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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Saputro, Setyo Wahyu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Journal : Jurnal Teknik Informatika (JUTIF)

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Implementation of Ant Colony Optimization in Obesity Level Classification Using Random Forest Wardana, Muhammad Difha; Budiman, Irwan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4696

Abstract

Obesity is a pressing global health issue characterized by excessive body fat accumulation and associated risks of chronic diseases. This study investigates the integration of Ant Colony Optimization (ACO) for feature selection in obesity-level classification using Random Forests. Results demonstrate that feature selection significantly improves classification accuracy, rising from 94.49% to 96.17% when using ten features selected by ACO. Despite limitations, such as challenges in tuning parameters like alpha (α), beta (β), and evaporation rate in ACO techniques, the study provides valuable insights into developing a more efficient obesity classification system. The proposed approach outperforms other algorithms, including KNN (78.98%), CNN (82.00%), Decision Tree (94.00%), and MLP (95.06%), emphasizing the importance of feature selection methods like ACO in enhancing model performance. This research addresses a critical gap in intelligent healthcare systems by providing the first comprehensive study of ACO-based feature selection specifically for obesity classification, contributing significantly to medical informatics and computer science. The findings have immediate practical implications for developing automated diagnostic tools that can assist healthcare professionals in early obesity detection and intervention, potentially reducing healthcare costs through improved diagnostic efficiency and supporting digital health transformation in clinical settings. Furthermore, the study highlights the broader applicability of ACO in various classification tasks, suggesting that similar techniques could be used to address other complex health issues, ultimately improving diagnostic accuracy and patient outcomes.
Accurate Skin Tone Classification for Foundation Shade Matching using GLCM Features-K-Nearest Neighbor Algorithm Syahputra, Muhammad Reza; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Sutaji, Deni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4723

Abstract

Foundation shade matching remains a significant challenge in the beauty industry, particularly in Indonesia where consumers exhibit three distinct skin tone categories: ivory white, amber yellow, and tan. Manual foundation selection often results in mismatched shades, leading to customer dissatisfaction. This study presents a novel automated skin tone classification system combining Gray Level Co-Occurrence Matrix (GLCM) feature extraction with the K-Nearest Neighbor (KNN) algorithm. The GLCM method extracts four key texture features (contrast, homogeneity, energy, and entropy) from facial images, while KNN performs classification. A comprehensive dataset of 963 facial images was used, with 770 training and 193 test samples collected under controlled lighting conditions. After testing K values from 1 to 15, the optimal K=1 achieved 75.65% accuracy. Compared to baseline color histogram methods (60% accuracy), our GLCM-KNN approach demonstrates 15.65% improvement in classification performance. This research contributes to computer vision applications in beauty technology, enabling the development of mobile applications for virtual foundation try-on and personalized product recommendations. The findings have significant implications for the cosmetics industry, particularly for automated cosmetic shade matching systems and enhanced customer experience in online beauty retail. Further research is recommended to explore deep learning approaches and expand dataset diversity to improve accuracy.
Co-Authors Adini, Muhammad Hifdzi Aflaha, Rahmina Ulfah Andi Farmadi Angga Maulana Akbar Anshory, Muhammad Naufal Badali, Rahmat Amin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Erick Kurniawan Fatma Indriani Febrian, Muhamad Michael Friska Abadi Herteno, Rudy Hertono, Rudy Irwan Budiman Kamil, Hawariul Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Radityo Adi Nugroho Rahmayanti Rahmayanti Rozaq, Hasri Akbar Awal Rudy Herteno Saragih, Triando Hamonangan Siti Aisyah Solechah Suryadi, Mulia Kevin Syahputra, Muhammad Reza Umiatin, Umiatin Wardana, Muhammad Difha YILDIZ, Oktay