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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal Pengabdian UntukMu NegeRI Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Applied Engineering and Technological Science (JAETS) Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Pendidikan dan Teknologi Indonesia Jurnal Abdi Masyarakat Indonesia Jurnal Pengabdian West Science Jurnal Abdimas Maduma Jurnal Elektronika dan Telekomunikasi
Hadipurnawan Satria
Department Of Informatics, Faculty Of Computer Science, Universitas Sriwijaya
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Pengelompokan Level Hipertensi Berbasis Tekanan Darah Menggunakan Algoritma K-Means Clustering Asoka, Egga; Fathoni, Fathoni; Satria, Hadipurnawan; Anggina, Edith
Jurnal Pendidikan dan Teknologi Indonesia Vol 6 No 3 (2026): JPTI - Maret 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1477

Abstract

Hipertensi merupakan salah satu komponen utama sindrom metabolik yang berkontribusi signifikan terhadap peningkatan risiko penyakit kardiovaskular. Deteksi dini dan pemetaan tingkat hipertensi menjadi penting untuk mendukung intervensi medis yang tepat. Penelitian ini bertujuan untuk menerapkan algoritma unsupervised learning Algoritma K-Means Clustering dalam mengelompokkan individu berdasarkan parameter tekanan darah sistolik dan diastolik. Dataset yang digunakan terdiri dari 1.878 catatan pasien, yang setelah proses pembersihan data menghasilkan 1.575 data unik. Data distandarisasi menggunakan StandardScaler, dan jumlah klaster optimal ditentukan melalui metode Elbow. Hasil klasterisasi menunjukkan empat klaster utama yang merepresentasikan segmentasi alami tekanan darah, mulai dari tekanan darah rendah hingga tinggi. Visualisasi dua dimensi dan reduksi dimensi menggunakan Principal Component Analysis (PCA) memperlihatkan pemisahan klaster yang relatif jelas. Temuan ini menunjukkan bahwa K-Means mampu mengidentifikasi struktur laten data tekanan darah secara objektif dan berpotensi menjadi dasar pengembangan sistem pendukung keputusan medis berbasis data untuk stratifikasi risiko hipertensi.
Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks Julian Supardi; Samsuryadi Samsuryadi; Hadipurnawan Satria; Philip Alger M. Serrano; Arnelawati Arnelawati
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.653

Abstract

Remote sensing imagery is a very interesting topic for researchers, especially in the fields of image and pattern recognition. Remote sensing images differ from ordinary images taken with conventional cameras. Remote sensing images are captured from satellite photos taken far above the Earth's surface. As a result, objects in satellite images appear small and have low resolution when enlarged. This condition makes it difficult to detect and recognize objects in remote-sensing images. However, detecting and recognizing objects in these images is crucial for various aspects of human life. This paper aims to address the problem of remote sensing image quality. The method used is a convolutional neural network. The results show the proposed method can improve PSNR and SSIM compared to previous methods
Pose Estimation Frameworks in Healthcare: A Systematic Review Egga Asoka Asoka; Fathoni Fathoni; Hadipurnawan Satria; Indra Griha Tofik Isa
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9779

Abstract

Human pose estimation has become increasingly important in healthcare applications such as fall detection, gait analysis, and rehabilitation monitoring. However, existing systematic reviews remain fragmented and largely descriptive, with limited comparative benchmarking and insufficient attention to clinical validation. This study addresses this gap by providing a structured comparison of major pose estimation frameworks in healthcare contexts. A systematic literature review was conducted using the PICOC framework and PRISMA guidelines. Studies published between 2020 and 2025 were retrieved from Scopus, Web of Science, IEEE Xplore, and PubMed based on predefined inclusion and exclusion criteria. Following screening and quality assessment, 41 studies were included in the final analysis. The results indicate that framework performance varies according to application requirements. OpenPose offers high anatomical precision but requires substantial computational resources, whereas MoveNet and MediaPipe enable real-time performance with lower latency, making them suitable for mobile and telehealth settings. Nevertheless, the evidence remains heterogeneous, with challenges related to occlusion, lighting variability, lack of standardized datasets, and limited real-world clinical validation. This study contributes by providing a theoretical synthesis and practical guidance for selecting appropriate pose estimation frameworks in healthcare applications.
Co-Authors Abdurahman Ahmad Nizar Ahmad Zarkasi Anggina, Edith Anggun Islami Arnaldo, Muhammad Arnelawati Arnelawati Arnelawati, Arnelawati Assaidah Assaidah, Assaidah Deris Stiawan Dian Palupi Rini Egga Asoka Egga Asoka Asoka Ermatita - Fathoni Fathoni Fathoni Fathoni Hadir Kaban Hasby Rifky Indra Griha Tofik Isa Indradewa, Rhian Iskandar, Ananda Haykel Islami, Anggun Jambak, Muhammad Ihsan Jorena Julian Supardi Julian Supardi Khairul Saleh M. Nazhif Rizani Menik Ariani Mohammad Eko Fitrianto Muhammad Afif Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Ichsan Hadjri Muhammad Naufal Rachmatullah Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Nugraha, Wibisena Nuzulastri, Sari Octavianus Cakra Satya Osvari Arsalan Philip Alger M. Serrano Primanita, Anggina Rahmat Budiarto Richardo, M Denny Rifkie Primartha Rizki Kurniati Samsuryadi Sahmin Samsuryadi Samsuryadi Samsuryadi Samsuryadi Serrano, Philip Alger M. Siti Nurmaini Sony Oktapriandi Sutarno Sutarno Syahroyni, Muhammad Widya Aprilini Yos Karimudin