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All Journal Elkom: Jurnal Elektronika dan Komputer Sistemasi: Jurnal Sistem Informasi Bina Insani ICT Journal Information Management For Educators And Professionals (IMBI) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Computer Science (CO-SCIENCE) JUSTIAN - Jurnal Sistem Informasi Akuntansi Jurnal Mandiri IT Journal of Artificial Intelligence and Engineering Applications (JAIEA) Paradigma Jurnal Insan Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Penelitian dan Pengabdian Masyarakat Jotika Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Indonesian Community Service Journal of Computer Science (IndoComs)
Badariatul Lailiah
Universitas Bina Sarana Informatika
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Other

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Evaluation of Machine Learning Algorithms in Sentiment Analysis of the Satu Sehat Application Marwan Suhendra; Badariatul Lailiah; Yanto Yanto; Lady Agustin Fitriana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1816

Abstract

This study aims to analyze and compare the performance of three sentiment classification algorithms—Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN)—in classifying user reviews of the Satu Sehat application. The data preprocessing stage involves several steps, including text cleaning through normalization, removal of punctuation, numbers, and irrelevant characters, as well as the elimination of stopwords. Subsequently, stemming is performed to reduce words to their root forms. Feature extraction is conducted using the CountVectorizer method with a bag-of-words approach, which converts textual data into numerical representations. The dataset is then divided into training and testing subsets using an 80:20 train-test split ratio. Model performance is evaluated through a confusion matrix, producing key evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the results of testing 9,192 user reviews, the SVM algorithm with a linear kernel demonstrated the best overall performance compared to NB and K-NN, as indicated by the highest accuracy score. These findings suggest that SVM is more effective in handling high-dimensional textual features, making it a highly suitable algorithm for sentiment analysis of digital health application reviews, particularly those related to Satu Sehat.
Analisis Tingkat Penerimaan Mahasiswa Terhadap Aplikasi Zoom Meeting Sebagai Media Perkuliahan Menggunakan Metode TAM Muhammad Ifan Rifani Ihsan; Rabiatus Saadah; Rizka Dahlia; Badariatul Lailiah; Hendri Mahmud Nawawi
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i1.973

Abstract

The increasingly advanced technology makes it easier for anyone to use it for various activities, such as lecture activities. Especially during the Covid-19 pandemic that has occurred since 2019 until this research was conducted. Covid-19, formerly known as SARS-CoV2, is an outbreak of pneumonia that originates from a virus. This virus was first heard in Wuhan, China in December 2019 (Ciotti et al., 2020). Lectures are conducted online to prevent the spread of this virus. One of the technologies widely used during the Covid-19 pandemic is video conferencing. With video conference meetings can be held even if the people present are far from each other. Zoom is a cloud-based application that is currently often used as a video communication (Azkiya, 2021). Zoom is a video conferencing application that is currently being used in various activities, including lectures. Because it is important to know how much student acceptance of the Zoom application they use for lectures is. The research was conducted using the Technology Acceptance Model or TAM method which consists of three constructs, namely Perceived Usefulness, Perceived Ease of Use and Acceptance of Technology. The data was obtained by distributing questionnaires with a Likert scale of 1 to 5. The data obtained were then calculated using the Structural Equation Model or SEM method consisting of the Outer Model and Inner Model calculations. SEM is an analytical technique that allows testing of a series of simultaneous relationships (Gardenia, 2018). The conclusion is that by taking the R-Square value, the construct in the TAM method is able to measure as much as 63% of student admission case studies on Zoom as a lecture medium. Keywords: Analysis, TAM, Zoom.
Co-Authors Agung Sasongko Ari Abdilah Ari Abdillah Ari Abdillah Asep Sayfulloh Dwi Yuni Utami Erni Erni Erni Erni Gata, Windu Hadikusumah, Prima Surya Hanggoro Aji Al Kautsar Hendri Mahmud Nawawi Kartika Handayani Lady Agustin Fitriana Laksono Trisnantoro Marien, Yunita Marwan Suhendra Monicka Nawar Monikka Nur Winnarto Muhammad Ifan Rifani Ihsan Muhammad Rezki Muhammad Rifqi Firdaus Muhammad Sony Maulana Nurmalasari Putri Fitri Astuti Rabiatus Rabiatus Sa'adah Rabiatus Sa'adah Rabiatus Saadah Rabiatus Sa’adah Rabiatus Sa’adah Rabiatus Sa’adah Sa’adah Rangga Pebrianto Reza Maulana Rizka Dahlia Sa'adah, Rabiatus Saadah, Rabiatus Sri Murni Sujiliani Heristian Verra Sofica Windu Gata Yanto Yanto