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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 SKANIKA: Sistem Komputer dan Teknik Informatika Telematika MKOM Jurnal Rekayasa elektrika Journal Of Artificial Intelligence And Software Engineering eProceeding of TIK Serambi Engineering Jurnal Teknologi Rekayasa Informasi dan Komputer
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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Journal : Journal of Applied Electrical Engineering

 1 
Model Rekomendasi Produk Perawatan Kulit Wajah Menggunakan Metode Content Based Filtering (CBF) Febrianti, Saskia; Hidayat, Rahmad; Mulyadi, Mulyadi
Journal of Applied Electrical Engineering Vol. 8 No. 2 (2024): JAEE, December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v8i2.8672

Abstract

Facial skin functions as a protective barrier against environmental pollution, including ultraviolet rays, which can cause wrinkles, aging, acne, and enlarged pores. Additionally, an unbalanced diet, lack of rest, and exposure to free radicals can further worsen skin conditions. Facial skincare is crucial as it relates to personal identity and health. Facial skin types are categorized into five groups: normal, dry, oily, combination, and sensitive, classified based on water and oil levels in the skin. A skincare product recommendation model is needed to assist consumers in finding products suitable for their skin issues. This need becomes increasingly significant given the wide variety of facial skincare products available in the market today. This study developed a recommendation model using the content-based filtering (CBF) method, which considers product characteristics such as ingredient composition. Experimental results show that the model effectively provides recommendations aligned with user preferences. The model demonstrated good performance, achieving an accuracy rate of 88.89%.
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 Fata, Akramul Febrianti, Saskia Gita Perdinanta Haris, Mifzal Hendrawaty Hendrawaty, Hendrawaty Huzaeni Huzaeni, Huzaeni Ilham Sahputra Khadafi, M M. Khadafi Mahdi Mahlil, Mahlil Muhammad Davi Muhammad Reza Zulman Muhammad Reza Zulman Mulyadi Mulyadi Mutammimul Ula Nanda Saputri Raihanunnisa, Farah Simbolon, Zulfan Khairil Syahdana, Irham Syahli Kurniawan Zuhendra, Muhammad Ihza