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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
Core Subject : Science,
Computer Science & IT
Bulletin of Computer Science Research covers the whole spectrum of Computer Science, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, Fault analysis, and Diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data • Cryptography • Model and Simulation • Image Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 462 Documents
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Implementasi Regresi Linier Berganda Untuk Prediksi Harga Mobil Bekas Di Indonesia Berbasis Gradio M Ridho Alfani; Elvia Budianita; Lestari Handayani; Siti Ramadhani
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1097

Abstract

The price of a used vehicle depends on various aspects that cause changes in the selling value in the market, such as model, year, transmission, mileage, fuel, tax, mpg, and cc. A common problem in used car transactions is determining prices that are still not fully based on measurable data analysis. The purpose of this study is to design a model to estimate the price of a used car through the multiple linear regression method and implement it in the User Interface. The data used in this study is secondary data obtained from the Kaggle public repository, and collected from several used car buying and selling forums in Pekanbaru and social media platforms such as Facebook that contain vehicle price information. The dataset contains 400 rows of data with a range of car years from 2005 to 2025. The research stages include data preprocessing in the form of categorical variable encoding and data normalization. Data is divided into training data and testing data, followed by the process of model building and model performance assessment. Evaluation is carried out using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The model was built using several independent variables, namely model, year, transmission, kilometer, fuel, tax, mpg, and cc, with vehicle price as the dependent variable. Based on the test results, the multiple linear regression method shows the ability to produce used car price estimates and has potential for application in decision support systems. The test results show that the MSE value on the training data is 0.004 and the testing data is 0.010, MAE on the training data is 0.046 and the testing data is 0.071, and RMSE on the training data is 0.062 and 0.100 on the testing data, the coefficient of determination (R²) on the training data is 0.985 and on the testing data is 0.955. The next model is implemented using the Python Gradio library so that users can predict vehicle prices through the User Interface.
Rancang Bangun Sistem Keamanan Laser Berbasis Internet of Things Menggunakan Metode Prototyping dengan Peringatan Dini dan Bukti Visual Real-Time Untuk Mencegah Pencurian Randikal Hikrenc Menono; Vilianty Rafida; Ahmad Fajri
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1098

Abstract

The rapid development of digital technology, particularly in the field of the Internet of Things (IoT), has brought significant changes to various aspects of human life, including security systems. Security is a crucial aspect for business owners, especially those operating in environments with a high risk of theft. This study aims to design and develop an IoT-based laser security prototype capable of providing real-time early warning notifications to users. The research employed the Prototyping method, which consists of communication, quick plan, modeling quick design, construction of prototype, and deployment and feedback stages. The system utilizes an ESP32-S3 Cam microcontroller as the main controller integrated with an LDR sensor and a SIM800L GSM module. The system operates by detecting interruptions in a laser beam caused by objects passing through the monitored area, which serve as intrusion triggers. When the laser beam is interrupted, the light intensity received by the LDR decreases significantly, prompting the microcontroller to automatically instruct the SIM800L module to place a phone call to the user. In addition, the ESP32-S3 Cam captures images of the monitored area and sends them through a Telegram bot as visual evidence. The testing results indicate that the system performs effectively, with an average response time of 8 seconds from intrusion detection to phone call notification. The success rate of phone call notifications reached 90%, while the visual evidence transmission through Telegram achieved a success rate of 100%. These results demonstrate that the system is capable of providing real-time alerts and visual information to users. The contribution of this research lies in the successful integration of intruder detection based on laser beam interruption, alarm notifications through telephone calls using the SIM800L module, and visual evidence transmission using the ESP32-S3 Cam and Telegram application into a single Internet of Things (IoT)-based security system capable of providing early warning notifications. When a laser beam interruption is detected, the system can automatically notify users through a phone call and send visual evidence in real time, thereby enhancing the effectiveness of remote security monitoring and response. However, the system performance is still affected by the stability of the GSM network used by the SIM800L module and is limited to detecting objects that interrupt the laser beam path. Therefore, the proposed system offers a smart, responsive, and cost-effective security solution that enables remote monitoring and supports theft prevention.
Klasifikasi Tingkat Kepuasan Pengguna Produk Body Care Menggunakan Algoritma Decision Tree Nur Jannah Hasibuan; Aidil Halim Lubis
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1110

