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All Journal Computatio : Journal of Computer Science and Information Systems JUTIK : Jurnal Teknologi Informasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas JUTIM (Jurnal Teknik Informatika Musirawas) Kurawal - Jurnal Teknologi, Informasi dan Industri Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Teknomatika (Jurnal Teknologi dan Informatika) Syntax: Journal of Software Engineering, Computer Science and Information Technology JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Ilmu Komputer dan Informatika Bulletin of Information Technology (BIT) Brilliance: Research of Artificial Intelligence Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Arcitech: Journal of Computer Science and Artificial Intelligence Agrivet: Jurnal Ilmu-ilmu Pertanian dan Peternakan DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Insand Comtech : Information Science and Computer Technology Journal Buletin Ilmiah Informatika Teknologi JOINTECOMS (Journal of Information Technology and Computer Science) MDP Student Conference Software Development Digital Business Intelligence and Computer Engineering Journal Information & Computer (JICOM) Jurnal Software Engineering and Computational Intelligence Computing Insight: Journal of Computer Science Applied Information Technology and Computer Science (AICOMS) JISCOMP (Journal of Information System and Computer) Journal of Informatics and Computer Engineering Research JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Hafiz Irsyad
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Journal : Progresif: Jurnal Ilmiah Komputer

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Pemanfaatan Fitur Tambahan Emosi Untuk Deteksi Hate Speech Media Sosial Bahasa Indonesia Michael Joy Clement; Hafiz Irsyad
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3338

Abstract

This study examines the importance of incorporatring emotion features and enhancing the temporal robustness of hate-speech detection models to improve classification accuracy. The research aims to analyze the impact of emotion features on an IndoBERT based model and to evaluate the model’s adaptability using an unsupervised self-learning approach. The dataset consists of two corpora, a public dataset from 2019 and twitter data from 2025, each divided into training, validation, and test sets with an 80%, 10%, 10% split. Model performance is evaluated using accuracy, precision, recall and F1-score calculated from confusion matrix. Experimental results show that adding emotion features increases accuracy by 1-2% across all scenarios. In cross-temporal testing, the supervised model performance declines duet o linguistic shifts whereas the self-learning method improves accuracy up to 77.67%. These findings indicate that emotion features and self-learning effectively enhance the model’s ability to adapt to evolving language and social context.Keyword: Emotion; Hate speech detection; IndoBERT AbstrakPenelitian ini membahas pentingnya penambahan fitur emosi dan peningkatan ketahanan model deteksi ujaran kebencian terhadap perubahan bahasa lintas waktu guna memperkuat akurasi klasifikasi. Tujuan penelitian adalah menganalisis pengaruh fitur emosi pada model berbasis IndoBERT dan mengevaluasi kemampuan adaptasi model menggunakan pendekatan unsupervised self-learning. Data menggunakan dua korpus yaitu dataset publik tahun 2019 dan data Twitter tahun 2025, yang masing-masing dibagi menjadi data latih dan data latih, validasi, dan uji dengan proporsi 80%, 10%, dan 10%. Model dievaluasi menggunakan accuracy, precision, recall, dan F1-score yang dihitung melalui confusion matrix. Hasil pengujian menunjukkan bahwa penambahan fitur emosi meningkatkan akurasi sebesar 1-2% di seluruh skenario. Pada pengujian lintas waktu, performa model supervised menurun akibat perubahan konteks linguistik, namun metode self-learning meningkatkan akurasi hingga 77.67%. temuan ini menunjukkan bahwa fitur emosi dan self-learning efektif meningkatkan adaptasi model terhadap dinamika bahasa serta konteks sosial.Kata kunci: Seteksi ujaran kebencian; Emosi; IndoBERT
Penerapan CNN Pada Klasifikasi Kepribadian Anak Sekolah Dasar Berdasarkan Citra Tulisan Tangan Muhammad Ishaq Maulana; Hafiz Irsyad
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2959

