Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
type: channel
description: Digital Zone journal publish by Fakultas Ilmu Komputer Universitas Lancang Kuning Online ISSN 2477-3255 Print ISSN 2086-4884) This journal publish two periode in a year on May and November Journal Digital Zone: Jurnal teknologi informasi dan Komunikasi has been accredited by National Journal Accreditation (ARJUNA) Managed by Ministry of Research, Technology, and Higher Education, Republic Indonesia since year 2021 to 2025 according to the decree No. 164/E/KPT/2021
publisher: Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning
language: en
publication name: Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
issn: 2086-4884
copyright: Authors retain copyright and grant the journal the right of first publication. Articles published in this journal are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
items: Diabetic Retinopathy Detection via PCA-MLP Using Independent GLCM and EfficientNetB3 Descriptors
items: Hyperparameter Optimization of IndoBERT for DeepSeek User Sentiment Analysis
items: Hierarchical Deep Language Models for Classifying Antibody Subtypes in Oncology Clinical Notes
items: Modeling Multidimensional Social Welfare Using Machine Learning Analysis
items: Analytical Study of Hash Algorithm Performance for Smart Grid Data Security
items: Optimization of Incung Ancient Manuscript Character Recognition Using Template Matching and Image Enhancement
items: A Review of Cloud Computing and AI Applications in Digital Marketing for Web-Enabled Enterprises
items: Decision Support System for Determining the Suitability of Vegetable Cultivation Using the Multifactor Evaluation Process Method
items: Evaluating Contextual Embedding Models for Multi-Label PICO Classification in Heart Disease: Addressing the Intervention - Comparison Bottleneck
items: Augmented Reality as a Catalyst for Innovation in the Ishihara Test for Color Blindness Detection
items: Implementing Offline Servers for Digital Library and Streaming Services in Areas with No Internet Access
items: Modelling the Hatching Success of Sea Turtle Eggs Using Long Short-Term Memory (LSTM) for Conservation Oriented Ecotourism
items: RoBERTa-BiLSTM-Conv1D Deep Learning Model for Detecting Persuasive Content in News
items: Integrated Named Entity Recognition and Identical-Entity Detection for Extracting Unique Information Sources in News Articles
items: Sensitivity Analysis of Parameter Control in Leukemia Classification Using Variable-Length Particle Swarm Optimization
items: Improvement of FPS and Efficiency of Parameters Mask R-CNN with MobileNetV3 Small for Cardboard Detection
items: Towards an Automated Essay Evaluation System NLP Based Text Embeddings and Similarity Metrics
items: Naïve Bayes Alpha Parameter Optimization with Ant Colony for Clinical Text Classification
items: Comparative Review of Machine Learning Models for Mobile Price Prediction Based on Specifications: A Systematic Literature Analysis
items: Analytical Hierarchy Process and Simple Additive Weighting Methods for Ranking Public Complaint Applications with ISO/IEC 25010
items: Clustering Of Library’s Patron Behavior Using Machine Learning
items: Deep Learning for Horticulture: Convolutional Neural Network Driven Classification of Banana Types
items: Hyperparameter Optimization of the Perceptron Algorithm for Determining the Feasibility of Research Proposals and Community Service
items: Comparison of K-Means and K-Medoids Algorithms for Clustering Poverty Data in South Sumatra Using DBI Evaluation
items: Comparative Analysis of KNN and Neavy Bayes Algorithms in Socio-Economic Data Classification in Indonesia
items: Solution Approach to the Minimum Spanning Tree Problem in Tsukamoto Fuzzy and Fermantean Fuzzy Environments
items: System Control of Prototype Forklift Using Android and ESP32 Based on MQTT Communication
items: Evaluation of Creative Economy and Tourism Industry Trends based on LDA Analysis with BERTopic
items: Implementation of Fuzzy Logic Sugeno on a Website-Based for Flood Monitoring and Early Detection System
items: Implementation of Virtual Try-On Product to Enhance Customer Satisfaction

Diabetic Retinopathy Detection via PCA-MLP Using Independent GLCM and EfficientNetB3 Descriptors
type: item
description: Diabetic Retinopathy (DR) is a microvascular complication of diabetes and a leading cause of preventable blindness. Manual diagnosis through fundus imagery is time-consuming and requires highly specialized expertise. This study proposes an automated DR detection system through a comparative multi-scenario framework that evaluates independent clinical and semantic descriptors using a Multi-Layer Perceptron (MLP) network. To prevent majority class bias, a random undersampling technique was applied to the MESSIDOR dataset, resulting in a balanced dataset of 700 fundus images (350 Grade 0 and 350 Grade 1 instances). The system implements a dual-stream preprocessing pipeline utilizing Ben Graham's method for illumination standardization and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance clinical lesions. This study independently analyzes texture representations from the Gray Level Co-occurrence Matrix (GLCM) and deep semantic features from a pre-trained EfficientNetB3 model. To mitigate computational overhead and explicitly prevent data leakage, feature standardization and Principal Component Analysis (PCA) were strictly isolated and executed within the training phase of each fold during the 5-Fold Cross-Validation protocol. The evaluation demonstrated that the proposed comparative framework achieves robust computational efficiency alongside a promising diagnostic testing accuracy of 97.00%. While these empirical results indicate strong internal reliability with minimal prediction errors, further evaluation involving external validation datasets is recommended to fully substantiate its utility as a supportive clinical screening tool.
subject: Articles
  topic: section
subject: Diabetic Retinopathy
  topic: keywords
subject: Fundus Image
  topic: keywords
subject: Feature Fusion
  topic: keywords
subject: Multi-Layer Perceptron
  topic: keywords
subject: Principal Component Analysis
  topic: keywords
subject: EfficientNetB3
  topic: keywords
creator: Loneli Costaner
creator: Lisnawita
creator: Guntoro
rights: Copyright (c) 2026 Loneli Costaner, Lisnawita, Guntoro https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 37
ending page: 48
doi: 10.31849/digitalzone.v17i1.33099

Hyperparameter Optimization of IndoBERT for DeepSeek User Sentiment Analysis
type: item
description: This study aims to evaluate the performance of the IndoBERT model in sentiment classification of user reviews for the DeepSeek application on the Google Play Store. The reviews were categorized into three sentiment classes: positive, neutral, and negative. The dataset was collected through web scraping of Indonesian-language reviews and processed using several preprocessing stages, including cleaning, stopword removal, and stemming. This study contributes by systematically comparing hyperparameter optimization methods using Grid Search and Random Search under two data split schemes (60:20:20 and 80:20). In addition, oversampling and Focal Loss techniques were implemented to address class imbalance and improve neutral class classification. Experimental results show that the best performance was achieved using Grid Search with an 80:20 data split, resulting in a testing accuracy of 80.40% and a macro F1-score of 70.85%. This configuration also produced a lower GAP value, indicating better model generalization and reduced overfitting. The findings demonstrate that appropriate hyperparameter optimization significantly improves IndoBERT performance for Indonesian sentiment analysis tasks
subject: Articles
  topic: section
subject: IndoBERT
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Hyperparameter Tuning
  topic: keywords
subject: DeepSeek
  topic: keywords
subject: NLP
  topic: keywords
creator: Andro Nicus Saragih
creator: Lisnawita*
rights: Copyright (c) 2026 Lisnawita*, Andro Nicus Saragih https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-31
publication date: 2026-05-31
starting page: 80
ending page: 94
doi: 10.31849/digitalzone.v17i1.33024

