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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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Desain ADC SAR 10-Bit Dua Kanal Simultan menggunakan Board FPGA Altera DE10 NUHA, MUHAMMAD ULIN; DHARMAWAN, HARI ARIEF; SAKTI, SETYAWAN PURNOMO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 1: Published January 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i1.16

Abstract

ABSTRAKDesain arsitektur ADC (Analog to Digital Converter) multi kanal simultan pada perangkat kontroller dapat mengurangi jumlah intruksi (task) program yang harus dijalankan oleh mikroprosessor dan dapat digunakan untuk membentuk pengukuran simultan. Paper ini memaparkan desain ADC SAR (Successive Approximation Register) 10-bit dua kanal simultan menggunakan Board FPGA (Field Programmable Gate Array) Altera DE10. FPGA dikonfigurasi untuk difungsikan sebagai sirkuit logika SAR dua kanal menggunakan bahasa VHDL (VHSIC-Very High Speed Integrated Circuit Hardware Description Language). Hasil pengujian menunjukkan kanal ADC_1 dan ADC_2 memiliki tingkat kesalahan rata-rata sebesar 1.05 % dan 0.90 %, tingkat akurasi sebesar 98.95 % dan 99.09 %, tingkat linearitas dengan koefisien korelasi sebesar 0.9999 dan 0.9999. Durasi waktu yang dibutuhkan dalam satu kali proses konversi ADC yaitu 104 μs. Didapatkan sampling-rate sebesar 9.6 KS/s. Daya yang dikonsumsi sebesar 842 mW. Kedua kanal ADC SAR yang telah dibuat mampu bekerja secara simultan.Kata kunci: ADC, dua-kanal simultan, FPGA, SAR, VHDL ABSTRACTDesing of simultaneous multi-channel ADC (Analog to Digital Converter) architecture on the controller device can reduce the number of program instructions (tasks) that must be executed by microprocessor and can be used to form simultaneous measurements. This paper describes design of simultaneous two channel 10-bit SAR (Successive Approximation Register) ADC by using Board FPGA (Field Programmable Gate Array) Altera DE10. FPGA is configured using VHDL (VHSIC-Very High Speed Integrated Circuit Hardware Description Language) language to function as two channels SAR logic circuit. Test results show that ADC_1 and ADC_2 channels have average error of 1.05% and 0.90%, accuracy of 98.95% and 99.09%, linearity level with correlation coefficient of 0.9999 and 0.9999. Time duration in one ADC conversion process is 104 μs. The sampling rate obtained is 9.6 KS/s. Power consumed is 842 mW. Design of two channels SAR ADC that has been made can work simultaneously.Keywords: ADC, two-channels simultaneous, FPGA, SAR, VHDL
THE ARTIFICIAL NOSE-BASED PMMA-rGO COMPOSITE COATED QCM SENSOR TO SNIFF COFFEE AROMA AT DIFFERENT ROASTING DEGREE Nalle, Ferry Chrismiadi; Sabarudin, Akhmad; Sakti, Setyawan Purnomo
al Kimiya: Jurnal Ilmu Kimia dan Terapan Vol 11, No 2 (2024): al Kimiya: Jurnal Ilmu Kimia dan Terapan
Publisher : Department of Chemistry, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/ak.v11i2.40381

