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Journal : Jurnal Teknik Informatika (JUTIF)

IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD IN CLASSIFYING SCHOOL LIBRARY BOOKS WITH COMBINATION OF TF-IDF AND WORD2VEC Cahyani, Salsabila Nida; Saraswati, Galuh Wilujeng
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1536

Abstract

The development of technology in education is integral to enhancing its quality, such as implementing information technology in school libraries. Searching for books in school libraries is time-consuming due to conventional book classification, lacking organization based on classifications. Therefore, implementing information technology in school libraries is crucial to improve library management effectiveness. An innovative solution optimizing library management involves leveraging artificial intelligence, particularly machine learning. In applying machine learning to library book classification, Support Vector Machine acts as an algorithm understanding patterns and characteristics of book titles, categorizing them into Dewey Decimal Classification (DDC). The dataset comprises 10 classes aligned with DDC. Random data collection follows an 80:20 scale for training and testing data. Data preprocessing is an initial research stage, addressing imbalanced data through oversampling. Testing the SVM algorithm with a linear kernel and C = 1 parameter is conducted three times using different feature extraction methods: TF-IDF alone, Word2Vec alone, and a combination of TF-IDF and Word2Vec. Model performance evaluation employs K-Fold Cross-Validation. After the three objective tests, the most accurate book classification results were obtained using a combination of TF-IDF and Word2Vec feature extraction. It's concluded that SVM's book classification method can be applied, yielding the highest accuracy of 73% with the TF-IDF and Word2Vec feature extraction combination. This outperforms other feature extraction methods, with precision at 83%, recall at 72%, and an F1-Score of 76%.
Co-Authors Achmad Naila Muna Ramadhani Adelia Rahmawati Adhitya Nugraha Aditya Wahyu Ramadhan Adji, Dian Restu Agus Winarno Ahmad Zainul Fanani Ajib Susanto Akbar Dwi Syahputra Angga Apriano Hermawan Azhara Devi Sandi Azzahra, Tarissa Aura Bagas Aditya Mahendra Cahyani, Almaun Tri Cahyani, Salsabila Nida Caturkusuma, Resha Meiranadi Danny Oka Ratmana Dianna Yanuaresta Didik Hermanto Dwi Puji Prabowo, Dwi Puji Erba Lutfina Etika Kartikadarma Fafaza, Safira Alya Fakhrurrozi Fakhrurrozi, Fakhrurrozi Febrianti, Ervina Febrianto, Nanang Filmada Ocky Saputra Filmada Ocky Saputra Fitasari, Ayu Tri Nur Garda, Kautsa Adi Guruh Fajar Shidik Gustina Alfa Trisnapradika Handoyo, Dhiky Resandi Wur Harisa, Ardiawan Bagus Heru Agus Santoso Iqlima Zahari Joel Justin Adrian Lakui Johary Lutfina, Erba Malik Aziz Ali Mandasari Kusuma Dyah Tantri Mardiantara, Naya Alifiah az Azar Putri Megantara, Rama Aria Meilani Dwi Permatasari Mellati, Pita Miranti Alysha Zulia Larasati Muhamad Ni'am Syukri Roni Asmi Muhammad Syaifur Rohman Muhammad Syaifur Rohman Muljono, - Mulyanto, Edy Nimasari, Azza Nur Inayati Nurun Najmi Amanina Pergiwati, Dewi Permana, Danang Juniar Prashanti, Eva Pratama, Zudha Pulung Nurtantio Andono Rahmat Trinanda Pramudya Amar Rama Tri Agung Ramadhan, Aditya Wahyu Ramadhani, Irfan Wahyu Ratmana, Danny Oka Renjiro Azhar Pramono Resha Meiranadi Caturkusuma Ricardus Anggi Pramunendar Rino Agung Rizky Syah Gumelar Rohman, Muhammad Syaifur Rohman, Muhammad Syaifur Saputra, Filmada Ocky Sri Winarsih, Nurul Anisa Wawan Darmawan Wildan Mahmud Winarsih, Nurul Anisa Sri Winasis, Galih Adi Yustiqomah, Evita Citra Zuhdi, Ahmad Muzaki