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All Journal MEDIA ELEKTRIKA Jurnal Teknologi Informasi dan Ilmu Komputer Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan Jurnal Tecnoscienza Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Electrical Engineering and Computer (JEECOM) JASEE Journal of Application and Science on Electrical Engineering Jurnal Fortech SinarFe7 Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Elconika: Jurnal Teknik Elektro Jurnal FORTECH Journal of Renewable Energy and Smart Device
Machrus Ali
Universitas Darul Ulum & Institut Teknologi Sepuluh Nopember
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Transportation Other

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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

Abstract

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

Abstract

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.
Development of an ANFIS-Based Method to Improve the Accuracy of Owner’s Estimated Cost in Construction Cost Management Markhaban Siswanto; Machrus Ali
Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan Vol 11 No 1 (2026): MARET
Publisher : Program Studi Teknik Sipil, Fakultas Teknik, Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/cvl.v11i1.1636

Abstract

An inaccurate Owner’s Estimate Cost (OEC) often triggers procurement failures in the purchasing process, thereby affecting cost performance and the success of government capital expenditure projects in Indonesia from a construction management perspective. The OEC serves as the primary benchmark for assessing the reasonableness of bids in construction procurement; calculation errors may lead to ineffective cost control, financial mismanagement, and regulatory non-compliance. Therefore, this study aims to improve OEC accuracy by developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model supported by a Linear Regression (LR) algorithm to predict price fluctuations that influence cost planning and procurement decisions for public building projects. Project data from state-owned building construction and unit price analysis data for the 2021–2024 period were analyzed to predict 2025 price changes (addenda) for various construction work items. The proposed model achieved strong accuracy, with Root Mean Squared Error (RMSE) values of 0.0108–0.0333 and Mean Absolute Error (MAE) values of 0.0099–0.0261 across multiple work descriptions, indicating a good model fit. These findings confirm that the model outperforms comparable studies in terms of precision and interpretability, and can serve as a data-driven approach to strengthen cost management, estimate planning, and procurement decision-making in construction management.
AI-Assisted PID Tuning for Voltage Control of an Axial-Flow Pico-Hydro Generator Ali, Machrus; Hidayatul Nurohmah; Muhammah Agil Haikal; Yanuar Mahfudz Safarudin
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.v3i2.691

Abstract

Pico hydropower is a renewable-energy option for isolated communities and low-head run-of-river sites, but axial-flow pico-hydro generators are vulnerable to voltage fluctuation when water flow, hydraulic head, or consumer load changes. This study proposes a novel and reproducible artificial-intelligence-assisted proportional-integral-derivative (PID) tuning framework for voltage control of a 220 V, 2 kW axial-flow turbine generator ZD760-LM-(18-20). The novelty lies in combining a voltage-control-oriented small-signal model of a low-head axial-flow pico-hydro unit, a nonminimum-phase hydraulic zero that represents inverse initial response, identical bounded PID-search constraints, and a composite objective that explicitly penalizes inverse dip, overshoot, settling time, ITAE, and IAE. The plant model combines actuator or electronic-load-controller dynamics, non-elastic water-column dynamics, turbine-generator dynamics, and sensor dynamics. PID gains obtained from Ziegler-Nichols (PID-ZN), Ant Colony Optimization (PID-ACO), and Particle Swarm Optimization (PID-PSO) are compared under Kp = 0-100, Ki = 0-50, and Kd = 0-10. Simulation results show that PID-ZN stabilizes the plant but requires a 6.80 s settling time and produces an ITAE of 2.9603. PID-ACO reduces settling time to 2.26 s and ITAE to 1.1320, whereas PID-PSO gives the lowest ITAE of 1.1311 with only 0.030% overshoot. Compared with PID-ZN, PID-PSO reduces settling time by 66.8% and ITAE by 61.8%. These results indicate that AI-based PID tuning can improve voltage quality in low-cost rural and off-grid pico-hydro systems using practical ELC or simple actuator implementations.
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

Abstract

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.
Co-Authors A.N. Afandi Abda Abda Achmad Zaini Afif Dwi Wijaya, Muhamad Agil Haikal, Muhammad Agus Raikhani, Agus Agus Siswanto Agustina, Devita Ahmad Yusuf Andrian Putra Alfian, Riski Ali Fikri Haiqal, Mochamad Anang Rohmad Jatmiko Anshoruddin, Ilham Arfaah, Saiful Arie Pambayun, Yoga Arrohman, Muhammad Arwan Satriaji, Faldy Askan, Askan Aslichah, Aslichah Asnun Parwanti Bahiyyah, Putri Burdirahaju, Rukmi Candra W.A, Farrel Ciptian Weried Priananda Dwi Ajatmo Dwi Ajiatmo Dwi Ajiatmo Dwi Ajiatmo Dwi Hariska, Maulana Efendi, Utoyo Faisal Faisal Fajardika, Risky Febrian Rizal Anas Haikal, Muhammad Agil Hasib Al Isbilly, Mohammad Hidayatul Nurohmah Hidayatul Nurohmah, Hidayatul Indra Gunawan, Enggal Intan Sekar Novitasari Izzatul Umami Jatmiko, Anang Rohmad JUNAEDI Kadaryono, Kadaryono Kusuma Apsari, Venda Lutfi Cahyanto, Iqbal Maida W, Avi Markhaban Siswanto Markhaban Siswanto Miftachul Ulum, Miftachul Muhammad Ruswandi Djalal Muhammah Agil Haikal Muhlasin Muhlasin Muhlasin, Muhlasin Muhlasin, Muhlasin Nafiardli, Rizqi Novrianto, Elfizar Nur S, Novi Nur Salam Parwanti, Asnun Puji Astoro, Fany Wahyu Putra, Ahmad Yusuf Adriyan Rahman Sujatmika, Arif Rakhwati Tri Atmayanti, Dyah Rizal Anas, Febrian Ryo Setyawan Soni Setiawan, Dafit Suhadak, Akemad Sula Cakra Buana, Arya Sunarti Sunarto Sunarto Triana, Triana Usman, Mualifi Yanuar Mahfudz Safarudin Zahwa, Aila Vairus