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Natalis Ransi
Univesitas Halu Oleo
Computer Science & IT

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SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KARYAWAN TERBAIK MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING STUDI KASUS RUMAH SAKIT HATI MULIA KENDARI Achirul Slamet Sugiarto; La Ode Saidi; Natalis Ransi; Andi Tenriawaru; Ferdinand Murni Hamundu; La Surimi
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 4 No 1 (2026): Juni 2026
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v4i1.122

Abstract

The Hati Mulia Kendari Hospital experiences difficulties in analyzing appropriate decision making. The process of determining the best employees still uses the method of adding up each criterion so that the data tends to be unstructured and inaccurate. This research aims to produce a decision support system for determining the best employees using a simple additive weighting method. Simple additive weighting is very important because it can increase efficiency, minimize errors, optimize resources, support evidence-based decisions, overcome complexity, increase decision certainty, increase competitiveness, facilitate innovation, and encourage transparency. This decision support system uses the waterfall development method to build the system, testing the system and data using the black box testing and confusion matrix method. The results of system testing using black box testing obtained results that were in accordance with user needs and the results of data testing with 36 test data using a confusion matrix with 3 outputs produced an accuracy of 94%, precision of 80% and recall of 100%. These results show that this decision support system can be implemented.
IMPLEMENTASI K-MEANS DAN DAVIES BOULDIN INDEX UNTUK PENGELOMPOKAN DAERAH DI SULAWESI TENGGARA BERDASARKAN INDIKATOR PENDIDIKAN DENGAN VISUALISASI SISTEM INFORMASI GEOGRAFIS Sahrul; Natalis Ransi; Muhammad Arfan
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 4 No 1 (2026): Juni 2026
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v4i1.133

Abstract

Penelitian ini menerapkan algoritma K-Means yang diintegrasikan dengan Davies-Bouldin Index (DBI) untuk mengelompokkan wilayah berdasarkan indikator pendidikan. Metode DBI berfungsi menentukan jumlah klaster optimal secara objektif sebelum proses klasterisasi dengan K-Means dilakukan. Hasil penelitian memperoleh jumlah klaster terbaik k=2 (nilai DBI 0,6035) yang mengelompokkan 17 wilayah menjadi dua klaster yang kohesif. Hasil klastering divalidasi secara visual menggunakan Principal Component Analysis (PCA) dan berhasil divisualisasikan dalam peta tematik berbasis Sistem Informasi Geografis (SIG) melalui sistem web yang dibangun dengan framework Streamlit. Simpulan penelitian ini adalah kombinasi K-Means dan DBI menghasilkan klasterisasi wilayah yang objektif dan terukur, serta sistem web memudahkan visualisasi hasilnya secara interaktif
Co-Authors Achirul Slamet Sugiarto Andi Tenriawaru Ferdinand Murni Hamundu La Ode Saidi La Surimi Muhammad Arfan Sahrul