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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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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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Design of Mechanics and Locomotion System For Box Culverts Inspection Robot Anwar, Khoirul; Juliarsyah, Mohammad Rizanto; Pungkiarto, Irwanda Yuni; Mohammad Abdullah
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4907

Abstract

In Indonesia there is often a flood caused by continuous heavy rain, based on the observation of the condition of the culprit is the cause of flooding in Indonesia. One of the problems owned by the public Works office today is the presence of Dutch-made sewers with concrete materials that have a depth of 6 meters below the surface so that the workers are hard to reach. The following problems need to be developed a technology that does not require human presence to directly monitor the circumstances that occur in the drain pipes (sewers) by using a mobile robot to perform monitoring. The result of this study obtained output in the form of a mobile robot that can work in various terrain, and to get a good response in the control system used Ziegler-Nichols for Tunning.
A Real-time Internet of Things-Based Wireless Livestock Tracking System for Theft Prevention Sandlana, Muzi; Mathonsi, Topside E.; Deon du Plessis; Tu, Chunling
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4910

Abstract

Livestock theft is a significant threat to the agricultural industry, necessitating innovative preventive strategies. This study proposes a Wireless Livestock Tracking System (WLTS) that uses real-time Internet of Things (IoT) technologies to prevent livestock theft. The WLTS integrates GPS sensors with Long Range Radio (LoRa) wireless communication modules, overcoming the limitations of Wi-Fi and Bluetooth-based systems. It uses a single LoRa network receiver to facilitate real-time communication between farmers and their livestock. Simulation results show the WLTS effectively mitigates livestock theft, enabling farmers to quickly identify and recover stolen animals. Geofencing alerts enhance the system's sensitivity to potential theft scenarios. The WLTS has a user-friendly interface, allowing farmers to remotely monitor their livestock. Data analytics capabilities enable predictive analysis of probable theft trends based on historical data. The findings pave the way for practical implementation, revolutionizing livestock protection and safeguarding farmers' livelihoods worldwide.
Enhanced Fake News Detection with Domain-Specific Word Embeddings: A TorchText-Based Method for News Semantics Representation. Ngwenya, Sikhumbuzo; Garidzira , Tinashe Crispen
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4831

Abstract

The prevalence of misinformation in digital media highlights the need for effective fake news detection methods. This paper presents a novel approach that leverages domain-specific word embeddings, trained specifically on news content, to improve the accuracy of fake news classification. Using TorchText, we generated 128-dimensional embeddings, optimized with Bi-LSTM and GRU models, achieving a test accuracy of 93.51% with a margin of error of 0.255. Two models were developed to classify fake news based on news headlines. The first model using pre-trained embeddings achieved a test accuracy of 96.51% with a margin of error of 0.102, and the second model trained without pre-trained embeddings, resulting in slightly worse resulting in a slightly lower accuracy of 96.23% with a loss of 0.104. The comparison highlights the significant impact of domain-specific integration on model performance. This study demonstrates the value of custom integration to improve semantic representation and fake news detection accuracy, providing a powerful tool to combat misinformation.
Enhanced Security Algorithm for Detecting Distributed Denial of Services Attacks in Cloud Computing Baloyi, Coster; Mathonsi, Topside E.; Plessis, Deon Du; Tshilongamulenzhe, Tshimangadzo
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4888

Abstract

Cloud Computing has the benefit of offering on-demand scalable services to its customers without having to invest much on hardware infrastructure, resources and software. Most private and public sectors are moving to the Cloud. As a result, Cloud Computing has become an ideal option due to its flexibility, scalability and cost efficiency. The existence of vulnerabilities in the network systems hosting Cloud have raised an opportunity for attackers to launch attacks in Cloud Computing. The intruders attack business applications such as webservers, financial servers, and other servers exploiting Distributed Denial of Service (DDoS) attacks. This paper proposed a Real-Time Network Traffic Attack Detection (RTNTAD) algorithm to detect DDoS attacks using real-time dataset to mitigate DDoS attacks. MATLAB was employed to evaluate the performance of RTNTAD. The proposed RTNTAD algorithm has achieved 99.2% detection rate, 99.5% classification of DDoS attacks, 0.9% connectivity time out and less than 18% false positive.
Klasifikasi Beban menggunakan Feed - Forward Neural Network pada Gedung Bertingkat Armanto, Ony; Aulia, Masyitah; Bahrul, Yasya
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4894

Abstract

Electricity consumption continues to increase year by year, leading to inefficiencies in energy management. This issue has become a major concern in modern power systems, particularly in energy monitoring systems based on Smart Grid technology. As the use of technology becomes more accessible, energy loads also grow significantly. Therefore, the ability to identify the types of electrical loads used in an installation is crucial, necessitating the implementation of load classification systems. To support the performance of electrical load classification, a Feed-Forward Neural Network (FFNN) is utilized. The results of this study show that the classification model achieved an accuracy of 99.03% with an error rate of 6.43%, and the RSME 0.098, indicating excellent classification performance
Dogfight dari Sudut Pandang Teori Permainan Rafi Prayoga Dhenanta; Aditya Purwa Santika
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4902

