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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ekonomi dan Bisnis Islam Konvergensi SYIRKAH Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Dialektika: Jurnal Ekonomi dan Ilmu Sosial Jurnal Informasi dan Teknologi MALIA: Journal of Islamic Banking and Finance Bima Abdi: Jurnal Pengabdian Masyarakat Brilliance: Research of Artificial Intelligence Insand Comtech : Information Science and Computer Technology Journal Journal of Artificial Intelligence and Digital Business
Ubaidi, Ubaidi
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Jurnal Teknologi Informasi dan Ilmu Komputer

 1 
POS Tagging Bahasa Madura dengan Menggunakan Algoritma Brill Tagger Dewi, Nindian Puspa; Ubaidi, Ubaidi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722449

Abstract

Bahasa Madura adalah bahasa daerah yang selain digunakan di Pulau Madura juga digunakan di daerah lainnya seperti di kota Jember, Pasuruan, dan Probolinggo. Sebagai bahasa daerah, Bahasa Madura mulai banyak ditinggalkan khususnya di kalangan anak muda. Beberapa penyebabnya adalah adanya rasa gengsi dan tingkat kesulitan untuk mempelajari Bahasa Madura yang memiliki ragam dialek dan tingkat bahasa. Berkurangnya penggunaan Bahasa Madura dapat mengakibatkan punahnya Bahasa Madura sebagai salah satu bahasa daerah yang ada di Indonesia. Oleh karena itu, perlu adanya usaha untuk mempertahankan dan memelihara Bahasa Madura. Salah satunya adalah dengan melakukan penelitian tentang Bahasa Madura dalam bidang Natural Language Processing sehingga kedepannya pembelajaran tentang Bahasa Madura dapat dilakukan melalui media digital. Part Of Speech (POS) Tagging adalah dasar penelitian text processing, sehingga perlu untuk dibuat aplikasi POS Tagging Bahasa Madura untuk digunakan pada penelitian Natural Languange Processing lainnya. Dalam penelitian ini, POS Tagging dibuat dengan menggunakan Algoritma Brill Tagger dengan menggunakan corpus yang berisi 10.535 kata Bahasa Madura. POS Tagging dengan Brill Tagger dapat memberikan kelas kata yang sesuai pada kata dengan menggunakan aturan leksikal dan kontekstual.  Brill Tagger merupakan algoritma dengan tingkat akurasi yang paling baik saat diterapkan dalam Bahasa Inggris, Bahasa Indonesia dan beberapa bahasa lainnya. Dari serangkaian percobaan dengan beberapa perubahan nilai threshold tanpa memperhatikan OOV (Out Of Vocabulary), menunjukkan rata-rata akurasi mencapai lebih dari 80% dengan akurasi tertinggi mencapai 86.67% dan untuk pengujian dengan memperhatikan OOV mencapai rata-rata akurasi 67.74%. Jadi dapat disimpulkan bahwa Brill Tagger dapat digunakan untuk Bahasa Madura dengan tingkat akurasi yang baik. Abstract Bahasa Madura is regional language which is not only used on Madura Island but is also used in other areas such as in several regions in Jember, Pasuruan, and Probolinggo. Today, Bahasa Madura began to be abandoned, especially among young people. One reason is sense of pride and also quite difficult to learn Bahasa Madura because it has a variety of dialects and language levels. The reduced use of Bahasa Madura can lead to the extinction of Bahasa Madura as one of the regional languages in Indonesia. Therefore, there needs to be an effort to maintain Madurese Language. One of them is by conducting research on Madurese Language in the field of Natural Language Processing so that in the future learning about Madurese can be done through digital media. Part of Speech (POS) Tagging is the basis of text processing research, so the Madura Language POS Tagging application needs to be made for use in other Natural Language Processing research. This study uses Brill Tagger by using a corpus containing 10,535 words. POS Tagging with Brill Tagger Algorithm can provide the appropriate word class to word using lexical and contextual rule. The reason for using Brill Tagger is because it is the algorithm that has the best accuracy when implemented in English, Indonesian and several other languages. The experimental results with Brill Tagger show that the average accuracy without OOV (Out Of Vocabulary) obtained is 86.6% with the highest accuracy of 86.94% and the average accuracy for OOV words reached 67.22%. So it can be concluded that the Brill Tagger Algorithm can also be used for Bahasa Madura with a good degree of accuracy.
Penerapan Hidden Markov Model (HMM) dan Mel-Frequency Cesptral Coefficients (MFCC) pada E-Learning Bahasa Madura untuk Anak Usia Dini Ubaidi, Ubaidi; Dewi, Nindian Puspa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722477

Abstract

Bahasa Madura is a regional language used in Madura island. This language has many variations of pronunciation and dialect that makes it not easy to learn, even by the local people especially children. There hasn’t been any interesting learning media to learn Bahasa Madura so far. In fact, a fun learning activity is needed to help children to enhance their ability in pronouncing animals’ names, numbers, fruits and things in Bahasa Madura. Thus, it’s considered important to create Bahasa Madura e-learning by implementing the recognition of voice patterns in order to make it easier for the children to learn Bahasa Madura which has several variations of pronunciation only for one single object. This Bahasa Madura e-learning application for young learners is used to introduce Bahasa Madura vocabularies by recognizing the voice pattern recordings which have been processed through MFCC technique as the extracted voice features and HMM as the learning techniques. The implementation of MFCC and HMM as the learning tool to introduce the pronunciation of regional language vocabularies especially Bahasa Madura has never been done before. Therefore, this research is expected to help the young learners to be able to pronounce Bahasa Madura vocabularies properly.  In this study, a number of young learners’ voices were recorded and were set as the trial data. Only the proper voice data that were used—voice data that were considered to be pronounced correctly. The trial method was done through one-single model and multi-model. After doing several simultaneous trials, the result showed the accuracy level. The average accuracy level for one-single model system was 73% (with the highest accuracy reached 75%) and the average accuracy level for multi-model system was 80% (with the highest accuracy reached 81%).
Co-Authors Abd Rohman, Abd Abd. Wahab Syahroni Aini, Septi Dariyatul Alim, Royfal Amir Hamzah Arifin, Mohammad Nazir Aziz, Mohammad Barokah, Selviana Nur Rizqi Dewi, Nindian Puspa Dwi Mayangsari Efendi, Reynal Widya Efendy, Satrio Ananta Fauzan Prasetyo Haris, Farras Maulana Hasbullah Hasbullah Huda, Moh Abroril Imron Mawardi Irfan, Moh. Irwan Darmawan Ismail Moadz, Ulin Najah Jannah, Ukhti Raudhatul Kusuma, R. Okky Firmansyah Laily, Nurhikmatul Mahendra, Mahendra Maulidia, Siti Nur Miftahus Surur, Miftahus Nindian Puspa Dewi Nor Hasanah Nuraini, Alief Pramadi, Walid Agel Prasetya, Andhis Yoga Puspa Dewi, Nindian Putra , Fauzan Prasetyo Eka Ramadhani, Nilam Rizki, Abdulloh Rosyidi, Luthfi Said, Badar Saputra, Rama Nurja Syafitri, Andriati Aziizah Syam, Abd Mu’iz Tamam, Alief Badrit Zakaria, Nor Balkish