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All Journal Journal of Applied Data Sciences Journal of Digital Market and Digital Currency
Tri Basuki Kurniawan
Postgraduate Program, Universitas Bina Darma, Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Deep Learning-Based Loan Approval Prediction Using Artificial Neural Network (ANN) and Feature Importance Analysis Sheeba Armoogum; Deshinta Arrova Dewi; Vinaye Armoogum; Nicolas Melanie; Tri Basuki Kurniawan
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.55

Abstract

The increasing demand for efficient and objective credit evaluation has motivated the adoption of artificial intelligence in financial decision-making. This study proposes a deep learning-based loan approval prediction model using an Artificial Neural Network (ANN) combined with feature importance analysis to enhance interpretability. The dataset, consisting of 2,000 loan application records with both financial and demographic attributes, was preprocessed through normalization and one-hot encoding to ensure consistent feature representation. The ANN model was trained using three hidden layers (64–32–16 neurons) with the ReLU activation function and optimized using Adam with early stopping to prevent overfitting. Experimental results demonstrate that the proposed ANN model achieves an accuracy of 92%, with a precision of 0.91, a recall of 0.93, and a ROC-AUC of 0.95, indicating excellent classification capability. The Permutation Feature Importance analysis revealed that Credit Score, Income, and Loan Amount are the most significant predictors influencing loan approval decisions. These findings confirm that the ANN model can capture complex non-linear relationships among financial attributes while maintaining transparency through explainable AI techniques. The proposed approach contributes both theoretically and practically by combining predictive power with interpretability, offering a reliable and explainable framework for automating loan evaluation in modern financial institutions.
Leveraging Data Analytics for Student Grade Prediction: A Comparative Study of Data Features Misinem Misinem; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mohd Zaki Zakaria; Che Mohd Alif Nazmi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.442

Abstract

In educational settings, a persistent challenge lies in accurately identifying and supporting students at risk of underperformance or grade retention. Traditional approaches often fall short by applying generalized interventions that fail to address specific academic needs, leading to ineffective outcomes and increased grade repetition. This study advocates for integrating machine learning algorithms into educational assessment practices to address these limitations. By leveraging historical and current performance data, machine learning models can help identify students needing additional support early in their academic journey, allowing for precise and timely interventions. This research examines the effectiveness of three machine learning algorithms: Naive Bayes, Deep Learning, and Decision Trees. Naive Bayes, known for its simplicity and efficiency, is well-suited for initial data screening. Deep Learning excels at uncovering complex patterns in large datasets, making it ideal for nuanced predictions. Decision Trees, with their interpretable and actionable outputs, provide clear decision paths, making them particularly advantageous for educational applications. Among the models tested, the Decision Tree algorithm demonstrated the highest performance, achieving an accuracy rate of 86.68%. This high precision underscores its suitability for educational contexts where decisions need to be based on reliable, interpretable data. The results strongly support the broader application of Decision Tree analysis in educational practices. By implementing this model, educational administrators can better identify at-risk students, tailor interventions to meet individual needs, and ultimately improve student success rates. This study suggests that Decision Trees could become a vital tool in data-driven strategies to enhance student retention and optimize academic outcomes.
Deep Learning Incorporated with Augmented Reality Application for Watch Try-On Andri Andri; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mashal Kasem Alqudah; Musab Kasim Alqudah; Mohd Zaki Zakaria; Putri Aisha Athira binti Hisham
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.529

Abstract

In evaluating the dynamic landscape of online shopping, the integration of Augmented Reality (AR) technologies has emerged as a transformative force, redefining the way consumers engage with products in virtual environments. This research project investigates the intersection of deep learning and AR in the context of online shopping, with a particular focus on a Watch Try-On application. The experimentation involves the use of SSD MobileNet's models for real-time object detection aimed at enhancing the user experience during online watch shopping. Training both SSD MobileNet's V1 and V2 models through 50,000 iterations, the results reveal intriguing insights into their performance. SSD MobileNet's V1 demonstrated superior results, boasting a mean average precision (mAP) of 0.9725 and a significant reduction in total loss from 0.774 to 0.5405. However, the longer training time of 7 hours and 42 minutes prompted the selection of SSD MobileNet's V2 for real-time applications due to its faster inference capabilities. Extending beyond traditional online shopping experiences, the research explores the potential of AR technologies to revolutionize product visualization and interaction. The choice of the Vuforia model target for the Watch Try-On application showcases the synergy between deep learning and AR, allowing users to virtually try on watches and visualize them in their real-world environment. The application successfully detects users' hands with high accuracy, creating an immersive and visually enriching experience. In conclusion, this project contributes to the ongoing discourse on the fusion of deep learning and AR for online shopping. The exploration of SSD MobileNet's models, coupled with the integration of AR technologies, underscores the potential to elevate the online shopping experience by providing users with dynamic, interactive, and personalized ways to engage with products.
Unveiling Criminal Activity: a Social Media Mining Approach to Crime Prediction Sheeba Armoogum; Deshinta Arrova Dewi; Vinaye Armoogum; Nicolas Melanie; Tri Basuki Kurniawan
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.350

