ComniTech : Journal of Computational Intelligence and Informatics
The Journal of Computational Intelligence and Informatics publishes original research on all aspects of applied computational intelligence and informatics, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence and informatics. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: Autonomous reasoning, Bio-informatics, Cloud computing, Condition monitoring, Data science, Data mining, Data visualization, Decision support systems, Fault diagnosis, Intelligent information retrieval, Human-machine interaction and interfaces, Image processing, Internet and networks, Noise analysis, Pattern recognition, Prediction systems, Power (nuclear) safety systems, Process and system controlReal-time systems, Risk analysis and safety-related issues, Robotics, Signal and image processing, IoT and smart environments, Systems integration, System control, System modelling and optimization, Telecommunications, Time series prediction, Warning systems, Virtual reality, Web intelligence, Deep learning
Evaluating University Students’ Acceptance of ChatGPT Using UTAUT2: Evidence from an Indonesian Computer Science Faculty
This study examines determinants of students’ behavioural intention to use ChatGPT in higher education by applying the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) within an Indonesian computer science faculty. A cross sectional survey was administered to 100 undergraduate students, and the structural relationships were assessed using partial least squares structural equation modelling. The results indicate that performance expectancy, effort expectancy, social influence, hedonic motivation, and habit are significant predictors of intention to use ChatGPT, whereas facilitating conditions and perceived price value are not significant. The findings contribute empirical evidence for understanding generative AI adoption in Indonesian higher education and suggest that universities should focus on task relevance, ease of use, responsible enjoyment, and habitual integration supported by academic integrity guidance and pedagogical scaffolding
Copyright (c) 2025 ComniTech : Journal of Computational Intelligence and Informatics
Random Forest Based Traffic Congestion Classification in Pekanbaru, Indonesia
Traffic congestion in Pekanbaru has become a critical urban challenge due to rapid growth in vehicle volume that is not matched by corresponding road capacity expansion. This study aims to develop a classification model that categorizes traffic congestion levels using original operational data obtained from the local Transportation Agency. The proposed approach employs a Random Forest classifier with three primary features: speed, traffic density, and the volume to capacity (V/C) ratio. Congestion levels are defined in three classes, low, moderate, and high, based on rule based thresholds derived from the Highway Capacity Manual (HCM) traffic engineering standards. The results demonstrate that the developed model achieves excellent predictive performance, reaching 98.86% accuracy on the testing dataset. Feature importance analysis further confirms that speed and the V/C ratio are the most dominant variables in determining congestion severity. Overall, the model is effective as a decision support tool for rapid identification of congestion hotspots and provides a practical foundation for developing a more responsive transportation management system in Pekanbaru
Sentiment Analysis of Teacher Bullying Discourse in Indonesia Using Naive Bayes, Support Vector Machines, and Random Forest
Bullying against teachers on social media has become an increasingly discussed issue, particularly on the X platform. This study aims to analyze public sentiment toward teacher bullying in Indonesia using a machine learning approach. Three classification methods are employed, namely Naive Bayes, Support Vector Machine (SVM), and Random Forest. The dataset consists of 3,351 tweets after preprocessing. Sentiment labels (positive, negative, and neutral) are assigned using a rule based approach, relying on a predefined keyword list. Feature extraction is conducted using TF-IDF, and classification performance is evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that Naive Bayes achieves an accuracy of 71.83 percent, Support Vector Machine 86.89 percent, and Random Forest 87.48 percent. Based on these findings, Random Forest demonstrates the best performance for classifying sentiment on the issue of teacher bullying on X
Deep Learning Based Web Application for Cardiovascular Abnormality Detection via Heart Sound Analysis Pekanbaru
Cardiovascular diseases remain a leading contributor to global mortality and early identification is essential for timely intervention. Conventional auscultation is inexpensive but subjective and depends on clinical expertise. This study proposes a deep learning based web application that classifies heart sounds as normal or abnormal to support rapid screening from phonocardiogram recordings. Recordings from the PhysioNet Computing in Cardiology Challenge 2016 dataset were denoised using a Butterworth bandpass filter, segmented into cardiac cycles, and transformed into Mel spectrogram images. Two transfer learning backbones, VGG16 and ResNet18, were fine tuned for binary classification. Using a 70 percent training split with 15 percent validation and 15 percent testing, the best model, ResNet18, achieved 94.50 percent accuracy with 95.63 percent precision, 96.73 percent recall, and 96.17 percent F1 score on the held out test set. The web interface enables users to upload heart sound recordings and receive probability based predictions in real time. Internal usability testing indicated very good overall acceptance with an average score of 89.5 percent. The results suggest that a lightweight web deployment of spectrogram based deep learning can provide accessible decision support for early cardiovascular abnormality screening, particularly in resource constrained settings.
