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All Journal bit-Tech Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Taruna Firlian Tama
Institut Teknologi dan Bisnis Ahmad Dahlan Lamongan
Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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RISIKO MENINGGAL KELOMPOK CONFIRM COVID-19, PDP, DAN ODP DI JAWA TIMUR Fibia Sentauri Cahyaningrum; Nola Riwibowo; Isna Ayu Saftri K D; Abdun Nafi Kurniawan; Taruna Firlian Tama
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.353

Abstract

Coronavirus Disease 2019 (COVID-19) is a disease caused by a new type of virus called Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Based on data up to April 9th 2020, confirmed cases of COVID-19 in East Java are 6.78% of the total confirmed cases in Indonesia. Meanwhile, cases of death due to COVID-19 are 6.07% of the total cases of death in Indonesia. In handling it, the Ministry of Health of the Republic of Indonesia classifies COVID-19 patients into 3 status levels, namely ODP, PDP and Confirmed. The results of the meta-analysis using the risk ratio with the Mantel-Haenszel fixed effect model obtained a pooled risk ratio for the PDP and ODP groups (RR = 33.66, 95% CI: 20.88-54.27), the confirmed and ODP groups (RR = 56.31, 95% CI: 32.07-98.87), and the confirmed and PDP groups (RR = 1.75, 95% CI: 1.2-2.53). The order of the group with the highest risk of death is Confirmed > PDP > ODP.
Evaluation of Distance Measurement Using Complete Linkage Method Fibia Sentauri Cahyaningrum; Isna Ayu Safitri Kusuma Dewi; Nola Riwibowo; Taruna Firlian Tama
bit-Tech Vol. 6 No. 2 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i2.1045

Abstract

Cluster analysis is the process of grouping a number of objects based on information obtained from data that explains the relationship between objects with the principle of maximizing similarities between members of one cluster and minimizing similarities between clusters. Cluster analysis is useful for identifying objects (recognition), supporting decision-making systems, and data mining. Cluster analysis consists of hierarchical (Average Linkage, Single Linkage, Complete Linkage, Ward's, and Centroid) and non-hierarchical (K-Means) methods. Each method generally has advantages and disadvantages. Apart from that, there are several distance measures that are commonly used in the grouping process, such as Euclidean, Canberra Metric, Czekanowski Coefficient, and others. In general, researchers will choose one or several cluster analysis methods as a comparison and a certain distance measure to be applied to the data in order to group objects based on certain criteria. In this research, a study and evaluation of Euclidean distance measures, Canberra Metric, and Czekanowski Coefficient were carried out using the Complete Linkage method based on simulated data. The conclusion obtained from evaluating measures of object similarity, namely Euclidean distance, Canberra Metric, and Czekanowski Coefficient by applying the Complete Linkage method, concluded that Euclidean distance is better used as a measure of object similarity in grouping cases compared to Canberra Metric and Czekanowski Coefficient.
Co-Authors Abdun Nafi Kurniawan Fibia Sentauri Cahyaningrum Isna Ayu Safitri Kusuma Dewi Isna Ayu Saftri K D N Riwibowo