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All Journal Teknika Journal of Economics, Business, & Accountancy Ventura SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Informatika dan Teknik Elektro Terapan JTT (Jurnal Teknologi Terpadu) Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Ideguru: Jurnal Karya Ilmiah Guru Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) SINTA Journal (Science, Technology, and Agricultural) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer J-Intech (Journal of Information and Technology) Jurnal Indonesia Raya Knowbase : International Journal of Knowledge in Database Jurnal Dehasen Mengabdi SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Muhammad Fikry
Universitas Islam Negeri Sultan Syarif Kasim Riau
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End-to-End Text-to-Speech for Minangkabau Pariaman Dialect Using Variational Autoencoder with Adversarial Learning (VITS) Fakhrezi, Muhammad Dzaki; Yusra; Muhammad Fikry; Pizaini; Suwanto Sanjaya
Knowbase : International Journal of Knowledge in Database Vol. 5 No. 1 (2025): June 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i1.9909

Abstract

Language serves as a medium of human communication to convey ideas, emotions, and information, both orally and in writing. Each language possesses vocabulary and grammar adapted to the local culture. One of the regional languages that enriches Indonesian as the national language is Minangkabau. This language has four main dialects, namely Tanah Datar, Lima Puluh Kota, Agam, and Pesisir. Within the Pesisir dialect, there are several variations, including the Padang Kota, Padang Luar Kota, Painan, Tapan, and Pariaman dialects. This study discusses the application of Text-to-Speech (TTS) technology to the Minangkabau language, specifically the Pariaman dialect, using the Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (VITS) method. This dialect needs to be preserved to prevent extinction and supported through technological development that broadens its use. The VITS method was chosen because it is capable of producing natural and high-quality speech. The research stages include voice data collection and recording, VITS model training, and speech quality evaluation using the Mean Opinion Score (MOS). The final results show a score of 4.72 out of 5, indicating that the generated speech closely resembles the natural utterances of native speakers. This TTS technology is expected to support the preservation and development of the Minangkabau language in the Pariaman dialect, as well as enhance information accessibility for its speakers.
PELATIHAN E-HERITAGE HISTORY ARTS AND CULTURE DALAM MENGENALKAN WARISAN SEJARAH DAN BUDAYA DI KOTA MALANG PADA KOMUNITAS JELAJAH JEJAK MALANG (JJM) Pebri Setiani, Puspita; Rahadian, Septa; Adi, Adi; Griz Ella, Cindi; Febian Pratama, Mohammad; Fikry, Muhammad
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 10 (2023): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v6i10.3523-3529

Abstract

Komunitas Jelajah Jejak Malang (JJM) merupakan komunitas yang bergerak dalam bidang pengenalan warisan dan peninggalan sejarah dan budaya yang ada di Kota Malang. Komunitas Jelajah Jejak Malang (JJM) melakukan aktifitasnya dengan aktif dalam suatu diskusi ilmiah dan perawatan peninggalan warisan-warisan budaya yang ada di Malang dengan mempromosikannya melalui platfom facebook, Instagram dan youtube dimana platfom tersebut sangat dirasa kurang dalam mempromosikan sejarah dan budaya Kota Malang ke tingkat dunia. Dalam pengabdian ini bertujuan untuk meningkatkan kualitas layanan dan pengalaman wisata di situs-situs sejarah Malang, dengan menyediakan tour guide secara virtual atau elektronik dengan e-heritage arts and culture yang di dukung oleh aktivitas komunitas Jelajah Jejak Malang (JJM). Metode yang dipakai dalam mendukung tujuan pengabdian ini terlaksana adalah dengan mengadakan pelatihan untuk komunitas Jelajah Jejak Malang (JJM) tentang e-heritage arts and culture sebagai upaya dalam mengenalkan warisan sejarah dan budaya Kota Malang lebih luas lagi. Sehingga hasil pengabdian ini menjadikan Komunitas Jelajah Jejak Malang (JJM) dapat memanfaatkan platfom teknologi informatika lebih luas dan lengkap dalam mengenalkan warisan dan budaya Kota Malang melalui e-heritage arts and culture.
Klasifikasi Sentimen Masyarakat Terhadap Prabowo Subianto Bakal Calon Presiden 2024 di Twitter Menggunakan Naïve Bayes Classifier Dwitama, Raja Zaidaan Putera; Yusra, Yusra; Fikry, Muhammad; Yanto, Febi; Budianita, Elvia
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7071

