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All Journal JIKO (Jurnal Informatika dan Komputer) Information System for Educators and Professionals : Journal of Information System IndoMath: Indonesia Mathematics Education JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Digit : Digital of Information Technology Mosharafa: Jurnal Pendidikan Matematika Jurnal Informatika Terpadu Jurnal Dinamika Informatika (JDI) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) Nuansa Informatika AMMA : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Bianglala Informatika Linier: Literatur Informatika dan Komputer
Ahmad Faqih
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Other

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Optimization of the K-Nearest Neighbors (KNN) Algorithm in Imbalanced Dataset Classification Using the SMOTE Technique Abi Fajar Ahmad Fauzi; Ahmad Faqih; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.756

Abstract

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.
Sales Data Classterization Analysis Using K-Means Method for Marketing Strategy Development Mifta Almaripat; Ahmad Faqih; Ade Rizki Rinaldy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.792

Abstract

In the digital era, utilizing sales data is very important to support strategic decision making. This research aims to overcome the problems faced by 9Doors Store in optimizing marketing strategies and stock management. The main problem faced is the lack of in-depth analysis of existing sales data, which results in difficulties in formulating appropriate marketing strategies and efficient stock management. For this reason, this research applies the K-Means Clustering method to group products based on customer purchasing behavior characteristics. The data used includes product categories, selling prices, initial stock, number of products sold, and total sales obtained from 9Doors Store during the period March to September 2024. The method used in this research is Data Mining approach with K-Means algorithm, which is implemented using RapidMiner software. The data analysis process goes through Knowledge Discovery in Databases (KDD) stages, including data collection, data cleaning (preprocessing), data transformation, and data mining using K-Means. Cluster evaluation is done using Davies-Bouldin Index (DBI) to assess the quality of clustering results. The results of this study show that the division of sales data into three clusters provides optimal results with the lowest DBI value (0.106), which indicates efficient clustering. This finding identifies products with high, medium, and low sales levels, which can be used to formulate more targeted marketing strategies. With these results, Toko 9Doors can improve stock management and design more effective promotions based on better customer segmentation.
Implementation of the Naive Bayes Method in Sentiment Analysis of Spotify Application Reviews Agung Triyono; Ahmad Faqih; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.824

Abstract

This study focuses on sentiment analysis of Spotify application reviews on Google Play Store using the Naive Bayes algorithm. As a leading music streaming platform, Spotify receives diverse user feedback that reflects their experiences, complaints, and satisfaction. Sentiment analysis aids in understanding user opinions, enhancing services, and innovating features. The research involves collecting user reviews via web scraping, followed by preprocessing steps such as text cleaning, tokenization, normalization, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF-IDF) method is employed to assign weights to words, highlighting their significance in reviews. The Naive Bayes algorithm categorizes sentiments into positive, negative, and neutral classes. Performance evaluation uses a confusion matrix to measure accuracy, precision, recall, and F1-score. Results indicate that Naive Bayes effectively classifies large volumes of unstructured data with high accuracy and efficiency. This study contributes practically by offering actionable insights to improve Spotify's services and theoretically by advancing sentiment analysis methodologies using machine learning. The findings highlight the algorithm's potential to understand user needs and address issues, reinforcing its value in text analytics for mobile applications.
Co-Authors Abdul Mukhyidin Abi Fajar Ahmad Fauzi Ade Irma Purnamasari Ade Rizki Rinald Ade Rizki Rinaldi Ade Rizki Rinaldy Adisty Tri Putra Agung Triyono Ahmad Rifa'i Ahmad Rifai Ahmad Rifa’i Ainun Nisa Sari Aliya Anisa Rahma Andi Setiawan Anjar Ayuning Lestari Arga Esa Putra Arif Rinaldi Dikananda Ariska Sari Aulia Nur Rochmah Azis Alma’as Azzahra Moudy Fajria Bambang Irawan Bisma Mahendra Chulyatunni’mah Denni Pratama Dinar Faris Naufal Dini Andriyani Fadli Iqbal Syaidin Fahmi Rahman Fathurrohman Fathurrohman Fathurrohman Fatkhan Mubarok Gifthera Dwilestari Haris Abdul Hadi Heliyanti Susana Iin Indah Ratna Ningsih Kaslani Kevin Salsabil Arlandy Kurniawan Fajar Abdulloh Luthfiyyah Iffah Adelia Madyant Marfelio Muhammad Fajid Martanto . Mifta Almaripat Mohamad Riad Solihin Muhamad Dadan Rifai Muhammad Daffa Ayyasy Muhammad Enricco Rizqy Nanda Permatasari Nining Rahaningsih Nisa Dieanwati Nuris Odi Nurdiawan Rudi Kurniawan Salsa Billa Agistina Sandy Eka Permana Siti Sa'diah Sri Darmayanti Yovi Yuliantin Yubi Aqsho Ramadhan Yusril Muhamad Izha Mahendra