Abstract:Tuberkolusis merupakan penyakit paru-paru disebabkan oleh Mycobacterium tuberculosis. TBC dapat menimbulkan gejala berupa batuk lama, Tuberkulosis diderita orang kebanyakan merupakan infeksi TBC, yaitu terdapat bakteri TBC yang pasif. Di Indonesia, Pemerintah berupaya melakukan akselerasi dalam upaya eliminasi TBC pada 2030. Akselerasi itu dilakukan melalui akses pencegahan, diagnosis, pengobatan, dan layanan kesehatan bagi seluruh penderita TBC Penyakit TBC ini memiliki keterkaitan yang sangat erat dengan kon… Show more
“…(Gustientiedina, Adiya, & Desnelita, 2019). Data mining, also known as knowledge discovery in database (KDD) can be used interchangeably by explaining the process of extracting hidden information (Toresa, 2020). Data Mining can be divided into four groups, namely prediction modeling, cluster analysis, association analysis and anomaly detection.…”
Indonesia is a country with unique tourist destinations from each region. The tourism sector has an impact on the Indonesian economy which can encourage economic growth and increase the country's foreign exchange from foreign tourist visits. Tourism growth in Indonesia was disrupted due to the Covid-19 pandemic with the imposition of major social restrictions which resulted in a decrease in tourist visits and the paralysis of the tourism sector. Based on the problems described above, the authors are interested in conducting research in order to classify data on foreign tourist arrivals based on the entrance of foreign tourist arrivals. This research uses data mining method and K-Means Algorithm to form 5 clusters. The 5 clusters are divided into groups of tourist entrances which are categorized as very high (C1), high (C2), moderate (C3), low (C4) and very low (C5). In forming the 5 clusters, the researchers used Ms. Excel and Rapidminer 10.1 to process data. The results of this study obtained that the tourist entrance group was categorized as very high (C1) with 1 data, high (C2) with 1 data, moderate (C3) with 1 data, low (C4) with 1 data and very low (C5). ) that is with 21 data. This study aims to provide suggestions and future considerations to the Ministry of Tourism and Creative Economy of the Republic of Indonesia (Kemenparekraf) to carry out policies so that the Indonesian tourism sector can return to normal.
“…(Gustientiedina, Adiya, & Desnelita, 2019). Data mining, also known as knowledge discovery in database (KDD) can be used interchangeably by explaining the process of extracting hidden information (Toresa, 2020). Data Mining can be divided into four groups, namely prediction modeling, cluster analysis, association analysis and anomaly detection.…”
Indonesia is a country with unique tourist destinations from each region. The tourism sector has an impact on the Indonesian economy which can encourage economic growth and increase the country's foreign exchange from foreign tourist visits. Tourism growth in Indonesia was disrupted due to the Covid-19 pandemic with the imposition of major social restrictions which resulted in a decrease in tourist visits and the paralysis of the tourism sector. Based on the problems described above, the authors are interested in conducting research in order to classify data on foreign tourist arrivals based on the entrance of foreign tourist arrivals. This research uses data mining method and K-Means Algorithm to form 5 clusters. The 5 clusters are divided into groups of tourist entrances which are categorized as very high (C1), high (C2), moderate (C3), low (C4) and very low (C5). In forming the 5 clusters, the researchers used Ms. Excel and Rapidminer 10.1 to process data. The results of this study obtained that the tourist entrance group was categorized as very high (C1) with 1 data, high (C2) with 1 data, moderate (C3) with 1 data, low (C4) with 1 data and very low (C5). ) that is with 21 data. This study aims to provide suggestions and future considerations to the Ministry of Tourism and Creative Economy of the Republic of Indonesia (Kemenparekraf) to carry out policies so that the Indonesian tourism sector can return to normal.
“…Metode ini efektif menghasilkan cluster-cluster didalam tumpukan data. (Toresa, 2020) 3. Karakteristik dari cluster yang terbentuk di setiap attribut yaitu Jumlah Kasus Baru TBC BTA+ dan Jumlah Kasus TBC anak pada cluster 2 mempunyai nilai paling tinggi dari cluster lainnya.…”
Tuberkulosis (TBC) ialah penyakit yang diakibatkan oleh infeksi organisme Mikroskopis Mycobacterium tuberculosis yang menjangkiti bagian organ paru-paru. Ditahun 2016 ada 10,4 juta kasus TBC di planet ini, identik dengan 120 kasus untuk setiap 100.000 jiwa. Cina, India, Filipina, Pakistan, dan Indonesia adalah negara dengan kasus paling penting. Sebagian besar kejadian TBC yang dinilai pada tahun 2016 terjadi di Lokal Asia Tenggara (45%) dimana Indonesia nomor satu dan 25% terjadi di kawasan Afrika. Indonesia memiliki masalah yang besar dalam menangani penyakit TBC. Pada tahun 2017 terdapat 420.994 kasus TBC di Indonesia. Di Indonesia, penyakit TBC hampir menyebar di semua wilayah, salah satunya daerah Kota Bandung, Wilayah Jawa Barat. Ditahun 2020 terdapat 10758 kasus, dan yang paling terdampak adalah Wilayah Bojongloa Kaler sebanyak 879 kasus. Mengingat jumlah kasus TBC di Kota Bandung yang terus meningkat, dapat dibayangkan bahwa diperlukan upaya pengobatan yang kuat. Penelitian ini bertujuan untuk menentukan wilayah penyebaran penyakit TBC di Kota Bandung pada tahun 2020, penelitian kuantitatif ini menggunakan data yang diperoleh dari Website Portal Data Kota Bandung sebesar 10758 kasus penyakit TBC. Metode Algoritma yang digunakan ialah K-Means dan diolah menggunakan Software Rapidminer. Berdasarkan hasil uji dapat ditarik kesimpulan bahwa penyebaran anggota cluster terbanyak ada di cluster 0 dengan 10 anggota, lalu penyebaran yang berukuran sedang ada di cluster 4 dengan 8 anggota, untuk yang terkecil berada di cluster 3 dengan 1 anggota.
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