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All Journal Sebatik Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah
Leo, Donatus
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Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Pengaruh Kualitas Pelayanan terhadap Kepuasan Lansia di Poli Lansia UPTD Puskesmas Emparu Kabupaten Sintang Leo, Donatus; Arifin, Arifin; Aripin, Sofjan
Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah Vol 6 No 2 (2024): Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah
Publisher : Research and Strategic Studies Center (Pusat Riset dan Kajian Strategis) Fakultas Syariah IAI Nasional Laa Roiba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/alkharaj.v6i2.5345

Abstract

Health services at the poly elderly puskesmas need to be considered and the quality of service is still often questioned, especially at UPTD Puskesmas Emparu. This quantitative study aims to obtain information on the quality of service satisfaction of the elderly who visit the Puskesmas. This study involved 75 elderly (men 23 elderly, women 52 elderly) as research subjects. Data on service quality and satisfaction of the elderly were collected by filling out questionnaires. The collected data was analyzed using classical assumption tests, simple regression analysis, validity tests, reliability tests, and t (partial) tests with the help of SPSS application version 30. Information was obtained that the dimensions of direct evidence (tangibles) and responsiveness (responsiveness) affect elderly satisfaction with services in the elderly poly. The follow-up that needs to be done for the elderly at the UPTD Puskesmas Emparu elderly poly is to update facilities and infrastructure as needed and respond quickly in serving
Music Genre Classification Based on Spectrogram Using CNN-MobileNet Leo, Donatus; Muhammad, Alva Hendi
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2634

Abstract

Music is a universal form of art that has a significant impact on human life. In the digital era, managing increasingly large music collections requires an effective classification system to facilitate searching and storage. One of the growing methods is music genre classification, which helps organize music based on specific characteristics. This study explores the application of Convolutional Neural Network (CNN) and the MobileNet architecture for music genre classification based on spectrogram images. Spectrogram representation is used to convert audio signals into visual form, allowing the classification problem to be approached as an image classification task. The dataset used is GTZAN, consisting of six genres: blues, classical, country, hiphop, jazz, and metal. Image augmentation is applied to increase the diversity of training data, including rotation, translation, zooming, brightness adjustment, and horizontal flipping. The evaluation results show that the CNN-MobileNet model achieves an overall accuracy of 83%, with a macro precision of 85%, macro recall of 83%, and macro F1-score of 84%. The classical genre achieved the best performance with an F1-score of 93%. This research demonstrates that spectrogram-based music genre classification using CNN-MobileNet is an effective approach for automatic music recognition tasks
Co-Authors Alva Hendi Muhammad Arifin Arifin Sofjan Aripin