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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 329 Documents
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Aplikasi Three Tier Sistem Informasi Manajemen Kepegawaian Menggunakan Model Prototype Setiowati, Dewi; Hidayah, Qori Halimatul; Nurmadewi, Dita
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.724

Abstract

Human resources is a service department that assists employees and leaders of an organization, as well as a division that handles individual or personal issues that, when applied to an organization, concern employees or staff. Balai Pengkajian dan Pengembangan Komunikasi dan Informatika (BPPKI) Surabaya has personnel data that needs to be stored and processed in order to facilitate computerized storage and reporting. The implementation of a web service information system is a collection of functions or methods contained on a server that can be called by clients remotely. Clients can be users who use the desktop application implemented in this study, or the web as a mobile communication device. The implementation of the Three Tier application model architecture is in terms of infrastructure and documents used as data exchange formats. The Human Resources Management Information System application was developed using the Three-Tier desktop-based method with the prototype method. The Human Resources Management Information System application consists of four key components: the transaction process information system for periodic salary increases, salary, rank promotions, and leave requests; simplifying report generation; and minimizing data input errors. The implementation and results of black box testing show that all form features tested on the human resources management system functioned successfully and as expected, making this application suitable for implementation to assist human resources management at Balai Pengkajian dan Pengembangan Komunikasi dan Informatika (BPPKI) Surabaya.
Question Answering System Zakat dengan Metode Long Short-Term Memory (LSTM) Tanuwijaya, Moch Apip; Jumadi; Eva Nurlatifah
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.728

Abstract

Zakat is a fundamental pillar of Islamic finance that serves as a mechanism for wealth redistribution. However, there is currently no Indonesian-language Question Answering System (QAS) capable of automatically and contextually responding to zakat-related queries. This study aims to develop a zakat-focused QAS using a Long Short-Term Memory (LSTM) model integrated into the Telegram platform. The dataset was compiled from the official BAZNAS zakat guidebook and processed through tokenization, padding, and label encoding. The model architecture consists of an embedding layer, two stacked LSTM layers (with return sequences, dropout, and recurrent dropout), followed by two dense layers (200 and 100 units) with additional dropout layers before the softmax output. The model was trained using the Adam optimizer (learning rate 0.003), a batch size of 24, and 100 epochs. Evaluation was conducted using a confusion matrix, resulting in a validation accuracy of 93%, with a precision of 0.94, recall of 0.93, and F1-score of 0.92 (weighted average). The system was deployed via the Telegram Bot API and demonstrated response times under two seconds, with stable performance across hundreds of question labels. This work contributes to the advancement of digital zakat education and presents a scalable solution that can be further extended within the ecosystem of Islamic Finance Technology and Digital Religious Education.
Evaluasi Kepuasan Pelanggan Dalam Berbelanja Online Pada Aplikasi Shopee Menggunakan Metode Importance Performance Analysis Anisa, Ayu; Saptari, Mochamad Ari; Fachrurrazi, Sayed
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.729

Abstract

Shopee is one of the most popular e-commerce platforms in Indonesia, especially among university students. At Malikussaleh University, it has become the preferred application for meeting both academic and personal needs. However, despite its high usage, there are still complaints regarding unsatisfactory service quality, such as delayed deliveries, difficulty in processing returns, and slow customer support responses. This study aims to analyze customer satisfaction with Shopee services using the Importance Performance Analysis (IPA) method to assess the alignment between service importance and performance. The study evaluates four key dimensions: Web Design, Fulfillment, Customer Service, and Security/Privacy. The results show that the average conformity level (TKI) across 17 service attributes is 94.59%. A TKI value below 100% suggests that the provided services have not fully met customer expectations. Moreover, the IPA quadrant analysis reveals that several attributes—such as delivery punctuality, responsiveness to complaints, and clarity of product information—fall into Quadrant A. Attributes in this quadrant are critical and underperforming, indicating top priorities for immediate improvement to enhance overall service quality.
Implementasi Algoritma Apriori dalam Menemukan Pola Asosiasi pada Data Penjualan Produk Retail Butsianto, Sufajar; Candra Naya; Anggi Muhammad Rifa'i
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.731

