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All Journal Techno.Com: Jurnal Teknologi Informasi CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Journal of ICT Research and Applications Journal of Information Systems Engineering and Business Intelligence JOIV : International Journal on Informatics Visualization Indonesian Journal of Information System Teknika ABDIMASTEK
Joko Siswantoro
Departement Of Informatics Engineering, Faculty Of Engineering, Universitas Surabaya, Jalan Raya Kali Rungkut, Surabaya, 60293
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Implementasi Game Berbasis Hand Gesture untuk Pelatihan dan Evaluasi Motorik Halus Anak di TK Yasmin Jember Arwoko, Heru; Siswantoro, Joko; Asmawti, Endah; Ariyani, Sofia
ABDIMASTEK Vol. 4 No. 1 (2025): Juli
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/abdimastek.v4i1.3554

Abstract

Kemampuan motorik halus merupakan aspek penting dalam perkembangan anak usia karena berperan dalam keterampilan dasar seperti menulis, menggambar, dan memegang objek kecil. Namun, metode pelatihan konvensional sering kali kurang menarik dan tidak menyediakan umpan balik objektif terhadap perkembangan anak. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan dan mengevaluasi keterampilan motorik halus anak melalui media game edukatif berbasis pengenalan gesture tangan (hand gesture) secara real-time. Program diterapkan di TK Yasmin Jember dengan melibatkan 32 anak usia 4–6 tahun. Game dirancang agar anak memindahkan objek virtual menggunakan gerakan tangan yang direkam dan dianalisis oleh sistem berbasis kamera. Data yang diperoleh diklasifikasikan ke dalam kategori performa (Baik, Cukup, Kurang) dan dibandingkan dengan penilaian guru. Hasil menunjukkan tingkat kesesuaian sebesar 93,75% antara klasifikasi sistem dan observasi manual guru. Selain peningkatan koordinasi tangan-mata dan fokus atensi, media ini juga mendapat tanggapan positif dari guru dan orang tua sebagai sarana pembelajaran inovatif. Temuan ini mengindikasikan bahwa pendekatan aplikasi berbasis gesture memiliki potensi sebagai alat bantu yang efektif, menyenangkan, dan objektif dalam mendukung perkembangan motorik anak usia dini.
Transliterating Javanese Script Images to Roman Script using Convolutional Neural Network with Transfer Learning Naufal, Mohammad Farid; Siswantoro, Joko; Soebroto, Juan Timothy
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2566

Abstract

The Javanese script holds immense cultural significance within Indonesia despite its diminishing usage in contemporary contexts. Its presence remains notable in specific regions of Java and remains integral to many historical documents and texts. Consequently, there is an urgent need for a transliteration system adept at converting Javanese script into contemporary scripts like Roman or Indonesian, thereby contributing to preserving Java's linguistic and cultural legacy. However, reading or transliterating Javanese script can be time-consuming, especially for longer texts, presenting considerable challenges for non-native readers. This study aims to develop an effective transliteration system for converting Javanese script into Roman script. This system addresses the pressing need to preserve Java's linguistic and cultural heritage by facilitating the readability and accessibility of Javanese script, especially for non-native readers. This study introduces an Optical Character Recognition (OCR) system tailored to identify Javanese script characters and transcribe them into Roman characters, explicitly focusing on fundamental nglegena and sandhangan swara characters. Individual characters are isolated by leveraging horizontal and vertical projection techniques, facilitating subsequent classification using a Convolutional Neural Network (CNN) employing transfer learning methodologies. The system's achievement of an impressive average similarity score of 90.78% is noteworthy, with the Xception architecture demonstrating superior efficiency in transliteration tasks. Implementing such a system harbors significant promise in safeguarding the Javanese script and enhancing its accessibility to a broader audience. This research contributes substantially to preserving and propagating Indonesia's rich cultural and linguistic heritage amidst the digital age.
Co-Authors Anasthasya Averina Anton Satria Prabuwono Anton Satria Prabuwono Arwoko, Heru Asmawti, Endah Azizi Abdullah Azizi Abdullah Bahari Indrus Edwin Indarto Helen Hadi Januar Rahmadiarto Jessica Millenia Mohammad Farid Naufal Mohammad Farid Naufal Mohammad Farid Naufal, Mohammad Farid Monica Widiasri, Monica Muhammad Alifian Fajar Pratama Muhammad Ghifari Kusuma Wicaksono Soebroto, Juan Timothy Sofia Ariyani Vincentius Riandaru Prasetyo