cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282370070808
Journal Mail Official
jurnal.bulletincsr@gmail.com
Editorial Address
Jalan sisingamangaraja No 338 Medan, Indonesia
Location
Kota medan,
Sumatera utara
INDONESIA
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
 29   30   31   32   33 
Perancangan Arsitektur Conversational Decision Support System Berbasis Agentic AI dan Large Language Models Hadijono, Ardijan
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.997

Abstract

The rapid advancement of information technology and the increasing complexity of organizational data have intensified the need for more adaptive and accessible Decision Support Systems (DSS), particularly for non-technical users. This study aims to examine and propose a Conversational AI–Driven Decision Support System as an evolution of DSS in the modern data era. The research adopts a Conceptual and Architectural Research (CAR) approach grounded in the principles of Design Science Research (DSR), with CRISP(Q) ML employed as the system development methodology. The main contribution of this study lies in the design of a conceptual architecture and an end-to-end process flow that integrates Agentic AI based on Large Language Models (LLM) with Business Intelligence infrastructure to support interactive data exploration. The proposed intelligent agents are capable of understanding user query context, performing step-by-step reasoning, and autonomously generating analytical queries, thereby overcoming the limitations of traditional NLP-based approaches. The findings indicate that conversational approaches have the potential to enhance analytical accessibility and support faster decision-making, while also identifying challenges related to data quality, governance, and user trust.
Implementasi Algoritma Kruskal untuk Menentukan Minimum Spanning Tree Rute Tempat Pembuangan Sampah Berbasis GUI Karyawan, Moch. Anang; Effendi, Yusuf; Ridwan, Muhammad Zakariya Alif; Adityawarman, Maulana Muhammad; Ikhwan, Muhammad Khairul
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.998

Abstract

Waste management in urban areas such as Surabaya faces dual challenges of increasing waste volume and operational efficiency. The city’s daily waste production of approximately 1,800 tons requires a precise logistics system to reduce transportation costs, which account for 60% to 85% of the total municipal waste management budget. This study aims to optimize waste collection routes in East Surabaya, a rapidly growing area. Kruskal’s algorithm was implemented to determine the Minimum Spanning Tree (MST) of the waste disposal network. The Waterfall software development model was applied to build a Java-based Graphical User Interface (GUI) application that visualizes optimal routes with minimal total distance. Distance data among six waste disposal sites (TPS) were obtained from actual road mapping and modeled as a weighted graph. The selection of only six TPS was intended as a case study to simplify the initial modeling process, which presents certain limitations in generalization but remains relevant for demonstrating route optimization potential. The results show that Kruskal’s algorithm produced an MST structure with a minimum total distance of 11 km. The developed application was tested and proved effective in supporting route planning and providing user-friendly graphical visualization. This research is expected to contribute to decision-making in optimizing urban waste collection logistics.
Sistem Pendukung Keputusan Optimasi Perencanaan Stok Obat Menggunakan Metode Weight Moving Average Opitasari, Opitasari; Yadarabullah, Yadarabullah
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.999

Abstract

A common issue in pharmacy inventory management is the occurrence of stockouts or overstock, which affects patient service. This study aims to design a decision support system that optimizes drug stock planning accurately and efficiently using the Weighted Moving Average (WMA) method. The system is developed as a web-based application using the Laravel framework and MySQL database, utilizing hisorical sales data as the basis for forecasting drug demand. The evaluation results indicate that the WMA method provides excellent prediction accuracy, achieving a MAPE value of 8.63%. This system assists pharmacists in making data-driven decisions, minimizing stock risks, and supporting the digitalization of inventory management in pharmacies
Prediction of Palm Oil Fresh Fruit Bunch Yield using Support Vector Machine (SVM) Widyawanti, Try; Fakhriza, M
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1002

Abstract

Palm oil fresh fruit bunch (FFB) production plays a crucial role in plantation management and decision making. However, fluctuations in environmental conditions and plantation characteristics often make yield estimation difficult to perform accurately. This study aims to predict palm oil fresh fruit bunch yield using the Support Vector Machine (SVM) algorithm as a machine learning–based approach. The dataset used in this research consists of monthly production data from 2020 to 2024, including several influential variables such as plant age, land area, rainfall, and soil characteristics. The data were preprocessed through cleaning, transformation, and normalization using the min–max scaling method to ensure consistency and stability during model training. The SVM model was implemented using the Radial Basis Function (RBF) kernel, which is suitable for handling nonlinear data patterns. Model evaluation was conducted by dividing the dataset into training and testing data with a ratio of 80% and 20%, respectively. The performance of the proposed model was measured using Root Mean Square Error (RMSE) and accuracy metrics. Experimental results show that the SVM model achieved an RMSE value of 1.316561 and an accuracy rate of 56.6%, indicating that the model is able to capture the general pattern of palm oil FFB yield data with a relatively small prediction error. Although the accuracy obtained is moderate, the results demonstrate that SVM can be applied as an initial predictive tool for estimating palm oil yield. The findings of this study are expected to support plantation managers in planning harvest activities and optimizing resource allocation.
Analisis Faktor Kinerja Penjualan Harian UMKM Menggunakan Metode Feature Engineering dan XAI Berbasis Gradient Boosting Regressor Raihan, Nur Afif; Shiroth, Salman Fathy
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1005

