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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,114 Documents
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Resolving SAP ERP Warehouse Management Module Implementation Issues in a Consumer Goods Company: A Case Study of PT XYZ Christian Herlando Indra Jaya
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4623

Abstract

This study analyzes the challenges associated with the implementation of the Warehouse Management (WM) module in SAP ERP at PT XYZ, a leading FMCG company in Indonesia, and proposes solutions to address them. The implementation of the WM module aims to reduce manual processes and enhance warehouse efficiency, thereby supporting operational success by improving stock data accuracy and integrating business processes. However, the current implementation has not fully met the company’s operational needs. The research adopts a qualitative approach using the Soft Systems Methodology (SSM). Data were collected through interviews with six respondents and operational observations. The analysis focuses on system quality, data quality, organizational culture, and user support to identify existing gaps. The findings reveal several issues, including non-real-time information, stock inaccuracies, reliance on manual processes, and the absence of knowledge management. Recommendations include integrating IoT, developing real-time applications for picking and stock counting, automating replenishment using AI, implementing SAP Analytics Cloud (SAC), and creating a knowledge management system. This study provides practical solutions for similar companies to enhance ERP implementation success, particularly in the Warehouse Management module.
Crime Link Prediction Across Geographical Location Through Multifaceted Analysis: A Classifier Chain Temporal Feature-Data Frame Joins Esan, Omobayo; Isaac Olusegun Osunmakinde; Bester Chimbo
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4627

Abstract

Crime link prediction across geographical locations is vital for law enforcement to uncover hidden connections between crime spanning different areas. Traditional methods often fails to capture the complexity and temporal dynamics of crime data, limiting g their predictive power. This research introduces a novel approach to enhance crime link prediction by leveraging multifaceted analysis that integrates multiple inputs and outputs. A classifier chain transformation is used for sequential multi-label classification, capturing interdependence between crime types across locations. The method facilitates a comphrensive understanding of crime patterns over time. Experiment conducted on South Africa Police Services (SAPS) crime dataset demonstrate the proposed model's superior performance compared to state-of-the-art methods, achieving precision, recall, F1-score, and accuracy of 0.98, 0.99,0.99, and 98.99%, respectively. This research aims to contribute to crime link prediction model's, offering a more nuanced and robust framework for forensic experts and law enforcement.
Peningkatan Kualitas Data Talent Karyawan pada Human Capital Management PT XYZ Sulaeman, Achmad Firmansyah; Ruldeviyani, Yova
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4628

Abstract

The importance of high quality data is a top priority for PT XYZ’s Human Capital Management (HCM) in handling Talent Management, Career Management, and Employee Performance Management. information security audit revealed several issues, such as delayed data updates and inconsistencies across functions. To address these issue, an assessment of the Data Quality Management (DQM) maturity level is needed to evaluate the implementation of consistency, accuracy, and integrity. This study uses David Loshin’s framework, with the results explained referring to DQM guidelines in DMBOK. Results show DQM maturity level is at level 2 (Repeatable), with an average score of 2.3. Three dimensions with the lowest scores are the main focus for improvement, which Data Quality Expectations (1.6), Data Quality Protocols (1.8) and Data Quality Technology (1.2). Recommendations from this study focus on enhancing these dimensions to improve data quality and address the issues highlighted in information security audit.
User Interface Improvement by Evaluating Usability and User Experience: Case Study of Indonesia’s Government Financial Management Information System Fachri Munandar; Harry Budi Santoso
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4630

