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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer Jurnal Abdidas Indonesian Journal of Data and Science Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Bulletin of Social Informatics Theory and Application International Journal of Artificial Intelligence in Medical Issues Media Elektrik
Syafie, Lukman
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Evaluating the Effectiveness of TBaWI for Imputation of Missing Rainfall Data Syafie, Lukman; Awangga, Narendra; Salim, Yulita
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3273.97-108

Abstract

Daily rainfall data plays an important role in hydrological and climatological analysis, especially in tropical regions characterised by high rainfall variability and sharp seasonal changes. However, observational data often has gaps, which can reduce model accuracy and obscure relevant climatological signals. This study addresses these issues by applying the Trend-Based Adaptive Window Imputation (TBaWI) method, an adaptive imputation approach that considers local temporal trends and seasonal dynamics in estimating missing rainfall values. This method was tested using CHIRPS data for the Makassar region for the period 2014–2023 with synthetic data loss scenarios of 10%, 15%, 20%, and 25%. The results show that TBaWI consistently provides a lower Mean Absolute Error (MAE) value, namely 6.14–7.65 mm, compared to linear interpolation, which produces 6.46–7.75 mm. The SMAPE value of TBaWI is also lower, for example 33.16% in the 15% data loss scenario, compared to interpolation at 35.06%. In addition, this method showed an improvement in the ability to identify dry days through the Zero Hit Rate (ZHR), which reached 60.08% in the 20% data loss scenario, higher than the interpolation of 58.32%, while the Rainy Hit Rate (RHR) remained in a stable range of 79–88%. These findings indicate that TBaWI is more effective in maintaining climatological consistency and numerical accuracy of tropical rainfall data. Further research is expected to integrate spatial aspects and optimise machine learning-based parameters to improve the generalisation of the method under various climatic conditions.
Smart IoT Relay Monitoring Box for Real-Time Monitoring and Control of Electrical Energy Consumption Muslihi, Muhammad Takdir; Syafie, Lukman; Alghamdi, Mona Ahmad; Puteri, Annisa Nurul; Achmad, Andita Dani
Jurnal Media Elektrik Vol. 23 No. 2 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i2.11899

Abstract

Background: The rapid increase in electricity consumption, particularly the 23.36% rise in Indonesia from 2019 to 2023, highlights the need for more efficient energy management systems. Limited user awareness and the lack of integrated monitoring and control solutions often lead to energy waste. Objective: This study aimed to design and evaluate an Internet of Things (IoT)-based smart relay system for real-time monitoring and remote control of electrical energy consumption. Methods: This study employed an experimental-based system development approach to create a portable prototype. The system integrated an ESP8266 microcontroller, a PZEM-004T sensor, a relay module, and a 16×2 LCD, with data transmitted to a web-based platform for monitoring and control. The system was tested using five different electrical loads, and performance was evaluated by comparing sensor measurements with standard measurement instruments. Results: The system achieved high accuracy in voltage measurement with an average error of 0.58% and moderate accuracy in power measurement with 2.78%. However, current measurement showed a higher average error of 15.06%, indicating sensitivity to fluctuating load conditions. The system supports real-time monitoring and remote control with an average response time of approximately one second. Compared to previous studies, the system demonstrates comparable voltage performance but higher deviation in current measurement. Novelty: This study presents an integrated IoT-based system that combines real-time monitoring, remote control, and energy estimation within a compact and portable platform. The proposed system provides a practical solution to enhance user awareness and enable more efficient energy management, extending beyond conventional monitoring-only systems.
Co-Authors Achmad, Andita Dani Admojo, Fadhila Tangguh Alghamdi, Mona Ahmad Annisa Nurul Puteri Aqsa, A. Anugrah Arifin, Muh Awal Awangga, Narendra Erick Irawadi Alwi, Erick Irawadi Gaffar, Andi Widya Mufila Huzain Azis Ifan Wahyudi Irawati Irawati Iskandar, M. Iqbal lilis nurhayati Lokapitasari Belluano, Poetri Lestari Muis, Ismunandar Muslihi, Muhammad Takdir Nidhom, Ahmad Mursyidun Purnawansyah Purnawansyah Rachman Manga, Abdul Ramdan Satra Rismayanti, Nurul Salim, Yulita Siska Anraeni Syam, Muhammad Farhan Syamsiar, Syamsiar