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Ronal Watrianthos
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Journal of Systems Engineering and Information Technology
Published by Organisasi Profesi IKATAN AHLI INFORMATIKA INDONESIA
ISSN : -     EISSN : 2829310X     DOI : https://doi.org/10.29207/joseit.*
Core Subject : Science,
Computer Science & IT
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 40 Documents
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Analysis of an Adaptive E-Learning System with the Adjustment of the Felder-Silverman Model in Moodle Jusuf, Heni; Andiani
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 1 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i1.6435

Abstract

In the digital era, adaptive e-learning has become essential in addressing students’ diverse learning preferences. This study aims to develop an adaptive e-learning system that integrates the Felder-Silverman Learning Style model (FSLSM) into Moodle using fuzzy logic and case-based reasoning. The system extracts behavioral attributes from student activity logs and classifies learning styles into four dimensions: processing, perception, input, and understanding. The experimental evaluation, conducted with and without substitution of the (ILS) questionnaire values, demonstrated varying levels of accuracy. Accuracy improved with ILS substitution as follows: processing (82.86%), perception (80.00%), input (80.00%), and understanding (74.29%). Without ILS substitution, the accuracies were as follows: processing (80.00%), perception (80.00%), input (74.29%), and understanding (62.86%). These findings confirm the system’s potential to support personalized learning by accurately identifying learning styles.
Comparison and Optimization of Parallel Clustering Algorithms for Chinese A-Share Stock Segmentation Based on Financial Indicators Hai Mo; Niu Yihan; Zhang Yuejin
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 1 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i1.6535

Abstract

This study presents a novel application of parallel clustering algorithms to segment stocks in the Chinese A-share market based on financial indicators. Using the Hadoop platform and Mahout software library, we implemented and compared the performance of the K-means and fuzzy K-means algorithms across five distance measures: Euclidean, squared Euclidean, Manhattan, cosine, and Tanimoto. The analysis utilized 15 financial indicators from 2,544 listed companies to reflect profitability, solvency, growth capability, asset management quality, and shareholder profitability. The experimental results demonstrate that for stock financial data clustering, the K-means algorithm with Tanimoto distance yields optimal execution efficiency and clustering quality, whereas the fuzzy K-means algorithm performs best with squared Euclidean distance. However, the K-means algorithm proved to be more effective overall, successfully categorizing 1,483 stocks into 26 meaningful segments compared to only 511 stocks in 27 segments using fuzzy K-means. The resulting stock segmentation framework divides the market into eight comprehensive categories based on investment value and security, thereby providing investors with practical guidance for stock selection. Our approach enables investors to understand the fundamental characteristics of each stock segment, discern their distinctive features, and identify undervalued stocks with appreciative potential. This study represents the first application of parallel big data clustering algorithms to segment the entire Chinese A-share market, offering significant practical value for investment decision-making.
Enhancing News Recommendations with Deep Reinforcement Learning and Dynamic Action Masking Dong Sang-hong; Ahn Jun-soo
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 1 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i1.6536

Abstract

A news recommender system is crucial for the transmission of news in new media. A deep reinforcement learning-based recommender system is suggested to integrate the characterization capabilities of neural networks with the strategic selection capabilities of reinforcement learning to enhance news recommendation efficacy. Dynamic action masks enhance the capacity to assess short-term interests of users. An optimized caching mechanism improves the efficiency of the experience cache, and a reward design characterized by region masking accelerates model training, thereby enhancing the performance of the recommender system for news recommendations. Experimental results indicate that the recommendation accuracy of the proposed model on the news dataset is on par with that of prevalent neural network recommendation techniques and surpasses existing state-of-the-art algorithms in ranking performance
Multi-Class CNN Models for Banana Ripeness Classification Rafaela S. Francisco; Gabriel de S. G. Pedroso; Thiago M. Ventura
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 1 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i1.6540

