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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Physics Student Journal Jurnal EECCIS Journal of Natural A Natural B The Journal of Pure and Applied Chemistry Research Jurnal Fisika Unand International Journal of Advances in Intelligent Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Science and Technology Indonesia al Kimiya : Jurnal Ilmu Kimia dan Terapan
Setyawan Purnomo Sakti
Department Of Physics, Brawijaya University, Malang
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Medicine & Pharmacology Physics

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Journal : Jurnal Fisika Unand

 1 
Optimasi Parameter PID Pada Sistem Kontrol Suhu Alat Roasting Biji Kopi Dewi Anggraeni; Saputra, Aditya Ilham; Sakti, Setyawan Purnomo
Jurnal Fisika Unand Vol 14 No 2 (2025)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.14.2.145-151.2025

Abstract

Coffee is an important commodity for Indonesia. This huge potential must be utilized optimally. One of the important stages in processing coffee beans is the process of roasting coffee beans. This research focuses on optimizing PID control parameters on coffee bean roasting equipment. The main stages in determining PID control parameters are carried out using MATLAB's PID Tuner and also the process of improving the parameter values. From this research it can be concluded that the PID parameters to improve the temperature control performance of coffee roasting equipment, in the temperature ranges of 210 °C, 215 °C, 220 °C, 225 °C, and 230 °C indicate that the PID parameters have been improved (Kp = 5, Ki = 0.017, and Kd = 2) has better performance compared to parameters obtained directly from PID Tuner Matlab (Kp = 33.4, Ki = 1, and Kd = 4.74). Parameters that have gone through the refinement process show a fairly fast rise time (123 seconds), small overshoot (2.8 °C) and no significant oscillations occur in the system. Thus, it can be concluded that the use of refined PID parameters is more optimal for controlling temperature in coffee bean roasting equipment, because it produces a better system response. By improving the PID parameter values, it is hoped that it can produce roasted coffee beans with accurate variations in maturity levels, so that users can determine the desired taste and aroma characteristics of coffee based on the maturity level.
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 Achmad Nurul Fauzie Adin Okta Triqadafi Agus Naba Akhmad Sabarudin Akhmad Sabarudin Aldi Dwi Putra Aprilia Nur Tasfiyati Arinto Yudi Ponco Wardoyo Cahyo Prayogo, Cahyo Dewi Anggraeni Didik Rahadi Santosa Didik Rahadi Santoso Didik Suprayogo Eko Malis Elvina Dhiaul Iftitah Farida Wahyuni Fauzi, Muhammad Rifqi Fenny Irawati Hari Arief Dharmawan Hariyadi Soetedjo Heru Nurwarsito Lalu Sahrul Hudha Layli Amaliya M. Yogi Riyantama Isjoni Masruroh Masruroh Masruroh MASRUROH MUHAMMAD ULIN NUHA Nalle, Ferry Chrismiadi Novanto Yudistira Nur Chabibah Oakley, Simon Rahmadwati Rizal Y Aji Santoso, Rikho Adi Saputra, Aditya Ilham Sigit Kurniawan Sri Sugiarti Sugeng Rianto Sukma Wahyu Fitriani, Sukma Wahyu Triswantoro Putro Umi Salamah Unggul P. Juswono Unggul Punjung Juswono Wayan Firdaus Mahmudy