Abstract

Micro, small, and medium enterprises (MSMEs) in the food and beverage sector typically possess daily transaction records, yet these data are often used only for bookkeeping. As a result, the drivers of sales fluctuations are not clearly identified and operational decisions tend to rely on intuition. This study aims to (1) analyze the key factors influencing daily sales performance of MSME MW and (2) develop an accurate and interpretable sales forecasting model. The main problem addressed is how to transform time series transaction data, which are affected by seasonality, changes in customer behavior, and shifts across digital channels, into informative predictors that can capture non-linear relationships while still providing actionable explanations. The proposed solution integrates time series feature engineering to construct 21 predictive features from POS and digital channel data spanning 19 months, applies a Gradient Boosting Regressor to model complex patterns and improve predictive accuracy, and employs Explainable AI using the SHAP method to quantify both global and local feature contributions to the model output. Preliminary results indicate strong forecasting performance, achieving an R² of 0.987265, an MAE of IDR 33,194.014, and an RMSE of IDR 42,625.808, suggesting a high level of agreement between predicted and actual daily sales. The SHAP analysis identifies “Total Items Sold” and “ATV” as the most dominant drivers of sales increases, while lag-based sales features exhibit non-linear behavior consistent with mean reversion following extreme spikes. Linguistically, these findings imply that revenue growth is driven not only by sales volume, but also by optimizing average transaction value, providing a clear direction for data-driven operational strategies.
Analisis Spasial Keterjangkauan Jarak Layanan dan Pola Persebaran Lembaga Pendidikan Menggunakan Buffering dan Nearest Neighbor Analysis Lestari, Widya Ayu; Saputro, Wahju Tjahjo; Widihasaniputri, Hanifatus Sa’diyah
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1014

Abstract

Equitable access to religious educational services is strongly influenced by the spatial distribution and service coverage of educational institutions. However, the growth of educational facilities is not always accompanied by balanced spatial planning, which can lead to accessibility disparities. This study aims to analyze service accessibility and spatial distribution patterns of educational institutions in Tlogomulyo District, Temanggung Regency, using a Geographic Information System (GIS) based quantitative descriptive analysis approach. Service accessibility was assessed using buffer analysis with a 500-meter radius, referring to neighborhood-scale educational facility service standards outlined in SNI 03-1733-2004, representing a reasonable service distance within rural settlement contexts, while spatial distribution patterns were examined using Nearest Neighbor Analysis (NNA) implemented in QGIS. The results indicate that most residential areas are within the service coverage of educational institutions, although several locations remain underserved. The NNA index value of 0.93 with a z-score of ?0.79 at a significance level of ? = 0.05 suggests a weak clustered distribution pattern that is not statistically significant at the 95% confidence level. These findings indicate that the spatial distribution of educational institutions tends to follow settlement patterns but has not yet achieved spatial equity. This study highlights the importance of GIS-based spatial analysis as a decision-support tool for planning and optimizing the distribution of religious educational services to promote equitable access within the study area.
Analisis Klasterisasi Wilayah Berdasarkan Tingkat Kepadatan Penduduk Menggunakan Algoritma K-Means Berbasis Sistem Informasi Geografis Athallah, Mustafa Iffat Shafi; Saputro, Wahju Tjahjo; Pasa, Ike Yunia
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1015

Abstract

This study aims to analyze and map the population density of regencies and municipalities in Jawa Tengah using a spatial analysis approach based on Geographic Information Systems (GIS) and the K-Means clustering algorithm. The main issue addressed is the lack of systematically classified and informative population density mapping to support spatial analysis and regional decision-making. Secondary data were obtained from the Central Bureau of Statistics (BPS), including total population, population growth rate, population percentage, population density per square kilometer, and administrative boundary spatial data. Prior to clustering, all variables were normalized using the Min-Max scaling method to prevent bias caused by differences in variable ranges in Euclidean distance calculations. The research employed a quantitative descriptive method with K-Means (K=3) to classify regions into low, medium, and high population density clusters. The results indicate that out of 35 regencies/municipalities, 7 regions (20%) fall into the high-density cluster, 22 regions (62.86%) into the medium-density cluster, and 6 regions (17.14%) into the low-density cluster. The implementation of the clustering results into a thematic map using a color scheme of red (high), yellow (medium), and green (low) effectively visualizes spatial distribution patterns, thereby supporting data-driven regional planning and spatial-based policy formulation.
Analisis Prioritas Distribusi Pupuk Bersubsidi untuk Ketepatan Sasaran Menggunakan Skoring Berbasis Sistem Informasi Geografis Huwae, Hans Andriano Pradana; Saputro, Wahju Tjahjo; Murhadi, Murhadi
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1016

