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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 1,127 Documents
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IPTSS Intelligent Preprocessing and Multi-Representation Analysis for Social Media Text Summarization with Clustering-Based Enhancement A. Ghanem, Fahd; C. Padma, M.; R. Naji, Wadeea
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5086

Abstract

        Social media platforms generate massive volumes of noisy, informal short texts, creating significant challenges for automatic text summarization. This paper presents IPTSS (Intelligent Preprocessing and Transformation System for Social Media Summarization), a unified framework that integrates intelligent preprocessing, multi-representation text modeling, and clustering-based extractive summarization into a single end-to-end pipeline. IPTSS incorporates a four-stage intelligent preprocessing pipeline for redundancy elimination, platform-noise removal, out-of-vocabulary normalization, and linguistic standardization, a multi-representation analysis layer spanning statistical, distributional, and transformer-based models, and a hybrid TF-IDF–weighted BERT representation that fuses corpus-specific lexical importance with contextual semantic information. Summarization is performed through clustering-based representative selection with redundancy control to ensure topical diversity and coverage. Extensive experiments on large-scale datasets collected from X (formerly Twitter) across the Monkeypox, COVID-19 Vaccine, and Climate Change domains demonstrate that preprocessing alone yields a 25.8% improvement in ROUGE-1, while representation sophistication produces a 38.4% gain from Bag-of-Words to Sentence-BERT. The proposed hybrid representation further improves performance by 7.0% over the best single-representation baseline, achieving the highest scores across all ROUGE metrics. The optimal configuration (Fuzzy C-Means + IPTSS Hybrid) reaches ROUGE-1 = 0.528, outperforming state-of-the-art statistical, graph-based, crisis-specific, neural, and optimization-based methods. Cross-dataset validation confirms strong generalizability, with low performance variance (CV ≈ 2.5%) across heterogeneous domains without dataset-specific tuning. These results demonstrate that effective social media summarization is driven primarily by preprocessing quality and hybrid representation design rather than algorithmic complexity alone, establishing IPTSS as a robust, scalable, and generalizable framework for large-scale social media extractive summarization.  
Predictive Analytics for Blood Supply and Demand: Review Rizgar, Firdaws; Adel Al-Zebari
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5089

Abstract

        The efficient management of the demand and supply of blood is a challenging problem to solve, mainly because of the nature of the blood supply chain, as the blood is a perishable item with strict storage conditions, while the demand is highly uncertain. Thus, an inefficient prediction of the demand may result in either a scarcity of blood, compromising the safety of the patients, or a surplus of blood, thereby increasing the waste. This review discusses the application of predictive analytics to solve the blood supply chain problem by combining the recent advancements of statistical prediction, optimization, machine learning, and deep learning. It discusses the various theoretical foundations of the problem, such as the basics of the blood supply chain, the concept of uncertainty, the theory of inventory management, and the prediction methodologies, while focusing on the significance of the application of predictive analytics to solve the problem by improving the accuracy of the prediction, the efficiency of the inventory management, and the quality of the decisions made. A comparative study of the various prediction methodologies reveals the evolution of the prediction from the traditional statistical prediction to machine learning and deep learning, often combined with optimization to solve the resource allocation problem. Although the prediction problem is solved to a large extent, there are still many challenges to be overcome with respect to the heterogeneity of the data, the interpretability of the results, the privacy of the data, and the infrastructure required to implement the prediction system.  
Development of A Secure Freelance Web-Based and Sentiment-Oriented Digital Platform for Local Artisans: A Case Study of Akungba Akoko Akinrolabu, Olatunde David; Amuda, Zikirullah; Abe, Samuel; Oluwatosin, Titilayomi; Ayomiposi Goodluck Adetula; Akinwale Moses Akinpetide; Akinjogunla Toluwalase Daniel
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5090

