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All Journal Halal Research Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Hermawan, Alfado Rafly
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Religion Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Exploring Semantic Similarity among MUI Fatwas: A Computational Analysis using Generalized Jaccard Similarity Hermawan, Alfado Rafly; Jannah, Shofa Wardatul
Halal Research Vol 4 No 2 (2024): July
Publisher : Halal Center ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j22759970.v4i2.1217

Abstract

Majelis Ulama Indonesia (MUI) plays a crucial role in the Islamic landscape of Indonesia, influencing religious discourse and societal norms. As a primary contributor to policy formulation and the issuance of Islamic fatwas, the MUI significantly impacts the lives of Muslims. However, challenges arise when certain fatwas exhibit similarities, necessitating deeper analysis to understand their differences. Despite limited prior research, there is an urgent need for a computational framework to comprehensively assess fatwa similarities. This study addresses this gap by employing the Generalized Jaccard Similarity method with WordNet, demonstrating its effectiveness compared to the Jaccard method with a 25.86% improvement in string matching quality for evaluating MUI fatwa titles. The Generalized Jaccard similarity analysis reveals that 73 documents exhibit similarity scores greater than 0.5, indicating significant resemblance, while 77,028 documents have scores less than 0.5, indicating lower similarity or dissimilarity. These figures reflect varying degrees of document similarity based on Generalized Jaccard.
Dilated-Convolutional Recurent Neural Network untuk Klasifikasi Genre Musik Fatichin, Mochammad Rizqul; Hermawan, Alfado Rafly; Siahaan, Raynaldi Anggiat Samuel; Indraswari, Rarasmaya
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 3 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i3.9347

Abstract

In the digital era, utilizing technology to automatically classify music genres has become very important, especially for applications such as music recommendation, music trend analysis, and digital music library management. This research evaluates the use of Dilated-Convolutional Recurrent Neural Network (D-CRNN) in classifying music genres. This method combines the advantages of Dilated-CNN in capturing longer temporal context with the temporal sequence recognition capability of CRNN. The data used is the GTZAN dataset consisting of 1,000 30-second audio recordings, categorized into 10 music genres. Data preprocessing involved converting the audio recordings into Mel-Frequency Cepstral Coefficients (MFCC) images. The model was tested using data without augmentation and with augmentation, resulting in a total of 15,991 images for training. The results show that the use of D-CRNN can improve the accuracy of music genre classification compared to the conventional CRNN method.
Co-Authors Fatichin, Mochammad Rizqul Indraswari, Rarasmaya Jannah, Shofa Wardatul Siahaan, Raynaldi Anggiat Samuel