Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
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: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning
Jurnal Digital Zone is licensed under aCreative Commons Attribution-ShareAlike 4.0 International License.
Optimization of Incung Ancient Manuscript Character Recognition Using Template Matching and Image Enhancement
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.
Copyright (c) 2026 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
Evaluating Contextual Embedding Models for Multi-Label PICO Classification in Heart Disease: Addressing the Intervention - Comparison Bottleneck
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
PICO classification, BioBERT, PubMedBERT, SciBERT, BiLSTM, clinical NLP, heart disease, contextual embedding, evidence-based medicine
Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi http://creativecommons.org/licenses/by-sa/4.0
Augmented Reality as a Catalyst for Innovation in the Ishihara Test for Color Blindness Detection
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
Implementing Offline Servers for Digital Library and Streaming Services in Areas with No Internet Access
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.
Modelling the Hatching Success of Sea Turtle Eggs Using Long Short-Term Memory (LSTM) for Conservation Oriented Ecotourism
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
LSTM, MAE, RMSE, Conservation, Sea Turtle, Ecoturism, Mangkai, Anambas
RoBERTa-BiLSTM-Conv1D Deep Learning Model for Detecting Persuasive Content in News
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
Integrated Named Entity Recognition and Identical-Entity Detection for Extracting Unique Information Sources in News Articles
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
Sensitivity Analysis of Parameter Control in Leukemia Classification Using Variable-Length Particle Swarm Optimization
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
Variable-Length Particle Swarm Optimization
Improvement of FPS and Efficiency of Parameters Mask R-CNN with MobileNetV3 Small for Cardboard Detection
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
Towards an Automated Essay Evaluation System NLP Based Text Embeddings and Similarity Metrics
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
Naïve Bayes Alpha Parameter Optimization with Ant Colony for Clinical Text Classification
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
classification, NLP, Naïve Bayes, ACO, medical text
Comparative Review of Machine Learning Models for Mobile Price Prediction Based on Specifications: A Systematic Literature Analysis
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 markets represented in available datasets, limited access to proprietary datasets, and a primary focus on specification-based features that exclude market sentiment analysis
Mobile price prediction, Machine learning, Random Forest, Support Vector Machine, Feature importance, Specification-based pricing
Clustering Of Library’s Patron Behavior Using Machine Learning
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.
Copyright (c) 2025 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Deep Learning for Horticulture: Convolutional Neural Network Driven Classification of Banana Types
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
Hyperparameter Optimization of the Perceptron Algorithm for Determining the Feasibility of Research Proposals and Community Service
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.
Copyright (c) 2024 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Comparison of K-Means and K-Medoids Algorithms for Clustering Poverty Data in South Sumatra Using DBI Evaluation
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.
Comparative Analysis of KNN and Neavy Bayes Algorithms in Socio-Economic Data Classification in Indonesia
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.
Solution Approach to the Minimum Spanning Tree Problem in Tsukamoto Fuzzy and Fermantean Fuzzy Environments
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.
System Control of Prototype Forklift Using Android and ESP32 Based on MQTT Communication
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
Evaluation of Creative Economy and Tourism Industry Trends based on LDA Analysis with BERTopic
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.
Implementation of Fuzzy Logic Sugeno on a Website-Based for Flood Monitoring and Early Detection System
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.
Implementation of Virtual Try-On Product to Enhance Customer Satisfaction
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.
Application of Backpropagation Neural Network in Predicting Mandatory Test Vehicle Parks
Backpropagation neural networks can be used in almost every aspect of human life, including the prediction of mandatory test vehicle parks. The goal of this study was to use BPNN (Backpropagation Neural Network) modeling to anticipate mandatory test vehicle parks based on the past data from the Department of Transportation at Purwakarta Regency, and to predict the results using the best model. This study makes use of mandatory test vehicle parks data from 2014 through 2023, which necessitates monthly testing. The test results show an accuracy level of 90.134% utilizing alpha 0.9, iteration number (epoch) of 10000, and MSE value 0.0064. Based on the best BPNN model into Matlab applications, the mandatory test vehicle park will be predicted from June 2023 to May 2024. The estimated value of the mandatory test vehicle park in December 2023 will be used to determine the requirement for proof of passing the periodic test in 2023 with a score of 7587
Digital Image Based DSS for Assessing Tomato Quality using AHP-TOPSIS Method
Tomatoes are a major export commodity in the country's plantation sector. This increases the urgency of efforts to increase tomato productivity, both in terms of quantity and quality. Evaluation of tomato quality currently relies on the degree of ripeness and skin texture. The conventional method currently used involves manual inspection, which can allow for misjudgment and economic loss. This research aims to use a digital image-based approach by utilizing a decision support system that combines the AHP and TOPSIS methods to assess tomato quality based on color and texture criteria. This research evaluates and ranks nine tomato images that have good quality, by giving higher priority to skin texture than skin color. Evaluation results from three tests showed that the system was able to determine the quality of tomatoes with an average kappa value of 0.78, which interpreted the results of good agreement between the system and expert judgments.
Decision Support System For Training Provider Selection Using Profile Matching Method
The process of selecting training providers at PCU (Pertamina Corporate University) is currently not very effective and efficient, still relying on conventional methods that take a long time to choose a training provider, requiring one month for each training session. Conventional document collection by applicants can lead to the loss of documents, and there is no method in the selection of training providers, resulting in frequent occurrences of sole providers and difficulties in choosing providers for specific training needs. The research has led to the development of a web-based system using the Profile Matching method. The Profile Matching method was chosen because it involves determining competency values (standard assessment criteria) required for specific types of training, serving as a reference to evaluate selected provider candidates. This method selects the best alternative from various options by comparing the individual competencies of providers with the desired training profile values, resulting in competency differences (Gains Across Product). The smaller the GAP, the higher the weight of the score generated. Providers with the highest score have the highest ranking and a greater chance of being selected as training instructors at PCU. Data collection techniques used include observation and interviews.
