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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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Development of IoT-Based Home Security Monitoring and Management Systems to Support Smart City Ecosystems Aldo Dwi Wahyudi; I Gede Puja Astawa; Faridatun Nadziroh
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.v14i2.4605

Abstract

The safety of the house and its contents is an essentialneed and must be maintained to avoid unwanted things. This is because of the increasing prevalence of criminal acts of theft in houses that their owners are abandoning. Along with the development of the times and the increase in technology, a home security device is needed to obtain accurate information, where homeowners can access and get reports about the house's conditionin real-time. To overcome these problems, it is necessary to have a tool that can monitor the house's condition and inform the house's state at any time. The device used in designing this security system, namely the Esp32-Cam microcontroller,will automatically send photos to Telegram,and the buzzer alarm and LED flash will be turned on;if this thief gets closer to the front door of the house, then the ultrasonic waves will also be activated.
Development of IoT-Based Smart Waste Management Systems for Organic and Non-Organic Waste in Smart Cities Aurelya Kirani Afkarien; I Gede Puja Astawa; Faridatun Nadziroh
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.v14i2.4606

Abstract

Public awareness of the importance of separating organic and inorganic waste and waste management is minimal, so there is a lack of public knowledge to distinguish between organic and inorganic waste. In addition, the slow transportation of garbage from the Temporary Disposal Site (TPS) to the Final Disposal Site (TPA) with a slow garbage truck can cause accumulation, so height monitoring that can be accessed remotely is needed. Based on these problems, it is necessary to have Organic and Inorganic Waste Detection tools and Internet of Things (IoT)-based altitude monitoring using infrared, capacitive, and inductive proximity sensors to distinguish between the two types of waste. This tool also monitors garbage height using ultrasonic sensors and the Ublox Neo 6mv2 GPS Module connected to the NodemCU ESP32 microcontroller to determine the longitude and latitude location point values. The altitude information will be sent to the Google Firebase server using WiFi and displayed on the App created using the MIT App Inventor.
Polinomial Jacobi dan T-Design untuk Kode Linear Susanto, Agnes Indarwati; Santika, Aditya Purwa
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.v14i3.4614

Abstract

This thesis explores the use of Jacobi polynomials and t-design properties in linear codes. The primary goal of the research is to develop a Python and SageMath program to compute the Jacobi polynomial for linear codes with multiple reference vectors. The methodology involves analyzing self-dual codes over various f ields to derive Jacobi polynomials under specific conditions. The results indicate that the analyzed codes do not satisfy the t-design criteria, as different random reference vectors yield varying Jacobi polynomials. The study offers insights into the relationship between linear codes and Jacobi polynomials, with suggestions for further exploration of more complex codes to meet the t-design criteria.
Optimizing the Performance of the PSHS CARC Knowledge Hub: A Mixed-Method Evaluation of a Moodle-Based LMS Cuyasen, Graceson
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.v14i3.4617

Abstract

Abstract—This study focuses on optimizing the performance of the Philippine Science High School – Cordillera Administrative Region Campus Knowledge Hub (PSHS-CARC KHub), a Moodle-based Learning Management System (LMS), by addressing key performance issues such as slow response times. A mixed-method approach, including a comprehensive literature review and experimental testing, was used to identify effective strategies for hardware, software, and front-end optimization. The study examined the impact of server configurations, memory upgrades, and Apache module optimizations on system performance. Results indicate that hardware optimizations (such as SSD deployment), software improvements (including database indexing and caching), and front-end enhancements (such as minimizing HTTP requests and optimizing images) led to measurable improvements in system scalability and responsiveness. While performance tests showed reduced response times and stable throughput, occasional delays for high-latency transactions were observed. These findings provide actionable insights for optimizing Moodle-based LMS platforms, improving both user experience and system efficiency.
Sebuah Deteksi Sampah Tenggelam menggunakan Modul Eksitasi Reseptif Ganda Todingan, Tomi Heri Julianus; Kutika, Imanuel; Lahimade, Vicky Nolant Setyanto; Sambul, Alwin M.; Lantang, Oktavian A.; Putro, Muhamad Dwisnanto
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.v14i2.4635

Abstract

Sunken litter poses a severe ecological challenge, threatening marine life and global ecosystems. Plastic litter is particularly concerning as it could disrupt the food chain, impacting the biodiversity and ecosystem. Over time, without intervention, this issue poses a severe threat to global food security, economic stability in coastal communities, and overall environmental balance. Addressing this problem requires effective monitoring systems for detection. This study enhances the YOLOv10 architecture with a novel Dual Receptive Excitation (DRE) module to improve sunken litter detection. The DRE module uses a dynamic dual-kernel approach to balance spatial and channel-wise processing in Convolutional Neural Networks, adaptively adjusting the receptive field, and capturing critical patterns across scales. Evaluations on the challenging Trash-ICRA19 dataset, sourced from J-EDI, demonstrate the model's robustness under diverse underwater conditions. The proposed system achieves a mean average precision (mAP) of 47.4% and processes 19.60 frames per second, outperforming other studies.
Variability in Makeup and Expressions: Impacts on Deep Learning Classifiers for Face Recognition: Assessing the Performance of Deep Learning Models in Diverse Facial Scenarios Egwali, Annie; Winifred, Sule
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.v14i3.4636

