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All Journal Dinamik Teknika Jupiter PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) International Journal of Artificial Intelligence Research Jurnal CoreIT Prosiding Seminar Nasional Darmajaya Jurnal Sinergitas PkM & CSR Jurnal Teknologi Informasi MURA Jurnal Informasi dan Komputer IJISCS (International Journal Of Information System and Computer Science) Jurnal Tekno Kompak Building of Informatics, Technology and Science JPGMI (Jurnal Pendidikan Guru Madrasah Ibtidaiyah Al-Multazam) Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian kepada Masyarakat Jurnal Sains Teknologi dan Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mandiri NEAR: Jurnal Pengabdian kepada Masyarakat SIENNA Jurnal Indonesia Sosial Sains Jurnal Ilmu Komputer, Sistem Informasi, Teknik Informatika (JILKOMSITI) Jurnal Ilmiah ESAI Jurnal Teknologi Informasi Mura Scientica: Jurnal Ilmiah Sains dan Teknologi Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora International Journal of Computer Technology and Science Journal of Software Engineering And Technology IJISCS (International Journal of Information System and Computer Science)
Sutedi Sutedi
IIB Darmajaya
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Journal : Jurnal Teknik Informatika (JUTIF)

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Brain Tumor Auto Segmentation On 3D MRI Using Deep Neural Network Agarina, Melda; Maulana, Muh Royan Fauzi; Sutedi, Sutedi; Karim, Arman Suryadi
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.2025.6.5.5106

Abstract

Accurate and automated segmentation of brain tumours from Magnetic Resonance Imaging (MRI) is crucial for clinical diagnosis and treatment planning, yet it remains a significant challenge due to tumour heterogeneity and data imbalance. This research investigation examines the effectiveness of a 3D UNet architecture for the segmentation of brain tumours utilizing MRI imaging modalities. The research employs the BRATS 2021 dataset, which consists of 675 MRI datasets across four distinct imaging modalities (FLAIR, T1-Weighted, T1-Contrast, and T2-Weighted) and encompasses four distinct segmentation label classes. The employed model integrated soft dice loss and dice coefficient as its loss functions, with the objective of achieving convergence despite the presence of imbalanced data. While constraints related to resources limited the training process, the model yielded promising outcomes, exhibiting high accuracy (99.43%) and specificity (99.5%), The model aids medical professionals in understanding tumor growth and enhances treatment planning via segmentation predictions in surgery. Nevertheless, the sensitivity, particularly concerning non-enhancing tumour classes, persists as a significant challenge, underscoring the necessity for future research to concentrate on data-centric methodologies and enhanced pre-processing techniques to improve model efficacy in critical medical applications such as the segmentation of brain tumours.
Integration of BERT and SVM in Sentiment Analysis of Twitter/X Regarding Constitutional Court Decision No. 60/PUU-XXII/2024 Irianti , Artia; Halimah, Halimah; Sutedi, Sutedi; Agariana, Melda
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4068

Abstract

This research analyzes public sentiment towards the Indonesian Constitutional Court's decision No. 60/PUU- XXII/2024 by utilizing natural language processing techniques using the BERT (Bidirectional Encoder Representations from Transformers) model and the Support Vector Machine model (SVM). The research methodology includes four stages: data preprocessing, data labeling using BERT, embedding extraction, and SVM model training. The data is taken from the Twitter platform, where various public opinions are reflected in three sentiment categories: positive, neutral, and negative. The preprocessing process results in the removal of approximately 23% of duplicate data, and sentiment labeling shows a dominance of the positive category. Evaluation results from the SVM model training demonstrated varying performance: negative sentiment achieved a Precision of 0.57, Recall of 0.36, and F1-score of 0.44; neutral sentiment had a Precision of 0.81, Recall of 0.62, and F1-score of 0.70; while positive sentiment recorded a Precision of 0.98, Recall of 1.00, and F1-score of 0.99. The model's overall accuracy reached 0.97. These findings indicate that the integration of BERT and SVM is effective for sentiment classification, but improvements are needed in the negative and neutral categories to achieve more balanced performance.
Co-Authors - Nurfiana Abda Abda Absor, M Ulil Adam Japal Adela, Cindy Nada Agariana, Melda Agarina , Melda Agung Nugroho Menggala Putra AGUS PURWANTO Anton Catur Atmoko Ardiansyah, Ari ARIF HIDAYAT Arman Suryadi Karim, Arman Suryadi DANU WIJAYA Diba Madya Margerefti Dona Yuliawati Edi Edi Pranyoto Fauzi Maulana, Muh Royan Fauzi, M.Royan Firdhayanti, Ayu Firmansyah Firmansyah Firmansyah Yunialfi Alfian Guntur Guntur Halimah Halimah handoyo widi nugroho Hartono Hartono Hasibuan, Said Hendra Kurniawan Hendri Purnomo Hendri Purnomo Hermanto, Muhammad Haris Ilhamiharja, Arbi Irianti , Artia Kaesar Azra Putra Zeva Kesuma, Martika Kurniawan, Putra Lestari, Mentari Puji Luh Putu Ratna Sundari M Said Hasibuan M Ulil Absor M. Royan Fauzi M.Ariza M.Royan Fauzi M.Royan Fauzi Maulana Maarif, Syaiful Mahfudzin, Abdul Halim Maulana, Muh Royan Fauzi Melda Agarina MS Hasibuan MUBAAROK, ABDURRAHMAN Muhamad Ariza Eka Yusendra Muhammad Ariza Eka Yusendra Nirma Nirma Nirmala Handayani Nirmala Handayani Ochi Marshella Febriani rinaldi rinaldi Rinaldo, Reyhandoni Rini Nurlistiani Risa Ayuna Sari, Erlinda Ratna Sri Karnila Sriyanto . Sriyanto, Sriyanto Stefanus Rumangkit Suryadi , Arman Susanti Susanti Susanti Susanti Susanti Tria Devi Miranti Triloka, Joko Ubaidilah, Romdan Muhamad Wasilah Wasilah Yusminar Yusminar