Abstract

This study aims to implement the Apriori algorithm in finding association patterns in retail product sales data, using the Association Rule Mining approach. Evaluating the ruler or association rules formed based on the support, confidence, and lift parameters, in finding association patterns in retail product sales data with a focus on the relationship between product categories. The data used consists of 500 sales data as sample data and 5,972 transactions as test data. The data mining process was carried out on the main product categories such as Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks, to find association rules that appear simultaneously with the Bulk Products category in one transaction time. The minimum support parameter was set at 0.02 and the minimum confidence was set at 0.5. By using these parameters, several significant association rules were obtained. One of the strongest rules shows that if products in the Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks categories are purchased together, then there is a 64.3% probability (confidence) that products in the Bulk Products category are also purchased at the same time. The support value of this rule reached 3.8%, and the lift value was 1.49, indicating a positive association and not a coincidence. Evaluation of the test data showed that this pattern was consistently found across 5,972 transactions, with a repeatability rate of 61.7%. The results of this study demonstrate that the Apriori algorithm is effective in identifying consumer purchasing patterns that can be utilized for product placement strategies, bundling offers, and inventory planning in retail management.
Klasifikasi Tingkat Kepuasan Peserta Pelatihan Balai Besar Pelatihan Vokasi dan Produktivitas Menggunakan Algoritma C5.0 Maulana, Fahmi; Kurniawan, Rakhmat
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.733

Abstract

The evaluation of training participant satisfaction at the Center for Vocational Training and Productivity Development (BBPVP) has traditionally relied on conventional methods, resulting in less accurate and unstructured outcomes. The core issue necessitates a data-driven solution to enhance objectivity and reliability. This study aims to develop a C5.0 algorithm-based classification model to automatically measure participant satisfaction levels and identify dominant influencing factors. The methodology includes collecting survey data from 300 respondents across five SERVQUAL attributes (reliability, assurance, responsiveness, empathy, tangibles), data preprocessing, dataset splitting (80:20), and model development using Python’s Scikit-learn library. Results indicate a model accuracy of 98.3% (12% higher than Naïve Bayes), with "assurance" as the most influential attribute (gain ratio: 0.638). Contributions of this research include: (1) providing BBPVP with an accurate data-driven satisfaction evaluation tool, (2) offering strategic recommendations to improve training quality, particularly in assurance, and (3) potential adoption of this method as a national vocational training evaluation standard.
Analisis Perbandingan Kinerja Algoritma K-Means dan K-Medoids dengan Reduksi Dimensi PCA pada Indikator Kesehatan dan Sosial Firdawanti, Aulia Rizki; Ahmad, Hafidlotul Fatimah; Agustiani, Nur
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.742

Abstract

Public health in West Java faces complex challenges, including disparities in healthcare access, malnutrition, and socio-economic inequalities across districts. These conditions require data-driven analysis to identify patterns of disparity and provide evidence-based guidance for policy intervention. This study aims to cluster districts/cities in West Java based on health and social indicators using Principal Component Analysis (PCA) for dimensionality reduction, followed by K-Means and K-Medoids algorithms for clustering. Data from 27 districts/cities during 2019–2024 were analyzed after standardization. PCA extracted two principal components explaining 61.4% of the total variance. Scree plot and silhouette results indicated three optimal clusters. Comparative analysis revealed that the average silhouette score of K-Means was 0.31, while K-Medoids achieved a higher score of 0.34, suggesting more stable and robust partitioning against outliers. In 2024, Cluster 1 consisted of regions with adequate healthcare facilities and lower prevalence of underweight children; Cluster 2 grouped regions with limited health infrastructure and higher malnutrition problems, while Cluster 3 showed intermediate conditions. Therefore, K-Medoids outperformed K-Means by producing more consistent clustering across years. These findings offer practical recommendations: Cluster 2 should be prioritized for interventions such as improving primary healthcare access and nutrition programs, Cluster 1 requires maintenance of service quality, and Cluster 3 should be targeted for gradual reinforcement.
Optimasi Algoritma Random Forest untuk Prediksi Eksport Kelapa Sawit Global Danny, Muhtajuddin; Muhidin, Asep
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.744

Abstract

Palm oil production is a strategic commodity in global trade, with a trend showing an increase from year to year. This study aims to optimize the Random Forest algorithm in predicting the amount of global palm oil production based on historical data. The dataset used consists of 12,458 observations with one dependent variable (Palm_Oil_00002577_) representing the amount of palm oil production, and four independent variables: country, Code, Year, and Palm_Oil_00002577_log. The data is divided into 80% for training (9,966 observations) and 20% for testing (2,492 observations). The model optimization process is carried out by adjusting the key parameters of Random Forest using Grid Search and Cross-Validation. The initial Random Forest model (without optimization) produces a Root Mean Squared Error (RMSE) value of 115.27 and an R-squared (R²) value of 0.9824 on the test data. After optimization using Grid Search and Cross-Validation on key parameters (n_estimators, max_depth, and max_features), the optimized model showed significant performance improvements, with the RMSE decreasing to 103.54 and the R² increasing to 0.9984. The decrease in the RMSE indicates a reduction in the model's average prediction error, while the increase in R² approaching 1 indicates the model's ability to explain almost all of the variation in global palm oil production data. These results indicate that parameter optimization in Random Forest can substantially improve prediction accuracy, enabling the model to be used as a production planning tool and strategic decision-making tool in the palm oil commodity trading sector.
Prediksi Kegagalan Perangkat Industri Menggunakan Random Forest dan SMOTE untuk Pemeliharaan Preventif Muhidin, Asep; Muhtajuddin Danny; Surojudin, Nurhadi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.745