Abstract

SAKTI is an Enterprise Resource Planning (ERP) system developed by Indonesia’s Ministry of Finance, integrating 11 modules to support State Budget management. Despite its critical role, a decline in the 2023 user satisfaction index highlighted usability and service quality issues. This study offers user interface improvement by evaluating usability and user experience of the contract data recording feature. Using mixed methods, data from 23 respondents were collected for both quantitative and qualitative analysis. Quantitative data, obtained through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ), yielded an initial SUS score of 67.39 (grade C) and a UEQ attractiveness index of 1.13. Qualitative data from open-ended questions, interviews, and usability testing identified 12 usability issues. A high-fidelity prototype, guided by User-Centered Design (UCD) principles, improved the SUS score to 84.35 (grade A+) and addressed usability challenges. It enhanced usability, creating an intuitive and efficient interface that increased user satisfaction.
Towards Efficient and Reliable Video Communication: A Survey on Scalability, Error Protection, and Multicasting Wijesekara, Patikiri Arachchige Don Shehan Nilmantha
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4639

Abstract

Efficient and reliable video communication is required to maintain high-quality and uninterrupted streaming in order to minimize bandwidth usage and to tackle network variability. Scalable Video Coding (SVC) introduces efficiency for video communication by introducing a base layer and a set of enhancement layers, while Unequal Error Protection (UEP) can provide high protection to important layers while having low redundancy for less important layers/bits/frames. Moreover, scalable video transmission’s efficiency can be further improved by multicasting a video to multiple recipients simultaneously over a network efficiently, where each user can adapt to network conditions. As existing surveys do not concentrate on discussing the improving efficiency and reliability of video communication by multicasting scalable video communication focusing on UEP, we review these factors individually and in combination. We first gathered 113 original research studies using qualification criteria searched using electronic libraries, leveraging an elaborative process. As per the review, video scalability has been achieved using temporal scalability, spatial scalability with spatial resolution, quality scalability using quantization steps, and slice grouping for region of interest scalability, while UEP is achieved using transceiver, packet level, bit level, and cross-layer methods. Moreover, simulcast, multiple access techniques, multi-resolution modulation, and antenna heterogeneity have shown to be the promising SVC multicasting techniques. Review analysis shows that from reviewed work, 10.3% provide H.265-based scalability, 19.2% use transceiver UEP, and 7.7% use simulcast. Finally, we conclude our review by discussing the advantages and challenges of the concept of SVC-UEP video communication and then presenting guidance to overcome them.
Sentiment Analysis of Public Responses Regarding The Use of Electric Cars in Indonesia with Support Vector Machine and Random Forest Methods Seraphina, Yessica Anglila; Gunawan, Putu Harry
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4649

Abstract

The diminishing use of fossil fuels has encouraged the search for alternative energy sources, one of which is the electric car. However, public acceptance of electric cars in Indonesia is not widely understood. This study aims to analyze public sentiment towards electric cars based on data from X social media. The dataset used consists of 3,450 data, which is analyzed using two machine learning methods, namely Support Vector Machine (SVM) and Random Forest. The research was conducted in three scenarios: SVM kernel comparison, Random Forest performance evaluation with various numbers of n-estimators (1, 10, 100), and performance comparison between the two methods. The experimental results show that Random Forest with 100 n-estimators produces the highest accuracy of 90.72% and F1-Score of 87.54%, while SVM with RBF kernel produces 89.35% accuracy and F1-Score of 85.15%. The performance difference of 1.37% shows that Random Forest is more effective in this sentiment analysis
Control the Sensitivity of the Encryption Key to Ensure the Security of Big Data Wafaa Ali; Alajali, Walaa; Abdulrahman D. Alhusaynat
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4660

Abstract

With the increasing technological development that has led to the complexity of big data systems, the importance lies in the challenge that ensures the security of sensitive information. Encryption is one of the basic methods used to protect data, hence the importance of the encryption key and its sensitivity, which play a vital role in the strength of encryption. Encryption sensitivity is the simple change in the encryption key that produces a very different encrypted text. This study is concerned with methods of controlling the sensitivity of the encryption key and its effect on the strength of encryption of big data. This context is in line with the nature of cloud data and the focus on the attacks that this data suffers from, such as brute-force attacks and statistical attacks. The research discusses the components that make up the encryption key, logistics maps, and chaos. The results reached by the study proved the merit of the research in terms of accuracy 1015 and appropriate key sensitivity 2128. This study discussed future challenges and the possibility of using artificial intelligence algorithms and adaptive security algorithms and solving quantum encryption problems.
Kinematics Analysis of Articulated Robot Mon Mon Thae
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4668