Hierarchical Deep Language Models for Classifying Antibody Subtypes in Oncology Clinical Notes
type: item
description: A hierarchical language model based on ClinicalBERT was developed to classify antibody subtypes in oncology clinical trial statements under severe class imbalance conditions. The study addresses the limitation of conventional flat multilabel classification approaches that ignore the hierarchical dependency between general antibody categories and their specific subtypes. The proposed framework applies a two-stage architecture in which antibody presence is detected before subtype identification, enabling conditional learning for monoclonal and bispecific antibodies. The model combines hierarchical optimization with imbalance-aware strategies including class weighting, focal loss, and limited undersampling to improve sensitivity toward rare therapeutic categories. Experiments were conducted using 17,701 annotated oncology trial statements derived from ClinicalTrials.gov protocols. Model performance was evaluated using macro F1, subtype-specific F1, precision–recall area under the curve, hierarchical accuracy, and calibration analysis across multiple random seeds. The hierarchical ClinicalBERT model consistently outperformed the flat multilabel baseline, with bispecific F1 increasing from 0.02 to 0.06 and precision–recall area improving from 0.01 to 0.03. Reliability analysis further demonstrated well-calibrated probability estimates suitable for evidence ranking and biomedical decision-support applications. The findings indicate that incorporating label hierarchy improves rare subtype recognition, robustness, interpretability, and classification stability in biomedical text mining tasks.
subject: Articles
  topic: section
subject: ClinicalBERT
  topic: keywords
subject: hierarchical text classification
  topic: keywords
subject: antibody subtype classification
  topic: keywords
subject: oncology clinical trials
  topic: keywords
subject: imbalanced learning
  topic: keywords
creator: Taslim Taslim
creator: Susi Handayani
creator: Dafwen Toresa
creator: Syahriatna Syahriatna
rights: Copyright (c) 2026 Taslim Taslim, Susi Handayani, Dafwen Toresa, Syahriatna Syahriatna https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-06
publication date: 2026-06-06
starting page: 95
ending page: 104
doi: 10.31849/digitalzone.v17i1.29945

Modeling Multidimensional Social Welfare Using Machine Learning Analysis
type: item
description: The measurement of regional welfare in Indonesia is still dominated by economic indicators and therefore does not fully capture broader social conditions. This study aims to model and analyze multidimensional social welfare across provinces in Indonesia using a machine learning approach. The analysis is based on official data from Statistics Indonesia (BPS), covering five key dimensions: health, economy, education, infrastructure, and employment. The data were transformed using Benefit–Cost normalization and analyzed using the K-Means clustering algorithm. The results reveal four distinct welfare clusters with varying characteristics. The highest welfare cluster achieves an average index value of 0.6143, while the medium and lower-middle clusters record values of 0.4673 and 0.4210, respectively. The lowest welfare cluster has an index value of 0.3468, indicating limitations in health, economic, and employment dimensions. These findings highlight significant disparities in regional welfare conditions and demonstrate the importance of a multidimensional analytical approach. This study contributes by integrating multidimensional welfare indicators with a machine learning clustering method to provide a more objective and data-driven classification of regional welfare.
subject: Articles
  topic: section
subject: Sosial welfare
  topic: keywords
subject: multidimensional analysis
  topic: keywords
subject: machine learning
  topic: keywords
subject: benefit–cost
  topic: keywords
subject: K-Means
  topic: keywords
creator: Rima Liana Gema
creator: Mutiana Pratiwi
creator: Silky Safira
rights: Copyright (c) 2026 Rima Liana Gema, Mutiana Pratiwi, Silky Safira https://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-31
publication date: 2026-05-31
starting page: 68
ending page: 79
doi: 10.31849/digitalzone.v17i1.32730

Analytical Study of Hash Algorithm Performance for Smart Grid Data Security
type: item
description: Data integrity is a critical requirement in Smart Grid systems due to the continuous exchange of high-frequency transactional data across distributed environments. Cryptographic hash functions play a key role in ensuring data authenticity and preventing unauthorized modification. This study presents a comparative analytical evaluation of three widely used hash algorithms SHA-256, SHA3-256, and BLAKE2b using simulated Smart Grid data. The evaluation focuses on computational efficiency, avalanche effect sensitivity, and demonstrative security properties under controlled experimental conditions. The experimental results show that all algorithms maintain strong diffusion characteristics, with avalanche effect values close to the theoretical ideal, indicating reliable sensitivity to input changes. In terms of computational performance, BLAKE2b demonstrates lower execution time and more stable performance compared to SHA-256 and SHA3-256 within the tested environment. However, the observed differences are relatively small and should be interpreted cautiously. This study contributes by providing a contextual evaluation of hash algorithm performance in relation to Smart Grid data characteristics, highlighting the balance between efficiency and security sensitivity. Nevertheless, the experiments are conducted using a limited synthetic dataset and a software-based runtime environment, and the security tests are demonstrative in nature. Therefore, the findings should be considered as preliminary analytical insights rather than direct recommendations for real-world Smart Grid deployment
subject: Articles
  topic: section
subject: Smart Grid
  topic: keywords
subject: Hash Algorithm
  topic: keywords
subject: SHA-256
  topic: keywords
subject: SHA3-256
  topic: keywords
subject: BLAKE2
  topic: keywords
subject: Avalanche Effect
  topic: keywords
subject: Time Efficiency
  topic: keywords
subject: Data Security
  topic: keywords
creator: Muhammad Ridwan
creator: Rusydi Umar
creator: Muhammad Kunta Biddinika
creator: Puguh Wahyu Prasetyo
rights: Copyright (c) 2026 Muhammad Ridwan S.Kom, Rusydi Umar, Muhammad Kunta Biddinika, Puguh Wahyu Prasetyo https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-20
publication date: 2026-06-20
starting page: 105
ending page: 116
doi: 10.31849/digitalzone.v17i1.30369