Abstract

Preventive maintenance is an essential strategy to minimize losses due to industrial equipment failures. This study aims to develop an equipment failure prediction model using the Random Forest algorithm with the SMOTE technique to address class imbalance. The dataset used is the AI4I 2020 Predictive Maintenance Dataset with 10,000 entries and six main input variables. Preprocessing includes normalization of numerical features, one-hot encoding for categorical features, and handling of missing values. The Random Forest model was optimized using GridSearchCV and compared with K-Nearest Neighbors. Results show that Random Forest with SMOTE achieved 97% accuracy, 0.47 precision, 0.75 recall, and 0.58 F1-score on the failure class. This model outperforms KNN in detecting failures, particularly in imbalanced data. These findings contribute to the development of an early warning system to support preventive maintenance in industrial environments.
Implementasi Metode Analitycal Hierarchy Process dan Multi-Objective Optimization by Ratio Analysis Untuk Rekomendasi Laptop Darwin, Ricalvin; Irwan, Irwan; Desnelita, Yenny; Siddik, Muhammad; Gustientiedina, Gustientiedina
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.746

Abstract

Laptops have become essential in the world of work, education, and society. With a laptop, tasks such as creating reports, sending data, learning, and even entertainment become easier. However, the variety of laptops available with different specifications can confuse people when choosing one that suits their profession and status. This confusion often leads to wasted time and the risk of choosing a laptop that does not meet their needs. Therefore, a decision support system (DSS) is needed to provide laptop recommendations based on desired criteria. In this study, the method used is a collaboration of Analytical Hierarchy Process (AHP) and Multi-Objective Optimization By Ratio Analysis (MOORA). AHP is used to calculate the weight of laptop criteria according to desired criteria, while MOORA is used to rank the recommended laptop values suitable for use. The implementation of the AHP and MOORA methods in this study resulted in laptop recommendations that meet the desired criteria and specifications of the community. Based on manual calculations in this study, the top-ranked laptop recommendation is alternative A8, the HP Victus Gaming Laptop 15 with a Yi of 0.424, followed by alternative A2, the HP Pavilion Gaming 15 with a Yi of 0.382. This study is considered successful because the results of manual calculations and those of the system built are consistent. Thus, the implementation of AHP and MOORA methods in a web-based system can be used for laptop recommendations.
Implementasi Simple Queue pada Router Mikrotik RB941-2nd untuk meningkatkan Quality of Service Jaringan Internet Kampus Laia, Desti Indah Sari; Hia, Daniel; Zalukhu, Yasri Astari; Zebua, Tutimurni; Kaban, Roberto; Br Ginting, Dewi Yohana
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.747

Abstract

The internet has become a primary necessity in supporting various activities in higher education, including academic, research, and administrative purposes. The availability of stable and high-quality internet services is crucial, as most campus activities have shifted to digital platforms such as online learning, access to scientific journals, email communication, and cloud-based applications. Institut Teknologi dan Bisnis Indonesia (ITB Indonesia), located in Medan, provides internet infrastructure that serves hundreds of users daily, consisting of lecturers, students, and administrative staff. The high number of users creates network load, which may decrease service quality if not properly managed. Therefore, an appropriate bandwidth management method is required to maintain network stability and performance. This study implements the Simple Queue method on MikroTik devices to analyze internet traffic patterns and evaluate Quality of Service (QoS) parameters, including throughput, delay, jitter, and packet loss. The Simple Queue method is chosen for its ability to distribute bandwidth proportionally among users, preventing excessive usage by certain individuals. The results show an improvement in throughput, where lecturers increased from 855 Kbps to 1000 Kbps, staff from 855 Kbps to 1000 Kbps, and students from 854 Kbps to 941 Kbps. Delay values decreased from 103 ms to 80.7 ms for lecturers, from 103 ms to 67.7 ms for staff, and from 419 ms to 68.3 ms for students, indicating reduced transmission latency. Jitter values decreased significantly from 119 ms to 18.9 ms for lecturers, from 119 ms to 11.2 ms for staff, and from 296 ms to 21.8 ms for students, reflecting more stable data transmission. Packet loss also decreased from 14.5% to 0% for lecturers and staff, and from 14.6% to 5.90% for students, showing improved data delivery reliability. The findings indicate that the implementation of the Simple Queue method effectively improves internet performance in the campus network. Furthermore, the results can provide useful recommendations for network administrators in optimizing bandwidth management, developing network infrastructure, and formulating strategies to enhance internet service quality in higher education environments.

Page 25 of 33 | Total Record : 329

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