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Journal : Jurnal Teknik Informatika (JUTIF)

DETECTION OF BULLYING CONTENT IN ONLINE NEWS USING A COMBINATION OF RoBERTa-BiLSTM Zamroni, Moh. Rosidi; Hamid, Rahayu A; Mujilahwati, Siti; Sholihin, Miftahus; Leksana, Dinar Mahdalena
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4140

Abstract

This research aims to build a bullying-themed online news classification system with a combined approach of RoBERTa embedding and BiLSTM. RoBERTa is used to generate context-rich text representations, while BiLSTM captures temporal relationships between words, thereby improving classification performance. The research dataset consisted of news from reputable portals such as Kompas.com, Detik.com, and iNews.com, labeled according to keywords relevant to the theme of bullying. The results of the experiment showed that the model achieved 95.2% accuracy, 98.2% precision, 93.6% recall, and 95.8% F1-score. Although there are few prediction errors (false positives and false negatives), this model shows excellent performance in detecting and classifying bullying-themed news. The main contribution of this research is the development of a new approach that combines RoBERTa and BiLSTM for the classification of complex bullying-themed news. This approach not only improves the accuracy of classification but can also be implemented in automated systems to detect negative content. Thus, this research has the potential to support the creation of a healthier digital space and encourage more responsible media practices.
Fine-Tuned Transfer Learning with InceptionV3 for Automated Detection of Grapevine Leaf Diseases Sholihin, Miftahus; Zamroni, Moh. Rosidi; Anifah, Lilik; Fudzee, Mohd Farhan Md; Ismail, Mohd Norasri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4717

Abstract

Grape leaf diseases pose a major threat to vineyard productivity, making early and accurate detection essential for modern grape plantation management. Despite advancements in computer vision, challenges remain in differentiating diseases with visually similar symptoms. This study addresses that gap by developing a grape leaf disease classification system using a fine-tuned deep learning model based on the InceptionV3 architecture. Three training scenarios were conducted with fixed parameters batch size of 32 and learning rate of 0.001while varying the number of epochs (25, 50, and 75). Results showed a consistent improvement in classification accuracy with increased training epochs, reaching 98.64%, 98.78%, and 99.09% respectively. Confusion matrix analysis revealed that most misclassifications occurred between visually similar diseases such as Black Rot and ESCA, but error rates declined as the number of epochs increased. Rather than merely applying transfer learning, this research highlights the impact of systematic tuning specifically epoch count optimization in enhancing model accuracy for difficult to distinguish disease classes. These findings underscore the urgency of developing high performance, automated disease detection tools to support precision agriculture and sustainable crop health monitoring.
Co-Authors Abdul Kholiq Abdul Kholiq Agus Setia Budi Ahmad Fauzi Hendratmoko Alfarisi, Muhammad Nur Fikri Alisya, Regina Dwirahma AlMuhibbi, Muhammad Rayendra Anam, M. Khairul Ansori, Yulian Arief Rahman Arief Rahman Arina, Faula Arshad, Mohamad Syafwan Asmaraningtyas, Kinanthi Trah Asshiddieqie, Rafi Ramadhan Atia Sonda Aulia Ikhsan Azizah, Luluk Nur AZZA ABIDATIN BETTALIYAH Azza Abidatin Bettaliyah Bagus Nur Bakti Aji Bagus Nur Bakti Aji Cindy Suryanti Darnis, Febriyanti Delano, M. Fabian Reinhard Dinar Mahdalena Leksana 1 Erna Hayati Erna Hayati, Erna ERRY ANGGRAINI Erry Anggraini Faiz, Syukron Farizki, Achmad Nurasel FATHARANI, ATIKA Fatkhul U, M. Miftah Febriyanti Darnis Firdaus, Muhammad Alvin Fudzee, Mohd Farhan Md Gusman, Taufik Hamid, Rahayu A Ichsan, Andhika Muhamad Ismail, Mohd Norasri Izz, Aiz Ahmad Fa’iz Dliya’ul KIKI SEPTARIA Lilik Anifah M. Ghofar Rohman M. Rosidi Zamroni M. ZAKI QOMARUDDIN Mahuda, Isnaini Masruroh MASRUROH Megawati Indriani Mohd Farhan MD Fudzee, Mohd Farhan Mufrody, Moh Adam Mustain Mustain Nafiiyah, Nur Nur Nafi'iyah Nur Nafi’iyah Nurroziqin, M Chabib Nurul Aswa Omar Nurul Ftria ApriLliani Pertiwi, Dinda Dwi Anugrah Prastowo, Diko Pratiwi, Putri Septiani Indah Prisma Nanda Prsatama, Febrian Abie Rahayu A Hamid Rahma, Midia Retno Wardhani Rofika Arista Sari, Putri Dina Setia Budi, Agus Sika Azkia, Czidni Silvia Agustin Siti Mujilahwati Sulaiman, Akhmad Nurali Surojuddin, Eko Titin Nurbella Udiansyah, Naufal Arrafi Ulum, M. Miftah Fatkhul Umam, Moch. Zuhrul Vanesta Ikhsana Putri Maulana Wati, Efi Neo WICAKSONO, AGUNG SATRIO Yulian Ansori Zirby, Qonit Zumrotus Shalekhah