Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) International Journal Software Engineering and Computer Science (IJSECS)
Tino Saputra
Universitas Esa Unggul
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Physics

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

Found 2 Documents
Search

 1 
Explainable Brain Tumor Classification Using EfficientNet-B2 and Grad-CAM on MRI Dataset　 Tino Saputra; Wahyu Purnama Magribi; Vitri Tundjungsari
Jurnal Penelitian Pendidikan IPA Vol 12 No 3 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i3.14179

Abstract

Brain tumors are life-threatening central nervous system disorders requiring early and accurate diagnosis for effective clinical management. Although MRI is the standard modality for detection, manual interpretation remains prone to inconsistency, particularly for complex cases such as glioma. This study proposes an explainable deep learning framework integrating EfficientNet-B2 with a threshold-based two-stage classification scheme and Grad-CAM interpretability analysis. In the first stage, a one-versus-rest binary classifier with an optimized threshold (τ = 0.20) performs glioma detection; the second stage classifies remaining cases into meningioma, pituitary tumor, or normal. The dataset comprises 7,023 MRI images across four classes from a public Kaggle repository. Preprocessing includes CLAHE contrast enhancement, normalization, and augmentation. EfficientNet-B0 serves as the baseline. EfficientNet-B2 achieves 97.9% overall accuracy, outperforming the baseline (96.7%), with a glioma F1-score of 0.988 at the optimal threshold. Grad-CAM visualizations confirm the model focuses on anatomically relevant regions, enhancing transparency and clinical trustworthiness. The proposed framework demonstrates that combining architectural capacity, threshold-based inference, and explainability yields a reliable system for computer-aided brain tumor diagnosis.
Data Mining for Predicting Creditworthiness in Credit Card Approval: A Systematic Literature Review Wahyu Purnama Magribi; Muhammad Fazly Qusyairy; Tino Saputra
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6618

Abstract

The growing volume of credit card applications has led financial institutions to seek faster and more reliable methods in the approval process. Manual evaluation is not only time-consuming but also susceptible to human error, which can result in poor credit decisions and measurable financial losses. This study conducts a Systematic Literature Review (SLR) to examine data mining techniques applied to creditworthiness prediction. Five research questions were formulated to identify: (1) commonly used data mining techniques, (2) frequently used datasets, (3) performance evaluation metrics, (4) algorithms with the strongest performance, and (5) recurring challenges and practical recommendations. A structured search across three academic databases — Scopus, Google Scholar, and GARUDA — yielded 8 relevant articles (7 primary experimental studies and 1 secondary study) published between 2021 and 2025. The findings show that Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors are the most widely applied methods. Tree-based algorithms such as Decision Tree and Random Forest consistently yield high accuracy, while K-Nearest Neighbors also delivers strong results in specific experimental settings. Naïve Bayes appears most frequently across studies, and its performance can be improved through metaheuristic approaches such as Particle Swarm Optimization (PSO). Standard evaluation metrics include accuracy, precision, recall, F1-score, and AUC-ROC. The review underscores the importance of data preprocessing, class imbalance handling, and hyperparameter tuning in building reliable prediction models — findings with direct implications for financial institutions seeking to reduce non-performing loan rates.
Co-Authors Muhammad Fazly Qusyairy Vitri Tundjungsari Wahyu Purnama Magribi Wahyu Purnama Magribi