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All Journal JISTECH: Journal of Information Science and Technology Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi INFORMAL: Informatics Journal Jurnal Khatulistiwa Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jiko (Jurnal Informatika dan komputer) JURTEKSI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI ScientiCO : Computer Science and Informatics Journal Progresif: Jurnal Ilmiah Komputer Jurnal Sistem Informasi dan Informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) MAHESA : Malahayati Health Student Journal Jurnal Sistem Informasi dan Teknik Informasi Journal of Embedded Systems, Security and Intelligent Systems Jurnal Teknik Informatika dan Teknologi Informasi
Yuliawan, Kristia
STMIK Pesat Nabire
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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Advancements in Brain Tumor Classification: Leveraging Mobilnet-V2 and Densenet121 For High-Precision Prediction Yuliawan, Kristia
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.11471

Abstract

Purpose – This study aims to compare the performance of MobileNet-V2 and DenseNet121 in classifying brain tumor types from MRI images under identical preprocessing, partitioning, and training conditions. Design/methods/approach – The study used a Kaggle-based dataset consisting of 3,264 MRI images, divided into 88% training data and 12% testing data. Both models were implemented using transfer learning and fine-tuning. Preprocessing included image resizing, normalization, and data augmentation through rotation, flipping, and zooming. The models were trained using the Adam optimizer, a learning rate of 0.0001, batch size of 32, and early stopping. Performance was evaluated using confusion matrix analysis, precision, recall, and F1-score. Findings – The results show that MobileNet-V2 achieved better overall performance than DenseNet121 in brain tumor classification. MobileNet-V2 produced more stable classification results and higher evaluation scores across most tumor classes, particularly in glioma and pituitary tumor prediction. In contrast, DenseNet121 showed a greater tendency to overfit, although both models performed well in identifying non-tumor images. Research implications/limitations – The study is limited by the relatively small dataset size, the use of a single dataset source, and the absence of external validation, which may affect generalizability. Originality/value – This study provides a direct comparative analysis of MobileNet-V2 and DenseNet121 for four-class brain tumor classification and highlights MobileNet-V2 as a more efficient and reliable model for this task.
Co-Authors Ester Ayuk Pusvita Hendra Muttu Herba, Mei Indriani, Alya Tasya Karubaba, Getreda Gabrilla Murib, Stevanus Natsir, Akhmad Amiruddin Nilam Tayang Parma Hadi Rantelinggi Parmahadi Rantelinggi Pilenaung, Jevan Prayitno, Gunawan Putra, Adie Purnama Simanjuntak, Rantika Tri Septyani Sri Yulianto Joko Prasetyo Sulastri Arung Sombolinggi Suryasatriya Trihandaru Sutarto Wijono Tania Lensi Basinung Tecuari, Martince Toroce Waum Wilar, Youce Albertus Wisna Bara’ Padalingan Yaumil Amne Akbar