Abstract

Indonesia has a rich culture. This creates dynamics in personality formation. In schools, teachers' understanding of students' personalities is key. So far, conventional methods such as observation, interviews and graphology have been used to classify children's personalities, which are less efficient. This study uses the CNN algorithm with the Mobilenetv2 architecture. Dataset was taken from 5th grade students from 3 SDN Palembang with a total of 246 data divided into 2 classes, namely extrovert 101 data and introvert 145 data. Then grayscale preprocessing, normalization, and augmentation. Ratio of training, validation, and test data is 80:10:10. Model was trained with Adam optimizer, learning rate 0.0001, batch size 20, and epochs of 12. The result is a model accuracy of 69.2% with a tendency for the model to classify images as introverts. This study is expected to help teachers gain insight into the best teaching approach in the classroom.Keywords: CNN; Graphology; Elementary School AbstrakIndonesia memiliki budaya yang kaya. Ini menciptakan dinamika dalam pembentukan kepribadian. Di sekolah, pemahaman guru terhadap kepribadian siswa menjadi kunci. Selama ini untuk mengklasifikasi kepribadian anak, digunakan metode konvensional seperti observasi, wawancara dan ilmu grafologi yang kurang efisien. Penelitian ini menggunakan algoritma CNN dengan arsitektur Mobilenetv2. Dataset diambil dari siswa kelas 5 dari 3 SDN Palembang dengan total 246 data yang dibagi menjadi 2 kelas, yaitu extrovert 101 data dan introvert 145 data. Kemudian dilakukan preprocessing grayscale, normalisasi, dan augmentasi. Rasio data latih, validasi, dan uji adalah 80:10:10. Model dilatih dengan Adam optimizer, learning rate 0,0001, batch size 20, dan epoch sebanyak 12. Hasilnya akurasi model sebesar 69,2% dengan kecenderungan model mengklasifikasi citra sebagai introvert. Penelitian ini diharapkan dapat membantu guru mendapatkan pandangan tentang cara pendekatan mengajar yang terbaik di kelas.Kata kunci: CNN; Grafologi; Sekolah Dasar
Co-Authors Abdul Rahman Abdul Rahman Adi Saputra Adrian Suparto Agnes Anastasia Putri Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Andreas Andreas Antony, Felix Arta Tri Narta Arta Tri Narta Aurelia, Reni Busdin, Rusdie Candra candra Chandra Wijaya Chandra, Kelvin William Christian Bautista Christy, Christy Cindy Meilani Daniel Wijaya Derry Alamsyah Devella, Siska dewa Dicko David K Dina Mariana Dwifa_Sophian, Muhammad Agus Edison, Nicholas Edward Pratama Eka Puji Widiyanto Fareza, Ivan Farisi, Ahmad Farisi, Ahmad Fariz Prasetya Ferdi Jiranda Sinaga Ferdilian, M Lazuardi Fernando Sugianto Putra Fernando, Kristian Franko, Billy Fujianto Graciela, Michelle Hansen, Hansen Hartati, Ery Hendra Nata Niko P Hidayat, Muhammad Syahrizal Hidayat, WIlliam Ibnusina, Fedri Ivander Destian Luis Jeason Lie Jocelyn, Jennifer Jolyn Lucretia jonathan stanly Jonathan Wijaya Juliana Nasution Julyo Armando Davincy Lin, Valen Kamilah, Nyimas Nisrinaa Kelly, Angel Kevin kevin Kevin Kevin Kotan, Jendraja Husein Kurniawan, Calvin Laksana, Jovansa Putra Leonardo Leonardo Lestari, Yehezekiel Gian levid, Jonathan Felix Lin, Jimmi M Ezar Al Rivan Meiriyama, Meiriyama Michael Joy Clement Michael Joy Clement Michael Wijaya Molavi Arman Muhammad Bemby Putra Mansyah Muhammad Ishaq Maulana Muhammad Rizky Pribadi Muhdhor, Umar Mutia, Silvi Narta, Arta Tri Novan Wijaya Novan Wijaya Novan Wijaya Novan Wijaya Ong, Jesen Pribadi, M Rizky Putra Darmansius, Albertus Dwi Andhika Renaldo, Florence Reynald Dwika Prameswara Rikky, Rikky Rizki Ambarwati Roshan, Muhamad Rizvi RR. Ella Evrita Hestiandari Russel Wijaya Samuel Effendi pratama Santoti, Jennifer Velensia Sanu, Intan Saputra, M Reynaldi Setiawan, Christofer Evan Shela, Shela Silfia Suhartoyo, Rayvin Tanuwijaya, William Taqwiym, Akhsani Taqwiym, Akhsani Taqwiym, Akhsani Tinaliah, Tinaliah Triana Elizabeth, Triana Verrino Adityya Virginia, Callista Wati, Retiana Krisna Wati, Risha Ambar Wijang Widhiarso Wijaya, Christian Richie Willyanto, Aldo Wilyanto, Nicholas Wong, Jeovanni Yohannes, Yohannes Yunarto Yunarto, Yunarto