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All Journal JOIV : International Journal on Informatics Visualization JURNAL ILMIAH INFORMATIKA Warta Pengabdian Jurnal Informatika Universitas Pamulang JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal ICT : Information Communication & Technology JISKa (Jurnal Informatika Sunan Kalijaga) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of Innovation Information Technology and Application (JINITA) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Ilmu Komputer untuk Masyarakat Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Journal of Informatics and Computing
Rizal, Randi
Informatika, Universitas Siliwangi, Tasikmalaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
VISUAL ENTITY OBJECT DETECTION SYSTEM IN SOCCER MATCHES BASED ON VARIOUS YOLO ARCHITECTURE Althaf Pramasetya Perkasa, Mochamad; El Akbar, R. Reza; Al Husaini, Muhammad; Rizal, Randi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2015

Abstract

In this study, a performance comparison between the YOLOv7, YOLOv8, and YOLOv9 models in identifying objects in soccer matches is conducted. Parameter adjustments based on GPU storage capacity were also evaluated. The results show that YOLOv8 performs better, with higher precision, recall, and F1-score values, especially in the "Ball" class, and an overall accuracy (mAP@0.5) of 87.4%. YOLOv9 also performs similarly to YOLOv8, but YOLOv8's higher mAP@0.5 value shows its superiority in detecting objects with varying degrees of confidence. Both models show significant improvement compared to YOLOv7 in overall object detection performance. Therefore, based on these results, YOLOv8 can be considered as the model that is close to the best performance in detecting objects in the dataset used. This study not only provides insights into the performance and characteristics of the YOLOv7, YOLOv8, and YOLOv9 models in the context of object detection in soccer matches but also results in a dataset ready for additional analysis or for training deep learning models.
ENSEMBLE MACHINE LEARNING WITH NEURAL NETWORK STUNTING PREDICTION AT PURBARATU TASIKMALAYA Al-Husaini, Muhammad; Lukmana, Hen Hen; Rizal, Randi; Puspareni, Luh Desi; Hoeronis, Irani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2421

Abstract

This research uses an ensemble model and neural network method that combines several machine learning algorithms used in the prediction of stunting and nutritional status children in Purbaratu Tasikmalaya. This ensemble method is complemented by a combination of the prediction results of several algorithms used to improve accuracy. The data used is anthropometry-based calculations of 195 toddlers with 39% of related stunting from 501 total data in Purbaratu Tasikmalaya City; high rates of stunting this research urgent to make a stable model for prediction. The results of this study are significant as they provide a more accurate and efficient method for predicting stunting and nutritional status in children, which can be crucial for early intervention and prevention strategies in public health and nutrition. The best accuracy value for some of these categories is 98, 21% for the Weight/Age category with the xGBoost algorithm, 97.7% of the best accuracy results with the Random Forest and Decision Tree algorithms for the Height/Age category, the Weight/Height category with the best accuracy of 97.4% for the Random Forest and xGBoost algorithms, and the use of neural network models resulted in an accuracy of 99.19% for Weight/Age and Height/Age while for Weight/Height resulted in an accuracy of 91.94%..
Co-Authors Al Husaini, Muhammad Al-Husaini, Muhammad Alam Rahmatulloh Aldya, Aldy Putra Althaf Pramasetya Perkasa, Mochamad Amien, Nashihun Candra, Fikri El-Akbar, R Reza Faisal Muhammad Dzikry Gunawan, Rohmat Indra Karyana Irani Hoeronis Komarudin, Arif Maulana Lukmana, Hen Hen Mohd. Zaki Mas’ud Nugraha, Cindera Syaiful Nur Widiyasono, Nur Nursihab, Cucu Oumarou, Hayatou Pendit, Ulka Chandini Purwayoga, Vega Puspareni, Luh Desi R. Reza El Akbar Rahmi Nur Shofa Ramli, Nuraminah Rasi Nuraeni Rianto Rianto Rianto, Rianto Rifki Maulana Insan Rizal Kurniawan Rochim, Rachma Verina Ruuhwan Ruuhwan Selamat, Siti Rahayu Septian Chandra Siti Annisa, Siti Siti Rahayu Selamat Siti Yuliyanti, Siti Sudiarjo, Aso Tiara Komala Sutra, Melanda Yahdi Siradj Yusuf Sumaryana