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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOLLT Journal of Languages and Language Teaching Journal of Computing Theories and Applications
Bonde, Lossan
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media

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Innovative M-Learning with Automatic Feedback: Enhancing Language Acquisition for Level 2 Indonesian Foreign Speakers (BIPA) Muzaki, Helmi; Susanto, Gatut; Widyartono, Didin; Bonde, Lossan; Moorthy, Thilip Kumar; Akhsani, Ilham
Journal of Languages and Language Teaching Vol 12, No 4 (2024)
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jollt.v12i4.11851

Abstract

Indonesian has become the official language of the UN. Many BIPA students want to learn Indonesian online. However, online learning creates obstacles such as time differences between teachers and BIPA students, internet connections, and providing less than optimal feedback. This research aims to develop m-learning with automatic feedback for BIPA level 2 learners. This research uses a 4D development model: define, design, develop and deploy. This study's instrument is  a questionnaire distributed to 2 validators and 18 BIPA learners. The results of this study are m-learning products that minimize internet connections; once installed, students only need to be connected to the internet when working on questions. In addition, m-learning is also equipped with automatic feedback that appears immediately after students answer questions. The results of the product trial show that students can use m-learning to learn anytime and anywhere, including in areas with minimal internet access. Automatic feedback in m-learning also helps students learn independently because they do not need to wait for feedback from teachers. The automatic feedback in M-learning is only for listening and reading questions while speaking and writing questions are still in the form of answer keywords or assessment rubrics that the teacher must correct. Based on expert validation and product trials with an average score of 88.8, we conclude that the development of m-learning is suitable for BIPA level 2 students.
Improving Credit Card Fraud Detection with Ensemble Deep Learning-Based Models: A Hybrid Approach Using SMOTE-ENN Bonde, Lossan; Bichanga, Abdoul Karim
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12021

Abstract

Advances in information and internet technologies have significantly transformed the business environment, including the financial sector. The COVID-19 pandemic has further accelerated this digital adoption, expanding the e-commerce industry and highlighting the necessity for secure online transactions. Credit Card Fraud Detection (CCFD) stands critical as the prevalence of fraudulent activities continues to rise with the increasing volume of online transactions. Traditional methods for detecting fraud, such as rule-based systems and basic machine learning models, tend to fail to keep pace with fraudsters' evolving tactics. This study proposes a novel ensemble deep learning-based approach that combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multilayer Perceptron (MLP) with the Synthetic Minority Oversampling Technique and Edited Nearest Neighbors (SMOTE-ENN) to address class imbalance and improve detection accuracy. The methodology integrates CNN for feature extraction, GRU for sequential transaction analysis, and Multilayer Perceptron (MLP) as a meta-learner in a stacking framework. By leveraging SMOTE-ENN, the proposed approach enhances data balance and prevents overfitting. With synthetic data, the robustness and accuracy of the model have been improved, particularly in scenarios where fraudulent examples are scarce. The experiments conducted on real-world credit card transaction datasets have established that our approach outperforms existing methods, achieving higher metrics performance.
Challenges of recommender systems in finance and banking: a systematic review Bonde, Lossan; Bichanga, Abdoul Karim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2559-2567

Abstract

Recommender systems are widely applied in various domains, including e-commerce, marketing, and education. Despite their popularity, recommender systems are not widely used in finance and banking. This paper aims to identify the challenges associated with using recommender systems in finance and banking and recommend directions for future research. Using a systematic literature review (SLR) method, 52 papers were selected and analyzed. A three-step process was used to make the selection. First, a keyword search was made to identify a seed list of sources. A snowball technique with specific inclusion and exclusion criteria was applied to expand the list. Finally, a quick study was made to produce the final list of sources to consider. Through the study of the 52 relevant papers, three main challenges: i) transparency, ethics, and data privacy; ii) handling complex content information and accounting for multiple user behaviors; and iii) explainability of AI models were identified. This study has established the barriers to adopting recommender systems in the finance and banking industry. Specific subjects of concern identified include cold-start problems, personalization, fraud detection, transparency, and data privacy. The study recommends further research leveraging advanced machine learning models and emerging technologies to fill the gap.
Innovative M-Learning with Automatic Feedback: Enhancing Language Acquisition for Level 2 Indonesian Foreign Speakers (BIPA) Muzaki, Helmi; Susanto, Gatut; Widyartono, Didin; Bonde, Lossan; Moorthy, Thilip Kumar; Akhsani, Ilham
Journal of Languages and Language Teaching Vol. 12 No. 4 (2024): October
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jollt.v12i4.11851

Abstract

Indonesian has become the official language of the UN. Many BIPA students want to learn Indonesian online. However, online learning creates obstacles such as time differences between teachers and BIPA students, internet connections, and providing less than optimal feedback. This research aims to develop m-learning with automatic feedback for BIPA level 2 learners. This research uses a 4D development model: define, design, develop and deploy. This study's instrument is  a questionnaire distributed to 2 validators and 18 BIPA learners. The results of this study are m-learning products that minimize internet connections; once installed, students only need to be connected to the internet when working on questions. In addition, m-learning is also equipped with automatic feedback that appears immediately after students answer questions. The results of the product trial show that students can use m-learning to learn anytime and anywhere, including in areas with minimal internet access. Automatic feedback in m-learning also helps students learn independently because they do not need to wait for feedback from teachers. The automatic feedback in M-learning is only for listening and reading questions while speaking and writing questions are still in the form of answer keywords or assessment rubrics that the teacher must correct. Based on expert validation and product trials with an average score of 88.8, we conclude that the development of m-learning is suitable for BIPA level 2 students.
Co-Authors Akhsani, Ilham Bichanga, Abdoul Karim Didin Widyartono Gatut Susanto Helmi Muzaki Moorthy, Thilip Kumar