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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ISTEK
Zaki Hamidi, Eki Ahmad
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering

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Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system Kamelia, Lia; Zaki Hamidi, Eki Ahmad; Muhammad Fadilla, Reno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4113-4120

Abstract

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.
Desain dan Implementasi Sistem Instrumentasi Aliran Fluida Berbasis Supervisory Control And Data Acquisition (SCADA) dan dan Programmable Logic Controller (PLC) Hasna Humaira, Raden Roro; Zaki Hamidi, Eki Ahmad; Ridwan, Azwar Mudzakkir
ISTEK Vol. 14 No. 2 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v14i2.2575

Abstract

One of the main challenges in the development of instrumentation systems for industrial fluid flow processes is the requirement for real-time pressure monitoring and control to prevent abnormal conditions that may lead to operational failures. This study aims to design and develop an instrumentation system capable of performing monitoring, control, and early warning functions through the integration of a pressure transmitter, Haiwell AC12M0R PLC, HMI, and SCADA using TCP/IP and Wi-Fi communication protocols. The system design includes hardware development, ladder diagram programming, and configuration of a cloud-connected HMI–SCADA interface. System testing was conducted by varying pressure setpoints to simulate different operating conditions. The test results indicate that the system operates as designed, as evidenced by alarm activation at a pressure of 6 bar, proportional opening of the Pressure Control Valve (PCV) when the pressure exceeds the 5 bar setpoint, and activation of safety functions under high-high pressure conditions at 9 bar, where the system triggers an alarm, closes the Shut Down Valve (SDV), and automatically stops the pump. The PCV response time was recorded at 0.1–0.2 seconds, while the SDV fully closed within 5–6 seconds. All process data can be monitored in real time through the HMI and cloud-integrated SCADA system, enabling remote monitoring via smartphone. Overall, the developed instrumentation system prototype functions in accordance with the control logic and performs reliably under the simulated operational scenarios.
Co-Authors Hasna Humaira, Raden Roro lia kamelia Mudzakkir Ridwan, Azwar Muhammad Fadilla, Reno