The ARIMA (Autoregressive Integrated Moving Average) method is a very appropriate method to use for the short term because the ARIMA method has very accurate accuracy. And also determine a good relationship between the variables to be forecasted with the value used for forecasting. This study uses the ARIMA method. The purpose of the study was to predict the production of Banggai Cardinalfish (Pterapogon kauderni) which is an ornamental fish commodity that is in great demand. High market demand and production predictions are able to provide the market, becoming important information so that potentials and opportunities can be exploited. The accuracy of the resulting forecast is calculated using the MSE (Mean Squared Error) and MAE (Mean Absolute Error) values. Forecasting results from Banggai Cardinalfish production using the ARIMA method were 830.33 in May 2017 with the resulting MAPE value of 176.93 and MAE of 975.23.
Pandemi COVID-19 dalam kurun waktu dua tahun berhasil menginfeksi jutaan orang di seluruh dunia dan menyebabkan banyak kematian. Guna menghentikan penyebaran virus, pemerintah melakukan tindakan yaitu menerapkan protokol kesehatan dan mewajibkan vaksinasi kepada masyarakat. Namun, kegiatan vaksinasi masih lamban untuk mencapai target. Maka dari itu, dalam penelitian ini akan dilakukan suatu pengelompokan untuk mengetahui tingkat persebaran vaksinasi di Indonesia menurut provinsi dengan data jumlah vaksinasi per-kategori masyarakat pada tanggal 1 Februari 2022. Salah satu algoritma pengelompokan dalam Data Mining yaitu Spectral Clustering. Pengelompokan spektral merupakan teknik yang mengikuti pendekatan konektivitas, dimana metode ini mengklasifikasikan titik-titik yang terhubung atau berbatasan langsung. Penelitian ini menghasilkan 3 klaster untuk masing-masing kategori, yaitu klaster daerah-daerah yang memiliki tingkat persebaran vaksinasi tinggi, sedang, dan rendah. Evaluasi klaster diukur menggunakan Davies-Bouldin Index (DBI) dengan rata-rata nilai DBI tiap kategori yaitu 1,01422.
The Human Development Index (HDI) is an indicator to measure the success of quality human life. Efforts to calculate HDI to the district/city level are very important, so a better understanding of local conditions is needed with more adequate data support for all districts/cities in Indonesia. Modelling of HDI in Central Java with the factors suspected to influence it is done by Geographically Weighted Logistic Regression (GWLR) with the Adaptive Gaussian Kernel weighting function. The analysis result shows that the deviance value is 32.3992, where the value is greater than the value of χ2 (0.1.4) table, which is 7.77943. So, there is at least one independent variable that significantly affects of HDI in Central Java. GWLR Model Parameter Testing Results with Adaptive Gaussian Kernel weighting function obtained factors that influence HDI in Central Java Province is health facilities.
The online population census was first launched in 2020. The purpose of the online population census 2020 is to provide data on the number, composition, distribution, and characteristics of the Indonesian population towards one Indonesian population data and provide demographic parameters and population projections and other population characteristics for population projections and SDGs indicators. These data are needed by the government as one of the bases for making decisions or policies in order to be able to accommodate all existing interests. This innovation with an online census approach is undoubtedly inseparable from social problems or constraints. Social, economic, and geographic factors affect the literacy of information and communication technology in society. The factual conditions in the field encouraged the team community service to take a strategic role by carrying out community service activities in Kecamaran Pabelan, Semarang Regency, in the form of online population census 2020 assistance activities. Mentoring methods are carried out by providing counseling, socialization, and technical guidance to the Public. The results achieved from this assistance to partners were an increase in the community response rate in Semarang Regency, more partners could participate and it was easier to fill data in the online population census 2020.
The human development index (HDI) is a measure to see an increase in regional development that has a very broad dimension, because it increases the quality of the population of an area in terms of life expectancy, education, and decent standard of living. In 2010 the Central Java HDI increased by 66.08% and increased by 4.44%, with the total HDI in 2017 of 70.52 percent. Spatial regression is the development of classical linear regression involving the region model. Spatial regression ensemble is a technique to be sent spasi spatial regression models by adding noise (additive noise). The type of spatial weighting used is Queen Contiguity. The selection of the best model using AIC and RMSE values. The purpose of this study is to provide an assessment of the distribution of HDI data in the Province of Central Java in 2017 and to do modeling using non-hybrid spatial ensemble regression regression. The results of this study are the SAR spatial method with ensemble giving results with AIC value of 143 and RMSE value of 1.3899 with a value of 90.09%. Significant variables on HDI are population density (X1), poverty (X2), school participation rates (X5), and average per capita per month for food and non-food (X7).
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