Design and Verification of a Dual Mode Smart Pet Feeder Using Boolean Logic Control
Irregular feeding schedules and inaccurate portioning are persistent contributors to obesity and related health risks in companion animals, especially when owners have limited time to supervise daily routines. Recent studies report high prevalence of feline and canine obesity and highlight that portion measurement by owners can be substantially imprecise, motivating solutions that enforce repeatable feeding events and support human override when needed. This paper presents a compact and explainable dual mode controller for an automatic and manual pet feeding system based on Boolean logic. The proposed design formalizes functional requirements as binary input signals, derives the canonical truth table, and synthesizes a minimized sum of products expression using established logic minimization principles. The resulting control law is F = M + (A·T), where A enables automatic operation, T is the scheduled timer event, and M is a manual feeding request. We further discuss a practical pulse shaping stage to prevent repeated dispensing when the timer signal remains high and outline hazard focused considerations for asynchronous inputs. Functional correctness is validated through exhaustive simulation across all input combinations and additional timing oriented test cases. Compared with a non minimized implementation, the minimized expression reduces gate count and logic depth while preserving safety relevant behavior such as manual override priority. The controller is suitable for low cost microcontroller or discrete logic realizations and provides a reproducible baseline for extending pet feeders with sensing, networking, and security features
Dimensionality Reduction and Clustering of Global Large Enterprise Data Using PCA, UMAP, and Gaussian Mixture Models
In the modern business landscape, large corporations generate high dimensional datasets that combine financial, operational, and market indicators, often producing complex and partially overlapping structures that are difficult to interpret in the original feature space. This study benchmarks linear dimensionality reduction using Principal Component Analysis (PCA) against non linear reduction using Uniform Manifold Approximation and Projection (UMAP), and examines how these representations affect clustering quality using k means and Gaussian Mixture Models (GMM). Data preprocessing includes missing value handling, categorical encoding, numeric coercion, and feature standardization to ensure scale comparable learning. Clustering performance is evaluated using the silhouette score, which jointly reflects within cluster cohesion and between cluster separation. The results indicate that the UMAP plus GMM pipeline achieves the best clustering quality with the highest silhouette score (0.57), suggesting that manifold based representations combined with probabilistic clustering more effectively capture heterogeneous corporate structures than linear projections and hard assignments. The findings support the use of non linear and model based pipelines for corporate segmentation tasks, particularly when clusters may overlap due to mixed business profiles and cross sector similarities.
Application of Fuzzy Logic to Evaluate Student Satisfaction with Library Services
Measuring student satisfaction with library services is crucial for enhancing the quality and effectiveness of academic support facilities. However, assessments are often subjective and fail to incorporate multiple influencing factors, such as service quality, infrastructure conditions, and staff responsiveness. This study aims to develop a decision support system (DSS) that determines student satisfaction levels using a fuzzy logic approach. The system accepts inputs consisting of service quality, facility conditions, and staff performance, which are then processed through the stages of fuzzification, rule base, inference, and defuzzification. The final output categorizes satisfaction levels into labels such as “Very Satisfied,” “Satisfied,” or “Dissatisfied.” Testing across various data scenarios demonstrated that the system’s results are consistent with actual user feedback. Therefore, the system can assist universities in more accurately evaluating library service performance and implementing improvements based on data-driven insights
Sentiment Analysis Of Halodoc Application User Satisfaction Using The Naïve Bayes Method, SVM and LSTM
The growth of digital technology in the health sector has encouraged the emergence of various health service applications, one of which is Halodoc. This study aims to analyze the sentiment of user satisfaction with the Halodoc application through reviews left on the Google Play Store. The method used in this research is Naïve Bayes, with stages including data collection, preprocessing (case folding, cleansing, tokenizing, stopword removal, stemming), weighting using TF-IDF, and sentiment classification. This research uses 350 review data as a dataset. The evaluation results showed that the Naïve Bayes-based sentiment classification model achieved 84% accuracy, with the majority of user sentiments being positive. The findings illustrate that the Halodoc application is generally well received by its users, but still needs improvement in some aspects of the service.
