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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 : Building of Informatics, Technology and Science

 1 
Difficulty Level Identification of Indonesian and Mathematics Multiple Choice Questions using Machine Learning Approach Shabrina Retno Ningsih; Ade Romadhony
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3649

Abstract

Examination question design is an important factor that could improve education, which could help teachers to analyze student understandings. Designing question should consider difficulty level, which commonly classified into three types: easy, medium, difficult. Predicting the difficulty level of questions is very important to help teachers form questions and know the level of student ability. In this study, we tackle question difficulty level identification as a classification problem. We use a dataset of Indonesian and mathematic question from elementary and junior or school exercise questions set and employ several machine learning methods on classification. We use Random Forest, Logistic Regression, SVM, Gaussian, and Dense NN on the experiment, with embeddings, lexical, and syntactic feature. The evaluation result shows that the best method on identifying question difficult level on Indonesian subject is Random Forest with 83% accuracy, while on mathematic subject the best method is Random Forest with 83% accuracy. Result analysis shows that embedding feature affect the model accuracy.
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