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All Journal JOIN (Jurnal Online Informatika) Al Ishlah Jurnal Pendidikan JURNAL PENDIDIKAN TAMBUSAI Kwangsan: Jurnal Teknologi Pendidikan JURNAL PENA EDUKASI Khazanah Pendidikan Islam Jurnal Syntax Imperatif : Jurnal Ilmu Sosial dan Pendidikan Wahana Dedikasi : Jurnal PkM Ilmu Kependidikan Jurnal Ilmiah Wahana Pendidikan Al-Ibanah Indonesian Journal of Innovation Studies DEVELOPMENT : Journal of Community Engagement Journal of English Education Program (JEEP) Digital Transformation Technology (Digitech) Indonesian Journal of Community Empowerment Jurnal Al Burhan Innovative: Journal Of Social Science Research PANDU: Jurnal Pendidikan Anak dan Pendidikan Umum Jurnal Al Maesarah : Jurnal Pengabdian Kepada Masyarakat Bidang Pendidikan, Sosial, dan Kemasyarakatan Jurnal Pengabdian Masyarakat dan Riset Pendidikan Bhinneka Tunggal Ika: Jurnal Pengabdian Kepada Masyarakat At- Ta'lim : Jurnal Pendidikan JOAPE International Journal on Educational Issues (IJEI)
Ayu Puji Rahayu
PPs STKIP Garut
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : JOIN (Jurnal Online Informatika)

 1 
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Yana Aditia Gerhana; Aaz Muhammad Hafidz Azis; Diena Rauda Ramdania; Wildan Budiawan Dzulfikar; Aldy Rialdy Atmadja; Deden Suparman; Ayu Puji Rahayu
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Chatbot for Signaling Quranic Verses Science Using Support Vector Machine Algorithm Undang Syaripudin; Deden Suparman; Yana Aditia Gerhana; Ayu Puji Rahayu; Mimin Mintarsih; Rizka Alawiyah
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.827

Abstract

The many verses in the Qur'an encourage finding the right way how to understand it thematically. The purpose of the research is to develop a chatbot application that can be used to explore and elaborate the content of verses in the Qur’an that hint at science. The support vector machine (SVM) algorithm classifies question and answers datasets in chatbot applications. The number of data sets used is 76, with test data as much as 10%. The test results show that the SVM algorithm is quite good in classifying, with an accuracy value of 87.5%. While the user test results obtained an average MOS of 8.4, which means the chatbot application developed is very effective in understanding the Qur'an, which implies science. This research is expected to provide an overview of the explanation of the Qur'an about science and technology.
Chatbot for Signaling Quranic Verses Science Using Support Vector Machine Algorithm Syaripudin, Undang; Suparman, Deden; Gerhana, Yana Aditia; Rahayu, Ayu Puji; Mintarsih, Mimin; Alawiyah, Rizka
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.827

Abstract

The many verses in the Qur'an encourage finding the right way how to understand it thematically. The purpose of the research is to develop a chatbot application that can be used to explore and elaborate the content of verses in the Qur’an that hint at science. The support vector machine (SVM) algorithm classifies question and answers datasets in chatbot applications. The number of data sets used is 76, with test data as much as 10%. The test results show that the SVM algorithm is quite good in classifying, with an accuracy value of 87.5%. While the user test results obtained an average MOS of 8.4, which means the chatbot application developed is very effective in understanding the Qur'an, which implies science. This research is expected to provide an overview of the explanation of the Qur'an about science and technology.
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Gerhana, Yana Aditia; Azis, Aaz Muhammad Hafidz; Ramdania, Diena Rauda; Dzulfikar, Wildan Budiawan; Atmadja, Aldy Rialdy; Suparman, Deden; Rahayu, Ayu Puji
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Co-Authors Aaz Muhammad Hafidz Azis Ade Herdiana Ahmad Andy Setiady Ahmad Saepurohman, Aam ALAWIYAH, RIZKA Aldy Rialdy Atmadja Ana Amalia, Ana Angga Surya Purnama Aris Somantri Asep Nurul Aripin Azis, Aaz Muhammad Hafidz Baari, Rosmalati Muthiatil Bambang Ruby Sugiarto Dedeh Rohayati, Dedeh Deden Suparman, Deden Deni Darmawan Deni Darmawan Desya Candra Dewi Dewi Nuraini, Dewi Dian Rahadian, Dian Diena Rauda Ramdania Dindin Sofyan Abdullah Dwi Rivana Dzulfikar, Wildan Budiawan Ely Susanti Enjang Aris Somantri Fauziyyah, Ayu Faza Fikri Rizkia Muhammad Fitri Eka Silviani Ginanjar, Egi Gumilar, Ramdan Hamdani, Nizar Alam Hesti Hevie Setia G Hevie Setia Gunawan Ibupoto, Mukhtiar Hussain Ika Sartika Imam Abdul R Imas Kurniasih Indra Budiman Jamilah, Jam Jam Jujun Junaedi, Jujun Kohar Kushendar Linda Herlinda Lindawati, Cucu Maharani, Syahna Mega Agitha Putri Masrini, Imas Mega Nurhayati Mia Ira Antika Mimin Mintarsih Mintarsih, Mimin Mohammad Sabarudin Muhamad Agus Nur Rohman Mulyani, Agustina Murharyana Mustopa, Endang Nasrulloh, iman Nina Nurwahidah Nurfaizi, Muhammad Fitra Nurjamil, Asep Nuroni, Roni Nurul Aziza Padilah, Ai Fitri Permana, Ajeng Ratna Permana, Fadilah Rahardian, Dian Rahmalia, Rika Rahmawati, Ine Riska Apriyanti Rismawati, Irma Rizka Alawiyah Robby, Asa Saepulloh, Saepulloh Saepurrohman, Saepurrohman Siti Asiah Siti Latifah Siti Maemunah, Imas Sobarudin, Refia Mustikaati Somantri, E. Aris Sonari, Heny Sopyan, Riyanti Astriani SUSANTI Tanjung, Ramdani Undang Syaripudin Undang Syaripudin, Undang Wahyudin Wahyudin Wildan Budiawan Zulfikar Yana Aditia Gerhana Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yeni Juarsih, Neng Yinshi Dong Yulia, Rini Zakia Arafat, Litsa