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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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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%.
Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web Alam, Syah Sury; Hidayat, Rahmad; Abdi, Musta'inul
JURNAL INTEGRASI Vol. 16 No. 2 (2024): Jurnal Integrasi - Oktober 2024
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v16i2.8523

Abstract

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15"“18 minutes conventionally to 5"“7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.
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 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