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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Ininnawa: Jurnal Pengabdian Masyarakat
Sheila Eunike Kakisina
Universitas Kristen Indonesia Paulus
Humanities Computer Science & IT Education Energy Engineering Public Health Social Sciences

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Improving Brain Tumor Classification Performance Using EfficientNetB0 Integrated with CBAM Attention Mechanism At Taqwa, Abd Salam; Muh., Satriawan; Sheila Eunike, Kakisina; Puput, Kusuma Dewi; Fettyana; Ayutri, Wahyuni
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15931

Abstract

Accurate classification of brain tumors using magnetic resonance imaging (MRI) requires robust automated methods to support clinical diagnosis, particularly when tumor types present subtle visual distinctions. In this study, the Convolutional Block Attention Module (CBAM) is incorporated into the EfficientNetB0 architecture to improve feature representation for multi-class brain tumor classification. The performance of the proposed model is evaluated against the baseline EfficientNetB0 under identical training and testing conditions. EfficientNetB0 with CBAM achieves a training accuracy of 99.76% and a validation accuracy of 99.45%, with corresponding training and validation losses of 0.0085 and 0.0241. On an independent test dataset, the model attains a test accuracy of 99.25% and a loss of 0.0207. In comparison, the baseline EfficientNetB0 model attains a training accuracy of 52.68%, validation accuracy of 46.20%, and test accuracy of 43.32%, accompanied by significantly higher loss values. At the class level, the proposed model demonstrates macro-average precision, recall, and F1-score of 0.99, whereas the baseline model yields macro-average values of approximately 0.54 for precision and recall, and 0.50 for F1-score. Although CBAM integration increases computational time per evaluation step from 395 ms to 601 ms, the marked improvement in classification accuracy and error reduction underscores the value of attention mechanisms. These results demonstrate that attention-based feature refinement significantly enhances deep learning performance for medical image classification, particularly in multi-class brain tumor diagnosis.
Model Pembelajaran Collaborative Digital Class Untuk Meningkatkan Kompetensi Robotika dan Web Development Siswa SMK Negeri 1 Gowa Fettyana Fettyana; Sheila Eunike Kakisina; Chris Batara; Muhammad Januansyah; Ahmad Fauzan
Ininnawa : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026): Volume 04 Nomor 01 (Mei 2026)
Publisher : Program Studi Manajemen FEB UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/9q63az75

Abstract

Perkembangan teknologi digital menuntut siswa SMK memiliki kompetensi di bidang Internet of Things (IoT), robotika, dan web development. Namun, pembelajaran yang masih bersifat konvensional menyebabkan keterampilan siswa belum berkembang secara optimal. Kegiatan ini bertujuan menerapkan model Collaborative Digital Class untuk meningkatkan kompetensi siswa SMK Negeri 1 Gowa Jurusan Teknik Komputer dan Jaringan serta Jurusan Elektro. Hasil kegiatan menunjukkan bahwa siswa mampu memahami konsep dasar IoT, robotika, dan web development, serta mampu membuat sistem monitoring sederhana berbasis web dan robot berbasis sensor. Selain meningkatkan kompetensi teknis, model pembelajaran ini juga meningkatkan kemampuan kolaborasi, kreativitas, dan pemecahan masalah siswa. Dengan demikian, model Collaborative Digital Class efektif digunakan sebagai pembelajaran inovatif untuk meningkatkan kompetensi teknologi digital siswa SMK.
Co-Authors Ahmad Fauzan At Taqwa, Abd Salam Ayutri, Wahyuni Chris Batara Fettyana Fettyana Fettyana Muh., Satriawan Muhammad Januansyah Puput, Kusuma Dewi