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All Journal Agents: Journal of Artificial Intelligence and Data Science
Muhammad Nur Akbar
Universitas Islam Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Sentiment Analysis Terhadap Review Aplikasi Maxim di Google Play Store Menggunakan Support Vector Machine (SVM) Muhammad Nur Akbar; Nur Hasanahlmar'iyah Rusydi; M. Hasrul H.; Nurul Shaumi Ramadhanti; Erfiana
AGENTS: Journal of Artificial Intelligence and Data Science Vol 2 No 2 (2022): Maret - Agustus
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1507.007 KB) | DOI: 10.24252/jagti.v2i2.39

Abstract

Before selecting and installing applications on the Google Play Store, users often read reviews of other users. This makes user review analysis very attractive for app owners to make future decisions. One of them is the Maxim application, a new online transportation application that provides different services from similar applications. This study aims to analyze user reviews of the maxim application on the Google Play Store using sentiment analysis. The research data is taken from the Google Play Store website, while the data taken is in the form of a review text. This user review analysis uses the Support Vector Machine (SVM) method producing an accuracy of 79%.
ANALISIS CLUSTERING UNTUK SEGMENTASI PENGGUNA KARTU KREDIT DENGAN MENGGUNAKAN ALGORITMA K-MEANS DAN PRINCIPAL COMPONENT ANALYSIS Muhammad Nur Akbar; Azizah Salsabila; Aldi Perdana Asri; Muhammad Syawir
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 1 (2023): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (832.897 KB) | DOI: 10.24252/jagti.v3i1.56

Abstract

Customer segmentation is a process used by companies to group customers based on common characteristics. The goal is to understand customer needs and preferences better so that companies can provide products and services that match customer needs. One way to segment customers is to use clustering algorithms, such as k-means. This algorithm groups data into adjacent clusters with randomly selected centroids. In the case of credit card customer segmentation, the k-means algorithm can be used to group customers based on characteristics such as number of transactions, amount of payments, and credit history. Thus, companies can better understand the needs and preferences of credit card customers and determine more effective marketing strategies. The advantages of the k-means algorithm and the clustering method are that the developed models can help companies determine more effective marketing strategies, easy-to-use algorithms with fast computation time and accurate results, and the PCA algorithm is also used to reduce dimensions and makes data visualization easier. Based on the test results and analysis of credit card customer data, the performance of the k-means algorithm is considered relatively good for segmentation with the number of clusters = 3 and the Davies Bouldin value = -0.778.
Co-Authors Aldi Perdana Asri Azizah Salsabila Erfiana M. Hasrul H. Muhammad Syawir Nur Hasanahlmar'iyah Rusydi Nurul Shaumi Ramadhanti