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All Journal Jurnal Simetris Kompak : Jurnal Ilmiah Komputerisasi Akuntansi E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING Krea-TIF: Jurnal Teknik Informatika EDUMATIC: Jurnal Pendidikan Informatika EVOLUSI : Jurnal Sains dan Manajemen JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi dan Komunikasi Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Journal of Engineering, Electrical and Informatics (JEEI) Dinamika Jurnal Publikasi Ilmu Komputer dan Multimedia Jurnal Teknik Informatika dan Teknologi Informasi JURNAL AKUNTANSI DAN BISNIS Community: Jurnal Pengabdian Pada Masyarakat Journal of Technology Informatics and Engineering Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Scientific Journal of Informatics Switch: Jurnal Sains dan Teknologi Informasi Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan Inovasi Sosial: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika dan Teknologi Informasi
Sindhu Rakasiwi
Universitas Sains Dan Teknologi Komputer
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Model SVM dengan Ekstraksi Fitur ResNet50 untuk Identifikasi Sel Darah Terinfeksi Malaria Adhesyah Putra, Maulana Damar; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8750

Abstract

Malaria remains a major public health challenge in Indonesia, with 279,865 reported cases in 2023 and an Annual Parasite Incidence (API) of 0.99 per 1,000 population. Although microscopic examination is still considered the gold standard for malaria diagnosis, it has several limitations, including dependency on trained experts, subjective interpretation, and relatively lengthy processing time. To address these challenges, this study aims to analyze the performance of a Support Vector Machine (SVM) classifier with feature extraction based on ResNet50 in a Computer-Aided Diagnosis (CAD) system for automatic detection of malaria-infected blood cells.ResNet50 was selected for its transfer learning capability to generate high-level feature representations from medical images, while SVM was chosen due to its strong performance on high-dimensional data and limited datasets. A feature vector of 2048 dimensions produced from the global average pooling layer was classified using SVM with a Radial Basis Function (RBF) kernel. The dataset used in this study was obtained from the National Institutes of Health (NIH) and consists of 27,558 microscopic blood cell images (Parasitized and Uninfected classes). The data were partitioned using stratified sampling with an 80:20 ratio for training and testing. Preprocessing steps included pixel normalization, resizing to 224×224 pixels, and basic augmentation to improve model generalization. Experimental results show that the proposed model achieved an accuracy of 93.94%, precision of 94%, recall of 93.43% (Parasitized) and 94.46% (Uninfected), and an average F1-score of 94%. The confusion matrix indicates 2,575 true positives, 2,606 true negatives, 153 false positives, and 181 false negatives, with a false negative rate of 6.57% and a false positive rate of 5.54%. These findings demonstrate that the combination of ResNet50 and SVM has strong potential as a fast and accurate image-based malaria detection method and is suitable for implementation in healthcare settings with limited resources.
Pendekatan Ensemble Multi-Arsitektur Convolutional Neural Network melalui Soft Voting untuk Klasifikasi Citra Histopatologi Kanker Payudara Fitriyani, Shelomita; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8797

Abstract

Breast cancer is one of the leading causes of mortality among women, creating a strong need for diagnostic methods that are accurate, consistent, and capable of handling the morphological variations present in histopathological images. This study aims to improve the stability and accuracy of breast cancer histopathology image classification through an ensemble multi-architecture Convolutional Neural Network approach. The BreakHis dataset, which consists of four magnification levels 40×, 100×, 200×, and 400× was used in this research. Three architectures, VGG19, ResNet50, and EfficientNetB0, served as the base models. All images underwent preprocessing, including resizing to 224×224 pixels, pixel-intensity normalization, and data augmentation. Each model was trained independently, and their probability outputs were combined using a soft voting mechanism to generate the final predictions. The experimental results show that the ensemble method provides the most stable and superior performance across all magnification levels. At 40× magnification, the ensemble achieved an accuracy of 92.00%, recall of 99.03%, and F1-score of 94.44%. At 100× magnification, the accuracy increased to 94.56%, with a recall of 99.07% and an F1-score of 96.18%. The 200× level produced an accuracy of 94.03%, recall of 97.61%, and an F1-score of 95.77%. Meanwhile, at 400× magnification, the model reached an accuracy of 90.11%, recall of 95.14%, and an F1-score of 92.88%. These consistently high recall and F1-score values highlight the model’s strong ability to detect malignant cases while maintaining balanced predictive performance. Overall, the findings demonstrate that combining multiple CNN architectures enhances feature representation and shows strong potential as a decision-support system for breast cancer diagnosis using histopathological images.
Co-Authors Abu Salam Adhesyah Putra, Maulana Damar Agus Cahyo Pangestu Agustinus Budi Santoso Albastomi, Taqius Shofi Andi Dharu Permana Andriana, Myra Arifin, Muhammad Farhan Ariyanto, Noval Arya Erlangga Astuti, Yani Parti budi hartono Cahaya Jatmiko Cahaya Jatmoko Cahyo Pangestu , Agus Candra Irawan Catur Supriyanto Daurat Sinaga Deddy Award Widya Laksana Dewi Agustini Santoso Dzaky, Azmi Abiyyu Edi Sugiarto Edwin Zusrony Edy Mulyanto Egia Rosi Subhiyakto Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Erna Zuni Astuti Erwin Yudi Hidayat Etika Kartikadarma Febryantahanuji Febryantahanuji Feri Agustina Fikri Budiman Fitriyani, Shelomita Guruh Fajar Shidik Haresta, Alif Agsakli Haryo Kusumo Haryo Kusumo Haryo Kusumo Heribertus Himawan Heru Lestiawan Ifan Rizqa Ika Novita Dewi Indra Laila Intan Nurul Alfiani Isnaini Khusnul Khotimah Jarot Dian Susatyono Jarot Dian Susatyono Jatmiko, Cahaya Junta Zeniarja Khani, Nadia Ifti Kurniawan, Defri Kusumo , Haryo Kusumo, Haryo Lalang Erawan Lalang Erawan Lutfi Ubaidillah Marjuni, Aris Moh Muthohir Mulyanto, Edy Munifah Murwoko, F Iwan Setyo Myra Andriana Norman, Maria Bernadette Chayeenee Nova Rijati Nur Rokhman Octaviani, Dhita Aulia Paramita, Cinantya Pulung Nurtantio Andono Putri, Chana Amelinda Rafsanjani, Muhammad Ivan Rifal Winazar Rifal Winazar Roymon Panjaitan Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Shier Nee Saw Sinaga, Daurat Sri Wahyuning Suprapti suprayogi Suprayogi Suprayogi Syah Putra, Fernanda Mulya T.Sutojo Tantik Sumarlin . Taqius Shofi Albastomi Taufik Kurnialensya Triginandri, Rifqi Ubaidillah , Lutfi Utomo, Danang Wahyu Widya Laksana, Deddi Award Yani Parti Astuti Yuli Fitrianto