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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Ida Bagus Gede Sarasvananda
Universitas Udayana
Computer Science & IT Engineering

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Optimasi Hyperparameter CART Menggunakan Particle Swarm Optimization (PSO) untuk Klasifikasi Penyakit Stroke I Putu Agus Wahyu Wirakusuma Putra; I Putu Gede Hendra Suputra; Ida Bagus Gede Sarasvananda
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p18

Abstract

Stroke is a leading cause of death and disability worldwide, including in Indonesia, making early diagnosis crucial. This study aims to enhance the accuracy of stroke classification using the Classification and Regression Tree (CART) algorithm optimized with Particle Swarm Optimization (PSO). A primary challenge in stroke classification is the prevalence of imbalanced datasets. To address this issue, the hybrid sampling technique SMOTEENN (Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors) was applied to balance the class distribution. The standard CART model (baseline) was first evaluated, achieving an accuracy of 94.41%. Subsequently, PSO was implemented to find the optimal hyperparameter combination for the CART model. The PSO optimization results improved the model's performance; the optimized CART model achieved an accuracy of 94.84%, an increase of 0.43% compared to the baseline model. This improvement demonstrates that the combination of the SMOTEENN method for handling imbalanced data and PSO for hyperparameter optimization is an effective and promising approach to enhance the accuracy of stroke classification models.
Segmentasi Pelanggan Berbasis RFMT Menggunakan K-Means dan Hierarchical Clustering I Komang Yosua Triantara; Made Agung Raharja; Ida Bagus Gede Sarasvananda
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p22

Abstract

The rapid growth of online retail has generated vast transactional data, creating significant opportunities for advanced customer segmentation. While the standard RFM (Recency, Frequency, Monetary) model is widely used in Customer Relationship Management (CRM), it possesses a key limitation by not capturing the temporal dynamics between customer purchases. This research addresses that gap by proposing an RFM-T model, which enhances the traditional framework with Interpurchase Time (IPT) to provide a more holistic view of customer behavior. Using a dual-clustering methodology on an online retail dataset, the K-Means algorithm is first applied for broad segmentation, followed by Hierarchical Clustering to explore deeper sub-segments within high-value groups. The process yielded four primary clusters, and the model's robustness was systematically validated through a strong Silhouette Score, a low Davies-Bouldin Index, and a high Calinski-Harabasz Index. This detailed analysis successfully identified distinct customer personas, such as 'Consistent Loyalists' (low IPT) and 'Periodic Premium Buyers' (high monetary value), which are crucial for developing targeted strategies. The findings demonstrate that this integrated RFM-T framework provides a quantitatively validated with Silhouette Score 0.410, Davies-Bouldin Index 0.720, and Calinski-Harabasz Index 1365.14 this score show actionable model for personalized marketing and effective customer retention.
Co-Authors I Komang Yosua Triantara I Putu Agus Wahyu Wirakusuma Putra I Putu Gede Hendra Suputra Raharja, Made Agung