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All Journal Jurnal Aplikasi Statistika & Komputasi Statistik J-Icon : Jurnal Komputer dan Informatika
Wa Ode Zuhayeni Madjida
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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PENGELOMPOKKAN PROVINSI DI INDONESIA BERDASARKAN JUMLAH KASUS COVID-19 DAN FASILITAS KESEHATAN Meinisa Fadillah Rahmi; Paulus Satria Prasetyo; Ratih Nurhabibah; Rizky Perdana; Wa Ode Zuhayeni Madjida
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 13 No 1 (2021): Jurnal Aplikasi Statistika dan Komputasi Statistik
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v13i1.274

Abstract

Tingkat penyebaran COVID-19 cukup cepat, hingga 14 November 2020 tercatat jumlah kasus terkonfirmasi positif di Indonesia mencapai 463.007 jiwa. Ketersediaan fasilitas kesehatan masing-masing provinsi menentukan kesiapan daerah dalam penanganan COVID-19 sehingga penting untuk menganalisis keadaan dan distribusi provinsi-provinsi terkait kesiapannya tersebut. Penelitian ini melakukan clustering menggunakan algoritma K-Means dan K-Means with Outlier Detection untuk mengelompokkan 34 provinsi di Indonesia berdasarkan jumlah kasus COVID-19 dan data fasilitas kesehatan, lalu menentukan metode terbaiknya, serta mengidentifikasi karakteristik masing-masing kelompok berdasarkan metode terbaik. Penelitian menghasilkan tiga cluster. Cluster 1 merupakan kelompok provinsi dengan jumlah kasus COVID-19 tinggi dan fasilitas kesehatan kurang memadai, cluster 2 memiliki jumlah kasus COVID-19 tinggi dan fasilitas kesehatan memadai, sedangkan cluster 3 memiliki jumlah kasus COVID-19 rendah dan fasilitas kesehatan menengah.
Perbandingan Metode Klasifikasi Multiclass untuk Pemetaan Zona Risiko COVID-19 di Pulau Jawa Jesica Nauli Br. Siringo Ringo; Wahyu Joko Mursalin; Nisrina Citra Nurfadilah; Dwiky Rachmat Ramadhan; Wa Ode Zuhayeni Madjida
J-Icon : Jurnal Komputer dan Informatika Vol 9 No 1 (2021): Maret 2021
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v9i1.3602

Abstract

Various attempts are needed to control the increment of COVID-19 cases in Indonesia, especially Java Island. One of the effective attempt to do this is through the preventive act by providing news about a region. Indonesia, through Satgas Penanganan COVID-19, has built a risk zone of district/city as a warning system for the public and the substance of policy making for government in region level. The risk zone is built by three kinds of indicator using a conventional technique named score weighting. By considering the importance of the risk zone for policy making in the government, this study aims to build a risk zone classification model for districts / cities in Java using several data mining classification techniques and determine the best classification model based on evaluation results. This study uses several classification technique on the purpose of comparation. These techniques are naive Bayes, decision tree, k-nearest-neighbor, and neural network. Before entering the modeling stage, data is being adjustedat the preprocessing stage where missing value and imbalanced data problems are identifies. These problems is being overcome by doing data imputation and oversampling techniques. The result of this study indicates that k-nearest-neighbor is the best model compared to other three models. This result is based on the evaluation measures of the four models where the k-NN model has the highest accuracy value, the macro average value for sensitivity, specivicity, and F1-Measure compared to other models.
Co-Authors Dwiky Rachmat Ramadhan Jesica Nauli Br. Siringo Ringo Meinisa Fadillah Rahmi Nisrina Citra Nurfadilah Paulus Satria Prasetyo Ratih Nurhabibah Rizky Perdana Wahyu Joko Mursalin