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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 23 Documents
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Search results for , issue "Vol 11, No 3: September 2023" : 23 Documents clear
Forecasting Carbon Dioxide Emission in Thailand Using Machine Learning Techniques Siriporn Chimphlee; Witcha Chimphlee
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4892

Abstract

Machine Learning (ML) models and the massive quantity of data accessible provide useful tools for analyzing the advancement of climate change trends and identifying major contributors. Random Forest (RF), Gradient Boosting Regression (GBR), XGBoost (XGB), Support Vector Machines (SVC), Decision Trees (DT), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), ensemble methods, and Genetic Algorithms (GA) are used in this study to predict CO2 emissions in Thailand. A variety of evaluation criteria are used to determine how well these models work, including R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and correctness.  The results show that the RF and XGB algorithms function exceptionally well, with high R-squared values and low error rates.  KNN, PCA, ensemble methods, and GA, on the other hand, outperform the top-performing models. Their lower R-squared values and higher error scores indicate that they are unable to accurately anticipate CO2 emissions. This paper contributes to the field of environmental modeling by comparing the effectiveness of various machine learning approaches in forecasting CO2 emissions. The findings can assist Thailand in promoting sustainable development and developing policies that are consistent with worldwide efforts to combat climate change.
The Success Factors in Measuring the Millennial Generation’s Energy-Saving Behavior Toward the Smart Campus Lola Oktavia; Okfalisa Okfalisa; Pizaini Pizaini; Rahmad Abdillah; Saktioto Saktioto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4885

Abstract

The millennial generation has a pivotal role in leading the industrial digital revolution. Energy-saving behavior and millennials’ awareness of energy consumption for educational context become crucial in performing a smart campus. This study tries to identify the success factors in measuring the millennial generation’s energy-saving Behavior toward the smart campus. The measurement model considers two significant constructs, including energy-saving attitudes with energy-saving education (organizational saving climate); energy-saving education and environment knowledge (personal saving climate); and energy-saving information publicity as sub-indicators, and construct energy-saving Behavior viz sub-indicators Behavior regarding energy and behavior control. In order to determine the preference level of each indicator and sub-indicator, the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach was executed by disseminating the questionnaire to 100 respondents from energy practitioners, students, and academicians in Indonesia. The calculation reveals that the energy-saving behavior construct has a higher priority value (0.94) than the energy-saving attitude (0.06). Meanwhile, energy-saving education and environment knowledge (personal saving climate) have been analyzed at the cutting-edge sub-indicator, followed by energy-saving information publicity and education (organizational saving climate). In addition, the sub-indicator for behaviors regarding energy becomes more demanding compared to behavioral control. As a novelty, the priority analysis of this Model aids the management of the campus and government in developing smart campus policies and governance. This Model can be used as a guideline for the management level to execute the smart campus practices. Thus, the effectiveness and optimization of smart campus transformation can be cultivated and accelerated. Besides, the potential coming of risks can be avoidable.
Investigation of Photovoltaic Hosting Capacity Using Minimum Generation Operation Approach Syafii Syafii; Thoriq Kurnia Agung; Dawam Habibullah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4856

Abstract

Photovoltaic (PV) have become a priority renewable energy source to be developed in Indonesia to achieve new and renewable energy (NRE) target of 23% in 2025 and 31% in 2050. The operation of a significant number of rooftop PV can also change the type of power system operating configuration to Distributed Energy Generation (DEG). The majority of DEGs which are NRE generators are capable of causing new problems because of their intermittent nature. Hosting Capacity is a high penetration limit for NRE without causing problems and limits on operational violations. The hosting capacity method used is based on the generator's minimum operation. In the test system consisting of 3 power plants such as hydro power plant, coal power plant, and geothermal power plant, the PV capacity that can be injected into the system is 139.1 MW. With PV injection based on hosting capacity, the system becomes better with the same average voltage profile as before PV injection, namely 0.991 p.u. System stability by reviewing the frequency, rotor angle, and rotor speed, the system after PV injection is better than before PV injection.

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