Abstract

Robot technology is applied to support the farmers in various stages, from seeding and farm maintenance to harvesting and packaging agricultural products. The purpose of this paper is to analyze the kinematic modeling of an articulated robot that can be used for sorting mangoes based on their weight. Two methods are used to analyze the robot arm: forward and inverse kinematics. The DH method is employed to analyze both forward and inverse kinematics in this paper. Transformation matrices for each joint were obtained using the DH method to derive the forward kinematics results. For inverse kinematics, a geometric approach is presented to determine the joint angles. A graphical user interface (GUI) is used to control the articulated robot. The results are validated by comparing the calculated outcomes with the Robo-Analyzer results. In this analysis, the articulated robot's accuracy was investigated by repeating the same predetermined movement and measuring the error. After implementing the articulated robot, an experimental results were obtained to measure the accuracy and repeatability of its performance. At the end of the experimental results, the highest positioning error of the articulated robot is 1.7205 mm, and the lowest positioning error is 0.2 mm, depending on the five predetermined positions.
Penerapan TAM dalam Evaluasi Penerimaan Aplikasi P-Care BPJS Pada Puskesmas di Kabupaten Gorontao Utara Gobel, Jimilin; Dai, Roviana; Ahaliki, Budiyanto
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4102

Abstract

P-Care BPJS is a web-based management information system that can be accessed through a web browser with the address https://pcarejkn.bpjs-kesehatan.go.id/eclaim/ which generally functions to check the validity of BPJS membership data. However, in its use, there was an inconvenience when using the P-Care BPJS application where the patient's social data entry failed, causing the P-Care BPJS officer to double-enter the patient's social data. Another problem found in the form of slow application performance when used which results in the length of work time and service response time by P-Care BPJS officers. This research was conducted to find out how far the acceptance of the existing P-Care BPJS application at Puskesmas in North Gorontalo district and what factors can affect user acceptance. This research uses descriptive quantitative methods and uses the TAM Model to measure user acceptance, the results of the analysis show that the P-Care BPJS application has been well received by users with an acceptance of 80.6.
Optimasi Model Algoritma Machine Learning Suppervised menggunakan Algoritma Genetika untuk Prediksi Kebakaran Hutan dan Lahan Utami, Putri; Sucipto; Risli , Andrea; Aurilia Viona, Tiara
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4395

Abstract

Forest and land fires are a common occurrence in Indonesia, particularly in the provinces of Sumatra and Kalimantan. One strategy for mitigating the impact of forest and land fires is to predict areas that are prone to such incidents. In this study, genetic algorithm (GA) optimization was employed to enhance the efficacy of the random tree and hyper-SVM algorithms, with a view to identifying the most optimal test results. The dataset utilized in this study comprises hotspot data and climate data for Ketapang Regency during the 2021-2022 period. The results of the training and testing demonstrate that the Random Tree +GA algorithm optimization with a PC value of 0.6 and Bolzmann selection parameters yields an accuracy of 99.77%, a recall of 94.88%, a precision of 95%, an RMSE of 0.015, and a Kappa of 0.9. In contrast, the Hyper-SVM +GA optimization, with a PC value of 0.6 and Bolzmann selection parameters, yielded an accuracy of 70.48%, a recall of 52.14%, a precision of 50.58%, an RMSE of 0.493, and a Kappa of 0.026. The results demonstrate that the Random Tree +GA algorithm model optimization exhibits superior performance compared to Hyper-SVM +GA optimization. Consequently, it can be inferred that the Random Tree +GA algorithm represents the most effective classification model for predicting the likelihood of forest and land fires in Ketapang Regency

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