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All Journal Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah JURTEKSI Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma Electronic Journal of Education, Social Economics and Technology Jurnal Informatika: Jurnal Pengembangan IT Journal of Informatics and Interactive Technology (JIITE) Journal of Digital Market and Digital Currency
Fandy Setyo Utomo
Universitas AMIKOM Purwokerto
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : JOIV : International Journal on Informatics Visualization

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Feature Selection Technique to Improve the Instances Classification Framework Performance for Quran Ontology Yuli Purwati; Fandy Setyo Utomo; Nikmah Trinarsih; Hanif Hidayatulloh
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1195

Abstract

The Al-Quran is the sacred book of Muslims, and it provides God's word in the form of orders, instructions, and guidelines for people to follow to have happy lives both here and in the afterlife. Several earlier research has used ontologies to store the knowledge found in the Quran. The previous study focused on extracting the relationship between classes and instances or the "is-a relation" by classifying instances based on the referenced class. Based on the performance testing of the instances classification framework, the test results show that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation had dropped the accuracy value to 65.41% when the test data size was increased to 30%. Likewise, with BPNN with TF-IDF and stemming operations. In the Indonesian Quran translation dataset with a test data size of 30%, the accuracy value drops to 57.86%. Instances classification based on the thematic topics of the Qur'an aims to connect verses (instances) to topics (classes) to get an overall picture of the topic and provide a better understanding to users. This study aims to apply the feature selection technique to the instances classification framework for the Al-Quran ontology and to analyze the impact of applying the feature selection technique to the framework with a small dataset and training data. The instances classification framework in this study consists of several stages: text-preprocessing, feature extraction, feature selection, and instances classification. We applied Chiq-Square as a technique to perform feature selection. SVM and BPNN as a classifier. Based on the experiment results, it can be concluded that the feature selection implementation using Chi-Square increases the value of precision, f-measure, and accuracy on the test data size from 40% to 60% in all datasets. The feature selection using Chi-Square and SVM classifier provides the highest precision value with a test data size of 60% on the Tafsir Quran dataset from the Ministry of Religious Affairs Indonesia: 64.36%. Furthermore, the feature selection implementation and BPNN classifier also increase the highest accuracy value with a test data size of 60% in the Quranic Tafsir dataset from the Ministry of Religion of the Republic of Indonesia: 63.09%.
Co-Authors Adiatma, Febriansyah Husni Adiya, Az Zahra Dwi Nur Afit Ajis Solihin Aisha Hukama Setyowati Aji Saeful Aji Septa, Adrian Ajis Solihin, Afit Amar Al Farizi Anas Nur Khafid Anggini, Melisa Anggraeni, Mutia Dwi Anggraini, Nova Anggriani, Epri Azhari Shouni Barkah Azmi, Mohd Sanusi Bagus Adhi Kusuma Baihaqi, Wiga Maulana Balit, Muhamad Naufal Burhanuddin Berlilana Berlilana Berlilana Burhanuddin Balit, Muhamad Naufal Churil Aeni, Agustina Chyntia Raras Ajeng Widiawati Darmono Dedi Purwanto, Dedi Didi Prasetyo Dwi Krisbiantoro, Dwi Dzaky Candy Fahrezy Fadhilah, Siti Nur Filanzi, Shendy Giat Karyono Giat Karyono Hanif Hidayatulloh Hendra Marcos, Hendra hidayatulloh, hanif Ilham, Rifqi Arifin Imam Tahyudin Indriyani, Ria Jamie Mayliana Alyza Kafilla, Princess Iqlima Kusuma, Bagus Adhi Kusuma, Velizha Sandy Lasmedi Afuan Lubna, Zuhriyatul Lukita, Dita Maharani, Titi Safitri Maulana Baihaqi, Wiga Mohd Fairuz Iskandar Othman Mohd Nazrin Muhammad Mohd Sanusi Azmi Muaziz, Imam Muhamad Naufal Burhanuddin Balit Muhtyas Yugi Murtiyoso Murtiyoso Nandang Hermanto Nanna Suryana Nikmah Trinarsih Nugroho, Khabib Adi Nur Cholis Romadhon Octavia, Annisa Suci Prayoga, Fandhi Dhuga Pungkas Subarkah Purbo, Yevi Septiray Purwidiantoro, Moch. Hari Putranto, R. Vitto Mahendra Pyawai, Hero Galuh Ramadhan, Aziz Ramadhan, Rio Fadly Rifqi Arifin Ilham RR. Ella Evrita Hestiandari Rujianto Eko Saputro Safitri Maharani, Titi Sagita, Selvi Samsul Arifin Sarmini - Sarmini Sarmini Sarmini Sekhudin Sekhudin Setiabudi, Rizki Setiawan, Ito Shafira, Lulu Slamet Widodo Sofa, Nur Sri Hartini Subarkah, Pungkas Suryana, Nanna Trinarsih, Nikmah Turino, Turino Utomo, Dadang Wahyu Wahid, Arif Mu'amar Wibisono, Arif Cahyo Wiga Maulana Baihaqi Yuli Purwat Yuli Purwati Yulianto, Koko Edy