Optimization of Incung Ancient Manuscript Character Recognition Using Template Matching and Image Enhancement
type: item
description: Ancient manuscripts represent valuable cultural heritage but are highly vulnerable to physical degradation and loss of public understanding. One such heritage is the Incung script manuscripts from Kerinci Regency, Indonesia, which remain underexplored in digital recognition research. This study proposes an optimized integration of image enhancement, template matching, and Convolutional Neural Network (CNN) methods to improve the accuracy and stability of Incung character recognition. Image enhancement is applied to improve contrast and legibility, template matching is used to capture structural character patterns, and CNN is employed as a complementary classifier to validate recognition results. Experimental results on segmented character images show that the proposed approach achieves a training accuracy of 93% and a validation accuracy of up to 100% with stable loss values, indicating effective learning under controlled conditions. Although performance decreases when applied to full manuscript images due to segmentation challenges and low contrast, the proposed method demonstrates strong potential for digital preservation of Incung manuscripts. While this study does not directly implement smart city services, the resulting digital manuscript data can support local wisdom–based smart city initiatives by enabling digital cultural documentation and access.
subject: Articles
  topic: section
subject: Ancient Manuscripts
  topic: keywords
subject: Smart City
  topic: keywords
subject: Enhancement
  topic: keywords
subject: Template Matching
  topic: keywords
subject: CNN
  topic: keywords
subject: Culture
  topic: keywords
creator: Devia Kartika
creator: Sri Rahmawati
rights: Copyright (c) 2026 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-23
publication date: 2026-02-23
starting page: 1
ending page: 10
doi: 10.31849/digitalzone.v17i1.29838

A Review of Cloud Computing and AI Applications in Digital Marketing for Web-Enabled Enterprises
type: item
description: This review examines how organizations are integrating cloud computing with artificial intelligence (AI) to enhance their digital marketing capabilities. We scrutinised scholarly articles, industry white papers, and illustrative corporate case studies covering the 2018–2025 period, with the aim of mapping the synergistic effects these technologies exert on widely recognised marketing pain points: difficulty in creating personalized customer experiences, wasting resources, and slow response to customer needs. Our analysis reveals five principal advantages: better business flexibility, improved customer tageting, real-time data analysis, automated processes, and cost saving. Quantified evidence suggests that firms adopting the AI-cloud model can lower operating costs by 70%, lift sales rates by 50%, elevate customer satisfaction scores by 60%, and accelerate decision-making by 40%. However, the implementation landscape features severe constraints, including data privacy reported by 78% of companies, a perceived over-reliance on external technology partners by 65% of respondents, and lack of skilled workers by 72%. Based on the findings, the study issues concrete guidance to executives: diversify cloud vendor portfolios, engineer interpretative user interfaces for AI applications, and commit to a robust training agenda. By synthesising contemporary evidence of advantages and vulnerabilities associated with AI-cloud marketing architectures, the research closes a significant knowledge void and furnishes a structured lens through which the evolving digital marketing landscape of the AI-cloud epoch can be systematically apprehended
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Digital Marketing
  topic: keywords
subject: Enterprise Systems
  topic: keywords
subject: Web-Enabled Technologies
  topic: keywords
subject: Marketing Automation
  topic: keywords
creator: Diyar Naaman
creator: Berivan Tahir Ahmed
creator: Subhi R. M. Zeebaree
rights: Copyright (c) 2026 Diyar Naaman, Berivan Tahir Ahmed, Subhi R. M. Zeebaree https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: 49
ending page: 67
doi: 10.31849/digitalzone.v17i1.27099

Decision Support System for Determining the Suitability of Vegetable Cultivation Using the Multifactor Evaluation Process Method
type: item
description: The challenge faced by farmers today is that it is still difficult to determine the types of vegetable crops that are suitable for cultivation according to environmental conditions, namely climate, soil type, and water requirements. Inaccuracies in choosing crop types are often caused by limited knowledge and lack of access to information related to the influence of environmental factors on the success of cultivation. This constraint makes it difficult for farmers to identify the characteristics of the selection of commodity types of vegetable crops that should be cultivated, this has an impact on suboptimal yields and limited sustainability in agriculture. Decision Support System (DSS) technology can help overcome this problem with its ability to determine the suitability of vegetable cultivation. The Multifactor Evaluation Process (MFEP) method is used in this study to assess and rank alternatives based on criteria that have been given weight values based on the parameters of temperature, sunlight, rainfall, humidity, slope, soil pH, and altitude. The results of the application of the MFEP method show that the system can provide recommendations based on validation testing accuracy values reaching 81.8%, precision 100%, recall 71.4%, specificity 100%, and f1-score 83.3%. These results show that DSS using the MFEP method can provide relevant recommendations by assisting farmers in choosing the type of vegetable crops that best suit the environmental conditions in their area. The benefits of this research can facilitate farmers in recommending types of vegetable crops that are suitable for cultivation to increase productivity and quality of crops.
subject: Articles
  topic: section
subject: Decision Support System
  topic: keywords
subject: Recommendation System
  topic: keywords
subject: Multifactor Evaluation Process
  topic: keywords
subject: Suitability of Vegetable Cultivation
  topic: keywords
creator: Anik Vega Vitianingsih
creator: Yiyin Safira
creator: Pamudi Pamudi
creator: Anastasia Lidya Maukar
creator: Achmad Muzakki
rights: Copyright (c) 2026 Yiyin Safira, Anik Vega Vitianingsih, Pamudi Pamudi, Anastasia Lidya Maukar, Seftin Fitri Ana Wati https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-03
publication date: 2026-06-03
starting page: 27
ending page: 36
doi: 10.31849/digitalzone.v17i1.26413

Evaluating Contextual Embedding Models for Multi-Label PICO Classification in Heart Disease: Addressing the Intervention - Comparison Bottleneck
type: item
description: Accurate extraction of Population, Intervention, Comparison, and Outcome (PICO) elements from clinical texts is essential for supporting evidence-based medicine, particularly in cardiology where clinical data complexity presents significant challenges. This study investigates the comparative effectiveness of three contextual embedding models—BioBERT, PubMedBERT, and SciBERT—integrated with a Bidirectional Long Short-Term Memory (BiLSTM) architecture for multi-label PICO element classification on heart disease datasets. The experimental framework involved pre-processing clinical sentences, transforming them into contextual embeddings, and classifying PICO elements using BiLSTM-based sequence modeling. Evaluation was conducted using five key metrics: accuracy, precision, recall, F1-score, and hamming loss, supplemented by confusion matrix analysis for each PICO element. Results demonstrate that the BioBERT-BiLSTM model achieved superior performance, with an accuracy of 73.89%, F1-score of 78.54%, precision of 81.60%, and recall of 76.64%. PubMedBERT-BiLSTM exhibited the highest precision (84.12%) but lower recall, while SciBERT-BiLSTM produced slightly inferior results overall. These findings confirm the importance of using domain-specific embeddings, particularly models pre-trained on biomedical corpora, to improve classification accuracy in specialized clinical text tasks. This study concludes that the BioBERT-BiLSTM combination offers a reliable approach for automated PICO element extraction in the cardiology domain, contributing to the development of more accurate and efficient clinical decision-support systems
subject: Articles
  topic: section
subject: PICO classification, BioBERT, PubMedBERT, SciBERT, BiLSTM, clinical NLP, heart disease, contextual embedding, evidence-based medicine
  topic: keywords
creator: Taslim Taslim
creator: Susi Handayani
creator: Walhidayat Walhidayat
creator: Dafwen Toresa
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 84
ending page: 95
doi: 10.31849/digitalzone.v16i2.28375

