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All Journal Journal of ICT Research and Applications JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Lukman, Selvi
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Computer Science & IT

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Studi Perbandingan Evaluasi Kinerja Metode Pembelajaran Eager Learning versus Lazy Learning Lukman, Selvi; Loekito, Jimmy; Yapinus, Pin Panji
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9197

Abstract

The major revenue in banking sector is generated long term deposits from customers. Many marketing strategies are implemented to target potential customers by examining their impacted characteristics for decision making. Therefore, machine learning as a scientific computing has drawn many interest in finding best potential customers especially in predicting whether a long term deposit is subscribed or not. In this research, lazy and eager learning of K-Nearest Neighbours (KNN) and Random Forest (RF) is compared. The computation procedure of the prediction makes a sharp distinction between them and accordingly, RF is proven to be more superior than KNN in the term of Accuracy as much as 96%, Precision 93% and F1 score 0.97. Therefore, the ultimate performance of RF relies on the ability to handle non-linearities and its resistance to overfitting makes RF a suitable choice for many predictive applications. Keywords— Classification; Easy learning; Lazy Learning, Term Deposit
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.
Co-Authors Loekito, Jimmy Pin Panji Yapinus, Pin Panji