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All Journal Jurnal Sains dan Informatika KOMPUTIKA - Jurnal Sistem Komputer Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Komputer dan Sistem Informasi Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Multimedia ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Ilmu Peternakan Terapan JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Bumigora Information Technology (BITe) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Among : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal Automation Computer Information System (JACIS) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika JAMU : Jurnal Abdi Masyarakat UMUS Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Sains Sistem Informasi Jurnal Pengabdian kepada Masyarakat JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Harliana Harliana
Fakultas Eksakta, Universitas Nahdlatul Ulama (UNU) Blitar
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Journal : Building of Informatics, Technology and Science

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Optimisasi Fungsi Aktivasi pada Arsitektur LeNet untuk Meningkatkan Akurasi Klasifikasi Citra Tumor Otak Harliana, Harliana; Rahadjeng, Indra Riyana; Winanjaya, Riki
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7108

Abstract

Brain hemorrhage is a critical medical condition that requires early and accurate detection to improve patient recovery outcomes. However, conventional image classification methods for brain hemorrhage still face limitations in terms of accuracy and efficiency. To address this issue, this study proposes optimizing the LeNet model using various activation functions—ReLU, Sigmoid, Tanh, and Swish—to enhance classification performance. Several optimization strategies were applied, including data augmentation techniques (flipping, rotation, shearing, rescaling) and fine-tuning of hyperparameters, to improve model generalization. Experimental results indicate that the model utilizing the Swish activation function achieves the most stable overall performance, with an accuracy of 55%, recall of 54%, precision of 54%, F1-score of 54%, and a ROC AUC value of 0.45. Although this performance is still below clinical application standards, the findings serve as an initial step toward exploring activation function optimization in CNN architectures. Further research is needed to significantly enhance classification accuracy and enable clinical viability.
Co-Authors Abd. Charis Fauzan Andri Syafrianto Anggara Setya Saputra Ayu Eviana Citra Mirna Wati Deny Restyo Nugroho Dewi Anggraini Dewi Anggraini Dewi Lestari Dwi Nurani FATRA NONGGALA PUTRA Fery Sofian Efendi Fiqih Ainul Qhabib Gian Fitralisma Gunawan Gunawan Hartatik Hasanah, Niswatin Imam Machfud Indra Riyana Rahadjeng Intan Nur Fauziyah Izzudin, Ahmad Kafi, Elfiya Khanatul Kartika Dewi Khalid Iskandar Khoiril Hikmah Khoirul Muanam, Mohammad Kirono, Sodik Leni Ayu Marlina Lestari, Febbi Sena Luthfi Hardiansyah Mei Maemunah Muhamad Bagas Gigih Muhamat Maariful Huda Muhammat Maariful Huda Mukidin Mukidin Nining Haryuni Nurul Aziz Tri Wahyuni Nurul Baety Tsani Otong Saeful Bachri Prabowo, Tito Putri Handayani Raden Mohamad Herdian Bhakti Rahmadita Sabrina Islahany Rizqi Darma Rusdiyan Yusron Roby Setiadi Rusdian Yusron, Rizki Darma Salnan Sabdo Wibowo Setyawan, Dimas Ari Sikone, Hilarius Yosef Siti Nuralia Subana Subana Suraji Suraji Titi Rahmawati Tito Prabowo Ulfa Nurfitria Sugandi Winanjaya, Riki Wiwi Widayani Yuniar Alam, Yuniar