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Journal of ICT Research and Applications
Published by Institut Teknologi Bandung
ISSN : 23375787     EISSN : 23385499     DOI : https://doi.org/10.5614/itbj.ict.res.appl.
Core Subject : Science,
Computer Science & IT
Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 304 Documents
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Examining Performance of Naïve Bayes and Support Vector Machine for Solid Waste Classification in Automated Sorting Systems Atillo, Guilbert Nicanor; Calumpang, Zenaida D.
Journal of ICT Research and Applications Vol. 19 No. 2 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.2.5

Abstract

The growing volume of global waste poses significant challenges to effective waste management, underscoring the need for innovative classification methods to improve recycling efficiency. This study evaluates the performance of two traditional machine learning models, Naïve Bayes and Support Vector Machines (SVMs), for classifying solid waste materials in an automated sorting system. A dataset of 284 JPEG images, categorized into five classes (cardboard, glass, metal, paper, and plastic), was utilized. Preprocessing involved resizing images to 512x384 pixels, normalizing pixel values, and extracting features using Histograms of Oriented Gradients (HOG) and Color Histograms. Naïve Bayes demonstrated computational efficiency with 98.90% accuracy and an F1-score of 0.908, but struggled with overlapping features, leading to misclassifications, particularly between glass and metal. In contrast, SVM outperformed Naïve Bayes, achieving 99.80% accuracy and an F1-score of 0.965 by effectively handling complex, overlapping features via optimal decision boundaries. The findings highlight SVM’s superior performance for complex datasets, while Naïve Bayes remains a viable option for simpler tasks. This study underscores the potential of traditional machine learning in waste classification. However, it suggests that integrating deep learning models could improve accuracy, scalability, and adaptability in real-world waste-sorting systems.
Fine-tuning NER for Triplet Extraction in Medical Knowledge Graph Construction Reinhart, Richard; Khodra, Masayu Leylia
Journal of ICT Research and Applications Vol. 19 No. 2 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.2.1

Abstract

This study presents a new approach for constructing a medical knowledge graph using Named Entity Recognition (NER) to identify entities such as diseases, drugs, or medical procedures, alongside part-of-speech (POS) tagging and dependency parsing to determine words that function as verbs and roots. These extracted words are then used as relations between entities, forming triplets in the format (entity, relation, entity). While the knowledge graph provides a structured representation of medical information, the evaluation primarily reflects the performance of the underlying NLP pipeline (NER, POS tagging, and dependency parsing) used to generate the triplets. Quantitative evaluation was performed using metrics such as precision, recall, and F1-score to assess the accuracy and completeness of entity and relation extraction. The qualitative evaluation involved medical domain experts to assess the relevance and validity of the relationships derived. The results indicate that fine-tuning a pre-trained model for NER and leveraging a pre-trained model for POS tagging and dependency parsing can effectively generate accurate triplets for constructing a medical knowledge graph. This approach demonstrated strong performance, achieving high evaluation scores in both quantitative and qualitative evaluations.
Enhancing Healthcare RFID Asset Tracking: A Multi-Objective Optimization Approach Considering Network Delay, False Identification, and Energy Efficiency Suakanto, Sinung; Nuryatno, Edi Triono; Fakhrurroja, Hanif; Rijadi, Safara Cathasa Riverinda
Journal of ICT Research and Applications Vol. 19 No. 3 (2026): (In Progress)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2026.19.3.1

Abstract

This paper discusses asset challenges detection in the healthcare industry, specifically delays and inaccuracies in asset monitoring caused by suboptimal RFID polling methods. The research question is how to determine an appropriate RFID polling interval that balances asset location accuracy, reader energy consumption, and network response time. Due to the increasing risk of mismanagement and equipment loss, an efficient approach is needed to improve asset-tracking accuracy. This study proposes a simulation-based multi-objective optimization approach by determining the optimal polling period to minimize network delay, reader energy consumption, and false identifications. Monte Carlo simulation models the stochastic movement of assets to evaluate system performance under different polling strategies. The results of one experiment showed that 100 assets, with an average moving rate of 2.48, reached the optimal scanning period of 1460 minutes. Additional experiments were conducted to analyze the sensitivity of the optimal polling interval to changes in asset population and movement rates. The contribution of this study is the development of a holistic model to determine the optimal scanning time to improve asset-tracking accuracy and reduce operational costs in RFID systems. Although evaluated in a healthcare context, the proposed framework is versatile and can also be used for other RFID-based asset monitoring scenarios with similar trade-offs.
Collision-Free Tool Path Optimization for Louvre Geometries Using an Adaptive Discrete Particle Swarm Framework Lukman, Selvi; Loekito, Jimmy
Journal of ICT Research and Applications Vol. 19 No. 3 (2026): (In Progress)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2026.19.3.2

Abstract

This study proposes a novel application of Particle Swarm Optimization (PSO) for tool path planning in complex louvre geometries utilized in heat transfer systems. Unlike conventional approaches, the proposed framework explicitly integrates geometric smoothness and collision avoidance into the optimization process, as it enables the generation of continuous and non-intersecting tool trajectories. This is particularly significant as surface quality in louvre fins directly influences boundary layer disruption, which in turn affects convective heat transfer efficiency and pressure drop characteristics. By minimizing abrupt tool movements and machining-induced surface roughness, the method addresses a critical gap between manufacturing precision and thermal performance. The PSO-based approach simultaneously optimizes machining time and trajectory feasibility, ensuring safe and efficient tool movements. The experimental results demonstrated rapid convergence with the objective function significantly decreasing within the first 50 iterations and stabilizing around iteration 80. The optimized solution achieved a machining time of 0.60 minutes (36 seconds) while maintaining consistent minimum objective values throughout the process. These findings highlight the robustness and stability of the proposed method. Overall, this work contributes a novel optimization framework that bridges advanced manufacturing and thermal performance considerations, establishing PSO as an effective solution for high-precision tool path planning in complex industrial geometries.

Page 31 of 31 | Total Record : 304

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