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Desain Optimasi PID Controller Pada Temperatur Heating Furnace Berbasis Ant Colony Algorithm (ACO) Kusuma Apsari, Venda; Ali, Machrus; Nurohmah, Hidayatul; Rukslin, Rukslin
Jurnal FORTECH Vol. 2 No. 2 (2021): Jurnal FORTECH
Publisher : FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56795/fortech.v2i2.204

A furnace is a tool for heating materials, oil, and so on, which usually uses gas, coal, and oil as fuel. Temperature is the main parameter that needs to be controlled in order to remain stable, precise, and of course improve fuel efficiency. As technology develops, there are several methods that can be used to control temperatures that are more reliable than conventional controls. The technology is Proportional Integral Derivative (PID) controller. PID controllers have been proven to be the best controllers and are widely used in industry. But to determine the gain from the PID value is still not accurate and can affect temperature stability, the response is also still slow to reach the desired set point. Therefore, this paper is to simulate a better PID gain value by using the artificial intelligence tuning method. The artificial intelligence method is Ant Colony Optimization (ACO). The simulation results and discussion show that the best design is PID-ACO with 0.0081 overshot, no undershot, and the fastest settling time is 35 seconds

Optimasi Kontrol Suhu Tungku Pemanas Menggunakan Metode Firefly Algorithm (FA) Rizal Anas, Febrian; Ajiatmo, Dwi; Nurohmah, Hidayatul; Ali, Machrus
Jurnal FORTECH Vol. 4 No. 2 (2023): Jurnal FORTECH
Publisher : FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56795/fortech.v4i2.4203

A furnace is a piece of equipment used to heat or change shape. Process control is becoming increasingly important in industry, as a consequence of global competition. Year after year, furnaces have progressed in both industrial processes and equipment. The tuning process ensures that system performance meets operating objectives. Intelligent control based on Artificial Intelligent (AI) has developed a lot to improve conventional control to control voltage loads and is always under constant variable assessment. The research results show that the best optimization method is produced by the PID-FA method which produces overshoot = 0.0721, undershoot 0.0081, and settling time at 30.4283 seconds. The PID-FA method produces better performance, according to the desired settings, so that fuel use can have a high level of efficiency

Optimasi Pembangkitan Ekonomis Berbasis Whale Optimization Algorithm Pada Sistem Multimesin Nurohmah, Hidayatul; Sula Cakra Buana, Arya; Rukslin, Rukslin; Ali, Machrus; Ruswandi Djalal, Muhammad
Jurnal FORTECH Vol. 6 No. 2 (2025): Jurnal FORTECH
Publisher : FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56795/fortech.v6i2.6102

This study addresses the problem of generation cost optimization for thermal power plants in the Sulbagsel multimachine power system. An advanced swarm intelligence approach, the Whale Optimization Algorithm (WOA), is employed as the primary optimization technique. WOA, inspired by the bubble-net hunting strategy of humpback whales, has emerged as a promising metaheuristic with strong capabilities in exploration and exploitation. The main objective of this study is to minimize thermal generation costs while ensuring effective performance under real system operating conditions. To provide a comparative benchmark, Particle Swarm Optimization (PSO) is also applied to the same problem. Statistical evaluation is conducted to assess convergence behavior, accuracy, and consistency of both methods. The results indicate that WOA demonstrates superior balance between exploration and exploitation, leading to stable convergence and reliable solutions. Under peak daytime load conditions, PSO achieves a cost reduction of 23.02%, whereas the proposed WOA-based method achieves a comparable reduction of 23.78%. Although PSO yields a slightly higher cost saving, WOA demonstrates stronger robustness and statistical reliability across multiple trials. These findings confirm that WOA is a competitive alternative for generation cost optimization in complex multimachine systems, offering significant potential for future applications in economic dispatch problems with larger-scale renewable energy integration.

Optimasi Pembangkitan Ekonomis Berbasis Whale Optimization Algorithm Pada Sistem Multimesin Nurohmah, Hidayatul; Sula Cakra Buana, Arya; Rukslin, Rukslin; Ali, Machrus; Ruswandi Djalal, Muhammad
Jurnal FORTECH Vol. 6 No. 2 (2025): Jurnal FORTECH
Publisher : FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56795/fortech.v6i2.6102

This study addresses the problem of generation cost optimization for thermal power plants in the Sulbagsel multimachine power system. An advanced swarm intelligence approach, the Whale Optimization Algorithm (WOA), is employed as the primary optimization technique. WOA, inspired by the bubble-net hunting strategy of humpback whales, has emerged as a promising metaheuristic with strong capabilities in exploration and exploitation. The main objective of this study is to minimize thermal generation costs while ensuring effective performance under real system operating conditions. To provide a comparative benchmark, Particle Swarm Optimization (PSO) is also applied to the same problem. Statistical evaluation is conducted to assess convergence behavior, accuracy, and consistency of both methods. The results indicate that WOA demonstrates superior balance between exploration and exploitation, leading to stable convergence and reliable solutions. Under peak daytime load conditions, PSO achieves a cost reduction of 23.02%, whereas the proposed WOA-based method achieves a comparable reduction of 23.78%. Although PSO yields a slightly higher cost saving, WOA demonstrates stronger robustness and statistical reliability across multiple trials. These findings confirm that WOA is a competitive alternative for generation cost optimization in complex multimachine systems, offering significant potential for future applications in economic dispatch problems with larger-scale renewable energy integration.

