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All Journal International Journal of Electrical and Computer Engineering Journal of Natural Science and Technology Adpertisi: Bidang Sains dan Teknologi
Buang, Misbahuddin
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Computer Science & IT Electrical & Electronics Engineering Energy Engineering

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PEMODELAN DAN PENGENDALIAN MOTOR DC TYPE DRIPPROOF-SEPARATELY VENTYLATED DENGAN TEGANGAN JANGKAR Buang, Misbahuddin
JNSTA ADPERTISI JOURNAL Vol. 3 No. 1 (2023): Januari 2023
Publisher : Aliansi Dosen Perguruan Tinggi Swasta Indonesia (Adpertisi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62728/jnsta.v3i1.390

Abstract

A dc motor is a machine that converts dc electrical energy into mechanical energy. One way to assess the work of a dc motor is to know the amount of current and speed in the initial conditions before adjustments/changes are made. Current and speed settings can be made by adjusting the motor armature voltage at starting. The greater the time the armature voltage reaches its nominal value, the armature current will be smaller so that it is safe for the motorbike and vice versa.
SMALL SIGNAL STABILITY SISTEM TENAGA LISTRIK Buang, Misbahuddin; Patandung , Oktavia Ronhab
JNSTA ADPERTISI JOURNAL Vol. 4 No. 2 (2024): Juli 2024
Publisher : Aliansi Dosen Perguruan Tinggi Swasta Indonesia (Adpertisi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62728/jnsta.v4i2.595

Abstract

Stabilitas sistem tenaga listrik merupakan kemampuan suatu sistem tenaga listrik untuk mempertahankan sinkronisasi dan keseimbangan dalam sistem akibat adanya gangguan. Secara umum permasalahan stabilitas sistem tenaga listrik terkait dengan kestabilan sudut rotor (Rotor Angle Stability) dan kestabilan tegangan (Voltage Stability). Small Signal Stability sistem tenaga listrik yang merupakan bagian dari kestabilan sudut rotor adalah kestabilan sistem untuk gangguan-gnagguan kecil dalam bentuk osilasi eletromagnetik yang tak teredam. Studi Small Signal Stability sangat diperlukan untuk menganalisis sistem supaya dapat bekerja secara efektif. Untuk mempelajari Small Signal Stability digunakan pemodelan terhadap komponen-komponen generator, saluran transmisi dan beban. Pemodelan diturunkan dari persamaan matematis berupa persamaan diffrensial linier untuk mewakili perilaku dinamik sistem. . Hasil simulasi menujukkan bahwa sistem stabil yang ditunjukkan dengan nilai eigen yang negatif. Dalam menganalisis Smal Signal Stability pada beban statik digunakan dua metode yaitu Analisis Eigen Value dan Time domain Simulation. Dari analisis eigen value diperoleh bahwa sistem stabil dengan nilai ril eigen yang bernilai negatif. Adapun Time Domain simulation, sistem diberi gangguan 0.01 pu, input unit step dengan jarak waktu 10 detik.Dari hasil simulasi diperoleh bahwa sistem mulai berosilasi pada saat 10 detik dan kembali stabil setelah diatas 50 detik.
Optimizing short-term energy demand forecasting: a comprehensive analysis using autoregressive integrated moving average method Aziz, Firman; Jeffry, Jeffry; Buang, Misbahuddin; La Wungo, Supriyadi; Nasruddin, Nasruddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5924-5933

Abstract

This study addresses the critical gap in short-term electricity demand forecasting in South Sulawesi, where inconsistencies between projected and actual peak loads hinder daily operational planning, system stability, and investment efficiency. While previous studies have applied approaches such as fuzzy logic, ARIMA-ANN, and hybrid models, few have focused on simple, robust ARIMA-based models validated across different time spans for daily operational use. To address this, the autoregressive integrated moving average (ARIMA) model is implemented within the Box-Jenkins framework, using automated model selection through the pmdarima library and Akaike’s information criterion (AIC) to identify optimal parameter configurations. The study analyzes daily peak load data from 2018 to 2023, producing realistic forecasts with high accuracy. The selected ARIMA model achieves a mean absolute percentage error (MAPE) of 1.91% and a root mean square error (RMSE) of 38.123, demonstrating its effectiveness in capturing short-term load trends. These results confirm the suitability of ARIMA for short-term forecasting in energy systems and its potential to enhance operational decision-making, reduce forecasting errors, and improve investment planning. The study also establishes a methodological foundation for future development, including the integration of ARIMA with machine learning and the use of extended datasets to support strategic energy management.
Co-Authors Firman Aziz Jeffry Jeffry La Wungo, Supriyadi Nasruddin Nasruddin Patandung , Oktavia Ronhab