Abstract

The increasing competition in the body care industry encourages companies to understand customer satisfaction as a basis for improving product quality and service performance. However, analyzing user satisfaction often produces complex data that are difficult to process manually. This study aims to apply the Decision Tree algorithm to classify the satisfaction levels of body care product users based on user characteristics and product evaluations. The research data were collected through questionnaires distributed to 250 respondents, including attributes such as gender, age, frequency of use, product quality, price, service quality, and satisfaction level as the target variable. The research stages consisted of data preprocessing, attribute selection, data transformation, splitting data into training and testing datasets, and building a classification model using the Decision Tree algorithm. Model evaluation was carried out using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the model was able to classify user satisfaction into four categories: very satisfied, satisfied, fairly satisfied, and dissatisfied, with an accuracy of 58%, precision of 57%, recall of 57%, and F1-score of 57%. This study contributes to the implementation of data mining for customer satisfaction analysis in the body care industry and helps companies identify dominant factors influencing user satisfaction, particularly product quality and service quality. In addition, the findings are expected to serve as a reference for developing customer satisfaction analysis systems based on data mining in the beauty and body care industry.
Klasifikasi Persepsi Publik Terhadap Perang Dagang Amerika Serikat Menggunakan Algoritma Naïve Bayes Classifier Bunga Nurul Manisa; Aidil Halim Lubis
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1112

Abstract

The import tariff policy implemented by the President of the United States on April 2, 2025 triggered tensions in global trade and provoked various public reactions. Differences in public perceptions of the policy generated diverse opinions, including support, criticism, and neutral responses, making sentiment analysis necessary to understand public opinion trends more systematically. This study aims to classify public perceptions of the U.S. trade war through sentiment analysis of Twitter data using the Naïve Bayes Classifier (NBC) algorithm. The dataset consists of 2,000 tweets collected using the keywords “trade war” and “import tariff increase” during April 3–30, 2025. Six preprocessing stages were applied: cleaning, case folding, tokenizing, slangword normalization, stopword removal, and stemming to improve data quality and consistency. Automatic labeling was conducted using a lexicon-based method with the InSet dictionary, yielding sentiment distributions of 83.5% negative, 12.8% positive, and 3.8% neutral. Feature representation was performed using TF-IDF, followed by an 80:20 train-test split. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Experimental results show that the NBC model without SMOTE achieved an accuracy of 83.5% but exhibited bias toward the majority class. After applying SMOTE, the dataset became balanced with 1,335 samples per class. Although overall accuracy decreased to 76%, the Macro F1-Score improved from 0.30 to 0.45, indicating improved model performance in handling multi-class classification more fairly. Additionally, the model achieved a recall of 43% for the positive class and 13% for the neutral class, providing a more representative evaluation of public sentiment toward the U.S. trade war issue.
Perbandingan Kinerja K-Medoids dan Improved K-Medoids Berbasis Crow Search Algorithm pada Klasterisasi Data Transaksi Penjualan Berdasarkan Silhouette Score dan Efisiensi Komputasi Melinda Putri Azzahra; Wahyu Syaifullah J.S.; Muhammad Nasrudin
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1117

Abstract

The development of digital transaction systems generates large amounts of data that need to be processed into meaningful information to support decision-making. One approach that can be used to analyze consumer purchasing patterns is clustering. The K-Medoids algorithm is known for its robustness against outliers; however, its iterative medoid search process leads to relatively high computational time. To address this limitation, an improved K-Medoids based on the Crow Search Algorithm (CSA) is employed, utilizing a metaheuristic optimization mechanism to determine optimal medoids. This study aims to compare the performance of the K-Medoids algorithm and the improved K-Medoids based on the CSA in transaction data clustering in terms of cluster quality and computational efficiency. The dataset used was obtained from a Point of Sale (POS) system of a fast-food restaurant and consisted of 18,814 transaction records. The research stages included data preprocessing, clustering using both methods, and performance evaluation based on the Silhouette Score and computation time. The results showed that both methods produced the same optimal number of clusters, namely K = 4. The K-Medoids algorithm achieved the highest Silhouette Score of 0.557791, while the improved K-Medoids based on the CSA obtained a Silhouette Score of 0.537240. In terms of efficiency, the improved K-Medoids based on the CSA required significantly shorter and more stable computation times than the conventional K-Medoids algorithm. These findings indicate a trade-off between clustering quality and computational efficiency, implying that the choice of method can be adjusted according to analytical requirements. The main contribution of this study is providing a comparative analysis of the K-Medoids algorithm and the improved K-Medoids based on the CSA on transaction data by jointly evaluating cluster quality and computational efficiency. The findings provide practical recommendations for selecting clustering methods according to analytical requirements and serve as a reference for future research on transaction data clustering.
Penerapan Extreme Programming dalam Pengembangan Sistem E-Tiket Berbasis Web pada Objek Wisata Lokal Tarso Tarso; Evi Martiani; Osi Krismonika
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1120

