Penggerombolan Data Panel Emiten Sektor Pertambangan selama Pandemi Covid-19
| dc.contributor.advisor | Alamudi, Aam | |
| dc.contributor.advisor | Afendi, Farit Mochamad | |
| dc.contributor.author | Nursyahban, Nadhif Muhammad | |
| dc.date.accessioned | 2022-04-02T01:24:20Z | |
| dc.date.available | 2022-04-02T01:24:20Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/111486 | |
| dc.description.abstract | The Covid-19 pandemic has made people start looking for new income, one of which is stock investment. Mining Stock recorded the highest sectoral index increase in 2020. The high increase in the mining sector index doesn’t indicate all of the stocks have a good performance. Clustering data of mining stock can help to see which stock has the best performance. Variables used in clustering are technical factors with details: return, trading volume, transaction frequency, bid volume, and foreign buy. Data in this research is longitudinal data from March 2020 until January 2022 and the clustering technique used is k-means. Clustering on outliers data and non-outliers data is done separately. Definition of outliers is exploratively with biplot analysis. Clustering on outliers data results obtained are five clusters and clustering on non-outliers data results obtained are two clusters. Best cluster is cluster who obtained ANTM because has highest value in return, transaction frequency, and foreign buy. | id |
| dc.language.iso | id | id |
| dc.publisher | IPB University | id |
| dc.title | Penggerombolan Data Panel Emiten Sektor Pertambangan selama Pandemi Covid-19 | id |
| dc.type | Undergraduate Thesis | id |
| dc.subject.keyword | clustering on longitudinal data | id |
| dc.subject.keyword | k-means | id |
| dc.subject.keyword | mining | id |
| dc.subject.keyword | stock | id |
| dc.subject.keyword | technical factor | id |