Abstract

The quality of a product, including coffee, can be determined by its aroma, which is influenced by various chemical compounds. Human olfactory-based assesment and other technologies have been developed to assess coffee aroma; however, these methods are often costly and require highly trained professionals. Gravimetric-based sensors, such as quartz crystal microbalance (QCM) sensors, offer high sensitivity, ease of use, and the capacity to modify their effective surface with nanomaterials. In this research, an artificial nose-based QCM sensor has been modified using a material-sensitive polymethyl methacrylate-reduced graphene oxide (PMMA-rGO) composite.The composite materials were synthesised using an in-situ polymerisation method in the presence of dual solvent. IR characterisation revealed PMMA and PMMA-rGO spectra to be highly similar, suggesting successful trapping of rGO within the PMMA matrix via physical interaction. Increasing the content of rGO resulted in a slight increase in the surface roughness of the QCM sensor.The composite-based QCM sensor demonstrated the capacity to detect coffee aroma at three distinct roasting temperatures (220℃, 225℃, and 230 ℃). The highest response was observed in sample PR1, with a value of -35.2 Hz (220℃), -44.3 Hz (225℃), and -83 Hz (230℃) for the variation in the amount of rGO in the polymer matrix. The presence of rGO with their surface area properties enhanced the QCM sensor to detect coffee aroma.
THE ARTIFICIAL NOSE-BASED PMMA-rGO COMPOSITE COATED QCM SENSOR TO SNIFF COFFEE AROMA AT DIFFERENT ROASTING DEGREE Nalle, Ferry Chrismiadi; Sabarudin, Akhmad; Sakti, Setyawan Purnomo
al Kimiya: Jurnal Ilmu Kimia dan Terapan Vol. 11 No. 2 (2024): al Kimiya: Jurnal Ilmu Kimia dan Terapan
Publisher : Department of Chemistry, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/ak.v11i2.40381

Abstract

The quality of a product, including coffee, can be determined by its aroma, which is influenced by various chemical compounds. Human olfactory-based assesment and other technologies have been developed to assess coffee aroma; however, these methods are often costly and require highly trained professionals. Gravimetric-based sensors, such as quartz crystal microbalance (QCM) sensors, offer high sensitivity, ease of use, and the capacity to modify their effective surface with nanomaterials. In this research, an artificial nose-based QCM sensor has been modified using a material-sensitive polymethyl methacrylate-reduced graphene oxide (PMMA-rGO) composite.The composite materials were synthesised using an in-situ polymerisation method in the presence of dual solvent. IR characterisation revealed PMMA and PMMA-rGO spectra to be highly similar, suggesting successful trapping of rGO within the PMMA matrix via physical interaction. Increasing the content of rGO resulted in a slight increase in the surface roughness of the QCM sensor.The composite-based QCM sensor demonstrated the capacity to detect coffee aroma at three distinct roasting temperatures (220℃, 225℃, and 230 ℃). The highest response was observed in sample PR1, with a value of -35.2 Hz (220℃), -44.3 Hz (225℃), and -83 Hz (230℃) for the variation in the amount of rGO in the polymer matrix. The presence of rGO with their surface area properties enhanced the QCM sensor to detect coffee aroma.
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.
Parallel Implementation of Gaussian Filter Image Processing on a Cluster of Single Board Computer Achmad Nurul Fauzie; Setyawan Purnomo Sakti; Rahmadwati
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 17 No. 3 (2023)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v17i3.1672

Abstract

Gaussian filters are widely used in image processing applications, such as edge detection, segmentation, and feature extraction. However, computationally intensive computations can take a long time to process large images. Therefore, a parallel algorithm implementation is necessary to accelerate the process. The authors proposed the use of Orange Pi SBCs for parallel image processing tasks involving a Gaussian filter. This paper outlines the steps for implementing a parallel Gaussian filter on a cluster of SBCs. The performance of the parallel implementation was evaluated in terms of speedup and efficiency, which are essential parameters for measuring the effectiveness of the approach. The parallel implementation speedup is described as the ratio of the time required by the serial implementation to that required by the parallel implementation. The parallel implementation efficiency is described as the speedup ratio of the number of SBCs in a cluster. The results of the performance evaluation show that the parallel implementation of the Gaussian filter on a cluster of Orange Pi SBCs can achieve significant speedup and efficiency compared to the serial implementation. The speedup increases with the number of SBCs used in the cluster. Using four SBCs can result in a speedup of up to 2.1 times faster than serial implementation. The efficiency also increases with the number of SBCs used in the cluster. Using four SBCs could achieve an efficiency of up to 53.4%.
Imputation of missing microclimate data of coffee-pine agroforestry with machine learning Nurwarsito, Heru; Suprayogo, Didik; Sakti, Setyawan Purnomo; Prayogo, Cahyo; Yudistira, Novanto; Fauzi, Muhammad Rifqi; Oakley, Simon; Mahmudy, Wayan Firdaus
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1439

Abstract

This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.
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