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All Journal Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) ALINIER: Journal of Artificial Intelligence & Applications Jurnal Fortech SinarFe7 Journal FORTEI-JEERI Jurnal JEETech Nucleus Journal
Dwi Ajiatmo
Universitas Darul Ulum
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : nucleus journal

 1 
Optimasi Thermal Oil Heater Menggunakan ACO Sebagai Tunning PID Controller Machrus Ali; Mochamad Ali Fikri Haiqal; Rukslin; Dwi Ajiatmo
Nucleus Journal Vol. 2 No. 1 (2023): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/nucleus.v2i1.2101

Abstract

The furnace is a piece of equipment used to heat materials or change their shape. Process control has become increasingly important in industry, as a consequence of global competition. Year after year, furnaces have improved in both industrial processes and equipment. The tuning process plays a role in ensuring that the performance of a system meets operational objectives. Intelligent control based on Artificial Intelligence (AI) has been developed to improve conventional control so that the output voltage is always considered constant under changing loads. From the simulation results of this research, it was found that the PID-ACO controller model is the best model for using a PID control system. This design without control never reaches a steady state, with the undershot being quite small, the PID-ACO control system has the fastest settling time and steady-state response. Even though PID-ACO has a higher overshoot than PID-Auto, the undershoot is higher than PID-Auto. PID-ACO has lower overshoot and undershoots than PID-Auto
Desain Controller Pada Heating Furnace Berbasis Metode Firefly Algorithm (FA) Febrian Rizal Anas; Dwi Ajiatmo; Hidayatul Nurohmah; Machrus Ali
Nucleus Journal Vol. 1 No. 2 (2022): November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/nucleus.v1i2.1202

Abstract

Furnace is an equipment used to heat or change shape. Process control has become increasingly important in industry, as a consequence of global competition, Year after year, furnaces have improved in both process and industrial equipment. The tuning process ensures that system performance meets operating objectives. Artificial Intelligent (AI)-based intelligent control has developed a lot to improve conventional controls to control voltage loads and is always under constant assessment of the variable. In this research task, it will be discussed about the control of the furnace temperature so that it remains constant with PID and by tuning the Firefly Algorithm (FA) with changes in the output voltage obtained which have better settling time, overshoot and undershoot.
Optimasi LFC (Load Frequency Control) Pada Mikrohidro Menggunakan Metode ACO-ANFIS dan BA-ANFIS Machrus Ali; Rizqi Nafiardli; Sunarto Sunarto; Dwi Ajiatmo
Nucleus Journal Vol. 3 No. 1 (2024): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/nucleus.v3i1.3104

Abstract

Micro-hydro Power Plant is a small-scale power plant. Micro-hydro plants are built with enough water potential to generate electrical energy. A common problem with micro-hydro generating systems is that the output of the generator is not constant. This is caused by changes in connected loads. Thus causing frequent fluctuations in the frequency and voltage of the system that can cause damage to electrical equipment. Because it is used Load Frequency Control (LFC) to control the frequency can be more stable. To obtain optimal control parameters on micro hydropower systems used by Artificial Intelligence (AI) is Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS data is retrieved from training data of PID controllers tuned using Ant Colony Optimization (ACO) and Bat Algorithm (BA). This study compared uncontrolled methods, PID-ZN control methods, PID-ACO method, PID-BA, PID-ACO-ANFIS, and PID-BA-ANFIS obtained the best control method. The result of this research is the control method of PID-ACO-ANFIS is the best control method with overshoot 0.00 and the fastest settling time is 0.00. The results showed that the smallest overshoot (0) in the PID-ACO-ANFIS model, the smallest undershoots (1,12x10-5) in PID-ACO-ANFIS and the fastest settling time (3.77 seconds) in the starting also at PID-ACO-ANFIS. The results of this study will be tried bengan other methods, which results may be better
Co-Authors Agus Raikhani Andi Imran Angga Yodhas Tangkara Arga Prabowo Aryo Hadi Wigantoro Budiman Febrian Rizal Anas Hari Sufitrihansyah Hendro Dripoyono Hidayatul Nurohmah Imam Robandi M Agil Haikal M. Teguh Proyoga Machrus Ali Mochamad Ali Fikri Haiqal Mochamad Ali Rofiq Muhammad Ade Yusuf Alghifrani Muhammad Ruswandi Djalal Muhlasin Nurul Azizah Rio Firmansyah Rizqi Nafiardli Rukslin Septa Dwi Candra Sunarto Sunarto Yanuangga Galahartlambang Zulhajji