Augmented Reality as a Catalyst for Innovation in the Ishihara Test for Color Blindness Detection
type: item
description: Color blindness affects approximately 8% of the global population, emphasizing the importance of early detection. The conventional Ishihara Test had several limitations, such as paper degradation, low interactivity, and difficulties in maintaining children’s focus. This research introduces AR-VISION, an Augmented Reality-based Ishihara Test application designed for inclusivity and child-friendliness. Employing a Research and Development approach with the Model Development Life Cycle (MDLC), AR-VISION was developed and evaluated through six main phases. The technical evaluation showed Algorithm C achieved the best compromise between speed, accuracy, and memory usage. User testing with 35 elementary school students indicated a significant increase in accuracy (from 72% to 91%) and engagement (from 60 to 87). In conclusion, AR-VISION enhanced the precision, interactivity, and motivation of children in color blindness screening, supporting SDG 3, 4, and 9 and Asta Cita No.4, while demonstrating the transformative potential of AR in health, education, and interactive learning
subject: Articles
  topic: section
subject: Augmented Reality
  topic: keywords
subject: Color Blindness Detection
  topic: keywords
subject: Ishihara Test
  topic: keywords
subject: Interactive Technology
  topic: keywords
subject: Digital Innovation
  topic: keywords
creator: Aisyah Mutia Dawis
creator: Rahmad Ardhani
creator: Faiq Fadhil Dzulfiqar Bariq
creator: Nalurita enggar Renosih
creator: Eka Marlina
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 150
ending page: 161
doi: 10.31849/digitalzone.v16i2.29233

Implementing Offline Servers for Digital Library and Streaming Services in Areas with No Internet Access
type: item
description: Access to digital libraries and streaming services is often limited in regions with no internet connectivity, commonly referred to as blank spot areas. This paper presents the design and implementation of an offline server system to provide digital library and multimedia streaming services in several villages on Bengkalis Island, Indonesia. The system was installed in three villages and utilised a local server equipped with over 1,200 digital learning resources, distributed to users through a wireless local area network (Wi-Fi) without the need for internet access. During the three-month pilot period, 94% of users (n = 127) reported successful access to e-books and educational videos via their personal devices, with average access speeds reaching 40–48 Mbps. Content updates were performed every four weeks via temporary internet connections. User satisfaction was high, with 91% of respondents stating that the system helped them access up-to-date learning materials and improved their learning motivation. These results demonstrate that the offline server solution effectively bridges the digital divide, enabling equitable access to information, educational resources, and multimedia content for underserved communities in remote areas.
subject: Articles
  topic: section
subject: offline server
  topic: keywords
subject: digital library
  topic: keywords
subject: streaming services
  topic: keywords
subject: blank spot areas
  topic: keywords
subject: wireless LAN
  topic: keywords
creator: Agus Tedyyana
creator: I Gusti Agung Putu Mahendra
creator: Fajar Ratnawati
creator: Onno W Purbo
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 162
ending page: 173
doi: 10.31849/digitalzone.v16i2.29045

Modelling the Hatching Success of Sea Turtle Eggs Using Long Short-Term Memory (LSTM) for Conservation Oriented Ecotourism
type: item
description: This study proposes a Long Short-Term Memory (LSTM) model to predict the hatching success of sea turtle eggs in the Anambas Islands Marine Conservation Area, Indonesia. Leveraging nesting data (2022–2024) provided by LKKPN Pekanbaru and associated environmental variables, the model’s performance was assessed across various configurations of time steps (2, 5, 7, 30, and 45 days) and data splits (ranging from 60:40 to 90:10). The optimal configuration—7-day time step with a 60:40 train-test split—yielded RMSE = 17.90, MAE = 8.67, and R² = 0.34. Results revealed strong seasonal nesting trends and statistically significant interspecies differences in incubation periods (p < 0.05). While the model demonstrated high predictive accuracy for standard incubation durations (30–45 days), performance declined in extreme cases, highlighting the need for location-specific environmental data. This research illustrates the practical application of LSTM for ecological time series forecasting and provides a machine learning framework to support decision-making in ecotourism scheduling and marine conservation planning in island-based coastal ecosystems
subject: Articles
  topic: section
subject: LSTM, MAE, RMSE, Conservation, Sea Turtle, Ecoturism, Mangkai, Anambas
  topic: keywords
creator: Agustriono Agustriono
creator: Susanti Susanti
creator: Lusiana Lusiana
creator: Mardainis Mardainis
creator: Rahmat Irfansyah
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-10-30
publication date: 2025-10-30
starting page: 135
ending page: 149
doi: 10.31849/digitalzone.v16i2.28856

RoBERTa-BiLSTM-Conv1D Deep Learning Model for Detecting Persuasive Content in News
type: item
description: The use of persuasive language is one of the defining features of native advertisements. Therefore, detecting persuasive content in news is essential, since native ads often appear disguised as legitimate news articles, it is crucial to identify and filter such content to maintain objectivity and improve the user experience. This study aims to detect news with persuasive content i.e. persuasive news in English language using a natural language processing (NLP) approach. The proposed method incorporates text summarization methods, pre-trained word embeddings, and deep learning models. An additional Conv1D layer has been added to improve the model’s performance. The model were trained on an Indonesian news dataset translated into English using Google Translate API. Experimental results show that our proposed RoBERTa–BiLSTM-Conv1D model, outperformed other models, achieving 92% accuracy in identifying persuasive news in English. These findings indicate that the persuasive content detection model can be used for application in mainstream media environments to detect native ads in English language. In the future, the model can incorporate Indonesian and English news as training data to develop a cross-lingual native ads detection model
subject: Articles
  topic: section
subject: deep learning
  topic: keywords
subject: persuasive content
  topic: keywords
subject: text summarization
  topic: keywords
subject: word embedding
  topic: keywords
creator: Arya Putra Kurniawan
creator: Daniel Siahaan
creator: Brian Darnoto
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 122
ending page: 134
doi: 10.31849/digitalzone.v16i2.27674

Integrated Named Entity Recognition and Identical-Entity Detection for Extracting Unique Information Sources in News Articles
type: item
description: Native advertising is often difficult to detect because it resembles regular news articles. One indicator is the absence of diverse information sources or the reliance on a single perspective. Therefore, it is necessary to employ an extraction technique capable of consolidating various forms of identical entity mentions. This study integrates an NER model based on XLNet+BiLSTM+CRF with identical entity classification using Levenshtein distance features and static and contextual vector representations. The results show an F1-score of 93.71% at the entity level and 92.84% for identical entity identification, along with a list of unique citation sources. These findings demonstrate that this unique list can be an additional feature in detecting native advertising, which often relies on a single source. With an average unique entity coverage of 97.40%, the proposed architecture can extract unique entities within news articles
subject: Articles
  topic: section
subject: Named Entity Recognition
  topic: keywords
subject: NLP
  topic: keywords
subject: Unique Information Source Extraction
  topic: keywords
creator: Adi Surya Suwardi Ansyah
creator: Daniel Oranova Siahaan
creator: Brian R izqi Paradisiaca
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-10-14
publication date: 2025-10-14
starting page: 72
ending page: 83
doi: 10.31849/digitalzone.v16i2.27687

