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All Journal TEKNIK INFORMATIKA Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JURNAL ILMIAH INFORMATIKA Generation Journal Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Terapan Dedication : Jurnal Pengabdian Masyarakat
M. Rosidi Zamroni
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Islam Lamongan
Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering Immunology & microbiology Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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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 Agung Wahyudi Ahmad Fauzi Hendratmoko Alfarisi, Muhammad Nur Fikri AlMuhibbi, Muhammad Rayendra Anam, M. Khairul Azizah, Luluk Nur Azza Abidatin Bettaliyah AZZA ABIDATIN BETTALIYAH Dinar Mahdalena Leksana 1 Erna Hayati, Erna Erry Anggraini ERRY ANGGRAINI Farizki, Achmad Nurasel FATHARANI, ATIKA Fatkhul U, M. Miftah Firdaus, Muhammad Alvin Fudzee, Mohd Farhan Md Hamid, Rahayu A Ismail, Mohd Norasri KIKI SEPTARIA Lilik Anifah M. Ghofar Rohman M. ZAKI QOMARUDDIN Miftahus Sholihin Mufrody, Moh Adam Nurroziqin, M Chabib Nurul Aswa Omar Prastowo, Diko Prsatama, Febrian Abie Qabilah Cita K. N. S Rahayu A Hamid Retno Wardhani Rohma, Riska Dwi Elida Yahyatul Siti Mujilahwati Sulaiman, Akhmad Nurali Umam, Moch. Zuhrul Zirby, Qonit