Performance Analysis of SVM and Naïve Bayes for Mango Image Classification Based on Ripeness Level and Variety
Mango is a seasonal fruit that is commonly known by people in Indonesia. This fruit that comes from India has many types that are widely favored by the people of Indonesia. However, in the case of the ripeness of this mango fruit, there are mangoes that have a fairly ripe color but after being purchased the mango is not fully ripe and there are also colors that we see ripe are actually fully ripe. The purpose of this study is to classify mangoes based on their level of ripeness using the color feature. Classification is carried out using the SVM and Naïve Bayes methods. The dataset used is 3 types of mangoes with 180 data samples, namely 20 unripe mangoes, 20 half-ripe, and 20 ripe. The accuracy results obtained by the Naïve Bayes method are 69% while the accuracy results obtained by the SVM method are 86%. The results of this study indicate that the SVM method is more efficient and effective in classifying mangoes based on their level of ripeness.
Optimizing IT Infrastructure at SMK Negeri 1 Kandis: A Fiber Optic and Hotspot Server Solution
This study aims to improve the performance of Information Technology (IT) infrastructure at SMK Negeri 1 Kandis through the implementation of a fiber optic network and hotspot server based on MikroTik with the MikroTik feature. The problems faced by the school include unstable internet connections, suboptimal network management, and the need for internet access that continues to increase along with the development of digital-based learning activities. The method used in this study is NDLC (Network Development Life Cycle), which consists of the stages of analysis, design, implementation, and evaluation of the network system. The implementation results show that the use of fiber optic as a network backbone can increase the speed and stability of the connection. At the same time, the hotspot server provides convenience in user management and internet access settings. The system built is also equipped with monitoring features and bandwidth settings to maintain the efficiency of network usage. By implementing this solution, SMK Negeri 1 Kandis has a more modern, stable, and secure network infrastructure and is able to support the teaching and learning process more effectively and efficiently.
Analysis and Development of Computer Network Infrastructure in Supporting the Implementation of Electronic-Based Government Systems in Pekanbaru City Using the Network Development Life Cycle Method
The implementation of Electronic-Based Government Systems (SPBE) enhances communication and interaction between governments and communities, enabling effective and efficient public service delivery. Pekanbaru City is among the regions adopting SPBE to improve its services. However, the success of SPBE relies heavily on robust computer network infrastructure, encompassing hardware, software, and interconnected data communication networks. This study aims to analyze the current state of Pekanbaru's computer network infrastructure, identify challenges, and provide recommendations for its development in alignment with SPBE requirements. Using the Network Development Life Cycle (NDLC) methodology, the research evaluates network configurations through Packet Tracer simulations. Key challenges include outdated hardware, limited software capabilities, and inefficient data networks. The findings emphasize the importance of addressing these obstacles and propose strategies to enhance infrastructure, supporting effective SPBE implementation. These recommendations aim to guide local governments in optimizing public service delivery through improved network systems
Cyber Security Risks in the Rapid Development of Generative Artificial Intelligence: A Systematic Literature Review
This study aims to identify the cybersecurity risks arising from the use of Generative Artificial Intelligence (GenAI). By employing a systematic literature review (SLR) method and following the PRISMA 2020 guidelines, this research systematically selects and analyzes relevant literature to discover and understand the risks associated with the use of GenAI. From the seventeen studies successfully collected and reviewed, various cybersecurity risks were identified, including phishing attacks, social engineering, ransomware, malware, deepfakes, misinformation, data leakage, misuse of personal data, executable attack code generation, privacy risks, and intellectual property violations. These findings provide crucial insights into the potential threats that may emerge from the irresponsible use of GenAI. The study is designed to offer valuable information for various stakeholders in their risk mitigation efforts and in the development of relevant regulations concerning the ethical use of GenAI. It is hoped that these findings will serve as a solid foundation for developing more effective security strategies and policies to address the challenges posed by this technology, and encourage the implementation of improved protective measures to tackle emerging risks.
generative artificial intelligence (GenAI), cybersecurity, systematic literature review.
