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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika International Journal of Computer and Information System (IJCIS) TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Nusantara Journal of Computers and its Applications Mumtaza: Journal of Community Engagement Journal of Artificial Intelligence
Zainal Arifin
Universitas Nurul Jadid
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Public Health Other

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Siamtek Unuja Security Analysis Using Two-Factor Algorithm Biometric Scan With Fingerprint zainal Arifin; Fuadz Hasyim
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 1 (2025): June 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i1.256

Abstract

Student data security is a critical priority in academic information systems, particularly at Nurul Jadid University (UNUJA), which utilizes the SIAMTEK platform to manage practicum and final project activities. This study aims to analyze the effectiveness of implementing Two-Factor Authentication (2FA) using biometric fingerprint scanning to enhance the security of the SIAMTEK application. The research employed an experimental approach that included literature review, system analysis, system design, development, and testing. The authentication process consisted of two stages: verification via username and password, followed by a fingerprint scan. System testing was conducted using a Likert scale questionnaire distributed to 4th and 6th semester students, who are active users of the SIAMTEK application. The results indicated a 62% satisfaction index for the user interface and a 65% index for the functionality of the 2FA system. These results suggest that users are moderately satisfied with the added layer of security. Unlike One-Time Password (OTP) methods, which depend on mobile credit or connectivity, fingerprint biometric scanning offers a cost-effective and secure solution without requiring additional user expenses. This study also reviewed previous research on 2FA and Multi-Factor Authentication (MFA), highlighting the vulnerabilities of single-factor systems, particularly to phishing attacks and unauthorized access. The findings confirm that implementing fingerprint-based 2FA in the SIAMTEK system is both feasible and beneficial, significantly reducing risks of data breaches and account misuse. The integration of biometric technology as an additional authentication layer positively contributes to digital academic system security and supports data protection efforts for students' personal and academic information.
Web-Based Classification Optimization of Grape Leaf Diseases Using Transfer Learning in CNN to Improve Model Accuracy and Efficiency Zainal Arifin; Diana Indri Rukmana; Fuadz Hasyim
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 2 No 2 (2025): December 2025
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v2i2.323

Abstract

Grapes are a high-value crop, but their yield is easily reduced due to leaf disease. This study focuses on developing a web-based grape leaf disease classification system that works automatically and in real time. The approach used is Convolutional Neural Network (CNN) with transfer learning using the EfficientNetB0 architecture. The research data consists of 8,000 grape leaf images divided into four classes (healthy, black rot, black spot, and leaf spot) with a composition of 80% for training, 10% for validation, and 10% for testing. The initial CNN model achieved an accuracy of 97%, which then increased to 99% after optimization using EfficientNetB0 with fine-tuning. The implementation of the system through Flask showed fast and accurate prediction results, proving that transfer learning plays an important role in improving classification performance.
Predicting Potential Deposit Customers Using CatBoost with Feature Selection and Hyperparameter Optimization Jiawei Han; Zainal Arifin
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 3 No 1 (2026): June 2026
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v3i1.671

Abstract

The banking industry requires increasingly targeted marketing strategies to identify potential customers for deposit products. This study aims to build a predictive model for identifying potential deposit customers using the CatBoost algorithm combined with feature selection based on feature importance and hyperparameter tuning using Hyperopt. This study employs an experimental method using the Bank Marketing Dataset from Kaggle, a secondary dataset consisting of 45,211 data points and 17 attributes, including a target variable indicating the customer’s decision to subscribe to a deposit product or not. The research stages included Exploratory Data Analysis (EDA), preprocessing, data cleaning, class balancing using SMOTE-NC, feature selection, hyperparameter tuning, modeling, and model evaluation. The EDA results revealed outliers in the duration and previous features, as well as class imbalance in the target variable. After preprocessing, the model was built using 10 selected features: duration, month, contact, day, outcome, balance, housing, pdays, age, and job. Evaluation results using a confusion matrix showed that the CatBoost model achieved an accuracy of 92.8%, a sensitivity of 91.0%, and a specificity of 94.8%. These findings demonstrate that the combination of CatBoost, feature selection, and hyperparameter tuning is capable of producing an accurate and efficient predictive model for handling both numerical and categorical data. This model can help banks develop more effective, cost-efficient, measurable, and data-driven deposit marketing strategies, while also supporting business decision-making that is more adaptive to customer behavior.
Co-Authors Abd Wafi Abu Tholib Diana Indri Rukmana Fathorazi Nur Fajri Hasyim, Fuadz Jiawei Han Kamil Malik Khoirun Nisa' Khoirun Nisa' Maulidiansyah Mochammad Faid Moh. Januar Rizqi Minallah Moh.Januar Rizqi Minallah Rian Hidayat Rio Ahmad Syaikhu Saidatul Maghfiroh Titasari Rahmati Wali Ja'far Shudiq Wali Wali Ja’far Shudiq