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All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Rita Yulfani
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Computer Science & IT

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Analisis Perbandingan Penerapan Metode AHP-SAW dan AHP-TOPSES Dalam Pemilihan Mahasiswa Terbaik Prodi Ilmu Keperawatan Muhamad Wahyu Tirta; Muhammad Khumaidi Nursyarif; Hamada Zein; Rita Yulfani; Melisa Nur Aini; Farhan Akbar
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 4 No. 1 (2024): Maret : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v4i1.2672

Abstract

Student graduation becomes a separate assessment reference in a higher education institution. Things that are taken into consideration are efforts to carry out assessments to determine the best students. The data used comes from the graduation achievements of students from the Nursing Profession Study Program, Faculty of Nursing, Muhammadiyah University, East Kalimantan, which consists of four criteria. Based on these problems, the author conducted research aimed at analyzing the use of Decision Support System methods. The method used in this research uses a combination of AHP-SAW and AHP-TOPSIS. The results obtained explain that both methods obtain ranking results in the same order even though the Priority value of each method is different. Where rank 1 for each method is A1 with an AHP-SAW Priority Value of 100 and AHP-TOPSES of 1, likewise in the next ranking order.
Application of the Naive Bayes Method for Sentiment Analysis of Sunscreen Product Reviews Based on the Female Daily Review Melisa Nur Aini; Rita Yulfani; Nurul Jariah
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 6 No. 01 (2024): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v6i01.421

Abstract

This study aims to apply the Naïve Bayes method for sentiment analysis of sunscreen product reviews based on data collected from the Female Daily platform. With the exponential growth of e-commerce, online reviews have become a valuable source of information for consumers seeking insights into product quality and user satisfaction. Sentiment analysis, a branch of natural language processing, plays a crucial role in extracting sentiments or opinions from text data. In this research, we focus specifically on sunscreen products and leverage the Naïve Bayes classifier to classify the sentiment polarity (positive, negative, or neutral) of reviews gathered from the Female Daily platform. The Female Daily platform provides a wealth of user-generated content, including detailed product reviews and ratings, making it an ideal dataset for sentiment analysis. By implementing the Naïve Bayes method, which is known for its simplicity and efficiency in text classification tasks, we aim to accurately identify sentiments expressed in sunscreen product reviews. The findings of this study are expected to contribute to the enhancement of consumer decision-making processes by providing valuable insights into the sentiment trends surrounding sunscreen products, ultimately aiding consumers in making informed purchasing decisions.
Co-Authors Farhan Akbar Hamada Zein Melisa Nur Aini Muhamad Wahyu Tirta Muhammad Khumaidi Nursyarif Nurul Jariah