Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Bangkit Indonesia
Atho’illah, Ibnu
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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

Found 2 Documents
Search

 1 
Prototype design of crowdfunding-based student tuition payment E-Wallet management application (startup) at STMIK Bandung Bali Andisana, I Putu Gd Sukenada; Smrti, Ni Nyoman Emang; Atho’illah, Ibnu
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 15 No. 2 (2025): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v15i2.72-86

Abstract

This research designed and developed a prototype e-wallet management application for crowdfunding-based tuition fee payment at STMIK Bandung Bali, addressing higher education cost challenges. Using Agile methodology, the development covered requirements analysis, UI/database design, payment gateway integration, and testing. Core functionalities include student data, academic history, billing, e-wallet balance, donor contributions, and campus operator disbursements. Functional testing showed 100% success across all 9 black-box test scenarios, confirming successful crowdfunding system implementation. However, load and stress tests on shared hosting (0.5 CPU, 256 MB RAM) revealed performance limitations. Response times increased sharply from 2.2 seconds (100 requests) to 14.6 seconds (200 requests), with over 95% system failure beyond 400 concurrent requests, indicating hosting resource constraints. Empirical user evaluations (10 students, 5 donors, 2 operators) confirmed high system effectiveness and usability, yielding average scores of 4.2 for effectiveness and 4.0 for usability (on a 5-point Likert scale). Security measures include private key API integration, AES password encryption, and restricted sensitive data access. This research's success lies in its specific technical solution for institutional tuition crowdfunding, integrating directly with STMIK Bandung Bali's financial management, differentiating it from general platforms.
Prediksi Gagal Jantung Berbasis Deep Learning dengan Algoritma Long Short Term Memory Atho’illah, Ibnu; Emang Smrti, Ni Nyoman; Madani, Annisa Fitri; Sukenada Andisana, I Putu Gd
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.436

Abstract

Heart failure is one of the leading causes of death in the world. Early detection and accurate analysis are essential for proper treatment. This study proposes the use of Long Short-Term Memory (LSTM) algorithm to analyse and predict the progression of heart failure disease based on patient medical data. The LSTM model developed uses the Python platform with TensorFlow and Keras libraries, as well as the “Heart Failure Prediction” dataset from Kaggle.com. The results showed that the LSTM model with training and testing data ratio of 70:30 (Model B) achieved the best performance with accuracy of 0.869, precision of 0.869, recall of 0.869, and F1-score of 0.869. The model showed consistent ability in identifying positive and negative cases of heart failure and was effective in reducing overfitting. Overall, this research contributes to the development of more accurate and efficient heart failure disease prediction methods.
Co-Authors Andisana, I Putu Gd Sukenada Emang Smrti, Ni Nyoman Madani, Annisa Fitri Ni Nyoman Emang Smrti Sukenada Andisana, I Putu Gd