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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Pseudocode Jurnal Pengabdian UntukMu NegeRI Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika JRST (Jurnal Riset Sains dan Teknologi) Conference SENATIK STT Adisutjipto Yogyakarta CYBERNETICS Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Building of Informatics, Technology and Science Indonesian Journal of Business Intelligence (IJUBI) International Journal of Informatics and Computation Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Masyarakat Berdaya dan Inovasi Jurnal Pengabdian Masyarakat Kebidanan HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Jurnal Informatika Terpadu Journal of Information Technology (JIfoTech) JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Ilmu Komputer untuk Masyarakat PELS (Procedia of Engineering and Life Science) Digital Transformation Technology (Digitech) Jurnal Ilmiah Teknik Informatika dan Komunikasi Journal Of Artificial Intelligence And Software Engineering Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Silmina, Esi Putri
Program Studi Teknologi Informasi Universitas ‘Aisyiyah Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Deteksi Bahan Pangan Tinggi Protein Menggunakan Model You Only Look Once (YOLO) Arjun, Restu Agil Yuli; Silmina, Esi Putri
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.v6i4.6889

Abstract

Stunting has a high prevalence of 21.6% from the government target of 14% and is one of the health problems in Indonesia. Lack of nutrition, especially protein, is the main cause that plays a role in child growth. One of the preventive solutions is to provide protein-rich complementary foods (MP-ASI). To enhance this solution, technology that can swiftly and precisely identify high-protein food components is imperative. This research seeks to create a high-protein food detection model utilizing the YOLOv11 framework, chosen for its efficacy in object detection, particularly in intricate environments and with overlapping items. The research methodology includes several stages: dataset collection and annotation, data pre-processing, model training, model evaluation, and model testing. The dataset is divided into three parts: 70% for the training set, 20% for the validation set, and 10% for the test set. The YOLOv11s model is used for training. Evaluation is based on precision, recall, and mean Average Precision (mAP) metrics to ensure the model’s detection accuracy. The evaluation results indicate a precision of 96%, recall of 92.3%, mAP50 of 96.4%, and mAP50-95 of 81.5%. During testing, the model achieved a success rate of 98.2%. These results demonstrate the model’s potential in detecting protein-rich foods, which could significantly contribute to addressing malnutrition and stunting.
Deteksi Penyakit Paru-Paru Berdasarkan Gambar Citra X-Ray Menggunakan Arsitektur Convolutional Neural Network (Arsitektur Mobilenetv2) Syaifurrahman, Rizky; Silmina, Esi Putri
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7457

Abstract

The lungs are vital organs in the respiratory system that exchange gases, such as oxygen and carbon dioxide. However, poor air quality can lead to health problems, including lung diseases such as pneumonia, pneumothorax, lung cancer, and tuberculosis. The objective of this study is to develop an automatic detection model that uses the Convolutional Neural Network (CNN) architecture, specifically MobileNetV2, to classify X-ray images into five categories: four types of lung disease and normal lungs. The dataset consists of 2,500 images, which are divided into five classes: 80% for training, 10% for validation, and 10% for testing. Preprocessing includes resizing images to 224 x 224 pixels, normalizing pixel values, and using augmentation techniques to increase data variation. The resulting model demonstrated good performance, achieving a training accuracy of 98.76% and a validation accuracy of 97.20%. Evaluation using a confusion matrix yielded an overall F1 score of 0.94, with the highest value of 0.98 for pneumothorax. These results suggest that the model can accurately detect and classify lung diseases with an overall accuracy of 94.4%. This research significantly contributes to developing an automated lung disease detection system that can be implemented in web- or mobile-based applications and performs well across all classes.
Co-Authors Aditiya, Aqni Alam Almon Lanang Prasojo Anggit Widcaksono Anton Yudhana Aris Rakhmadi Arjun, Restu Agil Yuli Bin Riedho, Al Bukhari Cendana, Willy Pratama Dana, Syaiful Rachmat Fadlillah, Umi Fikamelyalla, Naura Firdonsyah, Arizona Firmansyah Hanafi Fajar Hidayati, Ririn Wahyu Intan Mutiara Putri Intan Putri Irwanda Budi Pangestu Laily Maghfira Luluk Rosida Mufreni, Sadr Lufti Perkasa, Legawan Retantyo Wardoyo Rovalia Adhella Attya Amanda Salsyabila Vidia Nur Afni Salsyabila Vidia Nur Afni Sri Lestari Linawati Sri Winiarti Sunardi Suyani Suyani Syaiful Rachmat Dana Syaifurrahman, Rizky Syarifah Najmah Tikaridha Hardiani Wahyudi, M. Ilham Wijayanto, Danur Willy Pratama Cendana Yuliana Putri Yusuf Sulistyo Nugroho