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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Rekayasa Komputasi Terapan International Journal of Informatics and Computing
Ega Shela Marsiani
Department Of Informatics, Universitas Indraprasta PGRI, Indonesia
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics Neuroscience Transportation

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Penerapan Metode Moora dalam Keputusan Pemilihan Produk Layak Produksi Terbaik Natsir, Fauzan; Izzatilah, Millati; Marsiani, Ega Shela
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 9, No 3 (2025)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v9i3.28708

Abstract

Decision support system is a system that can be a problem solving quickly, especially in ranking and identifying the score of choices from highest to lowest. The research conducted aims to use the MOORA approach in determining superior products, so that it can help companies in the marketing process, selection, and production decisions based on the best product recommendations produced. In this research, the case study discussed is the selection of production-worthy products to meet quality. If the process is still done manually, it takes a long time and the evaluation process becomes inefficient. Therefore, a decision support system was designed to support the evaluation process. The MOORA method is used in the implementation of this system to test its accuracy. The results obtained from this system will be tested through sensitivity tests to criteria, value weighting, and correction tests, with the aim of knowing the number of criteria that can be added.
Support Vector Machine Based Machine Learning for Sentiment Analysis of User Reviews of the Bibit Application on Google Play Store Ega Shela Marsiani; Fauzan Natsir; Redo Abeputra Sihombing; Millati Izzatillah; Rajiansyah Rajiansyah
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing use of financial technology (fintech) applications has changed the investment patterns of users in Indonesia. Bibit, as one of the popular fintech investment platforms, receives many user reviews through the Google Play Store that reflect user perceptions and satisfaction levels. Although the volume of user reviews continues to increase, systematic analysis of user sentiment is still limited, making it difficult for developers to understand the needs and experiences of users. Therefore, an artificial intelligence-based approach is needed to efficiently and objectively extract and analyze user opinions. This study aims to conduct sentiment analysis of user reviews of the Bibit application using a Machine Vector Machine (SVM) based machine learning model. The research methodology includes data collection, pre-processing of texts, extraction of features using TF-IDF, as well as classification of sentiment into positive, negative, and neutral categories. Of the total review data, 7,801 data (79.99%) were used as training data, and 1,561 data (20.01%) were used as test data with a division ratio of 80:20 according to general standards in machine learning. The purpose of this study was to identify the dominant user sentiment and evaluate the classification performance of the SVM algorithm. The results of the experiment showed that the SVM model achieved high accuracy and was able to capture user opinions effectively, thus providing valuable input for developers in improving the quality of applications and user engagement on fintech platforms.
Co-Authors Alqomari Cahyo Ek Ajeng Rahmi Pinahayu Farras Raihan Rasyiq Fauzan Natsir Fauzan Natsir H. Nur Ali Hanggono Arie Prabowo Irawan Setiadi Izzatilah, Millati Karima Siti Masna Millati Izzatillah Muhammad Bahar Mubarok Natalia Tri Astuti Natalia Triastuti Nilma Nilma Nurul Khoirunnisa Rajiansyah Rajiansyah Redo Abeputra Sihombing Siti Suryani