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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) International Journal Software Engineering and Computer Science (IJSECS)
Wahyu Purnama Magribi
Universitas Esa Unggul
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Physics

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Analisis Komparatif Metode Pengurangan Derau Klasik dan Pembelajaran Mendalam untuk Meningkatkan Kualitas Citra Parasit Malaria Wahyu Purnama Magribi; Habibullah Akbar; Muhammad Fazly Qusyairy; Tino Saputra; Eric Julianto; Decky Ryansyah
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i4.14840

Abstract

Malaria diagnosis accuracy depends on microscopic image quality, often compromised by noise. This study comprehensively evaluates classical denoising (morphological, median, bilateral filters) against deep learning architectures (DnCNN, Autoencoder, U-Net) for malaria parasite images. Using the Cell Images for Detecting Malaria dataset with synthetic Gaussian, salt-and-pepper, and mixed noise, experiments measured PSNR, SSIM, and processing time. Results indicate U-Net achieved superior performance (PSNR 36.69 dB, SSIM 0.9577), significantly outperforming Autoencoder (PSNR 26.12 dB) and classical methods (PSNR 23.14 dB). The baseline DnCNN architecture did not achieve competitive performance (PSNR 8.42 dB), indicating that domain-specific parameter tuning and data normalization adjustments are necessary for effective application to microscopic imaging. Autoencoder demonstrated the highest computational efficiency (1.64 ms per image), though the 10.57 dB PSNR gap relative to U-Net suggests that the quality trade-off may limit its suitability in accuracy-critical diagnostic scenarios. U-Net best preserved morphological details crucial for diagnosis and is recommended as the primary choice for malaria diagnostic systems prioritizing accuracy, while Autoencoder represents the most computationally efficient alternative for resource-constrained deployment. These findings support developing robust computer-aided diagnosis systems and contribute a comprehensive quantitative benchmark for denoising methods in malaria microscopy.
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 Akbar, Habibullah Decky Ryansyah Eric Julianto Muhammad Fazly Qusyairy Muhammad Fazly Qusyairy Tino Saputra Tino Saputra