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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%20MEDIA%20INFORMATIKA%20BUDIDARMA

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Pengaruh Distribusi Panjang Data Teks pada Klasifikasi: Sebuah Studi Awal Said Al Faraby; Ade Romadhony
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4259

Abstract

In text classification, there is a problem with text domain differences (cross-domain) between the data used to train the model and the data used when the model is applied. In addition to the problem of domain differences, there are also language differences (cross-lingual). Many previous studies have looked for ways how classification models can be applied effectively and efficiently in these cross-domain and cross-lingual situations. However, there is one difference that is not given special attention because it is considered not very influential, namely the difference in text length (cross-length). In this study, we further investigated the cross-length condition by creating a special dataset and testing it with various commonly used classification models. The results showed that the difference in the distribution of text length between the training data and the test data could affect the performances. Cross-length transfers from long to short texts show an average decrease in F1-scores across all models of 14%, while transfers from short to long texts give an average decrease of 9%.
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