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p-Index From 2021 - 2026
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All Journal Jurnal Computech & Bisnis (e-Journal) Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Wilda Wilda Rina Hasibuan
Universitas Muhammadiyah Sumatera Utara
Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Library & Information Science

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Sentiment Analysis of SPayLater and SPinjam Features in the Shopee Application Using the Support Vector Machine (SVM) Algorithm Rahmad Rahmad Nawi Pane; Wilda Wilda Rina Hasibuan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2322

Abstract

The rapid development of information technology and the increasing use of e-commerce applications have generated a large number of user reviews that can be used to measure user satisfaction. SPayLater and SPinjam, as features in the Shopee application, receive various responses in the form of positive, negative, and neutral sentiments, making automatic sentiment analysis necessary. This study aims to analyze user sentiment and implement the Support Vector Machine (SVM) algorithm to classify reviews. The data used consist of 500 user reviews obtained from the Google Play Store. The method includes preprocessing, labeling, and classification using SVM. The results show that there are 231 positive, 230 negative, and 39 neutral sentiments. Model evaluation yields an accuracy of 74%, precision of 0.78, and recall of 0.84, indicating that the model performs fairly well. The developed system is also capable of processing data automatically and displaying classification results effectively. Therefore, the SVM algorithm is effective for sentiment analysis of SPayLater and SPinjam services in the Shopee application.
Expert System For Diagnosing Diseases In Honey Guava Plants Using The Certainty Factor Method Reza Reza Ananda Pandia; Wilda Wilda Rina Hasibuan
JURNAL COMPUTECH & BISNIS Vol. 20 No. 1 (2026): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/jcb.v20i1.09

Abstract

The rapid development of information technology has encouraged the use of artificial intelligence as a means of supporting decision-making, one of which is through the application of expert systems. Honey guava plants are a superior variety that is widely cultivated. In cultivation practices, the emergence of various diseases is one of the main obstacles, so that effective, fast, and precise handling measures are needed. The development of an expert system in this study was carried out as a solution to help identify diseases that attack honey guava plants through a certainty factor approach. The system's knowledge base is compiled from expert information and scientific references that cover 9 types of diseases and 32 related symptoms. The diagnosis process is carried out by analyzing the combination of symptoms selected by the user, then the system calculates the confidence level using expert weighting values ​​to produce the probability of the disease being detected. Based on the test results, the system output shows a level of accuracy that is in line with manual calculations and expert validation, so this system is considered capable of supporting the process of early disease diagnosis in honey guava plants.
Co-Authors Rahmad Rahmad Nawi Pane Reza Reza Ananda Pandia