FP Growth algorithm is widely used to analyze patterns from a huge amount of data with (frequent) repeated items. The objective of this research is to analyze playing pattern a badminton player, one of a popular sport in Indonesia. The data set was generated from a technical stroke during the game. The model used in this study was Jonathan Christie a top Indonesian badminton player. The method of data collection was done by dividing the playing field into various areas of the game. Observations were made by using the software, to calculate and classify the types of stroke that carried out by the athlete. The result of this research; the tactical approach of Jonathan Christie during this match was described. The data obtained would be very useful for the coach to improve the athlete’s performance. Another advantage obtained was the analysis of the athlete’s performance can be done with a quantitative approach so that it can enrich the current methods. As the conclusion, the FP Growth algorithms were able to describe the game pattern of a badminton athlete, JC by using PHP and MySQL. Sport science has become a necessity to develop to increase athletes’ competitiveness.
The economy of a region is affected by the stability of food supplies. If the market price of the food supply is stable, the purchasing power level will increase. The price stability of food supplies can be anticipated by using the Support Vector Regression Method, to predict the Consumer Price Index, known as CPI. In the Consumer Price Index assessment, using data based on recording, measurement and calculation of the goods and services average price which consumed by households in a certain period of time. Goods and services that are deemed to represent household expenses are then averaged. The CPI in this study is a type of food supply issued by the Indonesian Central Statistics, and the input variable is taken from the prices of staple commodities in the city of Surabaya, Malang and Kediri based on data from the Siskaperbapo website. To get the supported vector data, the hyperplane maximized by the SVR concept. This concept is able to overcome the overfitting, in order to obtain more accurate prediction results. In predicting the Consumer Price Index, reference data is divided as training data 2016-2019 and testing data 2017-2020. All four kernels were used in the test, namely Spline kernel, Gaussian-RBF kernel, Linear kernel and Polynomial kernel. All four kernels are compared to see their MAPE, this can be shown by the Mean Absolute Percentage Error (MAPE) of less than three, if by using Gaussian RBF kernel. The smallest MAPE value showed by Malang CPI value, which is 1.8242 with C = 50, followed by Kediri with the MAPE value of 2.251 with C = 50 and MAPE value of Surabaya which is 2.5279 with C = 50.
Knowledge Discovery in Data Base (KDD) is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data (base). FP-growth widely used to analyze data pattern from amount huge of data with frequent repeated items. In this research, this algorithm used to analyze in a sport activity, badminton. Specifically, the algorithm use to identify and considering frequent item set from particular technical stroke during the game. Finally, sport science is a must for Indonesia to win major tournament. This is our concern in this research.
Bank Sampah adalah suatu unit kegiatan masyarakat untuk mengatasi pencemaran lingkungan di wilayah perkotaan. Bank Sampah ini memungkinkan masyarakat untuk memperoleh penghasilan tambahan, dengan mengisi tabungan menggunakan sampah yang ditimbang dan diberi nilai uang (moneter), sesuai harga yang sudah ditentukan oleh BSI ( Bank Sampah Induk ) Kota Mojokerto. BSI merupakan unit pemerintah daerah yang penampung sampah yang terkumpul pada tiap bank sampah di masing-masing kelurahan di Kota Mojokerto. Permasalahan yang dihadapi oleh Bank Sampah “Gaposi Sejahtera “ adalah sering terjadinya kerepotan dalam transaksi dan laporan bulanan, sering tertukarnya jenis sampah dan harganya, harga sampah per kilo sering berubah, serta tidak adanya backup data yang bagus untuk menanggulangi data dari nasabah maupun jumlah tabungannya. Untuk mengatasi permasalahan diatas, peneliti berinisiatif untuk membangun sistem aplikasi untuk mengolah data tabungan pada Bank Sampah “Gaposi Sejahtera“. Perancangan menggunakan metode Waterfall, diharapakan memudahkan dalam pelayanan kepada pelanggan dan meningkatkan akurasi data.
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