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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Buana Informatika JURNAL INTEGRASI Jurnal Infomedia Jurnal Vokasi Journal of Applied Electrical Engineering Telematika MKOM Jurnal Rekayasa elektrika Journal Of Artificial Intelligence And Software Engineering eProceeding of TIK Journal Serambi Engineering (JSE)
Rahmad Hidayat
Politeknik Negeri Lhokseumawe, Lhokseumawe, Indonesia
Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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A Web-Based Laptop Purchase Recommendation Model Using Natural Language Processing (NLP) on Marketplace Reviews Syahdana, Irham; Hidayat, Rahmad; Khadafi, M
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6133

Abstract

This system will use the Natural Language Processing (NLP) method to analyze user reviews. In addition, the Naive Bayes classification algorithm will be used to provide recommendations based on the analysis. The methods used include collecting laptop user review data from the Shopee platform, Natural Language Processing (NLP) for text analysis, classification with the Naive Bayes algorithm, developing a recommendation system, and evaluating the system using relevant metrics. The results of the study show that this model achieves an accuracy of 0.86 with a precision of 0.93 for positive reviews and 0.69 for negative reviews. Of the total 42 reviews tested, the system provides a recall of 0.87 for positive reviews and 0.82 for negative reviews. The total reviews in the dataset consist of 96 positive reviews and 43 negative reviews. This study is expected to contribute to the development of review-based recommendation systems, so that users can make the right decisions.
A Comparative Study of Naïve Bayes and K-Nearest Neighbors (KNN) Algorithms in Sentiment Analysis of ChatGPT Usage Among Students Syahli Kurniawan; Hidayat, Rahmad; Muhammad Reza Zulman
Journal of Applied Electrical Engineering Vol. 9 No. 2 (2025): JAEE, December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v9i2.11464

Abstract

This study compares the performance of the Naïve Bayes and K-Nearest Neighbors (KNN) algorithms in sentiment analysis of Lhokseumawe State Polytechnic students toward the use of ChatGPT. The comparison is conducted due to the varied results of previous research, where the effectiveness of both algorithms largely depends on the data type and context. The model was developed using 9.800 external data collected from Twitter and Google Play Store, which were processed through text preprocessing and TF-IDF transformation stages, and then tested on 237 student questionnaire data as a case study. The initial evaluation showed that Naïve Bayes achieved an accuracy of 88% with a prediction time of 0,0063 seconds, while KNN recorded an accuracy of 83% with a prediction time of 0,4760 seconds. In the student questionnaire test, Naïve Bayes again outperformed with 79,75% accuracy compared to KNN’s 49,37%.
Co-Authors Abdi, Musta'inul Agus Harjoko Alam, Syah Sury Anny Kartika Sari Arhami, Muhammad Azhar Azhar Bancin, Muhammad Kadhafi Febrianti, Saskia Gita Perdinanta Haris, Mifzal Huzaeni Ilham Sahputra Khadafi, M M. Khadafi Mahdi Mahlil, Mahlil Muhammad Davi Muhammad Reza Zulman Mulyadi Mulyadi Mutammimul Ula Nanda Saputri Raihanunnisa, Farah Simbolon, Zulfan Khairil Syahdana, Irham Syahli Kurniawan