ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT
This study aims to apply clustering techniques to time series data. Time series models can be formed for all research data objects, so many research objects need to be grouped so that the resulting model becomes more efficient. The object used in this study was data on Covid-19 sufferers from 27 reg...
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| Format: | Article |
| Language: | English |
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Universitas Bina Bangsa
2023-08-01
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| Series: | Jurnal Lebesgue |
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| Online Access: | https://lebesgue.lppmbinabangsa.id/index.php/home/article/view/361 |
| _version_ | 1797680956397584384 |
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| author | Sarah Fadhlia |
| author_facet | Sarah Fadhlia |
| author_sort | Sarah Fadhlia |
| collection | DOAJ |
| description | This study aims to apply clustering techniques to time series data. Time series models can be formed for all research data objects, so many research objects need to be grouped so that the resulting model becomes more efficient. The object used in this study was data on Covid-19 sufferers from 27 regencies and cities in West Java Province. All objects were analyzed by time series to produce 27 models. All objects' data patterns and models have many similarities, so clustering can be done. Clustering models using the Ward method and the Piccolo dissimilarity measure. The optimum cluster uses the Hartigan and Ball indices to obtain 3 clusters |
| first_indexed | 2024-03-11T23:38:19Z |
| format | Article |
| id | doaj.art-b417d8fc1fb049f78970b1bb1ec4566a |
| institution | Directory Open Access Journal |
| issn | 2721-8929 2721-8937 |
| language | English |
| last_indexed | 2024-03-11T23:38:19Z |
| publishDate | 2023-08-01 |
| publisher | Universitas Bina Bangsa |
| record_format | Article |
| series | Jurnal Lebesgue |
| spelling | doaj.art-b417d8fc1fb049f78970b1bb1ec4566a2023-09-19T17:22:22ZengUniversitas Bina BangsaJurnal Lebesgue2721-89292721-89372023-08-014297097610.46306/lb.v4i2.361361ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARATSarah Fadhlia0Universitas Indraprasta PGRI JakartaThis study aims to apply clustering techniques to time series data. Time series models can be formed for all research data objects, so many research objects need to be grouped so that the resulting model becomes more efficient. The object used in this study was data on Covid-19 sufferers from 27 regencies and cities in West Java Province. All objects were analyzed by time series to produce 27 models. All objects' data patterns and models have many similarities, so clustering can be done. Clustering models using the Ward method and the Piccolo dissimilarity measure. The optimum cluster uses the Hartigan and Ball indices to obtain 3 clustershttps://lebesgue.lppmbinabangsa.id/index.php/home/article/view/361piccologerombolderet waktuclustertime series |
| spellingShingle | Sarah Fadhlia ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT Jurnal Lebesgue piccolo gerombol deret waktu cluster time series |
| title | ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT |
| title_full | ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT |
| title_fullStr | ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT |
| title_full_unstemmed | ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT |
| title_short | ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT |
| title_sort | analisis gerombol pada data deret waktu penderita covid 19 provinsi jawa barat |
| topic | piccolo gerombol deret waktu cluster time series |
| url | https://lebesgue.lppmbinabangsa.id/index.php/home/article/view/361 |
| work_keys_str_mv | AT sarahfadhlia analisisgerombolpadadataderetwaktupenderitacovid19provinsijawabarat |