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All Journal JSI: Jurnal Sistem Informasi (E-Journal) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Reswara: Jurnal Pengabdian Kepada Masyarakat Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Abdimas Maduma
Meylani Utari
Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Evaluating Netflix's User Experience (UX) Through The Lens Of The HEART Metrics Method Astriani, Yulia; Indah, Dwi Rosa; Utari, Meylani; Syahbani, M Husni
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.v8i2.8727

Abstract

Netflix is one of the most popular subscription video-on-demand (SVoD) platforms, offering a wide range of authentic, high-quality content and features that allow users to select, enjoy, and share their viewing experiences on social media. Despite its popularity, Netflix often receives complaints from users, including issues with accessing the application and various features related to viewing activities. The aim of this study is to evaluate the user experience of the Netflix application and provide recommendations for improvement based on data analysis. To achieve this, the HEART Metrics are utilized, which focus on the user's perspective, and apply the Importance-Performance Analysis (IPA) method to map performance and identify improvement priorities. The research reveals several areas that require enhancement, particularly three priority variables: the Happiness variable (Hp3), indicated by the statement "I like the appearance of the Netflix application"; the Retention variable, represented by "I enjoy using the features of the Netflix application"; and the Task Success variable (Ts4), reflected in "I can save movies in the Netflix application." To improve user satisfaction, Netflix can incorporate both light and dark themes, creating a more user-friendly interface. This update could enhance navigation, increase time spent on the platform, promote recommendations, and encourage subscription renewals.
Performance Comparison of Naive Bayes and Support Vector Machine Methods in Music Genre Classification Based on Audio Signal Feature Extraction Using Mel-Frequency Cepstral Coefficients (MFCC) Naserwan, Kevin Putrayudha; Miraswan, Kanda Januar; Utari, Meylani
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11770

Abstract

Music genre classification has gained increasing attention with the emergence of digital music platforms. One of the relevant features extracted from audio signals is Mel-Frequency Cepstral Coefficients (MFCC), which is widely recognized as an effective technique. MFCC features are extracted at the frame level and aggregated at the clip level to represent each music track, making them suitable for audio-based classification tasks. This study applies Naïve Bayes and Support Vector Machine (SVM) algorithms for classification using the GTZAN dataset consisting of 1,000 audio files from 10 music genres, each with a duration of 30 seconds. The performance of these methods is evaluated using accuracy, precision, recall, and F1-score. The results show that SVM demonstrates superior performance, achieving an accuracy of 95.25% compared to 50.37% for Naïve Bayes. This performance gap can be attributed to SVM’s ability to model non-linear decision boundaries and effectively handle high-dimensional MFCC feature spaces. The main contribution of this study lies in the systematic evaluation of multiple SVM kernel configurations and parameter settings, providing empirical insights into the robustness of classical machine learning methods for MFCC-based music genre classification. This study concludes that SVM is better than Naive Bayes in music genre classification with MFCC feature extraction.
Co-Authors Afif, Hasnan Al Ashri, Muhammad Rizky Apriansyah Putra Astriani, Yulia Bayu Wijaya Putra Darmawahyuni, Annisa Dewi Sartika DEWI SARTIKA Dian Palupi Rini Dwi Rosa Indah Fiftinova Florensia, Yesinta Hardini Novianti Jambak, Muhammad Ihsan Junia Kurniati, Junia Kanda Januar Miraswan M HUSNI SYAHBANI, M HUSNI Mastura Diana Marieska Miftahul Falah Naserwan, Kevin Putrayudha Novi Yusliani Primanita, Anggina Winda Kurnia Sari YUNITA