Article Per Year (5 Year)
p-Index From 2020 - 2025
4.51
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Teknologi dan Kejuruan MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Development Research JOIV : International Journal on Informatics Visualization Applied Information System and Management IJISTECH (International Journal Of Information System & Technology) Systemic: Information System and Informatics Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems IJISTECH Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Janitra Informatika dan Sistem Informasi SinarFe7 Bulletin of Social Informatics Theory and Application Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Yunifa Miftachul Arif
Department Of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

Published : 39 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search
Journal : Applied Information System and Management

 1   2 
Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices Sahi, Muhammad; Faisal, Muhammad; Arif, Yunifa Miftachul; Crysdian, Cahyo
Applied Information System and Management (AISM) Vol. 6 No. 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.29648

Abstract

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
Struggling Models: An Analysis of Logistic Regression and Random Forest in Predicting Repeat Buyers with Imbalanced Performance Metrics Mauludiah, Siska Farizah; Arif, Yunifa Miftachul; Faisal, Muhammad; Putra, Dony Darmawan
Applied Information System and Management (AISM) Vol. 7 No. 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.39326

Abstract

Predicting repeat buyers is essential for businesses seeking to improve customer retention and maximize profitability. This study examines the effectiveness of logistic regression and random forest algorithms in forecasting repeat buyers, utilizing an e-commerce dataset from Kaggle. Despite the theoretical strengths of these models, our results indicate significant performance challenges. Both models were evaluated on key metrics: accuracy, precision, recall, F1 score, and ROC-AUC. The findings revealed that the models logistic regression and random forest performed poorly, with accuracy hovering around 50%, precision and recall demonstrating imbalanced performance, and ROC-AUC scores barely exceeding random guessing levels. Such metrics highlight the limited discriminative power of these models in identifying repeat buyers. The analysis suggests that issues such as data quality, feature relevance, and class imbalance contribute to these shortcomings. Specifically, the models struggled to effectively learn from the data, leading to suboptimal predictions. These results underscore the need for enhanced feature engineering, better handling of class imbalance, and possibly exploring more advanced algorithms. This study provides a critical assessment of the limitations inherent in using Logistic Regression and Random Forest for predicting repeat buyers, hence implements feature engineering, SMOTE and hyperparameter tuning using RandomSearchCV to get better result.
Co-Authors AA Sudharmawan, AA Achmad Sabar, Achmad Ady Wicaksono Agariadne Dwinggo Samala Ahmad Fahmi Karami Alfachruddin, M. Nabil Fahd Aristantia, Yuliana Aziza, Miladina Rizka Bojic, Ljubisa Cahyo Crysdian Coelho, Diogo Pereira Dedy Kurnia Setiawan Diah, Norizan Mat Duvan Deswantara Putra Dyah Wardani Fachrul Kurniawan Fathir Fathir Fathir Fathurrahman Firmansyah, Rezky Fresy Nugroho Hamid, Abdulhalim Hamid Salih Hani Nurhayati Howard, Natalie-Jane Ihsan, Afif Nuril Ikhlayel, Mohammed Imamudin, M. Janitra, Geovanni Azam Juniardi Nur Fadila Junikhah, Allin Khadijah Fahmi Hayati Holle Khan, Nauman Linda Salma Angreani Mauludiah, Siska Farizah Mauridhi H Purnomo Mochamad Hariadi Mochammad Wahyu Firmansyah Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Mustofa, Ahmad Habibil Nadhifah, Rizqi Aulia Nauman Khan Norizan Mat Diah Novrindah Alvi Hasanah Putra, Dony Darmawan Putra, Duvan Deswantara Rawas, Soha Ririen Kusumawati Rizqi Aulia Nadhifah Rohma, Salma Ainur Rony, Zahara Tussoleha Roro Inda Melani Safitri A Basid, Puspa Miladin Nuraida Sahi, Muhammad Setiyawan, Niko Heri Sulika Sulika Supeno Mardi S. N, Supeno Mardi Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Tarranita Kusumadewi Tsoy, Dana Wahyuliningtyas, Lia Wardani, Dyah Wibowo, Muhammad Ismail Arjun Zainal Abidin Zulfiandri Zulfiandri