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All Journal English Review: Journal of English Education Jurnal Infortech Journal of Applied Data Sciences Jurnal Pendidikan Progresif
Permana, Randy
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science

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Penerapan Natural Language Processing Pada Sistem Chatbot Sebagai Helpdesk Obyek Wisata Menggunakan Metode Naïve Bayes Yuhandri, Yuhandri; Sovia, Rini; Syaiffullah, Afif; Yenila, Firna; Permana, Randy
Jurnal Infortech Vol 5, No 2 (2023): Desember 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v5i2.20911

Abstract

Keberhasilan pengembangan sektor pariwisata tidak hanya bergantung pada daya tarik wisata semata. Terdapat banyak faktor dalam penghambat perkembangan sektor tersebut. Salah satu faktor tersebut adalah kurangnya perkembangan dalam pola periklanan dan sistem pengelolaan informasi pariwisata. Sebagai akibatnya, sasaran pariwisata tidak terdefinisi dengan baik, dan wisatawan mungkin tidak memilih destinasi tersebut. Bukitinggi sebagai salah satu destinasi wisata yang terdapat di Sumatera Barat juga tidak lepas dari permasalahan tersebut. Kurang tersedianya informasi lengkap tentang potensi pariwisata di Kota Bukittinggi membuat wisatawan mengandalkan sumber-sumber seperti media sosial, internet, dan sumber lainnya untuk mendapatkan informasi. Namun, informasi yang ada belum mencakup seluruh aspek pariwisata, sehingga menyebabkan ketidakpastian bagi calon wisatawan. Untuk mengatasi permasalahan tersebut, dihadirkan sebuah sistem respons obrolan otomatis atau disebut dengan  Chatbot. Teknologi Chatbot merupakan salah satu bentuk sistem Natural Language Processing (NLP) dalam kecerdasan buatan. Chatbot berperan sebagai agen percakapan yang dapat berfungsi sebagai meja bantuan. Dalam konteks ini, helpdesk menjadi elemen penting yang menangani berbagai keluhan dari berbagai pihak dengan menyediakan informasi dan solusi. Dalam penelitian ini, dikembangkan sistem Chatbot menggunakan algoritma Naive Bayes untuk menjawab pertanyaan umum (FAQ) mengenai informasi pariwisata di Kota Bukittinggi.
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.
Enhancing EFL Listening Comprehension through a Customized Immersive Virtual Reality Application: A Study on Computer Science Students Amna, Shally; Christina, Dian; Permana, Randy; Gusta, Wienda
Jurnal Pendidikan Progresif Vol 16, No 1 (2026): Jurnal Pendidikan Progresif
Publisher : FKIP Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpp.v16i1.pp01-16

Abstract

Enhancing EFL Listening Comprehension through a Customized Immersive Virtual Reality Application: A Study on Computer Science Students. Listening comprehension is a crucial component of language acquisition, yet it remains a persistent challenge for students across diverse fields of study. Objective: This study develops a Virtual Reality app to address a gap and enhance computer science students' listening comprehension skills. Methods: This study employed a quantitative design with a paired t-test and a Likert-scale questionnaire. The participants in this study are 20 students from the Computer Science Faculty at the University of Putra Indonesia, YPTK Padang. Pre-test and Post-test were administered to evaluate improvements in listening Comprehension, as shown by a t-value of -5.501 and a p-value of < .001, while a questionnaire was also given to assess students' experiences and opinions after using the VR app. Findings: The VR app design required a few improvements in device settings, yet it still significantly enhances students' English comprehension skills, as shown by a paired t-test, and practically increases students' motivation. Conclusion: A VR app can be used in language learning to improve students' abilities, increase their motivation, and reduce anxiety, especially in listening comprehension. Keywords: EFL students, listening comprehension, virtual reality.
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.
Co-Authors Amna, Shally Anam, M Khairul Christina, Dian Gusta, Wienda Irzal Arief Wisky Rahmi, Nadya Alinda Ruri Hartika Zain Sovia, Rini Syaiffullah, Afif Yenila, Firna Yuhandri Yuhandri, Yuhandri