Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal ILKOM Jurnal Ilmiah Frontier Energy System and Power Engineering
Jehad A.H. Hammad
Al-Quds Open University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Classification of Engineering Journals Quartile using Various Supervised Learning Models Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rasyid; Anik Nur Handayani; Andrew Nafalski; Edinar Valiant Hawali; Jehad A.H. Hammad
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1483.101-106

Abstract

In scientific research, journals are among the primary sources of information. There are quartiles or categories of quality in journals which are Q1, Q2, Q3, and Q4. These quartiles represent the assessment of journal. A classification machine learning algorithm is developed as a means in the categorization of journals. The process of classifying data to estimate an item class with an unknown label is called classification. Various classification algorithms, such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM) are employed in this study, with several situations for exchanging training and testing data. Cross-validation with Confusion Matrix values of accuracy, precision, recall, and error classification is used to analyzed classification performance. The classifier with the finest accuracy rate is KNN with average accuracy of 70%, Naïve Bayes at 60% and SVM at 40%. This research suggests assumption that algorithms used in this article can approach SJR classification system.
Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN) Aji Prasetya Wibawa; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Agung Bella Putra Utama; Jehad A.H. Hammad
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p50-57

Abstract

Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In testing with z-score normalization, it showed a performance of MAPE of 10.6120%, RMSE of 0.7648, and R2 of 0.4142.
Co-Authors Ade Kurnia Ganesh Akbari Agung Bella Putra Utama Aji Prasetya Wibawa Akhmad Fanny Fadhilla Alfiansyah Putra Pertama Triono Andien Khansa’a Iffat Paramarta Andrew Nafalski Anik Nur Handayani Edinar Valiant Hawali Faradini Usha Setyaputri Harits Ar Rasyid Nastiti Susetyo Fanany Putri