Abstract

The Indonesian President who has served for 2 consecutive terms cannot nominate again to become President. The public's attitude towards the three presidential candidates, Prabowo Subianto, Anies Baswedan, and Ganjar Pranowo, who are predicted to run for the 2024 presidential election, is also a matter for netizens' opinions from which conclusions can be drawn. Testing will be carried out in this research using information collected from tweets posted by Twitter users. Naïve Bayes Classifier is a technique that will be applied for sentiment assessment. In the upcoming presidential election, this research will be a source when determining the presidential choice. 2100 tweets with the search keywords "Presidential Candidate" and "Prabowo Subianto" are data collected by dividing 1050 positive data and 1050 negative data. Then implementation was carried out using Google Colab starting from data processing (cleaning, case folding, tokenizing, normalization, negation handling, stopword removal, stemming) followed by classification using the Naïve Bayes Classifier. According to test findings using the Confusion Matrix with three experimental test data 90:10, 80:20 and 70:30. Obtained the highest accuracy results of 89%, with a precision value of 89.7%, 88.6% recall and 88.9% f1-score in the 90:10 trial test.
Klasifikasi Sentimen Terhadap Topik Pindah Ibu Kota Negara Pada Twitter Menggunakan Metode Naïve Bayes Classifier Dermawan, Jozu; Yusra, Yusra; Fikry, Muhammad; Agustian, Surya; Oktavia, Lola
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7475

Abstract

Towards the middle of 2019, President Joko Widodo announced plans to relocate Indonesia's capital city. This caused pros and cons in the community, which were widely observed in various social media. To quickly measure the level of public sentiment towards the policy of moving the National Capital City (IKN), whose construction is already underway, a classification system that has good performance is needed. This research proposes a classification of public sentiment on the topic using the Naïve Bayes Classifier method. The data used in this study amounted to 4000 tweets that have been classified into two classes, namely 2000 positive class data and 2000 negative class data. The purpose of this research is how to apply the Naïve Bayes Classifier method in classifying sentiment on the topic of moving the nation's capital and determine the accuracy level of the method. The application of the Naïve Bayes classification method using TF-IDF features to classify 10% of the data as testing data resulted in an accuracy of 77.00%, for a precision value of 77.06%, recall 77.08% and f1-score of 77.00%. Based on the results achieved, the Naïve Bayes Classifier method is good at text classification tasks, with a fairly good accuracy rate.
Co-Authors -, Yusra Adi Adi Ahadi, Ridho Alwis Nazir Ananda, Nuari Ananda, Silvia Andini, Nanda Angela, Angela Anggraeni . Anggraeni, Ni Ketut Pertiwi Anna Marina Annisa Annisa Ayu Fransiska Bahari, Bayu Dwi Prasetya Damayanti, Elok Dermawan, Jozu Detha Yurisna Dimas Pratama, Dimas Dinata, Ferdian Arya Dwitama, Raja Zaidaan Putera Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eko Sumartono, Eko Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvina Afriani Fadhilah Syafria Fakhrezi, Muhammad Dzaki Febi Yanto Febian Pratama, Mohammad Fitri Insani Fitri Insani Griz Ella, Cindi Harahap, Nazaruddin Safaat Hasugian, Leonardo Hidayat, Rizki Hutagalung, Yorio Arwandi Wisdom Ibnu Surya Ida Wahyuni Iis Afrianty Inggih Permana kurnia, fitra Lestari Handayani Lola Oktavia Lutfi, Raihansyah Mardiansyah, M Rizki Mei Lestari, Mei Muhammad Abdillah Muhammad Affandes Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Ravil Naharuddin Naharuddin Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Ndruru, Arlan Joliansa Nurcholis Sunuyeko, Nurcholis Nurdin Nurdin Nurhapiza, Nurhapiza nuryana nuryana, nuryana Oktavia, Lola Pebri Setiani, Puspita Pizaini Pizaini Prananda, Alga Putra, Wahyu Eka Putri Mardatillah Rahadian, Septa Rahma Yunita, Rahma Rahmat Rizki Hidayat Ramadanu Putra Reski Mai Candra Rinaldi Syarfianto Ritonga, Sinta Wahyuni Sagala, Ruflica Saputra, Ikhsan Dwi Sayed Omas Tutus Arifta Sayed Sentot Imam Wahjono Siti Ramadhani Sofiah Surya Agustian Suwanto Sanjaya Tarigan, Anggun Kinanti Taufik Hidayat Tiara Dwi Arista Wirdiani, Putri Syakira Yani, Muhamamd Yani, Susmi Syahfrida Yenggi Putra Dinata Yolanda, Khovifah Yossie Yumiati Yuda Zafitra Fadhlan Yulinazira, Ulfa Yusra Yusra Yusra . YUSRA YUSRA Yusra, Yusra Yusriyana, Yusriyana Zukhruf, Muhammad Firmansyah