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All Journal Modem : Jurnal Informatika dan Sains Teknologi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Wahyu Aji Dwi Pangestu
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Computer Science & IT

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Analisis K-Means Clustering pada Sistem Presensi Mobile dengan Fitur GPS Radius dan Foto Selfie untuk Pegawai Non-PNS di Puskesmas Kosambi Adih Adih; Wahyu Aji Dwi Pangestu; Muhamad Fauzi Akbar; Purnamasari Purnamasari; Saprudin Saprudin
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 3 No. 1 (2025): Januari : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v3i1.324

Abstract

Puskesmas Kosambi employs Non-PNS staff whose discipline, particularly regarding attendance and work location, needs to be evaluated. The previous manual attendance system was found to be ineffective in monitoring staff discipline. This study aims to develop a mobile-based attendance system equipped with GPS radius and selfie photo features to improve the accuracy and management of attendance. The GPS radius feature ensures that staff can only clock in within the designated area, such as the Puskesmas area, while the selfie photo feature verifies the identity of the staff member clocking in. This study involved 24 Non-PNS staff members and used the K-Means Clustering algorithm to group staff based on their discipline levels. The results showed that the system was effective in improving staff discipline, with 11 employees categorized as highly disciplined, 10 as moderately disciplined, and 3 as lowly disciplined. The implications of this study suggest that the implementation of a mobile-based attendance system can improve attendance monitoring and enhance work discipline at Puskesmas Kosambi.
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.
Sistem Pendukung Keputusan Penilaian Kinerja Pegawai Non-ASN Menggunakan Metode MOORA pada UPTD Puskesmas Kosambi Adih Adih; Wahyu Aji Dwi Pangestu; Purnama Sari; Joko Suwarno
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.888

Abstract

Employee performance represents the ability to fulfill tasks and responsibilities according to organizational standards. At UPTD Puskesmas Kosambi, the evaluation of Non-Civil Servant (Non-ASN) healthcare workers is still done manually and tends to be subjective, primarily based on attendance. This study aims to address the issue by developing a decision support system using the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method. A descriptive-applied approach and the waterfall development model were employed. Five evaluation criteria were used: attendance (25%), service quality (35%), report timeliness (20%), patient complaints (10%), and lateness (10%). Data from 20 Non-ASN healthcare workers in April 2025 were analyzed, with Anis Julianti achieving the highest MOORA score of 0.3839. The system underwent validation through black-box testing and user acceptance testing (UAT), both confirming its accuracy and usability. The findings were compared with previous studies. Harningsih et al. (2024) used the MOORA method for promotion evaluations at the North Sumatra Provincial Inspectorate Office and recorded a top score of 0.3616. Dewi Yohana br Ginting et al. (2024) applied the method at a beauty clinic and found a top score of 0.3747. These comparisons indicate that the MOORA results in this study fall within a consistent and valid range. The developed decision support system is expected to enhance the objectivity, transparency, and accountability of performance evaluations for Non-ASN employees in primary healthcare services, offering a more comprehensive alternative to manual assessment methods.
Co-Authors Adih Adih Farlin Wabula Ines Heidiani Ikasari Joko Suwarno Muhamad Fauzi Akbar Purnama Sari Purnamasari Purnamasari Saprudin Saprudin