Sensitivity Analysis of Parameter Control in Leukemia Classification Using Variable-Length Particle Swarm Optimization
type: item
description: Machine learning has the potential to support hematologists in classifying leukemia by identifying abnormal chromosomes and specific gene markers. One effective technique for feature selection is Variable-Length Particle Swarm Optimization (VLPSO), where its performance depends heavily on parameter control, specifically the inertia weight (w) and acceleration factors (c), which regulate the search process. In previous VLPSO, static types of parameter control were applied to the Factor, and time-varying types were used by the Factor. Although its results showed good performance in VLPSO, there was no separation in the treatment of training data and test data, leaving a gap in understanding their impacts for real-world applications. This study explores how different parameter control strategies (static, time-varying, and adaptive) affect the performance of VLPSO with two comparison adaptive parameter control approaches, Adaptive 1 and Adaptive 2, in the VLPSO framework, each designed to dynamically adjust the control parameters w and c in different ways. The 10-fold cross-validation shows that VLPSO with an Adaptive one-parameter setting achieves better generalization with low train-test differences, especially in Decision Tree and Naïve Bayes classifiers, though with higher variability. Adaptive 2-parameter setting of VLPSO offers more consistent results with narrower variability across different settings. Static methods are the least reliable, while time-varying controls show moderate but unstable performance. Adaptive parameter tuning is recommended to improve VLPSO's stability, flexibility, and classification accuracy in biomedical applications. The results provide recommendations for parameter settings using an adaptive approach that has been proven to enhance the performance of VLPSO
subject: Articles
  topic: section
subject: Leukemia Classification
  topic: keywords
subject: Variable-Length Particle Swarm Optimization
  topic: keywords
subject: Adaptive Parameter Control
  topic: keywords
subject: Generalization Consistency
  topic: keywords
subject: Parameter Sensitivity Analysis
  topic: keywords
creator: Siti Ramadhani
creator: Lestari Handayani Handayani
creator: Muhammad Fikri
creator: Theam Foo Ng
creator: Sumayyah Dzulkifly
creator: Roziana Ariffin
creator: Shir Li Wang
rights: Copyright (c) 2025 The Author(s) http://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-18
publication date: 2026-05-18
starting page: 58
ending page: 71
doi: 10.31849/digitalzone.v16i2.27473

Improvement of FPS and Efficiency of Parameters Mask R-CNN with MobileNetV3 Small for Cardboard Detection
type: item
description: Inventory management in warehouses often experiences discrepancies in recording the number of cardboard boxes due to errors during the manual recording process. To overcome this problem, a cardboard detection method was developed using the Default Mask R-CNN model and a modified model using MobileNetV3 Small. The training data was obtained from a collection of cardboard photos which then went through an annotation stage. In the cReonfiguration stage, various anchor scales were applied to determine the bounding box parameters, while the training process used Stochastic Gradient Descent (SGD). The default model is trained with the initial Mask R-CNN settings, while the custom model modifies the backbone and Feature Pyramid Network (FPN) adjustments. The test results show that the custom model has higher efficiency with a parameter count of 20,857,704 and an average FPS of 10.92. However, the accuracy level of the custom model is lower than that of the default model
subject: Articles
  topic: section
subject: Mask R-CNN
  topic: keywords
subject: MobileNetV3 Small
  topic: keywords
subject: Cardboard Detection
  topic: keywords
subject: Model Configuration
  topic: keywords
creator: Vikha Tri Vicika
creator: Jamaludin Indra
creator: Sutan Faisal
creator: Hanny Hikmayanti
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 26
ending page: 36
doi: 10.31849/digitalzone.v16i1.26349

Towards an Automated Essay Evaluation System NLP Based Text Embeddings and Similarity Metrics
type: item
description: This study aims to develop an automatic essay answer assessment system based on Natural Language Processing (NLP) to reduce the time and effort required for evaluation. The system uses Cosine Similarity and Manhattan Distance as evaluation metrics and implements two text embedding methods—Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)—to represent the user’s answer text. The methodology begins with text processing and pre-processing, followed by embedding and similarity calculation between the user’s answer and the reference text to generate an evaluation score categorized into three levels: good, sufficient, and poor. Based on Cohen’s Kappa analysis, the kappa value for Cosine Similarity reaches 0.78, indicating high agreement between the Cosine TF-IDF and Cosine BoW methods. In contrast, Manhattan Distance yields a kappa value of -0.05, indicating a discrepancy between the two Manhattan-based methods. The evaluation results suggest that Cosine Similarity is more suitable, whereas Manhattan Distance is not relevant for this task. At the modeling stage, the best classification models are Decision Tree and Random Forest, each achieving an accuracy of 96.67%. Although Random Forest demonstrates a higher AUC than Decision Tree, it requires a longer training time. Overall, the system is considered effective for assessing essay answers with both purpose and consistency, offering potential applications in the field of education
subject: Articles
  topic: section
subject: Automatic Essay Scoring
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Cosine Similarity
  topic: keywords
subject: Manhattan Distance
  topic: keywords
subject: Educational Assessment
  topic: keywords
creator: Regita Permata
creator: Rendika
creator: Luh Candra Julianty
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 37
ending page: 46
doi: 10.31849/digitalzone.v16i1.26541

Naïve Bayes Alpha Parameter Optimization with Ant Colony for Clinical Text Classification
type: item
description: This study addresses the challenges of text classification in domain-specific Natural Language Processing (NLP) within the medical field, which differs significantly from general NLP due to the presence of complex medical jargon and informal language in clinical documents. The primary objective of this research is to develop and evaluate a cancer-related text classification model by integrating the Naïve Bayes algorithm with Laplacian smoothing and optimizing its alpha parameter using Ant Colony Optimization (ACO). Specifically, the study aims to determine whether ACO can effectively identify the optimal alpha value that enhances the classification performance of the Naïve Bayes model. Experimental results demonstrate that with an alpha value of 0.27, the proposed model achieves an accuracy of 81.05%. This indicates that the combination of ACO and Naïve Bayes significantly improves classification efficiency and accuracy. The findings contribute to more accurate interpretation of clinical cancer-related texts, supporting better-informed decision-making in medical contexts
subject: Articles
  topic: section
subject: Classification
  topic: keywords
subject: NLP
  topic: keywords
subject: Naive Bayes
  topic: keywords
subject: ACO
  topic: keywords
subject: classification, NLP, Naïve Bayes, ACO, medical text
  topic: keywords
creator: Taslim Taslim
creator: Fajrizal Fajrizal
creator: Susi Handayani
creator: Dafwen Toresa
creator: Lisnawita Lisnawita
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 47
ending page: 57
doi: 10.31849/digitalzone.v16i1.24118