Copyright (c) 2024 ComniTech : Journal of Computational Intelligence and Informatics
Clustering of Oil Well Production Potential in Pertamina Hulu Rokan Regional 1 Zone 4 Limau Field
The variation in the performance of the oil wells in the Limau Field of Pertamina Hulu Rokan Regional 1 Zone 4 poses a challenge to the management and operational decision making. Basic segmentation and lack of visualization of well groupings based on their production potential characteristics can lead to inefficient resource allocation and improper decisions. This study aims to segment oil wells using K-means clustering to improve management effectiveness. The data used include gross production, net production, and water content (BSW). Clustering is performed to group wells based on their production potential characteristics in order for managerial decisions to be based on data-driven decision making. The evaluation process using Elbow Method and Davies-Bouldin Index showed optimal results with three clusters as the best number of centroids. The segmentation results provide a deeper insight into the well's production potential and support the optimization of resource allocation to improve well performance. It is anticipated that this research will make a substantial contribution to the improvement of operational efficiency and strategizing future oil well management with a data-driven approach.
Sentiment Analysis on UNHCR Accepting Illegal Immigrants into Indonesia Using Naïve Bayes and SVM Algorithms on Instagram
Technology encompasses everything that can be used to convey messages, process data, or address problems. Human resource development is a process of enhancing the quality, skills, and competencies of individuals to adapt to environmental changes and confront future challenges. This has led to a significant increase in social media usage, resulting in a higher volume of user comments and opinions. A concrete example is the issue of illegal immigrants entering Indonesia, which has sparked both positive and negative comments from users on the Instagram platform. Sentiment analysis is an appropriate technique for analyzing user opinions and sentiments on specific topics, including issues occurring in Indonesia. In this study, Naive Bayes and SVM methods were chosen for their effective ability to classify large text datasets with satisfactory accuracy.
The Local Binary Pattern (LBP) Approach for Cataract Disease and Classification Using Convolutional Neural Network (CNN)
The Eye is one of the human sensory organs that functions as a visual organ. In society, eyes affected by cataracts are often difficult to distinguish from normal eyes, making it challenging for many people to realize when they are showing signs of cataracts. With advancements in technology, the prediction and classification of cataracts have become easier through digital image processing. In this study, the authors conducted predictions using Machine Learning to identify cataract and normal eyes using the Convolutional Neural Network (CNN) method and LBP feature extraction. The dataset used consists of 1,411 digital images with two classes: cataract and normal. The data was split into 80% for training and 20% for testing, and the dataset was obtained from Kaggle, a platform specializing in data science. This study employed 100 epochs and achieved an accuracy of 87% on training data and 85% on test data. The results indicate that the Convolutional Neural Network (CNN) method performs well in predicting eye diseases.
The Implementation of the K-Means Clustering Algorithm for Awarding Scholarships to Outstanding Students
Offering scholarships is an important tactic to promote higher education and scholastic success among students. The purpose of this study is to improve the impartiality and efficiency of scholarship awarding decision making by developing a decision support system built based on the K-Means clustering method. Students were grouped based on relevant variables and academic achievement requirements using the K-Means clustering algorithm. This approach creates homogeneous groupings based on scholarship recipients' history and academic performance data. The result of this clustering helps in recognizing trends and attributes that form the basis for future scholarship grant choices. This strategy was put into practice by creating a decision support system linked to student information and academic tracking. On processing the clustering data, cluster 0 with the status of eligible scholarship recipients amounted to 43 data and the cluster contained 157 data. It seems to contain data. Cluster 1 includes the status of scholarship recipients who do not meet the requirements. From the results of data analysis, it can be concluded that the scholarship recipient students are really outstanding students.
