Pengelompokan Rumah Sakit di Jakarta Menggunakan Model DBSCAN, Gaussian Mixture, dan Hierarchical Clustering

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Karli Eka Setiawan
Afdhal Kurniawan

Abstrak

Perlu disadari bahwa setelah mengalami pandemi COVID-19, distribusi persebaran fasilitas rumah sakit harus menjadi perhatian utama dalam memenuhi hak-hak dasar dari masyarakat oleh pemerintah tingkat daerah dan tingkat nasional. Oleh karena itu diperlukan sebuah analisis data yang menggambarkan kondisi terkini mengenai persebaran fasilitas rumah sakit khususnya di Jakarta. Analisis Data dengan pendekatan teknik machine learning mampu memberikan beberapa manfaat seperti mendapatkan manfaat dalam memberikan pemahaman mengenai distribusi tenaga kesehatan, memberikan wawasan dalam rangka perencanaan sumber daya manusia, melakukan pemantauan kinerja rumah sakit, dan bahkan menjadi acuan target di masa ddepan dengan prediksi kebutuhan tenaga kesehatan. Penelitian ini mengajukan tiga buah model unsupervised learning, seperti model Density-based spatial clustering of applications with noise algorithm (DBSCAN), model Gaussian Mixture, dan model Agglomerative Hierarchical untuk melakukan pengelompokan sekumpulan data rumah sakit se-Jakarta, Indonesia yang berisi informasi data jumlah berbagai tenaga kerja medis di rumah sakit tersebut dan fasilitas tempat tidurnya. Dataset ini diperoleh dari proses web scrapping pada website Kementerian Kesehatan Republik Indonesia pada tahun 2022. Dari perbandingan ketiga model unsupervised, diperoleh bahwa model Gaussian Mixture menghasilkan nilai Davies Bouldin terkecil dengan nilai . Penelitian ini merupakan lanjutan dari penelitian kami sebelumnya, dimana penelitian tersebut menurut sepengetahuan kami merupakan penelitian pertama yang membahas  pengelompokan data daftar rumah sakit se-Jakarta berdasarkan informasi data jumlah tenaga medis dan jumlah fasilitas tempat tidur yang menjadikan kontribusi dari penelitian ini.

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Cara Mengutip
Setiawan, K. E., & Kurniawan, A. (2023). Pengelompokan Rumah Sakit di Jakarta Menggunakan Model DBSCAN, Gaussian Mixture, dan Hierarchical Clustering. Jurnal Informatika Terpadu, 9(2), 149–156. https://doi.org/10.54914/jit.v9i2.995
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Referensi

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