Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi
The purpose of this study was to screen toddlers who were experiencing severe malnutrition according to province. Sources of research data used were obtained from the Ministry of Health of the Republic of Indonesia. The variables used are toddlers who experience malnutrition according to the Provinc...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
Puslitbang Sinergis Asa Professional
2021-03-01
|
| Series: | BIOS |
| Subjects: | |
| Online Access: | https://bios.sinergis.org/index.php/bios/article/view/19 |
| _version_ | 1818533389728219136 |
|---|---|
| author | Muhammad Dwi Chandra Eka Irawan Ilham Syahputra Saragih Agus Perdana Windarto Dedi Suhendro |
| author_facet | Muhammad Dwi Chandra Eka Irawan Ilham Syahputra Saragih Agus Perdana Windarto Dedi Suhendro |
| author_sort | Muhammad Dwi Chandra |
| collection | DOAJ |
| description | The purpose of this study was to screen toddlers who were experiencing severe malnutrition according to province. Sources of research data used were obtained from the Ministry of Health of the Republic of Indonesia. The variables used are toddlers who experience malnutrition according to the Province. In this study using Data Mining Techniques using the K-means algorithm. It is expected that the results of this study can provide input to the central government to pay more attention to nutritional intake in infants, so as to increase the growth and development of toddlers in Indonesia. . And the data obtained by high clusters are 15 Provinsi yaitu (Aceh, Sumatera Utara, Nusa Tenggara Barat, Nusa Tenggara Timur, Kalimantan Barat, kalimantan Tengah, Kalimantan Selatan, Sulawesi Tengah, Sulawesi Selatan, Sulawesi Tenggara, Sulawesi Tenggara, Gorontalo, Sulawesi Barat, Papua Barat, Papua), dan cluster rendah ada 19 yaitu (Sumatera Barat, Riau, Jambi, Sumatera Selatan, Bengkulu, Lampung, Kep. Bangka Belitung, Kep. Riau, Dki Jakarta, Jawa Barat, Jawa Tengah, DI Yogyakarta, Jawa Timur, Banten, Bali, Kalimantan Timur, Kalimantan Utara, Sulawesi utara, Maluku Utara). |
| first_indexed | 2024-12-11T17:58:03Z |
| format | Article |
| id | doaj.art-50277eea63874b0380fb4a3a92f4459d |
| institution | Directory Open Access Journal |
| issn | 2722-0850 |
| language | Indonesian |
| last_indexed | 2024-12-11T17:58:03Z |
| publishDate | 2021-03-01 |
| publisher | Puslitbang Sinergis Asa Professional |
| record_format | Article |
| series | BIOS |
| spelling | doaj.art-50277eea63874b0380fb4a3a92f4459d2022-12-22T00:56:00ZindPuslitbang Sinergis Asa ProfessionalBIOS2722-08502021-03-0121303810.37148/bios.v2i1.1919Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut ProvinsiMuhammad Dwi Chandra0Eka Irawan1Ilham Syahputra Saragih2Agus Perdana Windarto3Dedi Suhendro4STIKOM Tunas Bangsa PematangsiantarSTIKOM Tunas Bangsa PematangsiantarSTIKOM Tunas Bangsa PematangsiantarSTIKOM Tunas Bangsa PematangsiantarSTIKOM Tunas Bangsa PematangsiantarThe purpose of this study was to screen toddlers who were experiencing severe malnutrition according to province. Sources of research data used were obtained from the Ministry of Health of the Republic of Indonesia. The variables used are toddlers who experience malnutrition according to the Province. In this study using Data Mining Techniques using the K-means algorithm. It is expected that the results of this study can provide input to the central government to pay more attention to nutritional intake in infants, so as to increase the growth and development of toddlers in Indonesia. . And the data obtained by high clusters are 15 Provinsi yaitu (Aceh, Sumatera Utara, Nusa Tenggara Barat, Nusa Tenggara Timur, Kalimantan Barat, kalimantan Tengah, Kalimantan Selatan, Sulawesi Tengah, Sulawesi Selatan, Sulawesi Tenggara, Sulawesi Tenggara, Gorontalo, Sulawesi Barat, Papua Barat, Papua), dan cluster rendah ada 19 yaitu (Sumatera Barat, Riau, Jambi, Sumatera Selatan, Bengkulu, Lampung, Kep. Bangka Belitung, Kep. Riau, Dki Jakarta, Jawa Barat, Jawa Tengah, DI Yogyakarta, Jawa Timur, Banten, Bali, Kalimantan Timur, Kalimantan Utara, Sulawesi utara, Maluku Utara).https://bios.sinergis.org/index.php/bios/article/view/19k-means algorithmtoddlerbpsprovincedata mining |
| spellingShingle | Muhammad Dwi Chandra Eka Irawan Ilham Syahputra Saragih Agus Perdana Windarto Dedi Suhendro Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi BIOS k-means algorithm toddler bps province data mining |
| title | Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi |
| title_full | Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi |
| title_fullStr | Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi |
| title_full_unstemmed | Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi |
| title_short | Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi |
| title_sort | penerapan algoritma k means dalam mengelompokkan balita yang mengalami gizi buruk menurut provinsi |
| topic | k-means algorithm toddler bps province data mining |
| url | https://bios.sinergis.org/index.php/bios/article/view/19 |
| work_keys_str_mv | AT muhammaddwichandra penerapanalgoritmakmeansdalammengelompokkanbalitayangmengalamigiziburukmenurutprovinsi AT ekairawan penerapanalgoritmakmeansdalammengelompokkanbalitayangmengalamigiziburukmenurutprovinsi AT ilhamsyahputrasaragih penerapanalgoritmakmeansdalammengelompokkanbalitayangmengalamigiziburukmenurutprovinsi AT agusperdanawindarto penerapanalgoritmakmeansdalammengelompokkanbalitayangmengalamigiziburukmenurutprovinsi AT dedisuhendro penerapanalgoritmakmeansdalammengelompokkanbalitayangmengalamigiziburukmenurutprovinsi |