Sentiment Analysis of Hotel Guests Using the Naive Bayes Classifier
The reviews given by visitors are critical and should be meticulously analyzed to enhance the quality of hotel services. Traditionally, reviewing each guest’s feedback manually is time-consuming and inefficient. Therefore, there is a need for an effective technique to aggregate and analyze large volumes of reviews. This research aims to provide a sentiment analysis of hotel visitor reviews using the Naïve Bayes algorithm. This study's findings are based on its comprehensive approach to automating sentiment analysis, which significantly reduces processing time and increases accuracy. For this purpose, we used Google Colaboratory to implement and evaluate the Naïve Bayes algorithm. The results reveal that the sentiment analysis model achieves varying levels of accuracy across different hotels: Hotel Arena with 0.74, K Hotel George with 0.94, Hotel Claridge Paris with 0.92, Suite Hotel 900 m zur Oper with 0.84, and Atlantis Hotel Vienna with 0.86. These findings underscore the potential of the Naïve Bayes algorithm for effectively capturing and analyzing customer sentiment, offering a valuable tool for hotel management to understand and improve guest satisfaction. This study is pioneering in its application of the Naïve Bayes algorithm in the context of sentiment analysis for multiple hotel brands, providing a scalable solution for the hospitality industry.
Review of Machine Learning Algorithm for Intrusion Detection System
Intrusion Detection Systems (IDS) are essential components in cybersecurity that aim to detect, identify, and mitigate threats to information systems. In recent years, the application of machine learning algorithms has significantly enhanced the effectiveness of IDS. This systematic literature review (SLR) analyzes and summarizes research studies on IDS using machine learning techniques from 2019 to 2023. The review focuses on key aspects such as datasets, machine learning algorithms, and types of attacks detected. The analysis reveals that Support Vector Machine (SVM) and Random Forest (RF) are the most frequently employed algorithms due to their high accuracy and robustness. Datasets such as NSL-KDD, KDD-Cup’99, and UNSW-NB15 are commonly used for training and evaluating IDS models. Various attack types, including Denial of Service (DoS), User to Root (U2R), Remote to Local (R2L), and Probing, are addressed in these studies. This SLR highlights the strengths and limitations of different machine learning approaches in IDS, offering insights into current trends and future research directions. The findings suggest a growing trend towards the use of ensemble methods and optimization techniques to improve IDS performance. Additionally, the review underscores the importance of diverse and realistic datasets for the accurate evaluation of IDS models. This comprehensive analysis aims to provide researchers and practitioners with a detailed understanding of the advancements in IDS using machine learning, guiding future research and development in this critical area of cybersecurity
Monitoring on Solid Foundation Servers Using Suricata Through Telegram Bot Notifications
This research was conducted to study the Telegram Bot-based Suricata technique, which is implemented in the application of data center server security where the data center is related to the center of the Drug and Narcotics Agency of the Republic of Indonesia. We collected data from observations and interviews conducted in the administration section of server data security. Furthermore, the data is analyzed to study the pattern of each technique used. Based on this pattern, a better technique is chosen for the security process to be carried out. Based on the experiments conducted, it is clear that each of these techniques has advantages and disadvantages. However, this Telegram Bot-Based Suricata technique is more recommended because it is simpler in implementation, the desired results can be achieved, and the system can be monitored anywhere.
Sentiment Analysis of Tiktok Shop Closure using Naïve Bayes Algorithm and Support Vector Machine
This research explores the closure of TikTok Shops in Indonesia triggered by the implementation of Minister of Trade Regulation (MOT) 31/2020. It focuses on analyzing user responses and sentiment towards this policy by utilizing Naïve Bayes and Support Vector Machine algorithms. While some netizens supported the move for security reasons, while others criticized it for limiting business opportunities, an analysis of 1000 datasets from Twetter with the keyword "close TikTok Shop" revealed that neutral sentiment dominated, indicating a lack of clarity or confidence regarding the reason for the closure. The results also showed that Support Vector Machine (SVM) had higher accuracy (94.4%) than Naïve Bayes (89.1%), signaling the superiority of SVM in classifying sentiment in this dataset. These findings provide deep insights into the public's perceptions and attitudes regarding the closure of TikTok Shop, providing an important basis for government and corporate understanding of the public's response to the policy
Sentiment Analysis, Tiktok, Tiktok Shop, Naïve Bayes, Support Vector Machine