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All Journal IJID (International Journal on Informatics for Development) Jurnal ABDI PAUD
Inggrid Yanuar Risca Partiwi
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences Other

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Sinergi Pendidikan Vokasi Dan Paud: Pelatihan Pembuatan Game Jumper Di Smk N 1 Pleret Bantul Fauzia Anis Sekar Ningrum; Ajie Kusuma Wardhana; Muhammad Ainul Fikri; Inggrid Yanuar Risca Partiwi; Yudha Riwanto
Jurnal ABDI PAUD Vol. 5 No. 2 (2024): DESEMBER
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/abdipaud.v5i2.40087

Abstract

Pendidikan vokasi memiliki peran penting dalam mencetak lulusan yang siap kerja, namun masih menghadapi tantangan ketidaksesuaian kompetensi dengan kebutuhan industri. Salah satu sektor yang membutuhkan inovasi pendidikan vokasi adalah Pendidikan Anak Usia Dini (PAUD), terutama dalam pengembangan media pembelajaran interaktif. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan keterampilan siswa SMK N 1 Pleret Bantul dalam pembuatan game edukatif berbasis Construct 2. Workshop ini melibatkan 30 siswa serta dosen dari Universitas Amikom, Politeknik Negeri Jember, dan Politeknik Negeri Malang. Metode yang digunakan meliputi ceramah, pelatihan langsung, serta bimbingan desain. Hasil pengabdian menunjukkan bahwa siswa memperoleh pemahaman dan keterampilan teknis dalam pembuatan game edukatif "Game Jumper", yang dirancang untuk mendukung tumbuh kembang anak PAUD. Selain itu, kegiatan ini membuka peluang kolaborasi antara siswa SMK dengan tenaga pendidik PAUD. Dengan demikian, pengabdian ini tidak hanya meningkatkan kompetensi siswa tetapi juga menunjukkan bahwa pendidikan vokasi dapat berkontribusi dalam sektor pendidikan anak usia dini, mendukung literasi digital, dan memperluas peluang kerja bagi lulusan SMK.
Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches Muhammad Ainul Fikri; Ajie Kusuma Wardhana; Yudha Riwanto; Inggrid Yanuar Risca Partiwi; Fauzia Sekar Anis Sekar Ningrum; Putra, Iqbal Kurniawan Asmar
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4890

Abstract

Osteosarcoma is an aggressive and highly malignant bone cancer primarily affecting adolescents and young adults, with males being more commonly affected. Although deep learning models such as YOLO (95.73% accuracy) and VGG19 (95.25% accuracy), have demonstrated effectiveness in osteosarcoma detection, their large model sizes and extensive computational requirements limit their feasibility in resource-constrained environments. This study proposes a lightweight AI approach that optimizes osteosarcoma detection while maintaining high diagnostic accuracy, leveraging machine learning models under 5MB, manually or semi-automatically extracted features, and SMOTE for data balancing. Experimental results show that Random Forest, SVM, and XGBoost achieve accuracies of 94.70%, 94.23%, and 94.39%, respectively, closely matching the performance of YOLO and VGG19 while maintaining computational efficiency. Furthermore, the inference time for SVM is under one second (0.97s), demonstrating the speed advantage of lightweight models. These findings highlight the potential of small-size (lightweight) machine learning models to deliver high diagnostic accuracy with minimal computational requirements, providing a scalable and practical solution for early osteosarcoma detection in resource-limited settings. By balancing simplicity, efficiency, and high performance, this study establishes a new benchmark for achieving state-of-the-art results with lightweight models and paving the way for improved healthcare accessibility in underserved regions.
Co-Authors Ajie Kusuma Wardhana Fauzia Anis Sekar Ningrum Fauzia Sekar Anis Sekar Ningrum Muhammad Ainul Fikri Putra, Iqbal Kurniawan Asmar Yudha Riwanto