Comparative Review of Machine Learning Models for Mobile Price Prediction Based on Specifications: A Systematic Literature Analysis
type: item
description: This systematic literature review analyzes machine learning approaches for mobile phone price prediction based on device specifications through a comprehensive examination of 25 research studies from 2018 to 2024.The review reveals that ensemble methods, particularly Random Forest (achieving up to 97% accuracy) and Gradient Boosting (R² = 0.9829), consistently outperform individual algorithms across various datasets. Support Vector Machine models demonstrate superior classification performance with 96-97% accuracy, while neural networks show perfect best-performer ratios but remain underutilized (4.88% of implementations). The following keywords were used in this systematic review's extensive search strategy across IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar: ("mobile phone price prediction" OR "smartphone price prediction") AND ("machine learning" OR "artificial intelligence") AND ("specifications" OR "features") AND ("classification" OR "regression"). Strict inclusion/exclusion criteria were used to select 25 studies from an initial pool of 45 studies, with an emphasis on empirical research with quantitative performance metrics published between 2018 and 2024. The study reveals RAM, internal memory, battery capacity, and processor specifications as the key determining features for mobile phone pricing. According to the study, the primary factors influencing mobile phone pricing are processor specifications, RAM, internal memory, and battery capacity. This review identifies critical research gaps, including insufficient neural network exploration, poor dataset reporting practices (52% of studies omit dataset sizes), and lack of real-time market dynamics integration. The findings provide evidence-based guidance for researchers, manufacturers, and consumers in selecting optimal prediction algorithms and understanding key price-determining features in the evolving smartphone market. Study limitations include geographic bias toward specific mark…
subject: Articles
  topic: section
subject: Mobile price prediction, Machine learning, Random Forest, Support Vector Machine, Feature importance, Specification-based pricing
  topic: keywords
creator: Diyar Naaman
creator: Berivan Tahir Ahmed
creator: Omar Sediq Kareem
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 96
ending page: 121
doi: 10.31849/digitalzone.v16i2.27096

Analytical Hierarchy Process and Simple Additive Weighting Methods for Ranking Public Complaint Applications with ISO/IEC 25010
type: item
description: An integrated decision support framework for evaluating and ranking public complaint applications operated by city governments in West Java Province, Indonesia, is presented in this study. The Analytical Hierarchy Process is employed to derive expert-based weights for the ISO/IEC 25010 software quality criteria, while the Simple Additive Weighting method is applied to generate objective rankings of the applications. A research gap in the e-government domain is addressed, as limited studies have systematically implemented a multi-criteria decision-making approach that integrates expert consensus with quantitative evaluation for prioritizing digital public services. Data were collected through expert panel assessments and structured questionnaires administered across nine city-level complaint systems. Significant variations in software quality among the evaluated applications were identified, with Depok and Bandung achieving the highest scores of 0.8053 and 0.7593, respectively, while Bekasi and Cimahi obtained the lowest scores. Specific recommendations for improvement are provided based on the eight quality dimensions defined in the ISO/IEC 25010 model, and a replicable evaluation model for government stakeholders is proposed. A theoretical contribution is established through the extension of multi-criteria decision-making applications in evaluating e-government software quality using the comprehensive ISO/IEC 25010 framework. A practical contribution is delivered through the provision of a structured benchmarking tool that enables local governments to prioritize improvements in digital complaint systems based on measurable software quality indicators
subject: Articles
  topic: section
subject: AHP
  topic: keywords
subject: ISO/IEC 25010
  topic: keywords
subject: Public Complaint Applications
  topic: keywords
subject: Rank
  topic: keywords
subject: SAW
  topic: keywords
creator: Winny Purbaratri
creator: Mia Utami
creator: Mardiana Purwaningsih
creator: Agnes Novita Ida Savitri
rights: Copyright (c) 2026 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2026-05-03
publication date: 2026-05-03
starting page: 11
ending page: 26
doi: 10.31849/digitalzone.v17i1.26991

Clustering Of Library’s Patron Behavior Using Machine Learning
type: item
description: Libraries collect a lot of important transaction data, but they rarely use this information to improve how consumers interact with them. This work tries to bridge this gap by offering a novel use of machine learning to analyze and classify library patron behavior. Customers were categorized based on their age range, checkouts, and renewals using the KMeans clustering technique. Dimensionality reduction methods like PCA and t-SNE were used to visually clarify the generated patterns. Our research revealed three different user groups: Rare Borrowers, who typically make 5.4 checkouts and 2.0 renewals; Occasional Borrowers, who typically make 20.8 checkouts and 7.1 renewals; and Frequent Borrowers, who often make 50.3 checkouts and 15.4 renewals. The clustering model performed quite well, as evidenced by its Calinski-Harabasz Index of 320.12, Davies-Bouldin Index of 0.45, and Silhouette Score of 0.62. Beyond these metrics, the study’s novelty lies in its practical implications—offering libraries a data-driven framework to tailor services, improve user satisfaction, and optimize resource allocation. This study highlights the transformative potential of machine learning in library science offering a data-driven framework for libraries to personalize services, optimize book recommendations, and enhance outreach efforts based on patron behavior. By segmenting users, libraries can better allocate resources and improve user experience. Limitation of this study lies on the data bias which may affect generalizability due to demographic differences across libraries. Additionally, KMeans clustering assumes predefined clusters, which may not fully capture nuanced behaviors.
subject: Articles
  topic: section
subject: Patron Behavior
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: University Library
  topic: keywords
subject: clustering
  topic: keywords
creator: Winda Monika
creator: Arbi Haza Nasution
creator: Febrizal Alfarasy Syam
creator: Chiranthi Wijesundara
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2025-05-01
publication date: 2025-05-01
starting page: 1
ending page: 11
doi: 10.31849/digitalzone.v16i1.19680

