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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Masyarakat Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ekonomi Bisnis dan Kewirausahaan Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Innovation in Research of Informatics (INNOVATICS) International Journal of Artificial Intelligence and Robotics (IJAIR) Advance Sustainable Science, Engineering and Technology (ASSET) Devotion: Journal of Research and Community Service JS (Jurnal Sekolah) Jurnal Ekonomi Bisnis dan Kewirausahaan JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Sri Winarno
Faculty of Information Science and Technology, Multimedia University
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Classification of Key and Time Signature in Western Musical Notation by using CRNN Algorithm with Bounding Box Soeroso, Dennis Adiwinata Irwan; Winarno, Sri; Luthfiarta, Ardytha; Aryanti, Firda Ayu Dwi
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.v6i4.6510

Abstract

This research seeks to employ the Convolutional Recurrent Neural Network (CRNN) algorithm to develop a method for classifying key and time signatures from sheet music images. The research design involved compiling a dataset of 285 sheet music images, which includes 15 types of key signatures and 19 types of time signatures. The methodology encompasses annotation using the bounding box technique, image preprocessing, and applying the CRNN model for classification using K-Fold Cross Validation because of the limited dataset. Then, the model is evaluated using the Multi Class Confusion Matrix and performance metrics. The primary findings of this study reveal that the developed model achieves 96% accuracy in key signature classification and 95% in time signature classification when utilizing bounding boxes. Conversely, the absence of bounding boxes substantially negatively impacted the accuracy of key signature classification, resulting in only a 58% accuracy rate. Time signature classification performed even worse, with an accuracy of just 19%. This research highlights the substantial accuracy enhancements achievable by incorporating bounding boxes. Therefore, we anticipate that this research will help singers, especially those in choirs, to understand and express music better using existing technologies while enhancing the accuracy of optical music recognition using the CRNN model.
Optimasi Algoritma Decision Tree Menggunakan GridSearchCV untuk Klasifikasi Tipe Obesitas Laurent, Feby; Winarno, Sri; Dewi, Ika Novita
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.v7i3.8638

Abstract

The rise in obesity cases in various countries, including Indonesia, has become a serious public health problem because it increases the risk of chronic diseases and affects individuals' psychological aspects. One of the main challenges in obesity management is the differences in obesity types in each individual, which are influenced by various factors. Therefore, accurate classification methods are needed to ensure more targeted treatment. In this context, machine learning-based technology is a potential solution for classifying obesity types. However, variations in individual characteristics make the classification process complex, as models often struggle to accurately distinguish obesity types. To overcome this problem, the Decision Tree algorithm was chosen because of its easy-to-interpret results. However, using Decision Tree with default parameters on datasets with many attributes and high variation tends to cause overfitting and decrease accuracy. Furthermore, Decision Tree performance is highly dependent on hyperparameter settings, requiring optimization techniques to achieve optimal results. Based on this, this study aims to optimize the Decision Tree algorithm using GridSearchCV to obtain the most optimal parameters to improve model performance in obesity type classification. The dataset used is from the UCI Machine Learning Repository, consisting of 2,111 rows of data and 17 attributes. Based on the initial test results, the default model achieved 92.58% accuracy, 92.58% recall, 92.66% precision, and 92.56% F1-score. After optimization, the accuracy increased to 95.69%, 95.69% recall, 95.72% precision, and 95.67% F1-score. The 3.1% increase in accuracy demonstrates the effectiveness of GridSearchCV in improving Decision Tree performance, resulting in a more accurate and stable prediction model. This research is expected to contribute as a basis for decision-making in early detection and prevention and treatment of obesity more efficiently and effectively.
Co-Authors Abiyyi, Ryandhika Bintang Adhitya Nugraha Al-Azies, Harun Alzami, Farrikh Anatri Desstya Andrean, Muhammad Niko Ardytha Luthfiarta Arga Retha, Helynda Mulya Ariansyah, M. Hafidz Aryanti, Firda Ayu Dwi Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Ayu Harini, Pradhita Rizka Cahya, Leno Dwi Cahyani, Ailsa Nurina Erika Devi Udayanti Fadlullah, Rizal Farandi, Muhammad Naufal Erza Fauzyah, Zahrah Asri Nur Fikri Budiman Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Guruh Fajar Shidik Harun Al Azies Hastuti, Tri Puji Ibad, M. Azka Khoirul Ika Novita Dewi Indra Gamayanto Junta Zeniarja Kamarudin, Fatkhurridlo Pranoto Khoirunnisa, Emila Krisna, Julius Immanuel Theo Kurniawan, Defri Kuswidiani, Erika Widya Laksono, Giffari Ilham Laurent, Feby Maulani, Ahmad Alaik Megantara, Rama Aria Muhammad Naufal, Muhammad Muttaqin, Almas Najiib Imam Nur Fitri, Esmi Pangestu, Aditya Gilang Pratama, Raffy Nicandra Putra Pratama, Rifky Ariya Pratiwi, Yunita Ayu Pulung Nurtantio Andono Putra, Permana Langgeng Wicaksono Ellwid Putri, Rusyda Tsaniya Eka Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rony, Zahara Tussoleha Rusnandari Retno Cahyani Sabilillah, Ferris Tita Sasono Wibowo Senata, Denny Soeroso, Dennis Adiwinata Irwan Sukamto, Titien Suhartini Sulistyono, Teguh Syifa Nurazizah, Syifa Wardhana, Faviola Proba Wijaya, Tan Nicholas Octavian Yudantiar, Mayang Arinda Zami, Farrikh Al