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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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Machine Learning Techniques for Enhancing Internet of Things (IoT) Performance A Review Hussein, Diana; Abdullah, Rebwar; Askar, Shavan; Ibrahim, Media
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.v14i1.4735

Abstract

The Internet of Things (IoT) is basically billions of interconnected smart devices that can communicate with little interference from humans, thus making life easier. The IoT is a fast-moving area of research, and the challenges are growing, thus requiring continuous improvement. As IoT systems become more challenging to improve, machine learning (ML) is increasingly incorporated into IoT systems to develop better capabilities. This article review explores several machine learning techniques aimed at enhancing the performance of IoT systems. It highlights the growing importance of integrating machine learning with IoT to address challenges such as data management, security, and real-time processing. The techniques discussed include supervised learning, unsupervised learning, reinforcement learning, deep learning, ensemble methods, anomaly detection, and federated learning. Each method is evaluated for its effectiveness in optimizing IoT applications, such as predictive maintenance, energy efficiency, and smart city solutions. The review emphasizes the potential of these techniques to improve decision-making processes, automate operations, and enhance user experiences. Additionally, it addresses the limitations and challenges associated with implementing machine learning in IoT environments, including data privacy concerns and the need for robust algorithms capable of handling diverse datasets. Overall, the article underscores the transformative role of machine learning in advancing IoT capabilities and suggests future research directions to further leverage these technologies for improved system performance and reliability.
Deciphering the Critical Success Factors of Application Development to Accelerate Digital Transformation and Service Innovation Local Bank Perspectives Apriansyah Pagua, Jeri; Raharjo, Teguh; Trisnawaty, Ni Wayan
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.v14i1.4736

Abstract

The rapid advancement of Industry 4.0 forces local banks to accelerate digital transformation and service innovation. However, Bank ABC faces a low project success rate of only 30%. This study identifies the Critical Success Factors (CSFs) that influence application development projects in local banks using the Analytical Hierarchy Process (AHP). Eight IT Subject Matter Experts (SMEs) from Bank ABC provided expert judgment, and the analysis was conducted using R Studio. The findings highlight five dominant CSFs: leadership, project team commitment, user support, project scale, and regulatory policies. These factors are crucial in improving project success rates, optimizing decision-making, and supporting local banks’ digital transformation. This study contributes to academic research and practical implementation by providing a structured framework for evaluating and prioritizing CSFs in banking application development projects.
Deep Learning Techniques for Network Security Yousif Mohammed Ismail; Diana Hayder Hussein; Shavan Askar; Media Ali Ibrahim
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.v14i1.4737

Abstract

This article explores the seven outstanding deep-learning techniques used to enhance network security. It provides a comprehensive analysis of how these techniques address various cybersecurity challenges, including intrusion detection, malware classification, and anomaly detection. This review highlights the effectiveness of deep learning models such as Convolutional Neural Networks (Recurrent neural networks (RNNs) and automatic encoders used in processing large datasets and identifying complex patterns representing security threats. The article also discusses the advantages and limitations of each technique, emphasizing the importance of feature extraction, model training, and real-time processing capabilities. By combining the findings of the current research, this review aims to guide future research and practical implementation of deep learning in securing network infrastructure against evolving cyber threats. The review provided a comprehensive summary of the deep learning techniques used in network security, highlighting their strengths and limitations. The findings showed that deep learning has significant potential to improve detection and response to network threats, although challenges related to model interpretability, data quality, and computational efficiency should be addressed.
Examining 1D and 2D CNN Architectures in Comparison for Sentiment Analysis in Sequential Data: A Case Study of Spotify Music Reviews Matobobo, Courage; Garidzira, Tinashe Crispen
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.v14i2.4740

Abstract

This study examines the comparative performance of one-dimensional (1D) and two-dimensional (2D) Convolutional Neural Networks (CNNs) in processing sequential data for sentiment analysis, using Spotify music reviews as a case study. Leveraging a custom dataset from Kaggle, the study examines the effectiveness of CNN architectures in extracting meaningful patterns from text input. The study integrates PyTorch and TorchText for efficient data preprocessing and model deployment. Both architectures are evaluated based on classification accuracy, computational efficiency, and ability to handle sequential dependencies. The results highlight the strengths and limitations of each method, providing insight into their suitability for similar tasks in text-based sentiment analysis. This research provides valuable guidance for researchers and practitioners working on sequential data tasks, emphasizing the role of architectural design in achieving optimal performance.
Perencanaan Strategis SI/TI Dalam Meningkatkan Kualitas Pelayanan Pasien : Studi Kasus RS XYZ Aditiyawijaya, Hendra; Harya Damar Widiputra
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.v14i1.4743

Abstract

Advances in information systems and information technology (IS/IT) have become an important element in improving the quality of health services. XYZ Jakarta Hospital faces challenges in integrating IS/IT to support health services to patients. This research aims to develop an IT strategic plan that is in line with the hospital's business objectives, using the IASA IT Strategic Planning methodology. The results showed that although XYZ Hospital has various information systems, their investment and implementation have not been fully integrated with the hospital's business processes. The resulting recommendations include strengthening infrastructure, improving system interoperability, and efficient patient data management. With the right strategy, it is expected that IS/IT can contribute to improving service quality and patient trust in a sustainable manner.
IoT Based Environmental Monitoring System for Residential Building with LoRa Technology Zin Mar; Lwin, Zin Mar; Hla, Tin Tin
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.v14i1.4745