Deep Learning for Horticulture: Convolutional Neural Network Driven Classification of Banana Types
type: item
description: One of the most widely grown horticulture fruits in Indonesia is the banana. In addition to its various health benefits, bananas are a good source of carbohydrates and vitamins A, C, and E. There are a lot of different kinds of bananas in Indonesia, and occasionally people have trouble telling them apart. This study uses a Convolutional Neural Network (CNN), a Deep Learning technique, to categorize bananas. Four different types of bananas—Cavendish, Kepok, Raja, and Tanduk—were classified. Planning, analysis, creating a banana classification model with CNN, and assessing the outcomes are the four phases of the research process. Data preprocessing, CNN model creation, training, and testing procedures are the next steps in the categorization model design process, which starts with the collection of banana data using a smartphone camera. The optimal model was obtained with the accuracy value of 96%, the average precision and recall values of 97% and 96% respectively. It was found based on test results with multiple tuning parameters, including dataset partition, optimizer use, and epoch. This study offers novelty in terms of the use of a large banana image dataset, extensive exploration of CNN parameters, and the potential application of the model in applications for the horticultural industry. In addition, this study contributes to the development of image-based AI technology in agricultural product classification, which is still relatively underexplored in Indonesia
subject: Articles
  topic: section
subject: Horticulture
  topic: keywords
subject: Classification
  topic: keywords
subject: Banana
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
creator: Ida Astuti
creator: Lutfi Nabhan Ibrahim
creator: Winda Widya Ariestya
creator: Syamsi Ruhama
creator: Diny Wahyuni
rights: Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 12
ending page: 25
doi: 10.31849/digitalzone.v16i1.23812

Hyperparameter Optimization of the Perceptron Algorithm for Determining the Feasibility of Research Proposals and Community Service
type: item
description: Higher education in Indonesia includes diploma, bachelor, master, specialist, and doctoral programmes organised by universities. The Institute for Research and Community Service (LPPM) is in charge of assessing lecturers' proposals. This research aims to optimise the Perceptron algorithm to assess proposal eligibility using Turnitin plagiarism scores and reviewer scores. The optimisation results show that Perceptron accuracy reaches 99.44% to 99.63% at various training data ratios. GridSearchCV achieved 100% accuracy, while RandomisedSearchCV recorded accuracy between 98.89% to 99.63%. GridSearchCV also had the lowest MSE , despite higher Loss values, indicating a sacrifice in generalisation ability. Perceptron Default and RandomisedSearchCV had higher MSE and Loss, but remained low. GridSearchCV's AUC reached 100%, while Perceptron Default and RandomisedSearchCV showed very high AUC, ranging from 99.25% to 99.98%. Overall, the Perceptron algorithm is effective in assessing proposal eligibility with high accuracy.
subject: Articles
  topic: section
subject: Research
  topic: keywords
subject: Community Service
  topic: keywords
subject: Perceptron
  topic: keywords
subject: LPPM
  topic: keywords
subject: GridSearchCV
  topic: keywords
subject: RandomisedSearchCV
  topic: keywords
creator: Lisnawita Lisnawita
creator: Guntoro Guntoro
creator: Loneli Costaner
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 172
ending page: 181
doi: 10.31849/digitalzone.v15i2.17812

Comparison of K-Means and K-Medoids Algorithms for Clustering Poverty Data in South Sumatra Using DBI Evaluation
type: item
description: This research focuses on the implementation and comparison of the K-Means and K-Medoids algorithms that function as poverty data clustering in South Sumatra Province, the poverty data is taken from the Central Statistics Agency of Indonesia (BPS Indonesia). This research also aims to analyze the poverty level in South Sumatra Province by including additional variables such as average years of schooling and per capita expenditure in the community in each regency or city in South Sumatra Province. Data clustering is done by both algorithms and then the performance value is Evaluated using Davies Bouldin Index DBI shows that K-Means gives better results, with a lower DBI value (0.204 at K=5) while K-Medoids has a DBI value of 0.239 at K=5, which indicates more compact and separated clusters. The superiority of K-Means is due to the homogeneous and minimal outlier characteristics of the dataset, which makes the centroid approach more optimal than medoids in K-Medoids. With these results, K-Means was chosen as the best algorithm for clustering poverty data in the region. The use of the K-Means algorithm produces a pattern in clusters related to education, economic inequality, and poverty distribution in various regions in South Sumatra. This implementation provides insight into how data clustering techniques can be applied to socio-economic data to provide policy makers in a region with information about the region, especially information about poverty-stricken areas.
subject: Articles
  topic: section
subject: K-Means
  topic: keywords
subject: K-Medoids
  topic: keywords
subject: Poverty Data
  topic: keywords
subject: Clustering
  topic: keywords
subject: Davies Bouldin Index (DBI)
  topic: keywords
creator: M. Dandi Akhda
creator: Ken Ditha Tania
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 233
ending page: 245
doi: 10.31849/digitalzone.v15i2.23624

Comparative Analysis of KNN and Neavy Bayes Algorithms in Socio-Economic Data Classification in Indonesia
type: item
description: The global economy continues to recover as trade flows, employment, and incomes improve. However, the economic recovery is uneven across countries and business sectors. The economic recovery has also resulted in structural changes, meaning that some sectors, jobs, technologies and behaviors will not return to pre-pandemic trends. Future developments depend on local economic conditions. The economy has the most important aspect in a country where the economy makes a country capable of meeting its needs by utilizing limited resources. This study aims to compare two data mining classification algorithms, namely Naïve Bayes and K-Nearest Neighbor, in analyzing socio-economic data in Indonesia. Based on this problem, the data mining classification method is used in determining the algorithm that is suitable for predicting socio-economic data in Indonesia. The two algorithms used are K-NN and Naive Bayes. After testing the two algorithms using confusion matrix and K-Fold Cross Validation, the results obtained from the two models have an accuracy of Naïve Bayes 98.25% and K-NN 97.78% and the results of K-Fold Cross Validation Naïve Bayes 98% and K-NN 96%. Naïve Bayes is superior to K-NN in this context of socioeconomic data classification in Indonesia, especially in terms of accuracy. Although K-NN shows good consistency, Naïve Bayes provides more accurate results.
subject: Articles
  topic: section
subject: Socio-economics
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Classification
  topic: keywords
subject: K-NN
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Python
  topic: keywords
creator: Kiki Buhori
creator: Andrianingsih
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 222
ending page: 232
doi: 10.31849/digitalzone.v15i2.23337

Solution Approach to the Minimum Spanning Tree Problem in Tsukamoto Fuzzy and Fermantean Fuzzy Environments
type: item
description: Solving Fuzzy Minimum Spanning Tree (FFMST) and Fuzzy Tsukamoto using modified Prim Algorithm for Undirected Graphs and modified Optimal Branching Algorithm for Directed Graphs in FFN environment. Since the proposed Algorithm includes FFN ranking and Arithmetic Operations, we use the improved FFN scoring function to compare the edge weights of the graphs. With the help of Numerical examples, the solution technique for the proposed FFMST model is explained. It aims to modify the Prims algorithm for oriktade graphing and the optimal result processing algorithm for re-graphing in Fuzzy Fermatean ( FFN )-miljö. They utilize the finite FFN function and the operation of fuzzy function operations to ensure victory in graphing. The fuzzy-inference process is based on the Tsukamoto method and also to get the best result from the existing catch. Numerical examples of presenters to perform the tasks of missing presenters. The results are seen in the effective Prim algorithm modifier lost Fuzzy Fermatean MST -problem for genome oriktade generator generated at minimum cost and fall with local banks. It is an optimal business growth modifier to optimize services for lenders, such as communication between financial consultants and commercial banks. This method will be effective and increase the desired parameters. Tsukamoto Fuzzy -This method includes a fuzzy-inference process to get the best answer in the minimum spanning tree problem. Kantvikter functions based on levels of capability and range functions. Theminimum spanning tree is achieved by the Prims algorithm, which may be performed with fuzzy values first.
subject: Articles
  topic: section
subject: Fuzzy;
  topic: keywords
subject: Tsukamoto;
  topic: keywords
subject: Fermatean;
  topic: keywords
subject: Algorithm;
  topic: keywords
subject: Graph;
  topic: keywords
creator: Desli de Haas
creator: Citra Palembang
creator: Aziz Rumakefing
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 208
ending page: 221
doi: 10.31849/digitalzone.v15i2.22826

