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All Journal Mechatronics, Electrical Power, and Vehicular Technology e-Jurnal Arus Elektro Indonesia Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan JE-Unisla JASEE Journal of Application and Science on Electrical Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science
Suprihadi Prasetyono
Jurusan Teknik Elektro, Fakultas Teknik, Universitas Jember (UNEJ)
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Proyeksi Kebutuhan Energi Listrik dengan Metode Regresi Linear Berganda di UP3 Mojokerto Tahun 2022 sampai 2027 Mirelle Ferent Hasnitha; RB. Moch. Gozali; Suprihadi Prasetyono
JASEE Journal of Application and Science on Electrical Engineering Vol. 4 No. 02 (2023): JASEE-September
Publisher : Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v4i02.427

Abstract

Demand for the availability of electrical energy in the future requires good planning and forecasting. Based on data from BPS (Badan Pusat Statik) East Java, the population and the number of electricity customers in East Java province has increased every year. So a forecasting can be done to find out the estimated demand for electrical energy needs in the future. In this study using multiple regression analysis method to project electricity demand in UP3 Mojokerto from 2022 to 2027. The results of the 2022 projection will be compared with the actual data in 2022 for data validation with the calculation of MAPE (Mean Absolute Percent Error). The resulting MAPE value is less than 10%, which means that this multiple linear regression method is a very good method used to project electrical energy demand at UP3 Mojokerto in 2022-2027. That way, the results of projected electrical energy demand with multiple linear regression methods in UP3 Mojokerto in 2022 amounted to 4,937,341 MWh, and in 2023 to 2027 the projected electrical energy demand increased successively by 5,323,722 MWh, 5,721,631 MWh, 6,131,543 MWh, 6,553,951 MWh, and 6,989,371 MWh. With an average growth from 2022 to 2027 of 6.30%.
IoT-based monitoring system for biodigester production and purification Suprihadi Prasetyono; Catur Suko Sarwono; Digdo Listyadi Setyawan; Azmi Saleh; Bambang Sri Kaloko; Muhammad Naufal An Nafi
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 17, No 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2026.1352

Abstract

Biogas is a promising renewable energy source, but its production and purification processes often lack real-time monitoring, leading to suboptimal yields and inconsistent gas quality. To address this gap, this study aimed to design a prototype for an integrated monitoring system based on the internet of things (IoT). The developed system facilitates the continuous observation of key parameters in both the anaerobic digestion and the subsequent purification stages. The prototype was constructed using an ESP32 microcontroller as the central processing unit, which collected data from a suite of sensors. These sensors measured critical process variables, including the digester's slurry temperature and pH, the volume of the produced gas in the gasholder, and the concentration of methane (CH₄) and hydrogen sulfide (H₂S) before and after the purification unit. Data were transmitted wirelessly via a Wi-Fi network to a cloud-based IoT platform, allowing for remote, real-time data visualization on a web dashboard. The results demonstrated that the prototype successfully captured and transmitted all parameter data with high reliability. The system provided a clear, real-time overview of the digester's operational stability and effectively quantified the increase in methane concentration and the reduction of impurities post-purification. Testing shows stable data transmission to Google Sheets and InfluxDB with minimal data loss. Delay times increase with distance in Google Sheets, from 3736.1 ms (2 m) to 3880.2 ms (8 m), while InfluxDB delay varies. RSSI values decrease with distance, with an accuracy range of 0.28 % to 5.11 %, peaking at 99.17 % accuracy at 6.05 meters.
Co-Authors Abil Huzail Dimasqi Adi Mulyadi Azmi Saleh Bambang Sri Kaloko Catur Suko Sarwono Digdo Listyadi, Digdo Erwin Setiyandani Fakhruddin Rafi Rizqulloh gozali Guido Dias Kalandro Gustio Riki Kartiko Anggara H.R.B. Moch. Gozali Jamaluddin Khalily Mirelle Ferent Hasnitha Moch Ghozali Muhammad Naufal An Nafi Muhammad Sany Aji Saputro Muhammad Zainal Roisul Amin Novitasari Novitasari R. B. Moch. Ghozali Riska Fita Lestari Rizkiyatul Mufaizah Widjonarko Widya Cahyadi