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All Journal Jurnal Sains dan Informatika Building of Informatics, Technology and Science
Arjun, Restu Agil Yuli
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Computer Science & IT Engineering Mathematics

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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.
Pemanfaatan Model YOLOv8 Untuk Mendeteksi Plat Nomor Kendaraan Mobil Pada Gerbang Masuk Universitas XYZ Silmina, Esi Putri; Arjun, Restu Agil Yuli
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.916

Abstract

Kendaraan bermotor merupakan salah satu transportasi yang sering digunakan oleh masyarakat Indonesia. Dalam penggunaan kendaraan bermotor ini, diperlukan adanya plat nomor sebagai Tanda Kendaraan Bermotor (TNKB) yang memuat kode wilayah, nomor registrasi, masa berlaku, dan memenuhi spesifikasi yang diatur. Sistem deteksi otomatis plat nomor kendaraan perlu disadari menjadi hal yang dibutuhkan guna memudahkan pencatatan, pengawasan, dan identifikasi kendaraan. Tujuan penelitian ini untuk mendeteksi plat nomor kendaraan yang masuk ke dalam lingkungan kampus Universitas XYZ. Metode penelitian yang digunakan yaitu pengumpulan data, anotasi data, pembagian data, preprocessing data, pelatihan model, implementasi model, dan pengujian model pada sistem. Tujuan penelitian ini untuk melakukan pendeteksian plat nomor kendaraan jenis mobil yang masuk ke lingkungan kampus Universitas XYZ menggunakan model YOLOv8. Hasil penelitian menunjukkan algoritma YOLOv8 dengan PaddleOCR memberikan nilai performansi yang sangat baik dengan nilai hasil pelatihan model mendapatkan mAP50 sebesar 98.9%, precision 98,8%, recall sebesar 96.5%, dan akurasi sistem sebesar 90% dalam skala likert.
Co-Authors Silmina, Esi Putri