System Control of Prototype Forklift Using Android and ESP32 Based on MQTT Communication
type: item
description: This research develops and tests a prototype forklift robot controlled via an Internet of Things (IoT)-based system using an ESP32 microcontroller and the MQTT protocol. Designed to overcome the limitations of traditional manual forklifts, the robot enhances warehouse operations by increasing efficiency and minimizing human error. The study presents the design, testing, and evaluation of the forklift robot's movement control, mechanical wheel performance, load capacity, and wireless connectivity across various Android devices and network providers. Testing was conducted over distances ranging from 1 km to 90 km with different Android devices (e.g., Samsung, Xiaomi, OPPO, Vivo) and providers (Indosat, Axis, Telkomsel, Smartfren). The robot successfully performed a variety of movements, including forward, backward, left, right, rotation, and lifting operations. It demonstrated stable load lifting capabilities up to 300 grams, with instability observed for heavier loads, and could operate smoothly across a range of network conditions. Connectivity tests showed consistent performance and stable communication, reinforcing the robot's practical use for remote control and real-time monitoring. This research emphasizes the potential of IoT-enabled forklift robots to optimize material handling processes in warehouses, contributing to safer and more efficient industrial environments
subject: Articles
  topic: section
subject: Forklift Robot
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: ESP32
  topic: keywords
subject: MQTT
  topic: keywords
subject: Mecanum Wheel
  topic: keywords
subject: Prototype
  topic: keywords
subject: System Control
  topic: keywords
creator: Faisal Faris
creator: Indah Sulistiyowati
creator: Agus Hayatal Falah
creator: Akhmad Ahfas
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 196
ending page: 207
doi: 10.31849/digitalzone.v15i2.22724

Evaluation of Creative Economy and Tourism Industry Trends based on LDA Analysis with BERTopic
type: item
description: Creative economy and tourism industry have a role in contributing country's foreign exchange. Efforts continue to be improved by utilizing social media. Latent Dirichlet allocation (LDA) and BERTopic topic model are used as topic models for creative economy and tourism trend analysis. The evaluation was carried out using a coherence matrix, topic distribution, similarity, and topic identification over the last five-years period. BERTopic has a higher coherence value of 0.53 compared to LDA 0.30 although the number of outlier topics dominates. The identification of the most relevant main topic trends is finance, travel, beaches and investment. These themes are interrelated in driving the growth of the creative economy and tourism, which increases local income and innovation in related sectors. BERTopic identifies hidden topics such as bitcoin cryptocurrency. In contrast, LDA provides a more even distribution of topics, revealing traditional trends such as beach tourism and travel. The evaluation offers key recommendations on creative economy and tourism policies to innovations about investment.
subject: Articles
  topic: section
subject: Trends
  topic: keywords
subject: Creative economy tourism
  topic: keywords
subject: Topic model
  topic: keywords
subject: LDA
  topic: keywords
subject: BERTopic
  topic: keywords
creator: Icha Nura Nugraha
creator: Ema Utami
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 182
ending page: 195
doi: 10.31849/digitalzone.v15i2.23796

Implementation of Fuzzy Logic Sugeno on a Website-Based for Flood Monitoring and Early Detection System
type: item
description: Floods are common in Indonesia, especially in Aceh, and are caused by various factors, such as high rainfall during the rainy season, resulting in material and life losses. Developing a flood monitoring and early detection system is essential to reduce the negative impact of flooding. This research introduces the implementation of the Fuzzy Logic Sugeno method on a web-based flood monitoring and early detection system, which uses ESP32 and accurate sensors to monitor water level and water flow. Testing is done through two approaches. First, the water level and flow parameters were manipulated manually to test the system's response to changing conditions. Secondly, tests were conducted automatically until the prototype aquarium was complete to see the system's ability to detect potential flooding. The test results show that the system can automatically recognize potential flooding, take preventive actions, such as opening or closing floodgates, and provide real-time condition information through a Website.
subject: Articles
  topic: section
subject: Flood
  topic: keywords
subject: Monitoring System
  topic: keywords
subject: ESP32
  topic: keywords
subject: Fuzzy Logic Sugeno
  topic: keywords
subject: Real-time Website
  topic: keywords
creator: Fadlyani Pohan
creator: Mukti Qamal
creator: Said Fadlan Anshari
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 159
ending page: 171
doi: 10.31849/digitalzone.v15i2.23356

Implementation of Virtual Try-On Product to Enhance Customer Satisfaction
type: item
description: In the digital age, customer experience is key to business success. Augmented Reality (AR) technology offers immense potential to enhance the shopping experience, particularly through virtual try-on features. This research aimed to analyze the impact of implementing AR Product, specifically the virtual try-on feature, on customer satisfaction at PT Dua Naga Kosmetindo. The study employed the System Development Life Cycle (SDLC) waterfall model to develop and implement the virtual try-on feature on the E-Colux website. Black Box testing and User Acceptance Testing (UAT) were conducted to assess the success of the implementation and user acceptance levels. Black Box testing revealed a 100% success rate, while UAT yielded an average score of 87.7%. The results indicated a significant positive correlation between the use of virtual try-on and customer satisfaction, particularly in the dimension of product satisfaction (86%-95%). Female respondents and younger generations exhibited higher satisfaction levels. This research demonstrates that the implementation of virtual try-on can significantly enhance customer satisfaction. However, further research with a larger and more diverse sample is needed to generalize these findings. Additionally, studies on the long-term impact of virtual try-on on customer loyalty and business performance are warranted.
subject: Articles
  topic: section
subject: augmented reality
  topic: keywords
subject: E-commerce
  topic: keywords
subject: Customer Satisfaction
  topic: keywords
subject: System Development Life Cycle (SDLC)
  topic: keywords
subject: Virtual Try-On
  topic: keywords
creator: Aisyah Mutia Dawis
creator: Rahmad Ardhani
creator: Sigit Setiyanto
creator: Aulia Uswatun Khasanah
creator: Muqorobbin
creator: Handoko Tantra
rights: Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
date: 2024-11-30
publication date: 2024-11-30
starting page: 147
ending page: 158
doi: 10.31849/digitalzone.v15i2.20845