Abstract

The rapid growth of digital technologies has transformed service delivery across many sectors; however, local artisans in semi-urban communities like Akungba Akoko continue to face limited market visibility, trust deficits, and a lack of structured digital platforms to connect with clients. This study addresses these challenges by developing a secure freelance web-based platform integrated with an AI-driven sentiment analysis engine designed to enhance transparency and improve artisan–client interactions. The main objectives of the research were to provide artisans with a centralised digital marketplace, ensure secure and reliable transactions, and enable intelligent review interpretation that supports trust-building and informed decision-making. The study identified that existing freelancing systems are not adequately tailored to service-based artisans and often lack security features, real-time feedback analysis, and localised accessibility. A mixed-methods approach was employed, beginning with a community survey involving over 2,000 respondents to assess platform needs and user expectations. The system was implemented using the MERN stack, MongoDB, Express.js, React.js, and Node.js combined with a Python-based sentiment analysis module utilising VADER and TextBlob for annotation. Security mechanisms such as JWT authentication, bcrypt password hashing, Paystack payment integration, and multilayer input validation were incorporated to safeguard user data and prevent common cyberattacks. Results showed that 60% of residents experience difficulty locating reliable artisans, while 85% of artisans expressed strong interest in a digital marketplace. System evaluation produced a 99% sentiment-classification accuracy, a 94% usability score from user testing, and a 99% success rate in security audits. These findings demonstrate the platform’s effectiveness in fostering trust, improving accessibility, and strengthening digital inclusion. Overall, the study demonstrates that the developed platform offers a scalable and impactful solution for enhancing artisan visibility and secure client engagement. It is recommended that future enhancements integrate advanced machine-learning models and mobile-application support to broaden user adoption and long-term sustainability.
FCS-MPC Strategy with Region-Based Voltage Vector Selection and Kalman Filtering in Shunt Active Power Filters for Power Quality Enhancement Phyu, Hnin Ei; Swe, Wunna
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5095

Abstract

Power quality issues caused by nonlinear and unbalanced loads, such as harmonics, reactive power demand, and current imbalance, pose significant challenges in modern distribution networks. Shunt Active Power Filters (SAPFs) provide an effective solution, but their performance depends on advanced control strategies. This study proposes a hybrid approach that integrates Finite Control Set Model Predictive Control (FCS-MPC) with region-based voltage vector selection to achieve fast dynamic response and reduced switching losses. The grid voltage cycle is divided into six regions, with only four candidate vectors evaluated per region, minimizing computational complexity. A Kalman filter further enhances prediction accuracy by mitigating measurement noise and control delays. MATLAB/Simulink implementation on a low-voltage distribution network demonstrates effective harmonic suppression, balanced source currents, and robust performance under sudden load variations. Results confirm IEEE 519 compliance, ensuring practical and reliable power quality improvement in smart grids.
Energy Management of Grid Connected PV-Wind-Battery Hybrid Power Supply System Using Model Predictive Control Nwe, Hnin; Swe, Wunna
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5097

Abstract

The increasing integration of renewable energy sources into modern power systems presents significant challenges in energy management due to their intermittent and uncertain nature. This paper proposes an energy management strategy for a grid connected photovoltaic (PV), wind and battery hybrid system based on Model Predictive Control (MPC). The main objectives are to enhance system efficiency, minimize grid power exchange and extend battery lifetime while ensuring reliable load supply. The proposed MPC-based EMS employs short-term forecasts of renewable generation and load demand to optimize power dispatch under system constraints. Its performance is evaluated and compared with a conventional rule-based EMS using MATLAB/Simulink. The results indicate that the MPC-based approach achieves smoother battery operation, improved state-of-charge regulation, enhanced renewable energy utilization and reduced grid dependency. Therefore, the proposed strategy provides on effective solution for advanced hybrid renewable energy systems.
Comparative Analysis of Fluidized Bed and Plasma Gasifier for Industrial Power Supply System Aung, Thet; Swe, Wunna
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5098

Abstract

The increasing demand for reliable industrial power generation has driven interest in gasification-based waste-to-energy technologies. This study compares fluidized bed and plasma gasifiers with a focus on electrical power performance. A steady-state energy analysis was conducted to evaluate syngas quality, cold gas efficiency, net electrical output, and overall electrical efficiency under similar operating conditions. Results indicate that fluidized bed gasification achieves higher net electrical efficiency and lower auxiliary power consumption, while plasma gasification provides greater feedstock flexibility. The study supports technology selection for industrial power applications. The power that can be generated from a fluidized bed gasifier and a plasma gasifier has been compared and presented using MATLAB
Optimization of Charging and Discharging Performance in a PV-Integrated Piston-Based Gravity Energy Storage System Mar Myint, Khin; Swe, Wunna
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5101

Abstract

The rapid integration of photovoltaic systems into modern power networks has created operational challenges due to their intermittent and fluctuating output. To maintain power balance and enhance grid stability, large-scale and efficient energy storage solutions are essential. This paper presents the dynamic modeling and simulation of a piston-based gravity energy storage system integrated with PV power plant. A detailed MATLAB/Simulink model is developed, including the PV array, power electronic converters, motor–pump system, piston-based gravitational storage mechanism, and generator–load interface. During periods of surplus PV generation, the system operates in charging mode by driving the motor–pump unit to lift the piston, thereby storing energy as gravitational potential. During low PV output, the stored energy is released through the turbine–generator unit to support the load. Simulation results verify stable electrical performance, smooth transition between operating modes, and effective mitigation of PV power fluctuations, demonstrating the technical feasibility of the proposed PGES configuration.

Page 113 of 113 | Total Record : 1127

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