Abstract

Social media platforms have become breeding grounds for abusive comments, necessitating the use of machine learning to detect harmful content. This study aims to predict abusive comments within a Mauritian context, focusing specifically on comments written in Mauritian Kreol, a language with limited natural language processing tools. The objective was to build and evaluate four machine learning models—Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine (SVM)—to accurately classify comments as abusive or non-abusive. The models were trained and tested using k-fold cross-validation, and the Decision Tree model outperformed others with 100% precision and recall, while Random Forest followed with 99% accuracy. Naïve Bayes and SVM, although achieving 100% precision, had lower recall rates of 35% and 16%, respectively, due to imbalanced data in the training set. Pre-processing steps, including stop-word removal and a custom Kreol spell checker, were key in enhancing model performance. The study provides a novel contribution by applying machine learning in a Mauritian context, demonstrating the potential of AI in detecting abusive language in underrepresented languages. Despite limitations such as the absence of a Kreol lemmatization tool and incomplete coverage of Kreol spelling variations, the models show promise for wider application in social media crime detection. Future research could explore expanding this approach to other languages and domains of social media crimes.
Recommender System for Book Review based on Clustering Algorithms Devi Udariansyah; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mohd Zaki Zakaria; Nur Syuhana binti Abd Hanan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.492

Abstract

Book reviews show the expression of the reviewers that are to be evaluated and describe the book. Today, the amount of the book is growing rapidly, and it offers people a lot of choices. The recommender system on book reviews is mostly mentioned, and we will recommend a book based on the keyword selected. This study highlights two primary objectives. The first objective is to identify the keywords of the book review, and the last objective is to design and develop a book review analysis visualization using the result of the k-means clustering algorithm. The methodology of this research consists of ten phases, which start with the preliminary study, knowledge acquisition and analysis phase, data collection phase, data pre-processing phase, and modeling phase. The research then continues with the design and implementation, dashboard development, testing and evaluation, and finally, the documentation phase. The data from this study is scraped from Amazon.com and focuses on three genres: Fiction and Fantasy, Mystery and Thriller, and Romance. All the data will be clean before it can be applied to k-means clustering. The result of clustering will define the keywords for every genre and will compare with the keywords for each book that was collected from Amazon.com.
Machine Learning Techniques for Distinguishing Android Malware Variants Irwansyah Irwansyah; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mohd Zaki Zakaria; Nurhafifi Binti Azmi
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.493

Abstract

The advancement of portable devices has been quickly and dramatically reshaping the usage trend and consumer preferences of electronic devices. Android, the most common mobile operating system, has a privilege-separated protection system with a complex access control mechanism. Android apps require permission to get access to confidential personal data and device resources. However, studies have shown that various malicious applications can acquire permission and target systems and applications by misleading users. In this study, we suggest a machine-learning approach to classifying Android malware variants by mining requested permissions, real permissions, suspicious calls, and API calls that were obtained and used in Android malware applications. Selected features were selected using a feature selection called KBest. Feature selection techniques are used to minimize the scale of the features and increase the performance. Two types of Naïve Bayes classifiers, called Multinomial distribution and multivariate Bernoulli distribution, are used and compared in malware family classification for text classification. Both naïve Bayes types are evaluated using a confusion matrix based on 4022 Android malware applications belonging to 10 families. Experimental findings show that the Multinomial distribution offers a reliable performance from three tests experiment with an average accuracy of 95%.
Co-Authors Andri Andri Che Mohd Alif Nazmi Deshinta Arrova Dewi Deshinta Arrova Dewi Deshinta Arrova Dewi Devi Udariansyah Irwansyah Irwansyah Mashal Kasem Alqudah Misinem Misinem Mohd Zaki Zakaria Mohd Zaki Zakaria Musab Kasim Alqudah Nicolas Melanie Nur Syuhana binti Abd Hanan Nurhafifi Binti Azmi Putri Aisha Athira binti Hisham Sheeba Armoogum Sheeba Armoogum Vinaye Armoogum