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All Journal Community Empowerment Bulletin of Computer Science Research
Harefa, Kecitaan
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Arts Humanities Computer Science & IT Education Public Health Social Sciences

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Sosialisasi Pemanfaatan Teknologi yang Dapat Digunakan Selama Masa Pandemi Covid-19 Rosyani, Perani; Rachmatika, Rinna; Harefa, Kecitaan; Herry, Ny. Ayni Suwarni; Priambodo, Joko
Community Empowerment Vol 6 No 3 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.066 KB) | DOI: 10.31603/ce.4525

Abstract

Pelaksanaan PKM dalam masa pandemi ini diperlukan teknologi – teknologi yang mendukung untuk tetap berjalannya kegiatan masyarakat. Maka dari itu, tujuan pengabdian ini adalah untuk mengenalkan tool–tool yang ada dalam Google seperti google form, google drive kepada karang taruna di Perumahan Pondok Karya, Pondok Aren. Kegiatan pengabdian dilaksanakan secara daring untuk menghindari kerumunan. Dengan adanya sosialisasi teknologi yang dapat digunakan selama masa pandemi ini, diharapkan para peserta dapat mensosialisasikan lagi kepada masyarakat agar dapat menghindari penyebaran wabah virus Covid-19 dan kegiatan perkumpulan warga dapat dialihkan dengan bantuan teknologi.
Pembuatan konten video pembelajaran menggunakan Filmora dan Youtube Herry, Ny. Ayni Suwarni; Rosyani, Perani; Rachmatika, Rinna; Harefa, Kecitaan; Priambodo, Joko
Community Empowerment Forthcoming issue
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.5043

Abstract

Kegiatan belajar mengajar secara daring menjadi kegiatan rutin selama masa pandemi Covid-19. Tidak terkecuali bagi guru-guru di SDN Pamulang Barat. Namun demikian, proses belajar hanya sebatas pemberian materi menggunakan Whatsapp. Hal ini sangat monoton dan membuat para murid menjadi jenuh. Oleh karena itu, pada kegiatan pengabdian masyarakat ini kami memberikan materi cara pembuatan konten video pembelajaran menggunakan aplikasi filmora dan Youtube. Tujuannya adalah agar para guru lebih kreatif dan inovatif dalam memberikan materi kepada para murid. Materi dapat disampaikan menggunakan suara atau dengan animasi-animasi menarik. Sehingga para guru dapat membuat video yang menarik untuk para murid dan meningkatkan minat belajar murid selama proses belajar daring.
Implementation of the LightGBM–CatBoost Ensemble Method for Obesity Risk Classification in Productive Age Harefa, Kecitaan; Priambodo, Joko
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.930

Abstract

Obesity is a health problem that continues to increase among individuals of productive age and has the potential to reduce quality of life and work productivity. One of the main challenges in obesity risk assessment is the limitation of conventional methods in accurately identifying obesity risk when dealing with complex, multidimensional data that include both numerical and categorical variables. Therefore, an artificial intelligence–based approach is required to provide a more accurate and stable obesity risk classification. This study aims to implement and evaluate a LightGBM–CatBoost ensemble method for obesity risk classification with a focus on the productive age population. The dataset used in this study was obtained from the Kaggle platform and consisted of 2,111 individual records containing physical attributes, eating habits, physical activity, and lifestyle factors. Although the dataset is synthetic and balanced, the included attributes and age-related variables are representative of individuals within the productive age range, making it suitable for modeling obesity risk in this demographic context. The research stages include data preprocessing, separate training of the LightGBM and CatBoost models, model integration using a probability averaging ensemble technique, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that both LightGBM and CatBoost achieved accuracy levels above 95%, while the ensemble model demonstrated superior performance with an accuracy of 96.69% and more balanced evaluation metrics across all obesity risk classes. These findings confirm that the ensemble approach improves classification stability and accuracy compared to single models. Therefore, the LightGBM–CatBoost ensemble method is effective for obesity risk classification and has the potential to be further developed as a decision support system in the health sector.
Co-Authors Herry, Ny. Ayni Suwarni Perani Rosyani Priambodo, Joko Rachmatika, Rinna