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All Journal Urania Jurnal Ilmiah Daur Bahan Bakar Nuklir Physics Student Journal Natural B Jurnal Fisika Unand JURNAL PENDIDIKAN TAMBUSAI Indonesian Physical Review Medika Kartika : Jurnal Kedokteran dan Kesehatan SOLUSI Journal Of World Science Jurnal Kreativitas dan Inovasi (Jurnal Kreanova) Jurnal Ilmiah Manajemen dan Bisnis (JIMBis) Jurnal Bisnis, Manajemen, dan Akuntansi JSITP Mamangan Social Science Journal Jurnal Riset Manajemen Indonesia DFAME Digital Financial Accounting Management Economics Journal Tematik International Journal of Contemporary Sciences (IJCS) Jurnal Ilmu-Ilmu Pertanian Interdisciplinary Explorations in Research Journal (IERJ)
Sugeng Rianto
Pusat Teknologi Bahan Bakar Nuklir (PTBBN), BATAN Kawasan Puspiptek-Tangerang Selatan 15314, Banten
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Journal : Jurnal Fisika Unand

 1 
Development of an Integrated Artificial Intelligence Model for Bottle Inspection Using Geometric Feature Extraction and ROI-Based Statistical Analysis Dewi Anggraeni; Santoso, Rikho Adi; Naba, Agus; Sakti, Setyawan Purnomo; Rianto, Sugeng
Jurnal Fisika Unand Vol 15 No 2 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.2.147-154.2026

Abstract

In the era of Industry 4.0, the demand for manufacturing systems that are fast, precise, and efficient has become increasingly urgent. This drives the adoption of artificial intelligence (AI) technologies as a promising solution, including in the field of automatic bottle sorting. However, many industries still use manual bottle sorting systems, which often have significant drawbacks. This study presents an integrated artificial intelligence (AI)-based inspection model for automated bottle inspection in the context of smart manufacturing. The proposed approach integrates geometric feature extraction with region-of-interest (ROI)-based statistical image analysis to improve classification accuracy and robustness. Geometric features extracted from bottle contours are combined with optimized ROI selection to enhance feature relevance prior to classification using a Random Forest algorithm. The dataset consists of four bottle types: plastic, glass, cans, and cardboard, captured under controlled imaging conditions. Experimental results show that the proposed integrated method achieves classification accuracy ranging from 96% to 97.72%. The findings confirm that ROI optimization significantly influences statistical feature characteristics and improves overall model performance. This integrated framework is suitable for implementation in automated visual inspection systems supporting Industry 4.0 applications.
Co-Authors Abdurrosyid, Imam abdurrouf Abdurrouf Achmad Syauqi Ade saputra Agus Naba Ahmadi Hasan Amda, Julfa Muhammad Amelia, Rr. Citra Aristi Anis Akhwan Dhafin Anjani, Mia Arif Agus P. Aseri, Akhmad Fauzi Bagus Abimanyu, Bagus Bunawas Bunawas Charliandri Saputra Wahab Cipta Raharja, Mahardika Dewi Anggraeni Ersa Agustin, Reni Fatimah Kunti Hentihu, Fatimah Kunti Fendy Prasetyawan Fikri Hidayat, Naufal Firdy Yuana Ghufron, Hanif Gusmao, Cristino Haryanto, Jepri Ina Fitria Ismarlin Iwan Setiawan Jalaluddin Jalaluddin Krisnawati, Lina Kusnilawati, Nunik Kuswardani, Dc Listyowati , Andang Andriani Muazidah, Lailatul Mulyantomo, Edy Odi Buana Hutapea, Odi Buana Prabasari, Bonita Pradipta, Aisyah Dianing Prasetya, Angga Ari Purwanto, Dika Raharjo, Susilo Margining Rahmadi, Gagad Rizki Amalia Santoso, Rikho Adi Sarah Dien Hawa Saumi Andrean Nofa, Muhammad Setyawan Purnomo Sakti Shutiawan, Muhammad Alviyan Sismadi, Adi Sri Herwiningsih Sukarni Sukarni Suryawadana, Edy Suryawardana, Edy SUSANTO Teguh Ariefiantoro, Teguh Triarjo Triarjo Trisna Maylika, Dwi Vankabo, Praditya Waskita, Arya Adhiyaksa Widyakto, Adhi