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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Ade Romadhony
Universitas Telkom
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)

 1 
Similar Questions Identification on Indonesian Language Subject Using Machine Learning Hasmawati; Ade Romadhony
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62582

Abstract

Question similarity is carried out to evaluate similarities between questions in a collection of questions in the question and answer forum and on other platforms. This is done to improve the performance of the question-and-answer forum so that new questions submitted by users can be identified as similar to existing questions in the database. Currently, research related to question similarity is still being carried out on foreign language datasets. The purpose of this research is to identify the similarity of questions in a collection of questions in Indonesian. The method used is Support Vector Machine and IndoBERT. For feature extraction, we evaluate the lexical features and syntax features of each question. For lexical feature extraction, we use the cosine similarity algorithm to calculate the distance between two objects which are represented as vectors. For syntax feature extraction we use the Indonesian part of speech tagger (POS Tag). The dataset used is a collection of questions on Indonesian subjects at the primary and secondary school levels. The results of this study show that the best performance of the Support Vector Machine is obtained from the use of the cosine similarity feature with an accuracy of 85%. While the use of the POS Tag feature or the combination of POS Tag and cosine similarity causes the model to be overfitted and the accuracy decreases to 77%. Meanwhile, for the IndoBERT model, an accuracy of 95% was obtained. 
Co-Authors Aditia Rafif Khoerulloh Agung Toto Wibowo Al Faraby, Said Ali Ridho Fauzi Rahman Ananda Affan Fattahila Ananda Wulandari Anisa Herdiani Arya Prima Al Aufar Bambang Pudjoatmodjo Bambang Pudjotatmodjo Bayu Tresna Ariesta Bedy Purnama Bhudi Jati Prio Utomo Bimmo Satryo Wicaksono Dade Nurjanah Dana Kusumo Dana S Kusumo Dana S Kusumo Dodi Wisaksono Sudiharto Donni Richasdy Ema Rachmawati Ema Rachmawati Farhan Ahmadi Javier Nur Fat’hah Noor Prawira Fat’hah Noor Prawira Fazainsyah Azka Wicaksono Fazmah Arif Yulianto Hamdy Nur Saidy Haryo Adi Nugroho Haryo Adi Nugroho Haryo Nugroho Hasmawat Hasmawat Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Kemas Rahmat S.W Kurniadi Ahmad Wijaya Lintani Afina Hajar Raudhoti Luh Putri Ayu Ningsih Mahmud Dwi Sulistiyo Moch. Arif Bijaksana Muhammad Arzaki Muhammad Arzaki Muhammad Aziz Pratama Muhammad Farrel Muhammad Rizki Ramadhan Setiawan Muhammad Taufik Wahdiat Muhammad Zaky Aonillah Nadine Azhalia Purbani Prawita, Fat’hah Noor Putu Harry Gunawan Rhesa Hermawan Ridea Valentini Peristiwari Siwabessy Rimba Whidiana Ciptasari Riska Junia Wulandari Said Faraby Selly Meliana Shabrina Retno Ningsih Siti Saadah