Abstract

A local tourism object serving as the research site still relies on a manual ticketing system, which has caused several operational issues including long queues at the entrance, transaction recording errors, and limited access to real-time visitor data that hinders management decision-making. This study aims to develop a web-based e-ticketing system as a solution to these problems by implementing the Extreme Programming (XP) method. XP was selected because it supports iterative and adaptive software development in response to changing user requirements through four main phases: planning, design, coding, and testing. The system was developed using PHP Native as the server-side programming language, MySQL as the database management system, and HTML, CSS, and JavaScript for the user interface, along with a ticket booking feature and WhatsApp-based notification. Testing was conducted using Black Box Testing to verify system functionality and User Acceptance Testing (UAT) to measure the level of end-user acceptance. Black Box Testing results on five main system functions showed that all features performed according to functional requirements with a 100% success rate. UAT results involving 15 respondents consisting of tourism managers and visitors yielded an average feasibility score of 88.03%, categorized as "Very Feasible." This study demonstrates that the XP method is effective in producing a functional web-based e-ticketing system that is accepted by end users. This study contributes by demonstrating the effectiveness of the Extreme Programming (XP) method in the context of local nature-based tourism, a domain that has received limited attention in previous research. In addition, the study provides a more comprehensive system evaluation through the combined use of Black Box Testing (100% success rate) and User Acceptance Testing (UAT) with a score of 88.03%, resulting in a more thorough validation compared to previous studies employing the XP methodology.
Implementasi Chatbot NgirimWA guna Optimalisasi Respons Pelanggan dan Peningkatan Kualitas Layanan Pelanggan pada UMKM Pakaian Bayi Fandevi Maitri; Claresta Antonia; Julius Sutrisno
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1123

Abstract

The rapid growth of the digital ecosystem encourages SMEs to improve customer service quality through digital communication platforms such as WhatsApp Business. However, the limited automation features available on WhatsApp Business still require customer service activities to be handled manually, especially during peak operational hours. This study aims to analyze the implementation of a knowledge-based chatbot using the NgirimWA platform to improve operational efficiency in the TISUKA baby clothing SME. The research applies a qualitative approach through observation, interviews, and literature studies. In addition, Value, Rare, Inimitable, Organization (VRIO), Strengths, Weaknesses, Opportunities, Threats (SWOT), Business Model Canvas (BMC), and Segmentation, Targeting, Positioning (STP) analyses were conducted to understand the business’s internal and external conditions before chatbot implementation. The findings indicate that the use of the NgirimWA chatbot accelerates customer communication, reduces repetitive manual responses, and improves the consistency of product information delivered to customers. The chatbot also helps business owners reduce operational workload, which previously required approximately 3–5 hours daily to respond to customer inquiries manually. Furthermore, chatbot implementation contributes to better customer service quality by providing faster, more informative, and continuously available responses outside operational hours. This study demonstrates that NgirimWA can serve as a relevant digital solution for SMEs to improve customer service efficiency and support business digital transformation.
Prediksi Saham Berdasarkan Data Teknikal Serta Fundamental Menggunakan Algoritma XGBoost Yoga Nur Pradana; Fitri Insani; Jasril Jasril; Siti Ramadhani
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1126

