Covid-19 is an infectious illness caused by a newly identified form of coronavirus. This is a new virus and illness that was previously unknown before the December 2019 outbreak in Wuhan, China. The number of confirmed cases of Covid-19 and the number of deaths due to this virus in Southeast Asia are increasing and quite alarming. Therefore this study will discuss the grouping of Cases and Deaths of COVID-19 in Southeast Asia. The method used is the K-Means Clustering Data Mining. By using this method the data that has been obtained can be grouped into several clusters, where K-Means Clustering Process is applied using RapidMiner tools. Data used are Country statistics, Area of recorded laboratory-confirmed cases of COVID-19, and April 2020 deaths from WHO (World Health Organization). Data is divided into 3 clusters: high (C1), medium (C2) and low (C3). The results obtained are that there are four countries with a high level cluster (C1), one country with a moderate level cluster (C2), and 6 countries with a low level cluster (C3). This can be an input for each country to increase awareness of the transmission of Covid-19.
Products are goods that are available and provided in stores for sale. Products provided in stores must be arranged properly to order to attract the attention of consumers to buy. Products arranged in a store will depend on the type of store.The product arrangement at a retail store will be different from the product arrangement at a clothing store. Store display will reflect a picture that is in the store so consumers know the types of products sold by product arrangement. An attractive arrangement will stimulate the desire of consumers to buy. In data mining there are several types of methods by use including prediction, association, classification and estimation. In the prediction method there are several techniques including the frequent pattern growth (FP-growth) method. FP-growth algorithm is the development of the apriori algorithm. So, the shortcomings of the apriori algorithm are corrected by the FP-growth algorithm. FP-growth is one alternative algorithm that can be used to determine the set of data that most often appears (frequent itemset) in a data set. Results of research on the application of the FP-growth algorithm to maximizing the display of goods. It is hoped that this research can be used to adjust the product layout according to the level of frequency the product is sought by the customer so that the customer has no difficulty finding the product they want.
Insurance is a mechanism of protection or protection from the risk of loss by transferring the risk to another party. Sometimes a product that has just emerged becomes a product that is superior in terms of sales, so that interest in a product is not absolutely measured from the year the product was released. The constraint factors include the marketing of the product when it was launched. Offering products with low premiums along with the benefits that customers want. However, insurance companies still have difficulty in classifying superior products that are in great demand by prospective customers. For this reason, a technique for grouping insurance products is needed to make it easier for companies to see superior products and choose products that suit the needs of their customers. Analyzing and processing data using the K-Means method in the clustering of insurance products is the aim of this study. The application of the K-Means algorithm is to help calculate the purity value from the results of the clustering carried out so that the clustering of insurance products is in accordance with the needs of its customers. The application of the K-Means method with clustering techniques for data mining produces information on insurance products that are more attractive to potential customers. This is very appropriate in grouping data types because it is easier to implement and its application can filter quickly and precisely. Calculations using the K-Means method with a data sample of 55 customers obtained 3 clusters, namely cluster 1 for fire insurance which has 30 customers, cluster 2 for accident insurance 24 people and cluster 3 for health insurance 1 person.
Penentuan dosen penguji skripsi dalam penelitian ini menggunakan metode ARAS, COPRAS dan WASPAS dengan 5 kriteria : masa kerja, fungsional, kompetensi, pendidikan dan tugas belajar serta ada 20 data alternatif. Penunjukan dosen penguji skripsi di STMIK Triguna Dharma, saat ini dilakukan masih secara langsung dan manual serta adakalanya mengesampingkan jumlah mahasiswa yang akan diuji sehingga kurang seimbang jumlahnya untuk setiap dosen. Masih ada ditemukan keputusan yang kurang optimal saat penunjukan dosen yang tidak sesuai dengan tema skripsi mahasiswa sehingga nilai kualitas karya ilmiah menurun. Untuk itu diperlukan sistem yang berguna dalam penentuan calon dosen penguji, agar sesuai dengan kompetensi calon dosen penguji dan tidak terlalu banyak jumlah mahasiswa yang akan diuji agar lebih fokus. Perbandingan metode dalam penelitian ini dengan melakukan perangkingan supaya diperoleh alternatif secara optimal dan objektif diawali dengan penentuan bobot dari setiap kriteria, hasil ranking diurutkan dari nilai yang tertinggi agar pemilihan dosen penguji mudah ditentukan dan sebagai alat rekomendasi bagi pihak pengambil keputusan. Untuk mendapatkan pilihan terbaik metode ARAS menggunakan nilai utilitas (Ki) tertinggi, metode COPRAS didasarkan pada skor penilaian utilitas quantitatif (Ui) tertinggi dan metode WASPAS menggunakan nilai preferensi (Qi) tertinggi untuk mendapatkan pilihan terbaik. Alternatif terbaik dengan metode ARAS menghasilkan (A6=0,9385) selanjutnya A5, A19, A3, A11, A14, A16, A18, A10, A4, A17, A9 layak untuk direkomendasi. Metode COPRAS menghasilkan (A6=100) dan metode WASPAS menghasilkan (A6=0,7083)
Non-Cash Food Assistance (BPNT) is social food assistance that is paid in non-cash form every month by the government. However, the problem of identifying BPNT recipients has not been identified properly and it is not certain that the poor really deserve to receive BPNT. So far, the existing system has not been optimal for selecting BPNT recipients with the existing criteria. Data management still uses a manual system and is not effective in determining who is entitled to receive BPNT and who is not. A creative solution to overcome this problem is the use of a web-based decision support system (DSS) using the Additive Ratio Assessment (ARAS) method. A system based on human and computer intelligence that creates options to improve decision making. The purpose of this research is to develop a system that helps decision making in deciding the acceptance of BPNT in poor families. This makes it easy to determine who is eligible and who is not eligible for assistance. This method is easy to apply for ranking by comparing it with other methods so that the results are more precise and accurate. The calculation process for the ARAS method using a very complex web-based system greatly facilitates and speeds up the determination of BPNT receipts. To facilitate implementation, 10 data with 5 criteria were used as sample data. Based on the calculation results of the potential beneficiaries on behalf of Selamat and Prayogi, the priority is getting assistance with a final score of < 0.07.
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