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All Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Ilmu Komputer dan Teknologi (IKOMTI)
Rahmiyati, Dwi
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Computer Science & IT Other

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Penerapan Algoritma K-Means untuk Pemetaan Kebutuhan Obat Sesuai Prioritas dalam Pengadaan Azizah, Laili Nur; Utari, Lis; Ningrum, Leny Tritanto; Rahmiyati, Dwi
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 6, No 1 (2025)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2025.v6i1.7943

Abstract

ABSTRACTEach Ministry/Institution that has a primary clinic has its own budget allocation to support the operational activities of the primary clinic health service. The budget is usually adjusted to the size of the organization and the needs of its employees. One of the budgets is for spending on medical supplies. It is expected that the Primary Clinic, which is within the Ministries or Institutions, can provide health services according to the standards and needs of the scope they serve in accordance with budget availability. For this reason, so that the available budget can be optimized according to existing needs and is right on target, it is necessary to have a careful drug procurement plan. Segmentation/mapping of drug use is said to be important to determine the priority level of drug needs and can be used as a reference in drug procurement. The priority scale of needs in drug procurement is a process for mapping the types of drugs based on their needs by considering the drugs needed and the available budget, to avoid buying drugs that are not needed by the employees. Ideally, the types of drugs with high use are groups of drugs which procurement needs to be prioritized with the aim of ensuring the availability of these drugs. This study uses drug use data for 1 (one) semester, namely January to June 2022, using total usage and number of transactions using the clustering method with the K-Means algorithm. The results of drug mapping resulted in 197 drugs with low priority, 13 drugs with medium priority and 3 drugs with high priority. The results of the clustering results evaluation test show the value of the silhouette coefficient results is 0.8630 or "strong structure
Penerapan Metode K-Means untuk Pemetaan Objek Wisata sebagai Rekomendasi Prioritas Pengembangan Pariwisata Rahmiyati, Dwi; Nuraeni; Ervan Britantono Siswantoro
Jurnal IT UHB Vol 6 No 1 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i1.1748

Abstract

Pariwisata menjadi salah satu faktor penggerak pertumbuhan ekonomi di Indonesia. Perkembangan destinasi yang tidak merata serta tidak adanya pemetaan yang jelas dapat berdampak pada penagalokasian dana dan prioritas pengembangan yang kurang tepat.  Pemetaan objek wisata dapat membantu pihak terkait dalam upaya menentukan objek wisata yang menjadi prioritas pengembangan. Tujuan penelitian dimaksudkan untuk mendapatkan pemetaan objek wisata yang akurat dan mengembangkan prototype aplikasi penerapan K-Means guna membantu menentukan rekomendasi prioritas pengembangan wisata. Sumber data penelitian yang digunakan adalah data objek wisata di wlilayah Bogor. Variabel yang digunakan meliputi Jumlah Kunjungan, Fasilitas, Aksesibilitas Kendaraan Umum, Harga Tiket Masuk, dan Luas Wilayah. Hasil perhitungan K-Means yaitu cluster 1 berjumlah 4 objek wisata, cluster 2 berjumlah 10 dan cluster 3 berjumlah 81 objek wisata. Berdasarkan uji kelayakan pada aplikasi yang dibangun diperoleh hasil 100% atau sangat layak. Uji pengguna dengan kuisioner PSSUQ diperoleh nilai Overall 86% yang berarti sangat layak digunakan. Uji validitas cluster menggunakan silhouette coefficient di dapat hasil 0.78 dalam tabel Kategori Silhouette disimpulkan cluster yang dibuat termasuk dalam kategori “Strong Structure”.
Co-Authors Ervan Britantono Siswantoro Laili Nur Azizah Ningrum, Leny Tritanto Nuraeni Utari, Lis