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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian Masyarakat IPTEK International Journal of Research in Vocational Studies (IJRVOCAS)
Friendly
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Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology 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 Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Speed and Accuracy Comparison of Person Detection Using Pretrained CenterNet and Yolov3 Friendly; Harizahayu; Sembiring, Zakaria
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 3 No. 4 (2024): IJRVOCAS - Special Issues - International Conference on Science, Technology and
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v3i4.17

Abstract

Implementations of object detection by using common devices for general purpose application is not widely used for speed and prone to errors. Most of research in speed detection using known method such as Centernet, Yolov3, Fast-RNN has variant result since the computer used are different. This experiment try to conduct experiments for person detection with Centernet and Yolov3 method using commonly used computer only using CPU. Based on the experiments, Yolov3 can give a much better precision for person detection by 98.42% of mAP point while Centernet only 97%. In terms of processing speed, Centernet can give much better speed where it can detect a person in average 370ms better than Yolov3 with average of 1050 ms or 1 second.
Predictive Modeling of Preeclampsia Risk Using Random Forest Algorithm within a Machine Learning Framework Harizahayu; Friendly; Purwo Seputro , Bintarto; Benar; Hermanto, Koko
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4779

Abstract

Preeclampsia is a serious pregnancy complication characterized by high blood pressure, potentially leading to organ damage, making early risk prediction crucial to reducing maternal morbidity and mortality. This study aims to develop a preeclampsia risk prediction model using medical and clinical data from 80 patients at Rumah Bersalin Sadan. The data include demographic profiles, blood pressure, weight, maternal age, preeclampsia history, body mass index, number of previous pregnancies, as well as genetic and environmental factors. The dependent variable is the risk of preeclampsia, either as a binary outcome (yes/no) or as a continuous risk score. The predictive model was built using multivariate linear regression and the Random Forest algorithm. The results showed that the Random Forest model achieved an accuracy of 65.22%, with an F-statistic of 7.345 and a very small p-value (1.908e-06), indicating that the model effectively explains data variability. However, the low Kappa value suggests room for improvement through feature refinement, hyperparameter tuning, or exploring other algorithms. Although these findings suggest that Random Forest is a promising method, further evaluation and model optimization are needed to enhance predictive performance and determine whether this method is the most suitable for the dataset used.
Perancangan Teknologi Multimedia Pada Mitra Komunitas Generasi Baru Indonesia Komisariat Politeknik Negeri Medan Supriadi Chan, Andi; Friendly; Bastanta Tarigan, Andre; Daniel Sinaga, Jose; Dwi Ilham, Muhammad; Karerina Kristanti Sianturi, Yessi
Jurnal Pengabdian Masyarakat IPTEK Vol. 5 No. 1 (2025): Edisi Januari 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/abdi.v5i1.10569

Abstract

Teknologi pada masa sekarang sudah menjadi salah satu aspek yang harus ada dalam setiap kehidupan umat manusia. Hal ini karena keberadaannya yang memberi kemudahan dalam semua aktivitas termasuk dalam hal pembelajaran. Dalam kegiatan Pengabdian ini, dilakukan dengan Komunitas Generasi Baru Indonesia (GENBI) komisariat Politeknik Negeri Medan. Divisi Pendidikan dan Kebudayaan sebagai mitra bertujuan untuk menerapkan teknologi multimedia sebagai media belajar pada pengenalan serta perawatan mata uang rupiah dan pengenalan komunitas Genbi komisariat Politeknik Negeri Medan berupa video edukasi, video company profile serta permainan edukasi. Divisi pendidikan dan kebudayaan GenBI komisariat Politeknik Negeri medan memiliki potensi dalam mendukung perkembangan media sosialisasi materi pengenalan dan perawatan rupiah dengan teknologi multimedia. Metode Pelaksanaan mencakup sosialisasi hasil, penerapan teknologi dan pendampingan.
Co-Authors Andi Supriadi Chan, Andi Supriadi Bastanta Tarigan, Andre Benar Daniel Sinaga, Jose Dwi Ilham, Muhammad Harizahayu, Harizahayu Hermanto, Koko Karerina Kristanti Sianturi, Yessi Purwo Seputro , Bintarto Sembiring, Zakaria