Completing cat products in meeting consumer demand is something that must be addressed. Sales are very important for sales. The amount of demand for goods increases, it will get a large income. The purpose of this study is to predict the sales revenue of paint products at UD. Masdi Related, makes it easy for the leadership of the company to find out the amount of money obtained quickly. This research also makes it easy for companies to take business strategies quickly and optimally. The data used in this research is the data of paint product sales for January 2016 to December 2018 which is processed using the Monte carlo method. Income prediction will be done every year. In addition to predicting revenue, the sales data is also used to predict product demand every year. To predict the sales of paint products using the Monte Carlo method. The results of this study can predict sales revenue of paint products very well. Based on the results of tests conducted on the system used to predict sales revenue of cat products with an average rating of 89%. With a fairly high degree of accuracy, the application of the Monte Carlo method can be estimated to make an estimate of the income and demand for each paint product every year. Necessary, will facilitate the leadership to choose the right business strategy to increase sales of cat product sales.
Family Planning aims to minimize birth rates in Indonesia. The target of family planning is couples of childbearing age, which refers to a married couple whose wife has a age range of 15-49 years. Contraception itself consists of 2 types of time periods, namely short and long. Where couples of childbearing age can choose according to what they want, therefore there is often a shortage of contraceptive supplies. Thus, it is necessary to predict the use of contraception using a method to be more efficient. The Monte Carlo method is used as a numerical analysis method that involves a random sample of random numbers. Where to use the previous year's data to get predictions of the number of usage of the following year in the form of numbers. And after a series of simulation results have been obtained the percentage results with an average of above 80%.
Digitalisasi dan otomasi dalam pelayanan mahasiswa di Perguruan Tinggi dapat menghasilkan big data. Amanat pemerintah dalam Peraturan Mentri Riset Teknologi dan Pendidikan Tinggi agar besaran Uang Kuliah Tunggal (UKT) di Perguruan Tinggi Negeri dibagi ke dalam 5 kelompok berdasarkan tingkatan kondisi sosial ekonomi orang tua. Dalam proses menetapkan UKT begitu banyak indikator sosial ekonomi orang tua yang harus dijadikan acuan sehingga menyulitkan dalam mengidentifiksi dan mencari formula yang tepat. Untuk mengelompokkan data mahasiswa ini dilakukan dengan teknik data mining menggunakan metode K-Means Clustering. Metode ini mengelompokkan besaran UKT mahasiswa berdasarkan pola atau kemiripan data sosial ekonomi orang tua. Data yang digunakan dalam penelitian ini adalah data calon mahasiswa baru Unversitas Negeri Padang. Pengelompokan ini bertujuan untuk membantu menetapkan besaran UKT calon mahasiswa baru pada Perguruan Tinggi Negeri. Hasil dari penelitian diperoleh 5 kelompok besaran UKT, terdiri dari UKT kategori 1 Rp. 500.000, UKT kategori 2 Rp. 1.000.000, UKT kategori 3 Rp. 2.000.000, UKT kategori 4 Rp. 3.000.000 dan UKT kategori 5 Rp. 4.000.000.
Scholarships are one of the factors that can increase learning motivation for students. This scholarship is one of the school programs to help parents of students to ease the burden of education costs of the students. In determining the scholarship recipients who meet the requirements and eligibility at the MA Raudlatul Ulum a decision support system is using the Weighted Product method. Decision making in the Weighted Product method is done by multiplication to connect the rating of each attribute, where the rating of each attribute must be raised by the weight of the attribute in question. In this study there are several criteria used in decision making, namely Average Value, Behavior, Extracurricular, Parent Income, and Dependents of Parents. The research carried out begins with determining the weight of each criterion, then the ranking process is carried out which will produce the most optimal alternative. Based on the results of testing that has been done, it can be concluded that the system is able to provide accuracy of 90% if compared with the results of testing manually.
Clustering K Mean digunakan untuk melakukan pengelompokan. Metode K-Means berusaha mengelompokkan data yang ada dalam beberapa kelompok yang unik, dimana data dalam satu kelompok memiliki karakteristik yang sama satu sama lainnya dan memiliki karakteristik yang berbeda dengan data ada dalam kelompok yang lain. Untuk melakukan pengelompokan mahasiswa potensi drop out diperlukan atribut. Total Sistem Kredit semester, Indek Prestasi Komulatif, dan Semester Total. Proses Clustering K- Mean dilakukan dengan menentukan titik centroid awal yang terdekat dalam satu kelompok mahasiswa yang potensial drop out. Hasil clustering K-Mean oleh Total Sistem Kredit semester, Indek Prestasi Komulatif, dan Total Semester. Hasil Clustering mahasiswa yang potensial drop out untuk angkatan 2014 berada pada cluster 0 berjumlah 4 orang mahasiswa atau 30,77% dari 13 Sampel, angkatan 2015 berada pada cluster 1 berjumlah 4 mahasiswa dan cluster 2 berjumlah 2 mahasiswa atau 66,7% dari 9 sampel, angkatan 2016 berada pada cluster 0 berjumlah 2 mahasiswa dan cluster 1 berjumlah 10 mahasiswa atau 50% dari 24 sampel, dan angkatan 2017 berada pada cluster 2 kekuatan 4 mahasiswa atau 22,22% dari 18 sampel .
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