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All Journal Jurnal Informatika Jurnal Ilmiah Matrik JURNAL ILMIAH INFORMATIKA Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Abdimas Mahakam METIK JURNAL Building of Informatics, Technology and Science Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sains Komputer dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Terpadu Jurnal Informatika Teknologi dan Sains (Jinteks) Sci-Tech Journal Sewagati: Jurnal Pengabdian Masyarakat Indonesia Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Publikasi Teknik Informatika (JUPTI) Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) GEMBIRA (Pengabdian Kepada Masyarakat) Buffer Informatika JSE Journal of Science and Engineering
Naufal Azmi Verdikha
Teknik Informatika, Universitas Muhammadiyah Kalimantan Timur
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : JSE Journal of Science and Engineering

 1 
Multilayer Perceptron and TF-IDF in the Classification of Hate Speech on Twitter in Indonesian Syahrandi, Akmal; Latipah, Asslia Johar; Verdikha, Naufal Azmi
JSE Journal of Science and Engineering Vol. 2 No. 1 (2023): Journal of Science and Engineering
Publisher : LPPI Universitas Muhammadiyah Kalimantan Timur (UMKT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30650/jse.v1i1.3773

Abstract

Twitter nowadays is one of the popular social media which currently has over 300millions accounts, twitter is the rich source to learn about people’s opion and sentimental analysis. However, this also brings new problems where the practice of hate speech. This research classifies of hate speech on social media. Evaluation using dataset from previous research Ibrohim&Budi (2019), then using classification method Multilayer Perceptron which combined with feature extraction to be able to detect negations and weighting uses Term Frequency – Inverse Document Frequency (TF-IDF). Results show that the F1 score gives an accuracy rate of up to 74.51%. This research has a reasonably good effectiveness from combining the TF-IDF and Multilayer Perceptron methods, considering the results obtained from the F1 Score evaluation value.
Indonesian Automated Essay Scoring with Bag of Word and Support Vector Regression Verdikha, Naufal Azmi; Dwiagam, Junianda Haris; Hasudungan, Rofilde
JSE Journal of Science and Engineering Vol. 2 No. 2 (2024): Journal of Science and Engineering
Publisher : LPPI Universitas Muhammadiyah Kalimantan Timur (UMKT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30650/jse.v1i2.3841

Abstract

Essay is one of the test questions to measure students' understanding of learning. Respondents can organize the answers to each question in their own language style, so it takes time to make corrections. It takes a system that can assess essay answers automatically quickly and accurately. Auto Essay Scoring (AES) is a tool that can assign grades or scores to answers in the form of essays automatically. In giving grades automatically, AES requires machine learning with training data that contains answer data that has been given a value by the assessor. In this study, AES was used to assess the Indonesian language midterm exams using the Bag of Word extraction feature and using Support Vector Regression. The Root Mean Square Error value obtained when evaluating AES is 1.99.
Co-Authors Abdul Rahim Achmad, Arda Fahmi Adhistya Erna Permanasari Ahmad Ilham Ahmad Ridhani Mubaraq Arbansyah Aspianur Asslia Johar Latipah Assliah Johar Latipah Bulan Suci Cahayawati Dwiagam, Junianda Haris Evitasari, Yuliana Dilla Fahrezi, Irqi Anbi Faldi Faldi Faldi Faldi Fendy Yulianto Fiqri, Mohammad Hiqmal Fitri Damayanti Habid, Reza Hasudungan, Rofilde Hendra Saputra Hery Tri Waloyo Hidayati Ramadhani, Novia Husni Thamrin Ilham, Muhammad Fauzan Nur Johar Latipah, Asslia Latipah, Asslia Johar Muhamad Wahyu Tirta Muhammad Najeri Al Syahrin Muhammad Taufiq Sumadi Muhammad Taufiq Sumadi Muthmainnah Muthmainnah Nuraini, Nabila Nurdiansyah, Rendy Octaviany, Dinda Nur Pranata, Aditya Pranata, Ricky Albin Rahmad Fahrozi, Mu. Aldi Reza Andriyanti Ridhoi, Reno Ririn Wahyuni Rudiman Rudiman, Rudiman Sari, Septa Intan Permata Sayekti Harits Suryawan Susanto, Muhammad Riza Radyaka Syahrandi, Akmal Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Takhta Perlawanan Putra Sinawang Teguh Bharata Adji Tri Wahyudi Trisha NurHalisha Tyas, Intan Kinanthi Damarin Wawan Joko Pranoto Wawan Joko Pranoto Zayni Zayni