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Noviyanto Noviyanto
Universitas Gunadarma
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Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 Berdasarkan Negara di Benua Asia Noviyanto Noviyanto
Paradigma Vol 22, No 2 (2020): Periode September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (844.578 KB) | DOI: 10.31294/p.v22i2.8808

Abstract

Abstrak  - COVID-19 disebabkan oleh SARS-CoV-2, yaitu virus jenis baru dari coronavirus (kelompok virus yang menginfeksi sistem pernapasan). Infeksi virus Corona bisa menyebabkan infeksi pernapasan ringan sampai sedang, seperti flu, atau infeksi sistem pernapasan dan paru-paru, seperti pneumonia. Maraknya penyebaran penyakit yang diakibatkan oleh virus COVID-19 yang telah ditetapkan sebagai pandemi oleh WHO pada tanggal 12 Maret 2020 , akibat virus COVID-19 banyak pasien yang terjangkit mengalami kematian. Dalam mengelompokkan jumlah kematian penderita Covid-19 menggunakan teknik data mining metode k-means clustering. Data diambil dari dari link https://www.worldometers.info/coronavirus/. Hasil dari penelitian ini adalah cluster jumlah kematian penderita Covid-19 kedalam 3 cluster. terdapat 4 negara dengan cluster tingkat tinggi yaitu: Turki, Iran, India dan China dengan cluster tingkat sedang sebanyak 4 negara yaitu : Pakistan, Indonesia, Jepang, dan Piliphina dan dengan cluster rendah adalah 41 negara lainnya. Kata kunci: Data mining, Clustering, K-Means, Covid-19 Abstract - COVID-19 is caused by SARS-CoV-2, a new type of virus from coronavirus (a group of viruses that infects the respiratory system). Corona virus infections can cause mild to moderate respiratory infections, such as flu, or respiratory and lung system infections, such as pneumonia. The rise of the spread of diseases caused by the COVID-19 virus which has been established as a pandemic by WHO on March 12, 2020, due to the COVID-19 virus many patients who have contracted have died. In classifying the number of deaths of patients with Covid-19 using data mining techniques k-means clustering method. Data is taken from the link https://www.worldometers.info/coronavirus/. The results of this study are the cluster number of deaths of patients with Covid-19 into 3 clusters. there are 4 countries with high level clusters, namely: Turkey, Iran, India and China with medium level clusters as many as 4 countries namely: Pakistan, Indonesia, Japan, and the Philippines and with low clusters are 41 other countries. Keywords: Data mining, Clustering, K-Means, Covid-19
Data Mining Menggunakan Algoritma K-Means dengan Weka Interface untuk Klasterisasi Dosen Yang Memiliki Jabatan Fungsional pada Perguruan Tinggi Swasta dilingkungan LLDikti Wilayah III Noviyanto Noviyanto; Prita Ekasari
Paradigma Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (220.845 KB) | DOI: 10.31294/paradigma.v24i1.1112

Abstract

Permanent lecturers and lecturers who have functional positions absolutely must be owned by every university in Indonesia in order to fulfill the provisions of the legislation. Article 48 paragraph (2) of Law Number 14 of 2005 concerning Teachers and Lecturers states that the level of academic positions of permanent lecturers consists of asisten ahli, lektor, lektor kepala dan profesor. Lecturers still have to comply with these rules by submitting proposals for functional positions if they fulfill the qualification, in addition, universities where lecturers work must also proactively encourage lecturers to fulfill their obligations. Many lecturers from various universities, even though they have fulfill qualification, did not immediately take the initiative to propose their functional positions. In grouping the data for permanent lecturers and lecturers who have functional positions using data mining techniques, the k-means clustering method. The data is taken from the link https://pddikti.kemdikbud.go.id/. The results of this study are clusters of the number of permanent lecturers and lecturers who have functional positions into 3 clusters. There are 15 private universities (PTS) in the LLDikti III region with clusters of permanent lecturers and lecturers who have the most functional positions, then the medium level cluster is 45 private universities and the lowest cluster is 228 private universities.
Co-Authors Prita Ekasari