Abstract

Facial recognition technology serves as an integral component of security, access management, and identification systems. This study addresses the challenges this technology faces due to makeup and varying facial expressions, which can lead to misidentification. We investigate the effectiveness of five deep learning models—ResNet, InceptionV3, EfficientNet, Xception, and SENet—in recognizing faces with makeup and diverse emotional expressions. Using five publicly accessible datasets, including KDEF, CelebA, and UTKFace, we measure performance with metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Our analysis evaluates the benefits of transfer learning with pre-trained models and their robustness against new data. We find that InceptionV3 achieves peak accuracy of 85.2% on CelebA with high performance across all datasets, with an average accuracy of 79.8%. These results highlight how makeup and emotional expressions affect recognition accuracy and emphasize the need for improving facial recognition technologies for security and accessibility applications.
Investigasi Tantangan dalam Penerapan Tata Kelola Keamanan Informasi di Sektor-Sektor Utama: Analisis Komparatif Lintas Negara Saraswati, Karisa; Purwandari, Betty; Trisnawaty, Ni Wayan
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.v14i2.4637

Abstract

This study investigates the challenges of implementing information security governance across countries. Initial analysis was carried out from cases in Indonesian organizations. Using Kitchenham’s systematic literature review to identify challenges and information security governance expert interviews to validate the result, the research analyzes thirty-four issues and compares them with those in other developing and developed countries. The objective is to identify common challenges, highlight differences, and propose recommendations for improvement. Findings reveal that Indonesia faces difficulties that are similar to those of other developing nations, such as limited leadership support and resource constraints. In contrast, developed countries struggle with overlapping regulations and maintaining compliance despite having stronger frameworks. The study emphasizes the importance of cohesive frameworks, enhanced training, and management support to improve governance practices. These results provide actionable insights for policymakers and organizations to strengthen information security governance and address the increasing complexity of global cybersecurity challenges.
Optimasi Klasifikasi Gestur Tangan Menggunakan Metode CNN Dengan Implementasi Strategi Landmark Berbasis Warna Komplementer Agus Nugroho; Jasmir; M. Riza Pahlevi. B, S; Roby Setiawan
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.v14i2.4645

Abstract

The growth of hand gesture recognition technology has positively impacted various sectors. However, classification errors often occur due to the similarity of gesture shapes, which are challenging for models to differentiate. This study aims to develop a classification method based on Convolutional Neural Network (CNN) using a landmark modification approach with complementary colors. This approach applies significant color contrast to enhance the model’s ability to extract unique features from similar hand gestures. The dataset used includes gestures with color modifications on landmarks using an HSV-based color wheel to create maximum contrast. The data is then processed through bounding box creation, resizing, and transfer learning using the Teachable Machine architecture. The study results show a significant improvement in classification accuracy for models with landmark modifications compared to those without. Metrics analysis, including precision, recall, and F1-score, confirms that this approach effectively reduces classification errors caused by similar hand gestures.
Klasifikasi Sentimen Tweet dengan Arsitektur Hybrid Transformers-CNN pada Platform Twitter Safrizal Ardana Ardiyansa; Abdi Negara Guci; Jemmy Febryan; Dian Alhusari; Haidar Ahmad Fajri
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.v14i3.4653

Abstract

Twitter, now known as X, is a popular platform used to express opinions on the latest trends, making it a valuable source of data for sentiment analysis research. The huge volume of data makes manual analysis impractical because it requires a long time and human resources, so it is necessary to automate the sentiment classification process through machine learning. Machine learning can be used to classify sentiment on a large scale quickly and accurately by utilising patterns. Machine learning models such as Transformers-CNN show the most superior performance with accuracy reaching 85.71% on test data and 99.90% on training data. The accuracy on the test data was better than other architectures namely LSTM, CNN, BERT, Transformers-LSTM, and LSTM-CNN with accuracies of 84.73%; 82.27%; 77.34%; 85.71%; 84.24% respectively. Transformers-CNN also has a training time of 30.17 minutes which is shorter than Transformers-LSTM, but longer than the other architectures.
Designing a Web-Based Vehicle Tracking Application using the Design Thinking Method Amalia, Endang
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.v14i3.4655

Abstract

The primary challenge faced by fleet managers in mining companies is the lack of sufficient visibility and control over their fleet operations. Without accurate, real-time information about the location, status, and performance of vehicles, companies may struggle to optimize routes, maintain fleet safety, and provide quality service to customers. Firm measures and strict supervision are necessary to ensure the continuity of mining product distribution. Therefore, a web-based application was designed to address these issues. In designing the web-based vehicle tracking application that aligns with user needs, the design thinking method was employed because this method places users at the core, ensuring that the resulting prototype truly meets users' needs and preferences. The prototype was then implemented as a web-based application using Vue.js and the OpenLayers Library to produce an application consisting of a login page, dashboard, vehicle tracking, device management, master data, violation, and reports that meet user needs based on acceptance criteria tested using the User Acceptance Test (UAT) method, thereby becoming a solution to users' needs.

Page 101 of 112 | Total Record : 1114

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