Abstract

Dogfight is one of many scenarios happening in a battle for air-superiority. This research delves deeper into dogfight, using the perspectives of game theory. The purpose of this research is to model a strategy that can be used in a dogfight. This research models dogfight into game theory’s extensive-form-games and then simulates the model ccompuationally. From the simulation, the model developed in this research increases the winning rate of a certain player significantly.
Implementing an Information Verification System to Prevent Academic Fraud by Employees Using a Hybrid of ANN and RF Algorithms Lebopa, Lebogang Vinnas; Tonderai Muchenje; Topside E. Mathonsi; Solly P. Maswikaneng
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4909

Abstract

Academic fraud, particularly the falsification of qualifications, poses a growing threat to organizational integrity and professional credibility. This study proposes an Information Verification System (IVS) to combat employee credential fraud using a hybrid of Artificial Neural Network (ANN) and Random Forest (RF) algorithms. The method follows a two-step process: first, ANN extracts key certificate features, such as digital signatures, logos, and serial numbers, then RF classifies the certificate as authentic or fraudulent based on these features. Tested on 4,830 certificates from Mopani TVET College, alongside 1500 replicas, the system achieved near-perfect results: 98.90% accuracy, 96.75% precision, 99.33% recall, and a 98.03% F1-score, outperforming Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Logistic Regression models. By integrating with institutional databases, the IVS offers a scalable, secure solution to automate verification processes so that only legitimate qualifications are accepted. These results suggest that the proposed IVS offers a scalable and secure solution for institutions and employers, significantly improving the efficiency and reliability of academic credential verification.
Unraveling the Structure of India’s Railway Network: Insights from Network Analysis Al Afgani, Fadil
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4920

Abstract

India's railway station network is a vast and complex system that plays a crucial role in the country's transportation infrastructure. In this analysis, we will explore the network of Indian railway stations using network analysis techniques. Network analysis is a statistical approach to analyzing the relationships between variables in a network. A network is a graphical representation of the relationships (edges) between variables (nodes). The study will involve constructing a network representation of the railway station network, where each station is represented as a node, and the connections between stations are represented as edges. This visualization allows us to identify the type of network, communities, overlapping communities, cascade failure, and heterogeneous information network within the network. Based on the analysis results, the formed network is a scale-free network. The community detection analysis using the Leiden algorithm shows that there are 23 clusters formed with a quality value of 0.97662. Overlapping communities are present when the value of K ≤ 3, and there is the potential for cascade failure or an epidemic when the node with the highest degree is assigned the status of infected. The formed India's railway station network is a HINs (heterogeneous information network) as it consists of various types of entities with different characteristics.
Perancangan Kapabilitas Security Operations Center di Public Cloud Computing Environment: Studi Kasus PT. XYZ Fadholi, M Ryan; Rizal Fathoni Aji
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4945

Abstract

Cybersecurity has become a major challenge for financial institutions, including PT. XYZ. Although PT. XYZ has established a Security Operations Center (SOC) to safeguard its digital assets, the current SOC team lacks optimal capability to monitor the organization's newly adopted public cloud environment. This gap increases the risk of undetected cyberattacks targeting the cloud infrastructure. This study aims to develop recommendations for enhancing SOC capabilities in PT. XYZ’s public cloud environment using the Design Science Research (DSR) method. The initial SOC condition was analyzed through document review and observation. Capability gaps were identified through focus group discussions (FGD) guided by the SOC-CMM screening tool. The NIST Cybersecurity Framework (CSF) was then employed as the foundation for defining target capabilities. The study resulted in a set of 35 practical recommendations to improve the SOC team's capabilities, categorized according to the SOC-CMM domains.
Enhancing Diabetes Prediction Accuracy Using Stacked Machine Learning and Deep Learning Models: A Public Health Approach Islam, Md Ziarul; Mohd Khairul Azmi Bin Hassan; Amir 'Aatieff Bin Amir Hussin; Md Salman Sha
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4947

Abstract

Diabetes mellitus is a growing public health issue in Malaysia, affecting 7 million adults aged 18 and older. By 2025, 20.1% of Malaysians will have diabetes, with the International Diabetes Federation predicting 5 million by 2030. A study aims to improve diabetes prediction accuracy and reliability. The Indian PIMA Diabetes dataset was used to develop stacked machine learning and deep learning models, with 70% ML and 30% DL achieving optimal results. The weighted soft voting ensemble (70% ML, 30% DL) outperformed individual stacking models in terms of reliability and balanced performance, improving diabetes classification with 75.65% accuracy, 67.89% precision, and 81.41% ROC-AUC. The ensemble method, optimized for medical diagnosis tasks, showed improved accuracy, robustness, and generalization. However, ethical considerations, data privacy, and algorithmic biases are crucial for maximizing AI's potential in diabetes care, highlighting the need for scalable solutions.

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