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All Journal REKA ELKOMIKA International Journal of Artificial Intelligence Research
A. TAUPIK RAHMAN
Jurusan Teknik Elektro Institut Teknologi Nasional Bandung
Computer Science & IT Education Electrical & Electronics Engineering

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Peramalan Beban Puncak Jangka Pendek Khusus Hari Libur Nasional Berbasis Algoritma Fuzzy Subtractive Clustering, Studi Kasus di Jawa – Bali RAHMAN, A. TAUPIK; HARIYANTO, NASRUN; ANWARI, SABAT
REKA ELKOMIKA Vol 2, No 2 (2014)
Publisher : REKA ELKOMIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.153 KB)

Abstract

ABSTRAK Peramalan merupakan upaya memperkirakan apa yang terjadi pada masa mendatang berdasarkan data pada masa lalu, berbasis pada metode ilmiah dan kualitatif yang dilakukan secara sistematis.Penelitian ini mengkaji tentang algoritma Fuzzy Subtractive Clustering (FSC) untuk peramalan beban puncak harian jangka pendek. Tujuan dari penelitian ini adalah membandingkan hasil peramalan beban puncak antara algoritma FSC dengan metode PLN, yaitu Koefisien Beban. Data historis menggunakan data pengeluaran beban listrik dari P3B PT.PLN (Persero) Area III Jawa Barat UPB-Cigereleng tahun 2006 sampai dengan 2012, setiap 30 menit dalam 6 jam mulai dari pukul 17.00 sampai dengan 22.00 WIB. Cara perhitungan dilakukan dengan menggunakan algoritma FSC untuk mengetahui tingkat akurasi prediksi dan nilai rata-rata error peramalan beban puncak. Melalui perhitungan dan hasil simulasi didapatkan rata-rata error peramalan beban puncak dengan menggunakan metode Koefisien Beban sebesar 3,11% dan rata-rata error peramalan beban puncak dengan menggunakan algoritma FSC sebesar 0,002%. Sehingga dapat menyimpulkan, bahwa peramalan beban menggunakan algoritma FSC memberikan hasil peramalan yang lebih akurat dibanding dengan algoritma Koefisien Beban. Kata kunci: Fuzzy Subtractive Clustering (FSC), Koefisien Beban, Prediksi Beban Puncak Jangka Pendek. ABSTRACT Forecasting is an attempt to predict what happens in the future based on the data in the past, based on the scientific method and qualitative systematic. This study examines the algorithm of Fuzzy Subtractive Clustering (FSC) for forecasting short-term daily peak load. The purpose of this study was to compare the results between the peak load forecasting algorithm with the method FSC PLN, i.e. Load Coefficient. The historical data used expenditure data from the electrical load PT PLN P3B (Persero) Area III West Java UPB-Cigereleng 2006 to 2012, every 30 minutes in 6 hours starting from 17:00 until 22:00 pm. The calculation was done by using FSC algorithm to determine the level of accuracy of prediction and the average value of the error in the FSC algorithm. Through the calculation and simulation results, it was obtained the average peak load forecasting error by using the method of Load Coefficient at 3.11% and the average error of peak load forecasting using FSC algorithm of 0.002%. So that, it could be concluded, that the load forecasting that using FSC algorithm gave more accurate forecasting results than the algorithm as well as the expense coefficient. Keywords: Fuzzy Subtractive Clustering (FSC), coefficient Expense, Short-Term Peak Load Prediction.
Design and Development of a Competency Certificate Surveillance System for Electrical Technical Personnel using a Disruptive RSM Design Approach Mulyati, Rika; Lubis, Muharman; Suakanto, Sinung; Rahman, A. Taupik
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1660

Abstract

The competency certificate for electrical technical personnel serves as formal evidence that an individual is qualified to work in the electricity sector and has a validity period of three years, requiring periodic surveillance to ensure regulatory compliance. In practice, the surveillance process is still largely conducted manually by Competency Certification Bodies (LSK), resulting in administrative inefficiencies, delays in certificate renewal, fragmented documentation, and limited traceability of surveillance records. These challenges not only burden certification bodies and certificate holders but also affect regulatory supervision performance. This study aims to design and develop a competency certificate surveillance information system for electrical technical personnel using a disruptive Recognise–Scrutinize–Materialize (RSM) design approach. Data were collected through observations, semi-structured interviews, and document analysis to identify existing problems and system requirements. The RSM method was applied to systematically align stakeholder needs with national regulations and international standards, including ISO, IEEE, and NIST guidelines. The results of this research produce a regulation-based surveillance system design in the form of a structured mock-up that integrates automated reminders, digital document validation, standardized surveillance workflows, and real-time monitoring dashboards. The proposed system is expected to improve efficiency, data accuracy, transparency, and regulatory compliance in the surveillance and renewal process of competency certificates. This research contributes novel insights into the digitalization of competency certificate surveillance, a topic that has received limited attention in previous studies, particularly within the electricity sector. 
Co-Authors Muharman Lubis Mulyati, Rika NASRUN HARIYANTO SABAT ANWARI Sinung Suakanto