Abstract

This study develops and evaluates Convolutional Neural Network (CNN) models for classifying banana maturity stages using images, thereby addressing a significant challenge in the banana supply chain. The banana industry represents a major agricultural sector worldwide, with Brazil exporting 56.2 thousand tons in 2023. Accurate maturity classification is essential for optimizing harvest timing, reducing postharvest losses, and extending shelf life. We utilized a public Brazilian dataset of 1,000 images of Prata Catarina banana categorized into eight ripening stages based on peel coloration standards established by the Brazilian Program for Horticulture Modernization. The images were preprocessed to a standardized 200 × 200-pixel resolution, and we evaluated the effectiveness of the data augmentation techniques, including horizontal flip, vertical flip, rotation, and zoom. Our CNN architecture consisted of five convolutional blocks with a dropout layer prior to flattening. We conducted six experiments to compare three classification scenarios (eight, five, and two ripeness classes) with and without data augmentation. Our findings demonstrate that CNN models can effectively classify banana ripeness, with performance improving significantly as classification granularity decreases. The best-performing model achieved 89.5% accuracy, 87.2% precision, and 89.6% recall when classifying bananas into two categories.
Machine Learning Models for Air Pollution Health Risk Assessment Lipatova A.V; Potapchenko T.D
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 1 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i1.6544

Abstract

This study explored the application of machine learning (ML) models and artificial neural networks (ANNs) in the assessment of public health concerns associated with air pollution. Utilizing a dataset comprising over 12,000 records from India and Nepal, encompassing both quantitative measurements and visual data, several classification models have been constructed and evaluated to predict air quality index (AQI) categories indicative of varying health risk levels. The implemented models comprise a decision tree (DT), support vector machine (SVM), random forest (RF), XGBoost, and deep neural networks (both convolutional and recurrent). The methodology entailed data preprocessing, feature significance analysis, and model assessment using accuracy metrics and ROC curves. The findings revealed a high classification accuracy across all models (>90%), with ensemble-based methods demonstrating enhanced performance. XGBoost attained superior accuracy with optimal resource efficiency; however, artificial neural network (ANN) models, particularly long short-term memory (LSTM), obtained accuracy levels of 98% by the 15th training epoch. Feature significance analysis revealed that AQI, PM2.5, and PM10 were the primary predictors of health risk categorization. Correlation analysis demonstrated robust associations between particulate matter measures (PM2.5, PM10), underscoring their significance in air quality evaluation. This study proposes a methodological framework for automating risk assessment procedures using machine learning approaches to facilitate more effective environmental health monitoring. The findings suggest that ensemble models offer an optimal balance between precision and computing efficiency for real-time air quality classification systems with potential applications in early warning systems and public health intervention techniques.
BERTopic-Driven Identification of Emerging Technology Topics: A Multi-Source Framework with Empirical Validation in New Energy Vehicles Dakun Wang; Bolin HUA
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 2 (2025): September 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i2.7169

Abstract

This study addresses the critical challenge of accurately detecting emerging technology topics from large-scale heterogeneous data sources. The methodology encompasses four sequential stages: (1) multi-source data acquisition from academic papers, patents, policies, and technical reports; (2) BERTopic-based topic modeling utilizing BERT embeddings and c-TF-IDF for enhanced semantic representation; (3) topic consolidation through cosine similarity analysis of topic vectors; and (4) emerging topic identification via a weighted evaluation system incorporating novelty, growth, continuity, and impact dimensions. Applied to the new energy vehicle domain using data from 2010-2022, the framework successfully identified 16 candidate emerging technology topics through analysis of 27,058 academic papers and 54,572 patents. Validation results indicate that 12 of the 16 identified topics (75% accuracy) align with technological priorities outlined in government policies and industry reports. The method effectively captures cross-domain technological convergence, with four common topics identified between academic and patent datasets, primarily concentrated in battery technology domains.
An Empirical Evaluation of ChatGPT as an Automated Machine Learning Code Generator for Image Classification Dedi Mardianto; Milla Apriliana; Eva Oktavia; Ideva Gaputra; Widya Wahyuni
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 2 (2025): September 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i2.7174

