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All Journal Jurnal Buana Informatika Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Made Satria Bimantara
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Computer Science & IT Engineering

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Inspired GWO-based Multilevel Thresholding for Color Images Segmentation via M. Masi Entropy I Made Satria Bimantara; I Wayan Supriana; I Komang Arya Ganda Wiguna; Ida Bagus Gede Sarasvananda
Jurnal Buana Informatika Vol. 16 No. 2 (2025): Jurnal Buana Informatika, Volume 16, Nomor 02, Oktober 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Image segmentation is crucial in image processing and computer vision, with multilevel thresholding (ML-ISP) offering robust solutions for complex images. However, effectively applying ML-ISP to RGB color images remains a challenge due to computational complexity and the limitations of traditional optimization algorithms, such as the Grey Wolf Optimizer (GWO). This study proposes an Inspired Grey Wolf Optimizer (IGWO) to address these issues and enhance ML-ISP for RGB color images. The performance stability of IGWO is comprehensively evaluated using three distinct objective functions: the Otsu method, the Kapur Entropy, and the M. Masi Entropy. Qualitative and quantitative analyses using PSNR, SSIM, and UQI were conducted on benchmark images. Results consistently demonstrate that IGWO, particularly with M. Masi Entropy, achieves superior segmentation quality. This research incorporates GridSearch-based hyperparameter tuning. The findings highlight the effectiveness and robustness of the proposed IGWO approach for complex ML-ISP tasks on color images.
Perbandingan Metode Clustering K-Means, GMM, dan DBSCAN Berbasis Fitur RFM Putu Nadya Putri Astina; I Wayan Supriana; I Made Satria Bimantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 2 (2026): JNATIA Vol. 4, No. 2, Februari 2026
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.2026.v04.i02.p07

Abstract

In the digital banking era, understanding customer behavior has become essential for delivering relevant services and maintaining competitiveness. This study aims to develop and evaluate customer segmentation models by leveraging an extended RFM (Recency, Frequency, Monetary) model, incorporating both behavioral and demographic attributes. Preprocessing, feature engineering, handling outliers, and standardization were done on the data using a dataset of 100,000 bank transaction records from Kaggle. DBSCAN, Gaussian Mixture Model (GMM), and K-Means were the three clustering techniques that were employed and contrasted. The clustering performance was evaluated using the Silhouette Score, Calinski-Harabasz Index (CHI), and Davies-Bouldin Index (DBI). The output of DBSCAN was too noisy to be useful in the business world, despite having the highest scores on Silhouette: 0,667 and lowest score on DBI: 0,396. K-Means offered the most interpretable segmentation with five ideal clusters (Silhouette: 0,308; DBI: 0,957; CHI: 6191), identifying customer groups ranging from highly active to potentially inactive. The findings highlight the synergy between transactional features and clustering algorithms in generating actionable insights for banking strategy.
Co-Authors I Komang Arya Ganda Wiguna I Wayan Supriana Ida Bagus Gede Sarasvananda Putu Nadya Putri Astina