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All Journal Cakrawala: Jurnal Pengabdian Masyarakat Global Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Farlin Wabula
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Sosialisasi dan Edukasi Penggunaan Aplikasi Presensi Berbasis Mobile Bagi Pegawai Non PNS di Lingkungan Puskesmas Kosambi untuk Meningkatkan Kedisiplinan Kerja Adih Adih; Bagas Syahputra; Obay Sobarnas; Farlin Wabula; Valentino Liu; Ade Irma Nizar; Sukron Anggara; Muhammad Azhar Prasetyo; Fashya Mulya; Purnamasari Purnamasari; Abdullah Muhajir
Cakrawala: Jurnal Pengabdian Masyarakat Global Vol. 3 No. 4 (2024): Cakrawala: Jurnal Pengabdian Masyarakat Global
Publisher : Universitas 45 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30640/cakrawala.v3i4.3332

Abstract

This community service activity aims to improve the discipline and efficiency of non-civil servant employees at Kosambi Public Health Center (Puskesmas) through the implementation of a mobile attendance application. Currently, the manual attendance system used at Kosambi Puskesmas has numerous limitations, such as susceptibility to record errors, lack of transparency, and inability to monitor attendance in real time. To address this, socialization and educational activities were conducted on the use of the mobile attendance application, which is expected to enhance employees’ understanding and encourage attendance discipline. The methods used in this activity include socialization, discussion, and hands-on training. The results indicate that implementing this application can help improve discipline and transparency in managing employee attendance and has the potential to enhance the quality of healthcare services provided to the community at Kosambi Puskesmas.
Literature Review : Penggunaan Machine Learning Berbasis SVM untuk Klasifikasi Penyakit Diabetes Adih Adih; Wahyu Aji Dwi Pangestu; Muhamad Fauzi Akbar; Purnamasari Purnamasari; Farlin Wabula; Ines Heidiani Ikasari
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 1 (2025): Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i1.616

Abstract

Diabetes is one of the diseases that poses a significant global health challenge, with a considerable impact on quality of life and mortality rates. This study examines the use of the Support Vector Machine (SVM) algorithm for diabetes classification through a literature review. SVM was chosen due to its ability to handle imbalanced and complex data. The aim of this study is to assess the effectiveness of SVM compared to other machine learning methods in detecting diabetes. The results of the literature review indicate that SVM achieves higher accuracy than other methods such as Naïve Bayes and Decision Tree, with some studies showing accuracy above 90%. This study is expected to provide deeper insights into the development of machine learning-based diagnostic systems for diabetes.
Co-Authors Abdullah Muhajir Ade Irma Nizar Adih Adih Bagas Syahputra Fashya Mulya Ines Heidiani Ikasari Muhamad Fauzi Akbar Muhammad Azhar Prasetyo Obay Sobarnas Purnamasari Purnamasari Sukron Anggara Valentino Liu Wahyu Aji Dwi Pangestu