Abstract

This paper introduces an environmental monitoring system for residential buildings that combines PIC microcontroller functionality with LoRa technology. The system integrates motion, flame, gas, vibration, temperature, and humidity sensors to provide real-time monitoring of environmental conditions and safety risks. The PIC microcontroller acts as the system's core, gathering and processing data from the sensors before transmitting it over a LoRa network to a remote station. LoRa technology is employed for its long-range and low-power communication capabilities, making the system energy-efficient and reliable for residential applications. By delivering timely alerts and insights into potential hazards, this system enhances safety and livability in residential settings. The results highlight the practicality of the proposed design and its potential for integration into smart building applications, offering a scalable and efficient solution for modern environmental monitoring challenges.
Analysis on Light Extraction Efficiency of Aluminum Gallium Nitride-Based Ultraviolet-C Light Emitting Diode with Patterned Surface in FDTD Htwe Ei Ei Khin; Hla, Tin Tin
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.v14i1.4746

Abstract

This paper presents the analysis on light extraction efficiency (LEE) of each polarization mode for Aluminum Gallium Nitride (AlGaN)-based Ultraviolet-C (UV-C) light emitting diodes(LEDs) with patterned substrate surfaces. LEE is severely constrained by the substantial refractive index contrast and polarization-dependent losses, especially transverse magnetic (TM) mode polarized light that predominates at shorter wavelengths. This study employs two dimension (2D) Finite-Difference Time-Domain (FDTD) simulations to investigate the effects of flat, equal triangular patterned, and isosceles triangular patterned surfaces on the LEE of both transverse electric (TE) and transverse magnetic (TM) modes in order to solve this issue. The specific objective of this study is to evaluate the maximum light extraction efficiency for each polarization mode of AlGaN-based UV-C LED with flat surface and patterned surfaces. Simulation results show that the patterned surfaces have better performance than flat surface. This study concludes that isosceles triangular patterning is the most effective approach for maximizing light extraction efficiency compared with equal triangular patterning and flat surfaces in AlGaN-based UV-C LEDs, providing a pathway toward the development of high-efficiency deep UV emitters.
PLC Based Automatic Stamping Machine for labelling the Boxes Pie, Khin Thet; Hlaing, May Su; Hla, Tin Tin
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.v14i1.4747

Abstract

Stamping machine plays a vital role for automation industy. There have consisted of two main parts. The results of this system has been demonstrated with supervisory control and data acquisition (SCADA) systems software and hardware. MS 32MT PLC has been applied to control and sensors and limit switches has been used to get good accuracy and start and stop condition of this system. The combination of PID and PLC have been and the desired value in conveyor system aims to maintain and the the system is stable. This system has been used for labelling the boxes in automation industry that have been carried on conveyor and chosen the box’s size by the use of laser distance sensors at robot arm system. The correct box’s size has been detected with proximity sensor and stamped the boxes. The aim of automatic stamping machine is the prototye machine to apply in automation industry.
Development of a Backend Architecture for an Online Mental Counselling Platform to Enhance Performance and Security Misna Asqia
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.v14i2.4748

Abstract

The increasing demand for mental health services during the COVID-19 pandemic emphasizes the importance of developing a web-based psychological counselling platform. This study aims to create a robust backend architecture to support the functional and non-functional requirements of an online mental counselling platform. The backend plays a crucial role in managing business logic, data management, user authentication, counselling session settings, and integration with third-party services. The Research and Development (R&D) methodology was implemented through the steps of requirements analysis, architecture design, module creation, and performance and security testing. The backend was built with a layered architecture approach, ensuring optimal load management, information security, and scalability. The application of authentication features with JSON Web Token (JWT) provides an extra layer of protection for user data. System tests were conducted using Postman through three main scenarios. The initial trial showed a "User does not exist" error (code 400) when user data could not be found. The second test resulted in an "Incorrect password" (code 400) when the password entered was incorrect. The third trial showed that the login was successful with code 200 OK, issuing an access token to the user. These results demonstrate the stability and accuracy of the backend implementation in managing user validation. Research findings include the design of an Entity Relationship Diagram (ERD) for data management as well as the development of backend modules that support CRUD functions. This backend platform improves service efficiency, protects user privacy, and enables wider access, including to remote areas. This study makes a meaningful contribution to the innovation of technology-enabled mental health services, creating opportunities for further development in support of inclusive and sustainable digital psychology services.
Weather Monitoring and Prediction System for Rice Cultivation in Mandalay Using IoT and Machine Learning Khaing Zar Zar Myint; Aye, Maung; Hla, Tin Tin
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.v14i1.4749

Abstract

The purpose of this research is to monitor and predict temperature, humidity and carbon-dioxide with the objective of increasing rice yields in the rice fields located east of Mandalay. This research focuses on monitoring the temperature and humidity of rice fields near MTU. The data are displayed on a LCD and uploaded to a server to ensure timely access for farmers. Monthly weather forecasts are provided to assist farmers in making advance preparations. The energy generated by the solar system is sufficient to meet the system’s low power consumption requirements. An ESP32 collects weather data from DHT11 sensor. CO2 data from the DM118 sensor is sent to the ESP32 via Arduino UNO using the UART protocol. These data are uploaded to the AWS Lightsail server. LSTM well-suited for time-series and sequence prediction tasks. Additionally, the data is presented in the farmers’ native language to ensure readability for non-English speakers.

Page 97 of 112 | Total Record : 1114

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