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All Journal JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Infomedia CSRID (Computer Science Research and Its Development Journal) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika (JUTIF) Journal of Applied Computer Science and Technology (JACOST) SmartComp Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Abdulloh, Ferian Fauzi
Universitas AMIKOM Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Nursing

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Sentiment Analysis of Online Vehicle Tax Renewal Application Users Using Support Vector Machine Algorithm Fauzy, Muhamad Ilham; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8654

Abstract

This study examines user sentiment towards online vehicle tax renewal applications by utilizing the Support Vector Machine (SVM) algorithm. The data was collected from user reviews on the Google Play Store for three major applications: New Sakpole, Sapawarga, and Timsalut. The reviews were preprocessed through steps including normalization, case folding, tokenization, and stopword removal. The SVM algorithm was then applied to classify the reviews into positive or negative sentiments. A comparative analysis was performed with K-Nearest Neighbors (KNN) and Naïve Bayes, with SVM demonstrating the best performance, achieving an accuracy of 76.5%. In addition to accuracy, metrics such as precision, recall, and F1-score were also evaluated to provide a more comprehensive assessment of the models. The results indicate that while these applications help facilitate vehicle tax payments, there remains significant user dissatisfaction, particularly related to technical issues and usability concerns. This study offers valuable insights for application developers, highlighting areas for improvement in functionality and user experience to better meet public expectations.
Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price Putra, Dhendy Mardiansyah; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8671

Abstract

This study aims to compare the performance of three clustering algorithms, namely Fuzzy C-Means, K-Means, and DBSCAN, in grouping houses based on their specifications and prices. The data used includes features such as price, building area, land area, number of bedrooms, number of bathrooms, and availability of garages. The performance of these algorithms was evaluated using Silhouette Score and Davies-Bouldin Score to determine the quality of cluster separation. The results indicate that K-Means achieved the best performance with the highest Silhouette Score of 0.7702 for two clusters, followed by Fuzzy C-Means, which excelled in handling overlapping clusters. DBSCAN, while effective in detecting outliers, showed suboptimal performance for this housing dataset. These findings suggest that K-Means is the most suitable clustering method for housing data, while Fuzzy C-Means and DBSCAN can serve as alternatives depending on the data characteristics. This research is expected to assist in making the house searching and classification process more efficient and provide additional insights for developers in shaping housing market strategies.
Co-Authors Abdul Muin Afrig Aminuddin Agun, Pasipikus Yosua Ali Mustopa, Ali Anggita, Sharazita Dyah Aprilliandhika, Wahyu Arfan Yoga Aji Nugraha Asadulloh, Bima Pramudya Buana, Rifqi Genta Devi Wulandari Devi Wulandari Fauzy, Muhamad Ilham Favian Afrheza Fattah Febrilia Hayyu Pradaningrum, Febrilia Fetrus Jari Harianto Fitriyani, Nurul Khasanah Harianto, Fetrus Jari Jeki Kuswanto Joang Ipmawati Kusnawi Kusnawi Majid Rahardi Maulana Abrari, Muhammad Naufal Moch Farid Fauzi Nafiatun Sholihah Nurcholis, Muhammad Tofa Oktaviani, Inggit Dwi Pambudi, Iqbal Rilo Putra, Dhendy Mardiansyah Rahman, Rizki Rifqi Genta Buana Rismawati Nurul Ikhsani Teuku Muhammad Dista Wahyudi, Hasyim Sri Yusuf Amri Amrulloh