Abstract

The capital market has an important role in the economy as a means of investment and fundraising, with banking sector stocks being one of the main contributors to market capitalization in Indonesia. However, the investment decision-making process often faces obstacles in the form of limited investors' ability to comprehensively analyze fundamental and technical data, as well as irrational behavior that causes decisions to be less than optimal. This conditions encourage the need for a more objective and data-driven approach to help predict stock price movements. The results of the model evaluation on the test data showed excellent performance: BCA obtained a MAPE of 2.8% and an R² of 0.9488; BNI with MAPE 3.06% and R² 0.8863; Bank Mandiri with a MAPE of 4.70% and R² 0.9114; and BRI with MAPE of 2.48% and R² 0.8872. Based on this model, the results of the share price prediction for 2026 show that BCA is predicted to experience a significant increase from IDR 7,756 (January) to IDR 7,846 (June), while Bank Mandiri is predicted to grow from IDR 5,211 (January) to IDR 5,930 (June). BNI and BRI are predicted to experience an increase in share prices, respectively from IDR 3,327 (January) to IDR 3,683 (June) and from IDR 4,124 (January) to IDR 4,541 (June). This research contributes by presenting a stock prediction model that combines technical and fundamental data at once, applied to four major Indonesian banks Bank Central Asia, Bank Rakyat Indonesia, Bank Mandiri, dan Bank Negara Indonesia in a single modeling framework. This approach has proven to produce good accuracy with an average MAPE of 3.13% and R² 0.919, as well as being a more objective alternative for investors in analyzing stock price movements. However, the prediction results obtained in this study are analytical tools and are not intended as direct investment recommendations.
Penerapan XGBoost dan SMOTE untuk Klasifikasi Metode Pembayaran Pelanggan pada Data Transaksi Tidak Seimbang Fadilah Nuria Handayani; Vihi Atina; Aprilisa Arumsari
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1127

Abstract

The increasing use of digital payment methods in retail transactions highlights the importance of analyzing customer payment behavior. This study aims to classify customer payment methods using the XGBoost algorithm and to evaluate the effect of Synthetic Minority Over-sampling Technique (SMOTE) in handling class imbalance. The dataset consists of 287,422 transaction records processed using the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, which includes data understanding, data preparation, modeling, and evaluation stages. Experimental results show that the XGBoost model without SMOTE achieved an accuracy of 92.83% and a ROC-AUC of 0.7759, but performed poorly in identifying the minority class (Card), with a recall of 0.14, indicating a strong bias toward the majority class. After applying SMOTE, the model’s ability to detect the minority class improved, with recall increasing to 0.53 and F1-score reaching 0.28, although accuracy decreased to 78.50% and ROC-AUC to 0.7529. This study contributes by implementing XGBoost combined with the SMOTE method for customer payment method classification on imbalanced data and evaluating model performance using multiple classification metrics. This trade-off indicates that SMOTE improves sensitivity toward minority classes while affecting overall predictive accuracy. The findings highlight that evaluation of imbalanced classification models should not rely solely on accuracy but must also consider precision, recall, F1-score, and ROC-AUC to obtain a more comprehensive assessment. Overall, while SMOTE enhances minority class detection, further improvements are still required to achieve more stable and reliable classification performance.
Analisis Spasial Temporal Pola Gempa Bumi Menggunakan GIS dan Visualisasi Peta Animasi Mahreza Rakha Indra Putra; Suharyadi Suharyadi
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1128

Abstract

Central Aceh Regency is one of the regions in Aceh Province influenced by the tectonic activity of the Sumatra Fault, resulting in relatively high seismic activity. This study aims to analyze the spatial and temporal changes in earthquake patterns in Central Aceh Regency during 2004–2015 using Geographic Information Systems (GIS) and animated map visualization. The data used in this study were obtained from the United States Geological Survey (USGS) and the Meteorology, Climatology, and Geophysics Agency (BMKG) in CSV format, including parameters such as event time, magnitude, depth, latitude, and longitude. The research stages consisted of data preprocessing, data de-duplication, Kernel Density Estimation (KDE) analysis, spatial-temporal analysis, and visualization using the QGIS Temporal Controller. The coordinate system applied in this study was WGS 84 (EPSG:4326). The results indicate a shift in earthquake epicenter distribution from the northwest toward the southeast of Central Aceh during the observation period. In 2004, 2,006 earthquake events were recorded with an average magnitude of 4.49 Mw and an average depth of 84.82 km, while in 2015, 116 events were recorded with an average magnitude of 5.1 Mw and an average depth of 30 km. Heatmap analysis revealed increasing seismic concentration near the active Sumatra Fault zone. The animated map visualization successfully illustrated the temporal dynamics of seismic activity and can support spatial-based disaster mitigation planning. The main contribution of this study is the development of a GIS-based spatial-temporal analysis model that not only presents earthquake point distribution but also dynamically shows changes in seismic concentration through heatmaps and animated visualization. The findings can serve as an initial basis for identifying earthquake-prone zones and supporting spatial data-based disaster mitigation planning.

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