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All Journal Jurnal INSYPRO (Information System and Processing) Information Management For Educators And Professionals (IMBI) JURNAL INSTEK (Informatika Sains dan Teknologi) Ainet : Jurnal Informatika Journal Software, Hardware and Information Technology Agents: Journal of Artificial Intelligence and Data Science
Muhammad Nur Akbar
UIN Alauddin Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Journal : Agents: Journal of Artificial Intelligence and Data Science

 1 
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.
Analisis Sentimen Komentar Pengguna Aplikasi Threads Pada Google Playstore Menggunakan Algoritma Multinominal Naive Bayes Classfier Muhammad Nur Akbar; Nirwana Samrin
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 2 (2023): Maret - Agustus
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v3i2.67

Abstract

The advancement of technology, especially internet and social media technology, has enabled people across the country to connect and interact with each other. In this context, Instagram has become a popular platform for content creators to share their work. In an effort to compete with other platforms, Instagram launched an integrated app called Threads, which shares some features similar to Twitter. Threads allows users to share text-based posts and provides various other features. To enhance the quality of this application, developers need to review user comments. However, the influx of comments is substantial, making manual review inefficient. Therefore, an automated application is required to categorize comments and analyze user sentiment. By utilizing text mining techniques for sentiment analysis, developers can easily sort comments into positive and negative categories. Multinomial Naive Bayes was chosen as it's specifically designed for data with frequency occurrences, such as in text analysis. It is expected that this application can assist developers in improving the quality of the generated app. From the results of this study, an accuracy of 76% was achieved, which is  relatively good and offers potential for further development to attain better results.
ANALISIS SENTIMEN KOMENTAR PENGGUNA TERHADAP GAME MOBA LOKAPALA DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Muiz, Rafiul; Ishar, Rahmat Fajri; Febrianto, Andi; Akbar, Muhammad Nur
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 2 (2024): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i2.79

Abstract

In this modern era, games are heavily influenced by technological advancements. The development of increasingly complex and captivating games can be played online by millions of players worldwide. The gaming industry in Indonesia has shown significant progress with the emergence of various games from local developers, one of which is Lokapala, a Multiplayer Online Battle Arena (MOBA) game that highlights the uniqueness of Indonesian culture. However, this game has received various responses from users on Google Play Store. This study aims to analyze user sentiment towards the Lokapala game on Google Play Store using the Support Vector Machine (SVM) algorithm. User review data were collected and pre-processed through stages such as data cleaning, tokenization, stopwords removal, and stemming. Subsequently, features were extracted using the TF-IDF method. The analysis results show that SVM with Radial Basis Function (RBF) kernel successfully classified user sentiment with an accuracy of 90% from a total of 300 reviews analyzed. This process not only helps in understanding overall user perceptions but also identifies specific aspects of the game that receive appreciation or criticism. Thus, game developers can use the results of this analysis to improve quality and user satisfaction, and strengthen the game's competitiveness in markets.
Perancangan Game Flashcard dengan Fitur Time Tracker pada Anak Usia Dini Berbasis Android Salsabilah, Fitriyah; Hasrul H, M.; Nur Akbar, Muhammad
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 2 (2024): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i2.81

Abstract

The presidential candidate election in Indonesia is a hot topic on social media, especially Twitter. This study analyzes public sentiment regarding the 2024 presidential candidate election using the IndoBERT model, which is specifically designed for the Indonesian language, on a dataset of 8,442 tweets. This research follows the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data was collected through crawling with keywords related to the election, followed by preprocessing and manual labeling before being processed by the model. The results show that IndoBERT achieved an accuracy of 98%, with precision, recall, and F1-score also at 98% at the 10th epoch. Batch size evaluation indicated that a batch size of 4 yielded the best performance. This model is effective in classifying sentiment related to the 2024 presidential candidate election and serves as a useful tool for understanding public opinion.
Co-Authors A. Muh Nur Hidayat Aldi Perdana Asri Andi Irsandi Ramadani ANDI MUHAMMAD NUR HIDAYAT Andi Muhammad Nur Hidayat Andi Muhammad Syafar Asep Indra Syahyadi Azizah Salsabila Darmatasia Darmatasia DARMATASIA, DARMATASIA Erfiana Febrianto, Andi Hariani Hasrul H, M. Ishar, Rahmat Fajri M. Hasrul H M. Hasrul H. Meidinah, Nur Mubarak Mubarak Muh Syahwal Muhammad Syawir Muiz, Rafiul Mustikasari Mustikasari Nirwana Samrin Nur Annisa Safitri Yusuf Nur Hasanahlmar'iyah Rusydi Nurul Shaumi Ramadhanti Salman, Nur Salsabilah, Fitriyah Syarif , Ririn Suharni Yoga Yuniadi Yulia Ardana