Article Per Year (5 Year)
p-Index From 2021 - 2026
2.218
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Intechno Journal : Information Technology Journal
Agastya, I Made Artha
Unknown Affiliation
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Perbandingan Algoritma Support Vector Machine, Decision Tree, Naïve Bayes, dan Neural Network dalam Klasifikasi Email Wicaksono, Dika; Agastya, I Made Artha
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6949

Abstract

This study aims to compare the effectiveness of four machine learning models in email classification, namely Support Vector Machine (SVM), Decision Tree, Naive Bayes, and Neural Network. This research uses datasets obtained from the Kaggle website. The first dataset contains 18,650 phishing emails (7,328 phishing and 11,322 non-phishing). The second dataset is the result of merging two different datasets containing Indonesian spam emails, resulting in a total of 4,681 emails (2,670 spam and 2,011 non-spam). The merging was done to obtain a more representative amount of data for model evaluation. The results of the study of the two datasets above showed that the Neural Network achieved the highest accuracy with an average of 96.60%. Then, followed by SVM with an average accuracy of 96.43%. Meanwhile, Decision Tree has a fairly high accuracy with an average of 92.38%. In contrast, Naive Bayes recorded the lowest performance with an average accuracy of 90.22%. Although Neural Network has the highest accuracy, other models may be more suitable depending on the needs of the system. Models with lower accuracy, such as Naive Bayes, can be more useful in systems with computational limitations due to their efficiency. SVM offers a balance between high accuracy and computational efficiency, making it an ideal choice for systems that require optimal performance without too much computational burden. Decision Tree is superior in result interpretation, making it suitable for applications that require transparency in decision making.
Peramalan Multivariat Saham Bank Indonesia dengan Model ARIMA dan LSTM Ramadhan, Akhdan Ferdiansyah; Agastya, I Made Artha
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7352

Abstract

Stock price forecasting is a crucial aspect of financial market analysis, particularly in supporting more accurate and informed investment decision-making. This study compares the performance of the statistical Autoregressive Integrated Moving Average (ARIMA) model with three variants of the Long Short-Term Memory (LSTM) architecture, namely Vanilla LSTM, Bidirectional LSTM, and Stacked LSTM, in predicting closing prices and trading volumes of Indonesian bank stocks—specifically BBCA.JK, BBRI.JK, and BMRI.JK. The data were obtained from Kaggle and processed through normalization, transformation, and model training stages using Google Colab and TensorFlow. Evaluation was conducted using RMSE, MAE, and MAPE metrics. The results indicate that ARIMA performs better in forecasting closing prices, achieving an average MAPE of 1.9%, while Bidirectional LSTM yielded the best results in forecasting trading volumes, particularly for BBRI and BMRI stocks. However, the prediction error for volume data remains relatively high (average MAPE of 36.4%) due to its volatile nature. These findings suggest that data characteristics significantly influence model effectiveness. LSTM-based models demonstrate superior capabilities in capturing complex non-linear patterns and exhibit advantages in multivariate forecasting compared to the ARIMA model. This study is expected to serve as a reference for selecting appropriate forecasting models in the context of Indonesian banking stock markets. The results highlight a trade-off between ARIMA, which excels in modeling linear patterns such as closing prices, and LSTM, which is more adaptive to non-linear patterns like trading volumes.
Co-Authors Adhani, Muhammad Azmi Afrig Aminuddin Arief Setyanto Dini Oktarina Dwi Handayani Kusrini, K Kusrini, Kusrini López, Alba Puelles Martínez-Béjar, Rodrigo Mohamad Firdaus, Mohamad Mohammad Haris Muzakki, Mohammad Haris Noor Akhmad Setiawan Nugroho, Agung Prasetyo, Martinus Juan Pratama, Muhammad Egy Ramadhan, Akhdan Ferdiansyah Robert Marco, Robert Rohman, Wahyutri Nur Saputra, Gilang Ardhi Sukaria, Petra Nugra Teguh Bharata Adji Wicaksono, Dika Wiryawan Sujana, Rajendra Artanto