Enhancing Dental Image Segmentation Techniques: Edge Detection and Color Thresholding
Rapid advancements in medical technology, particularly in the field of dentistry, have led to significant progress in the application of medical imaging techniques to generate valuable image data. The resulting images often exhibit heterogeneous intensity distributions, with boundaries not always distinctly clear between the tooth roots and bone, along with variations in shape and pose. This study specifically aimed to identify the optimal image for segmenting specific parts of the dental structures. Image segmentation is crucial for ensuring effective diagnosis in the context of dental medicine. To achieve optimal dental image segmentation, this research combines edge detection methods with the determination of color thresholds, specifically grayscale and Hue, Saturation, Value (HSV). The research findings revealed that edge detection using the Sobel gradient operator yielded a relevant count of 17,099 pixels. Using RGB=3 and HSV=0.3 the color thresholds show an enhancement in the brightness of the resulting HSV-segmented image, while in the RGB-segmented image, the selected object appears more prominent. The findings of this study contribute significantly to the evolution of dental image segmentation techniques, potentially enhancing the accuracy and effectiveness of diagnoses within the realm of modern dental practice
Comparative Study of the Effect of Datasets and Machine Learning Algorithms for PDF Malware Detection
This research presents an innovative approach to detecting malicious PDFs through machine learning algorithms, focusing on the expansion of the Evasive-PDFMal2022 dataset. The objective is to enhance the accuracy of detecting malicious PDFs by enriching the dataset, augmenting its representation and diversity, and developing a practical tool—a website—for extracting and detecting malicious PDFs. The methodology involves updating and enlarging the dataset with additional malicious PDFs sourced from CVE and Exploit-db, along with non-malicious PDFs from diverse origins. Features are then extracted using the PDFID tool, and these 20 features serve as the foundation for implementing K-Nearest Neighbor (KNN), Random Forest, and Random Committee algorithms. The outcomes demonstrate that the model trained with the expanded dataset achieves a remarkable 99% accuracy, surpassing the performance of models relying solely on the Evasive-PDFMal2022 dataset. Additionally, this research significantly enhances the representation and diversity of the dataset while delivering a practical solution in the form of a website tailored for the extraction and detection of malicious PDFs.
Recommendation System to Determine Achievement Students Using Naïve Bayes and Simple Additive Weighting (SAW) Methods
Giving appreciation to outstanding students can motivate students to compete with each other in learning. MA Tanwirul Qulub Tanggungan often experiences difficulties in determining outstanding students due to There is no application that can assist school management in identifying outstanding students, the implementation is considered less than optimal. besides that, the determination of outstanding students is still based on report cards that are only ranked, and there are no criteria that refer to the K-13 curriculum. The purpose of this research is to offer a solution to create a recommendation system for selecting outstanding students using the parameters of midterm exams, final exams, assignments, attendance, attitude, extracurricular activities, organizations, and award certificates using decision support system techniques. Extracurricular grades are taken from Scouting activities only because students are generally required to participate in them. Naïve Bayes and Simple Additive Weighting methods are used in this research, where the Naïve Bayes method classifies the categories of outstanding students and not, while the SAW method is used for ranking. The contribution of this research has the potential to increase school efficiency in student assessment and support efforts to improve the quality of education by rewarding students appropriately. The validation test results of Naïve Bayes and SAW techniques get an accuracy value of 100%, which shows that the SAW method can produce the best alternative recommendations
Application of the Naïve Bayes Algorithm in Sentiment Analysis of Using the Shopee Application on the Play Store
This research aims to find out the opinions of users of the Shopee application on the Play Store using the Naive Bayes Naive Algorithm and to find out the suitability of the correct application of the Naive Bayes algorithm in carrying out sentiment analysis with the classification of three sentiment classes. The dataset used in this study consisted of 2000 customer reviews obtained from the Play Store in 2024 collected by the scraping process using the Python library. The dataset has 1,198 examples of negative attitudes, 583 examples of good sentiment, and 219 examples of neutral sentiment. The results of this study are expected to be used as evaluation material for Shopee Apilkation to improve the performance of Shopee applications. Research findings show that the Bayes naive approach reaches accuracy determined by various aspects, such as the quantity of data collections and positive and negative data distribution. This study shows that the Bayes naive algorithm can function properly as a technique to evaluate user sentiment for applications in the Play Store. However, with the classification of three classes, another algorithm is needed to produce higher accuracy.
Implementation of a CNN-trained model for coffee type detection in an Android app with photo input of beans, fruits, and leaves
Coffee is the most consumed type of drink in the world. Each type of coffee has different physical characteristics from leaves, fruits to seeds. Now technology is needed in the world of agriculture in making decisions. To determine the type of coffee with fission characteristics, there are still many people who do not understand in distinguishing the physical characteristics of coffee plants. In this case, an application was developed using the RAD method by utilizing the flutter framework and the Convolutional Neural Network model that has been trained. The pre-train model used is NasNet Mobile with a dataset of 900 photos and 100 epochs with early-stopping utilization and heti at epoch 55 with an accuracy of 90.67%. In this study, implementing existing models into Android applications using the Flutter framework. With the implementation process carried out by the application can help the detection process using an android device. The implementation results get good test results with a score of 0.97. This application can help the process of identifying the type of coffee and minimize errors in identifying directly.