Abstract

The emergence of large language models such as ChatGPT has created unprecedented opportunities for automating software development processes, particularly within the machine learning domain. This study aims to empirically evaluate the effectiveness of ChatGPT in generating machine learning code for image classification tasks using the Keras framework. The research employs an experimental methodology utilizing the MNIST dataset, comprising 70,000 handwritten digit images. A systematic series of experiments was conducted through progressive prompting strategies, ranging from basic model construction to comprehensive evaluation protocols. The findings demonstrate that ChatGPT successfully generated 100% executable code without errors, with the resulting models achieving 99% accuracy on the test dataset. A notable discovery emerged in the form of "intelligent deviation" phenomena, wherein ChatGPT autonomously provided Convolutional Neural Network (CNN) architectures despite explicit requests for fully connected layers, demonstrating sophisticated contextual understanding. The generated code quality met professional standards with robust multi-library integration capabilities. This research provides the first systematic empirical contribution regarding large language model capabilities in machine learning code generation, offering significant implications for democratizing artificial intelligence technology access within educational and research contexts.
Big Data-Driven Transformation of Social Science Research Tianhuan PENG
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 2 (2025): September 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i2.7170

Abstract

This study examines the transformative potential of big data integration in social science research and proposes systematic approaches for implementing data-driven methodologies through institutional innovation. The methodology encompasses systematic analysis of traditional research limitations, mechanistic investigation of big data integration effects on seven critical research phases (topic identification, literature synthesis, theoretical framework development, data acquisition, analytical processing and visualization, knowledge dissemination, and outcome evaluation), and institutional design analysis for philosophy and social science laboratory development. The analysis reveals that big data fundamentally transforms social science research through multiple mechanisms. The proposed laboratory framework addresses critical implementation challenges through seven strategic dimensions: institutional awareness enhancement, differentiated development pathways, computational infrastructure strengthening, specialized tool and platform development, interdisciplinary talent cultivation, comprehensive data resource construction, and supportive policy framework establishment.
BERT-TBGH: A Graph Attention Network Approach for Sentiment Analysis in Online Health Communities Pu Han; Ye Dongyu
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 2 (2025): September 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i2.7172

Abstract

This research proposes a semantic-enhanced sentiment analysis framework that integrates dependency parsing, graph attention networks, and prior sentiment knowledge to improve classification accuracy in Chinese online health community texts. Comprehensive experiments conducted on 31,718 online health community comments demonstrate the effectiveness of the proposed approach. The BERT-TBGH model achieves 90.77% accuracy, representing substantial improvements of 10.57% and 7.79% over baseline TextCNN and BiLSTM models, respectively. Ablation studies reveal that incorporating sentiment knowledge contributes 1.85% accuracy improvement, while character-level dependency syntactic information adds 1.00%. The dual-channel architecture outperforms single-channel approaches, with TextCNN\BiLSTM showing 0.64% and 3.57% F1-score improvements over individual BiLSTM and TextCNN models. Graph Attention Networks demonstrate superior performance compared to Graph Convolutional Networks for dependency parsing integration, with GAT-based models achieving 0.86% higher accuracy than GCN alternatives.
Towards Effective Classification of Intrusion in Internet of Medical Things using Random Forest Classifier for Sustainable Health Care Jimoh, RG; Oyelakin, A.M; Sanni, S.A; Salau-Ibrahim, T.T; Akanbi, M.B; Saka, B.A; Lasisi, I.O; Ibrahim , T.L
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 4 No 2 (2025): September 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i2.6984

Abstract

Information Technology is extensively utilized in contemporary healthcare settings. Nevertheless, healthcare facilities are frequently targeted by malicious actors aiming to disrupt services and exfiltrate sensitive data. Intrusion detection systems play a crucial role in monitoring and analyzing computer systems or networks for indications of such attacks. To safeguard healthcare technologies, including the Internet of Medical Things (IoMT), it is imperative to protect them from these threats. Machine learning models have demonstrated superiority over traditional methods in detecting intrusions. However, some of these models still exhibit limitations, such as generating false alarms when identifying attacks on IoMT. Consequently, the development of more accurate models for detecting these attacks is essential. Security experts have developed datasets that simulate various attack scenarios for testing purposes, one of which is the IoMT Attack Testbed. This study proposes a three-step methodology for constructing an effective intrusion detection model. It employs a Random Forest classifier to categorize intrusions within the dataset. Through dataset analysis, the study provides insights into feature handling, addresses data imbalance issues, and identifies significant features for the model. The model's parameters were optimized to enhance its performance. The model was evaluated in two scenarios, with results indicating superior performance in the second scenario when data imbalance was addressed, critical features were selected, and parameters were fine-tuned.

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