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All Journal English Review: Journal of English Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Sebatik Jurnal Infortech Journal of Applied Data Sciences Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo
Randy Permana
Universitas Putra Indonesia YPTK Padang
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Hybrid Data Mining For Member Determination And Financing Prediction In Syariah Financing Saving And Loan Cooperatives Ondra Eka Putra; Randy Permana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5683

Abstract

Syariah Financing Saving And Loan Cooperatives (KSPPS) is an Islamic financial institution aimed at people who are on the lower middle scale to lift the economy of small communities through microfinancing programs. Problems that often occur in member recommendations to get KSPPS financing are often not on target. In addition, The amount of member financing is often problematic due to a lack of analysis, resulting in poor financing instalments. This research aims to present an analysis model for clustering and classification using hybrid data mining algorithms. This research method is using hybrid data mining Algorithms, namely K-Medoids, Naïve Bayes, and k-Nearest Neighbors (k-NN). This study uses the historical dataset of the last two years on KSPPS BMT Dadok Tunggul Hitam as a total of 70 data samples. The analysis parameters consist of income, business, residence Status, financing application, billing history, and balance amount. The best analysis Model will be obtained by comparing the results between Naïve Bayes with K-Medoids, and K-Nearest Neighbor (k-NN) with K-Medoids. The results of this research showed the best performance is using the hybrid Naïve Bayes data mining model with K-Medoids which has an accuracy of 90.91% for data split 70:30, while performance with K-fold cross-validation shows an accuracy of 93.49% using this algorithm. Overall, the results of this study can provide an effective analysis model to determine the status of the loan.
Multi-Process Data Mining with Clustering and Support Vector Machine for Corporate Recruitment Zain, Ruri Hartika; Randy Permana; Sarjon Defit
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6197

Abstract

Having an efficient and accurate recruitment process is very important for a company to attract candidates with professionalism, a high level of loyalty, and motivation. However, the current selection method often faces problems due to the subjectivity of assessing prospective employees and the long process of deciding on the best candidate. Therefore, this research aims to optimize the recruitment process by applying data mining techniques to improve efficiency and accuracy in candidate selection. The method used in this research utilizes a multi-process Data Mining approach, which is a combination of clustering and classification algorithms sequentially. In the initial stage, the K-Means algorithm is applied to cluster candidates based on administrative selection data, such as document completeness and reference support. Next, a classification model was built using a Support Vector Machine (SVM) to categorize the best candidates based on the results of psychological tests, medical tests, and interviews. The experimental results show that the SVM model produces high evaluation scores, with an AUC of 87%, Classification Accuracy (CA) of 90%, F1-score of 89%, Precision of 91%, and Recall of 90%. With these results, it can be concluded that this model is able to improve accuracy in the employee selection process and help companies make more measurable and data-based recruitment decisions.
A DEVELOPMENT OF ENGLISH LEARNING COMPANION USING IMMERSIVE VIRTUAL REALITY APPLICATION Amna, Shally; Permana, Randy; Christina, Dian
English Review: Journal of English Education Vol. 12 No. 1 (2024)
Publisher : University of Kuningan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/erjee.v12i1.8928

Abstract

This research developed an educational application based an immersive virtual reality application using a 360-degree camera and a software engine called Unity. The content in the application contains conversations with topics given in the English II course and listening comprehension skill exercises tailored to the needs of users, especially for Computer Science Faculty Students. Each exercise is given a score or value to monitor the improvement of students' listening skills before and after using the application. This research is part of Research and Development (R & D) research. In this study, a descriptive qualitative approach was used to explain the process, appearances, and results of application validation by six English lecturers. The result of this research was  an immersive-based educational application with virtual reality technology for listening comprehension exercises using several features like text to speech, rotating 360-degree ability, and scoring system. This application achieved a validation value of 94 percent.The highest value of the validation results was the suitability of the application to the needs of students, while the lowest value of this application was the video display which still has to be improved again.
Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques Sovia, Rini; Anam, M. Khairul; Wisky, Irzal Arief; Permana, Randy; Rahmi, Nadya Alinda; Zain, Ruri Hartika
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1092

Abstract

This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.
A Multi-Model Framework for Autonomous Schema Discovery and Hybrid Natural Language Generation-Driven Augmented Business Intelligence Randy Permana; Sarjon Defit; Gunadi Widi Nurcahyo
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1479

Abstract

Traditional Business Intelligence (BI) frameworks rely heavily on data professionals to manually map relational metadata into structured star schemas during the ETL process, creating a significant operational bottleneck in data preparation and downstream interpretation of insights. To address these limitations, this study introduces an augmented analytics framework for autonomous schema discovery and hybrid Natural Language Generation (NLG) driven Augmented BI. In the data representation layer, a weighted hybrid feature fusion mechanism combines structural database metadata with contextual text embeddings produced by a pre-trained Sentence Transformer (all-MiniLM-L6-v2). In the multi-model machine learning layer, a multi-paradigm execution engine combines unsupervised geometric clustering models (K-Means, K-Medoids, DBSCAN) and supervised classifiers (SVM, Random Forest), and performance is evaluated using Leave-One-Out Cross-Validation (LOOCV). The resulting schemas are then dynamically projected into an in-memory OLAP cube. At the downstream insight interpretation layer, a Hybrid NLG engine combines a context-aware, rule-based router with an autoregressive generative decoding mechanism to autonomously produce adaptive, actionable business commentaries triggered by user-driven OLAP exploration states. Experimental results demonstrate that applying linear semantic scaling optimization (α) substantially mitigates statistical semantic blindness and protects the framework from structural schema misclassification. On the E-Commerce dataset, the proposed K-Medoids+Semantic configuration demonstrated topological superiority, achieving a peak Silhouette Score of 0.611 and a compressed Davies-Bouldin Index of 0.514. Meanwhile, on the high-dimensional Superstore dataset, the pipeline maintained high functional flexibility, stabilizing overall classification accuracy up to 94.74%. Furthermore, the downstream Hybrid NLG engine (Template+Generative) demonstrated high factual integrity and linguistic flexibility, achieving a ROUGE-1 score of 0.85, a ROUGE-2 score of 0.82, and a BLEU score of 0.15. This research provides ABI frameworks that enable accelerated executive decision-making through seamless, data-to-insight automation.
Co-Authors Adiddo Restiady Anam, M Khairul Andrianof, Harkamysah Aulia Hadi Dewi Eka Putri Dewi Eka Putri Dian Christina Eka Praja Wiyata Mandala Febri Hadi Firna Yenila Gunadi Widi Nurcahyo Hanippa Prima Putra Irzal Arif Wisky Lusinia, Shary Armonitha M.Hafizh Muhammad Reza Putra Ondra Eka Putra Rahmi, Nadya Alinda Ravhis Al Amin Riandana Afira Ruri Hartika Zain Sarjon Defit Shally Amna Sovia, Rini Syaiffullah, Afif Vera Arnelis Syam Vincensius Edward Litya Yanto, Musli Yuhandri Yuhandri, Yuhandri