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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS)
Ritonga, Ali Akbar
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Journal of Computer Science and Information Systems (JCoInS)

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Analisis Sentimen Ulasan Produk Suncreen Wardah Pada Marketplace Shopee Menggunakan Metode Naïve Bayes Rambe, Nurhayati; Harahap, Syaiful Zuhri; Ritonga, Ali Akbar; Bangun, Budianto
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7992

Abstract

The development of the digital world and the popularity of online marketplaces such as Shopee have changed the way consumers interact and review products. Reviews of Wardah sunscreen products, which have an important role in skin health, are one of the most widely found. Understanding the sentiment of these reviews is crucial for manufacturers to improve product quality. Therefore, this study aims to analyze and classify consumer sentiment towards Wardah sunscreen products on Shopee. Using the Naïve Bayes classification method, the reviews will be categorized into positive, negative, and neutral sentiments to get an overall picture of the public perception of the product.
Penerapan Data Mining Untuk Memprediksi Prestasi Akademik Siswa SMKS IT Shah Hamidun Majid Menggunakan Algoritma Decision Tree Sahbana, Ahmad; Nasution, Fitri Aini; Ritonga, Ali Akbar; Suryadi, Sudi
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7939

Abstract

Education is the main foundation in the development of superior human resources, especially in the digital era that demands the use of Information Technology. One of the main challenges is how schools are able to effectively manage and analyze academic data. Data mining comes as a solution in extracting hidden information from educational data so that it can support strategic decision making. This study focuses on the application of Decision Tree algorithm in predicting student academic achievement in SMKs It Shah Hamidun Majid. The Decision Tree algorithm was chosen because it is easy to understand and is able to provide accurate classification based on various variables, such as attendance, grades, and student background. By utilizing academic data for the 2023/2024 school year, this study is expected to produce predictive models that help schools identify factors that affect student achievement, provide personalized coaching recommendations, and support data-based policies. The results of this study are expected to be a real contribution in the development of academic information systems that are adaptive, inclusive, and oriented to improving the quality of education at the private vocational school level.
Co-Authors Andriani, Reza Silvia Bangun, Budianto Irmayani, Deci Nasution, Fitri Aini Nasution, Marnis Rambe, Nurhayati Ritonga, Aldi Rapandi Sahbana, Ahmad Suryadi, Sudi Syaiful Zuhri Harahap Trisno Trisno