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All Journal Jurnal Pengembangan Riset dan Observasi Teknik Informatika JARES (Journal of Academic Research and Sciences) Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Manajemen Informatika dan Sistem Informasi Journal Zetroem JAMI: Jurnal Ahli Muda Indonesia Journal of Artificial Intelligence and Engineering Applications (JAIEA) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Science Contribution to Society Journal Jurnal Informatika Polinema (JIP)
Sri Lestanti
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Transportation

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PENERAPAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK KLASIFIKASI ALAT MUSIK TRADISIONAL DI BLITAR RAYA Razaan, Muhammad Rizal; sri lestanti; Udkhiati Mawaddah
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 9 No. 3 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/jkzynb61

Abstract

Penelitian ini dibuat dikarenakan menurunnya minat generasi muda terhadap alat musik tradisional, terutama di wilayah Blitar Raya, yang mengancam pelestarian budaya lokal. Untuk mengatasi hal ini, sistem klasifikasi gambar alat musik tradisioanal yang dikembangkan menggunakan algoritma Convolutional Neural Network (CNN). Studi eksperimental ini menggunakan bahasa pemrograman Python dan antarmuka visual yang dibangun dengan framework Streamlit. Dataset mencakup berbagai kategori alat musik tradisional dari daerah Blitar Raya. Model tersebut berfungsi dengan baik dalam mengklasifikasikan gambar alat musik, dengan hanya sedikit kesalahan penandaan. Antarmuka aplikasi sederhana, ramah pengguna, dan berfungsi secara efektif dalam melakukan analisis gambar. Penelitian ini menyoroti bagaimana pembelajaran mesin dapat mendukung pelestarian digital budaya lokal dan pendidikan. Hal ini juga menunjukkan potensi teknologi untuk meningkatkan kesadaran dan apresiasi terhadap alat musik tradisional, terutama di kalangan audiens muda saat ini.
SiPuTiH: Model Convolutional Neural Network untuk Sistem Pengenalan Tulisan Tangan Hijaiyah Saiful Nur Budiman; Sri Lestanti; Sandi Widya Permana
JAMI: Jurnal Ahli Muda Indonesia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v6i2.390

Abstract

This research presents the development of SiPuTiH (Handwritten Hijaiyah Character Recognition System) using the Convolutional Neural Network (CNN) algorithm to address the challenges of handwriting variability in Arabic scripts. The methodology includes dataset acquisition and preprocessing, CNN architecture design, model training, and performance evaluation. The dataset consists of 1,680 handwritten images representing 30 Hijaiyah characters, divided into 80% training and 20% testing data. The proposed CNN architecture employs four convolutional and pooling layers with a total of 6.8 million trainable parameters. Experimental results show that SiPuTiH achieved a 99.7% accuracy rate in recognizing Hijaiyah characters, with only one misclassification between ‘ta’ (ت) and ‘tsa’ (ث) due to morphological similarity. The trained model was implemented in an interactive Streamlit-based application that includes learning modules, quizzes, and real-time handwriting prediction. SiPuTiH demonstrates high reliability not only as a handwriting recognition system but also as an engaging educational platform for learning Arabic letters. This study confirms the effectiveness of CNNs in handling the morphological complexity of Hijaiyah characters and contributes to the development of intelligent educational tools. Future work may explore larger datasets, transfer learning architectures, and contextual (word-level) recognition to enhance system scalability and performance.
A Convolutional Neural Network Model for the Handwritten Hijaiyah Recognition System (SiPuTiH) with Domain-Specific Data Augmentation Sri Lestanti; Sandi Widya Permana; Nur Budiman, Saiful
JARES (Journal of Academic Research and Sciences) Vol 10 No 2 (2025): September 2025
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/jares.v10i2.5430

Abstract

This paper presents SiPuTiH, a Convolutional Neural Network (CNN)-based approach for handwritten Hijaiyah character recognition that addresses performance degradation caused by morphological variations in handwriting. The study employs a dataset of 1,680 handwritten images representing 30 Hijaiyah characters, where domain-specific data augmentation is applied solely during the training phase. The augmentation strategy incorporates controlled geometric and stroke-based transformations, including rotation, scaling, shear, slant variation, and stroke thickness adjustment, to model realistic handwriting diversity. The proposed CNN architecture consists of multiple convolutional layers with ReLU activation, max-pooling operations, and a softmax classifier. Experimental results show that the proposed method achieves an accuracy of 99.70%, with weighted precision and F1-score of 99.85% and 99.77%, respectively. Furthermore, the use of domain-specific data augmentation effectively reduces misclassification among visually similar characters, such as ta and tsa, demonstrating improved robustness and generalization capability.
Co-Authors Akhmad Alfan Fauzi Alviansyah, Alif Ardina Desi Susana Cita Puspita Inayati Dini Kustiari Dwi Lailatus Saadah Erwan Erwan, Erwan Febrinita, Filda Hesty Puspita Sari Huandito, Dion Tri Kusumawijayanti, Anita Reta Laili Neni Sutanti Nur Budiman, Saiful Pratama, Bagus Kurniawan Razaan, Muhammad Rizal Rizki Dwi Romadhona Sabitul Kirom Saiful Budiman Saiful Nur Budiman Salsa Billa Dewi Mahmudi6 Sandi Widya Permana Toni Hardian Udkhiati Mawaddah Yusniarsi Primasari Zunita Wulansari