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Journal : Building of Informatics, Technology and Science

Penerapan Metode Trend Moment Untuk Memprediksi Jumlah Pertumbuhan Penduduk Amalia, Laily Rizky; Ramdhan, William; Kifti, Wan Mariatul
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (476.135 KB) | DOI: 10.47065/bits.v3i4.1396

Abstract

Population growth is caused by three components, namely birth (fertility), death (mortality), and migration. These three components greatly affect the process of population growth that occurs. The development of population growth in one sub-district is very important to be detailed so that the development of the subdistrict can be improved. Forecasting is an important tool in effective and efficient planning. An important step after forecasting is done is verification of forecasting in such a way that it reflects past data and the underlying causal systems of such growth. As long as the forecasting representation is reliable, forecasting results can continue to be used. The purpose of this study is to apply the Trend Moment method to predict the population growth in Joman Water District in the next few years based on three components, namely birth (fertility), death (mortality), and migration. The results of this study can predict the number of population growth using the Trend Moment Method in 2022 with birth rates of 1573, 641 arrivals, 601 arrivals, and 235 displacements with errors below 10%
Analisis Kelayakan Penerima Bantuan Covid-19 Menggunakan Metode K–Means Nursia, Akris Nursia; Ramdhan, William; Kifti, Wan Mariatul
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (684.745 KB) | DOI: 10.47065/bits.v3i4.1399

Abstract

The government provides several types of assistance during the covid-19 pandemic that is distributed through the agencies of each village throughout Indonesia, one of which is Punggulan Village air joman subdistrict. The types of assistance that have been distributed to citizens are Cash Social Assistance (BST), Social Safety Net Assistance (JPS), Non-Cash Sembako Assistance, and Cash Direct Assistance (BLT). So far the assistance provided by Punggulan Village is still done manually, so it is possible to occur errors in the collection and distribution of assistance. To solve the problem, the author applies one of the data mining algorithms, the K-Means algorithm, to determine the recipient of covid-19 assistance that is done by collecting population data by the specified attributes. Then the data is weighted to facilitate the calculation of K-Means, after that build a system to implement the K-Means algorithm and perform testing. Population data used is 50 data recipients of aid 2021 as a sample using 3 attributes, namely, income, dependents, and other beneficiaries. The result of this system is prospective recipients of Covid-19 assistance with 2 feasible clusters (C1) and unfit (C2)