Abstract

The distribution of subsidized fertilizer plays an important role in supporting agricultural productivity; however, its implementation at the regional level still faces mismatches between allocation and actual land requirements. This study aims to determine the priority of subsidized fertilizer distribution for paddy agricultural land in Central Java Province using a scoring method based on Geographic Information Systems (GIS). The variables applied in this study include harvested area, land productivity, and elevation. The data used are secondary data obtained from the Central Statistics Agency (BPS) for the period 2022–2024. The scoring process was conducted using Microsoft Excel with a five-level ordinal scale and equal weighting for each variable. The scoring results were then integrated with spatial analysis using QGIS to produce thematic maps of fertilizer distribution priorities. The results indicate that each regency in Central Java Province has a different priority level, which is classified into low, medium, and high priority categories. Out of 35 regencies/cities analyzed, 17 regions (48.57%) were classified as high priority, 11 regions (31.43%) as medium priority, and 7 regions (20%) as low priority. Validation results show that most high-priority areas also receive relatively large amounts of subsidized fertilizer, although some discrepancies remain due to policy considerations and regional characteristics. This study demonstrates that a GIS-based scoring method can be used as a supporting tool for planning and evaluating subsidized fertilizer distribution in a more objective and spatially informed manner.
Implementasi Algoritma K-Means Pada Sistem Persediaan Barang Khaliq, Achsyanul; Nawangsih, Ismasari; Majid, Annisa Maulana
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1017

Abstract

Inventory is an important component in a company's operational activities, especially in the trade sector, because it directly affects the smoothness of sales and the level of customer satisfaction. Unstructured inventory management can lead to stockpiling of goods, stock shortages, and inappropriate decision making. Maisa Building Materials Store located in Semerap, Kerinci Regency, Jambi, currently still records inventory manually, so the shop owner has difficulty in identifying items with high and low sales levels. This study aims to implement the K-Means Clustering algorithm in grouping inventory based on sales levels to support more effective and efficient stock management. The research method used is data mining with the stages of data collection, preprocessing, manual calculation of the K-Means algorithm, and implementation using RapidMiner software. The analyzed data amounted to 507 inventory items that have gone through a data cleaning process so that they are suitable for use in grouping. Grouping is done with two clusters, namely a cluster of goods with a low sales level and a cluster of goods with a high sales level. The results of the study indicate that 494 items, or 97.44 percent, fall into the low-sales cluster, while 13 items, or 2.56 percent, fall into the high-sales cluster. These results indicate that most products have relatively low sales turnover, while only a small proportion contribute significantly to total store sales. The information generated from this clustering process can be used as a basis for decision-making in inventory management, particularly in determining stocking priorities, stock control, and developing appropriate, data-driven marketing strategies.

Page 33 of 33 | Total Record : 329

 29   30   31   32   33 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 6 No. 2 (2026): February 2026 Vol. 6 No. 1 (2025): December 2025 Vol. 5 No. 6 (2025): October 2025 Vol. 5 No. 5 (2025): August 2025 Vol. 5 No. 4 (2025): June 2025 Vol. 5 No. 3 (2025): April 2025 Vol. 5 No. 2 (2025): February 2025 Vol. 5 No. 1 (2024): December 2024 Vol. 4 No. 6 (2024): Oktober 2024 Vol. 4 No. 5 (2024): August 2024 Vol. 4 No. 4 (2024): June 2024 Vol. 4 No. 3 (2024): April 2024 Vol. 4 No. 2 (2024): Februari 2024 Vol. 4 No. 1 (2023): December 2023 Vol. 3 No. 6 (2023): October 2023 Vol. 3 No. 5 (2023): Agustus 2023 Vol. 3 No. 4 (2023): June 2023 Vol. 3 No. 3 (2023): April 2023 Vol. 3 No. 2 (2023): Februari 2023 Vol. 3 No. 1 (2022): Desember 2022 Vol. 2 No. 3 (2022): Agustus 2022 Vol. 2 No. 2 (2022): April 2022 Vol. 2 No. 1 (2021): Desember 2021 Vol. 1 No. 3 (2021): Agustus 2021 Vol. 1 No. 2 (2021): April 2021 Vol. 1 No. 1 (2020): Desember 2020 More Issue