DESAIN FREKUENSI KONTROL PADA HIBRID WIND-DIESEL DENGAN PID- PARTICLE SWARM OPTIMIZATION Nurohmah, Hidayatul; Choiruddin, Choiruddin
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 1 No. 1 (2017): PROSIDING SEMNAS INOTEK Ke-I Tahun 2017
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v1i1.394

Sistem pembangkit listrik hibrid adalah jaringan terkontrol dari beberapa pembangkit tenaga energi terbaharukan seperti; turbin angin, sel surya, mikrohidro dan sebagainya. Fluktuasi frekuensi pada pembangkit terbarukan sangat mempengaruhi kualitas daya dalam hal ini turbin angin yang dihibrid dengan diesel. Permasalahan tersebut disebabkan, seperti tidak optimalnya setting gain dan kecilnya waktu konstan pada Automatic Voltage Regulator, terlalu banyak jaringan transmisi yang panjang sehingga kemampuan lemah (weak line). Dalam penerapannya sistem wind-diesel dikontrol dengan kontroler PID, penyetelan nilai gain dari PID masih dalam metode konvensional saja, sehingga sulit untuk mendapatkan nilai optimal. Dalam penelitian ini diterapkan desain kontrol dengan menggunakan Metode Cerdas dalam mencari nilai optimum Proporsional Intergral Derivatif (PID) untuk mengatur frekuensi beban dengan program Matlab/ Simulink. Pemodelan wind-diesel menggunakan fungsi transfer dari diagram turbin angin dan diesel. Respon sistem dengan Matlab/ Simulink dengan membandingkan dengan sistem tak terkontrol dan dengan metode PID-Trial Error, menunjukkan bahwa besar overshoot dan respon keadaan mantap (Settling Time) pada sistem terkontrol PID-PSO menjadi lebih halus dan lebih cepat.

RTC-Scheduled ESP32 IoT Prototype for Automated Hydroponic Nutrient Irrigation Nurohmah, Hidayatul; Soni Setiawan, Dafit; Ali, Machrus; Ciptian Weried Priananda
Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v3i2.694

Reliable nutrient circulation is essential for small-scale hydroponic cultivation, but many Internet of Things (IoT) hydroponic systems depend on multi-parameter sensing, cloud-based decision making, or artificial-intelligence-assisted architectures that can be costly and difficult to reproduce in household and educational settings. This study designs and functionally evaluates a low-cost real-time-clock (RTC)-assisted ESP32 IoT prototype for scheduled hydroponic nutrient irrigation. The practical contribution is a reproducible entry-level automation baseline that helps household users, school laboratories, and community demonstration sites maintain predictable nutrient circulation without continuous manual checking. The system integrates an ESP32 microcontroller, DS3231 RTC, DHT11 temperature-humidity sensor, relay-driven DC nutrient pump, LCD, and Blynk monitoring interface. The main novelty is the use of battery-backed RTC scheduling as a local-first mechanism for routine nutrient-pump actuation, while the cloud dashboard is retained for supervision rather than as the sole timing dependency. This position differentiates the prototype from cloud-centered hydroponic systems whose irrigation execution may depend on network availability. The prototype was programmed to activate the nutrient pump at 07:00 and 16:00 for 10 s per event. Functional validation used four dimensions: environmental reading consistency, RTC timing consistency, pump actuation reliability, and IoT monitoring availability. Daytime DHT11 observations ranged from 29.1 to 31.2 °C and 62 to 68% RH, with mean values of 30.28 °C and 64.50% RH. The RTC showed a recorded 0-s difference from the daily reference time over five observation days within the resolution of the test. The pump executed all observed scheduled ON-OFF events, yielding 100% schedule execution success for two scheduled activations and 100% relay-pump state reliability for four observed states. The Blynk interface displayed temperature, humidity, and pump status during testing. These results demonstrate engineering feasibility for a reproducible scheduled nutrient-irrigation baseline suitable for household-scale hydroponic practice, student laboratories, and introductory IoT learning. The scope is deliberately bounded to prototype-level engineering feasibility: the study evaluates scheduling, actuation, and monitoring, but does not claim nutrient-dosing precision, flow-rate calibration, pH/EC regulation, or crop-yield improvement. Future work should include calibrated reference instruments, pH/EC and flow-rate measurement, nutrient-volume accuracy testing, network-performance analysis, power and cost benchmarking, and controlled plant-growth trials.

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