Berdikari: Jurnal Ekonomi dan Statistik Indonesia
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Comparison of Regression Analysis with Machine Learning Supervised Predictive Model Techniques
Full text linkThe happiness index is a parameter used to measure the level of happiness and well-being of people in a particular country or region. This research aims to determine the factors that contribute to people's happiness. In terms of modelling, this study will compare several regressions modelling using machine learning, including regression trees, random forests and Support Vector Regression (SVR). The SVR model has a minor error value in terms of MSE, RMSE and MAE compared to the other three models. The same thing happened when viewed from the value of R2 that the SVR model has an enormous value. This result indicates that SVR modelling is the best of the four models. A comprehensive policy is needed to increase a country's happiness index
Assessing The Impact of Jajar Legowo Planting System on Wetland Paddy Productivity and Income of Farmers in Indonesia
Full text linkThis study aims to assess whether Jajar Legowo planting system has a significant impact on increasing the productivity of wetland paddy and the income of the paddy growers in Indonesia. We applied a linear regression model to the results of the 2017 National Cost Structure of Paddy Cultivation Household Survey conducted by BPS-Statistics Indonesia in all 34 provinces. The main contribution of this study is to provide an evaluation of the performance of Jajar Legowo planting system in increasing paddy productivity and income of the farmers. Therefore, our research can be used by the government as a reference for future improvement of the implementation of Jajar Legowo cultivation system. Our findings show that the new cultivation system has a significant impact on increasing the productivity of wetland paddy. Without controlling for other variables affecting productivity, the estimation result pointed out that on average, the new cultivation system can increase productivity by about 10 per cent. However, after controlling for other variables (the farmers and other cultivations characteristics), the magnitude decreases to around 5 per cent. Moreover, our estimation results also show that the income of the farmers rises by around 12 per cent by implementing Jajar Legowo. Our study indicates that the implementation of Jajar Legowo planting system results in better efficiency than that of the conventional one
Modeling Rice Production in West Java by Means Geographically Weighted Regression
Full text linkIn Indonesia, rice production varies from province to province, resulting in both large and small disparities between provinces. In Indonesia, East Java, Central Java and West Java Provinces have the highest rice production. In contrast to East Java and Central Java, however, the total rice consumption per year in West Java is the highest. In linear regression, the coefficients are the same for all regions, while each region sometimes has different influencing factors, resulting in spatial diversity. Consequently, the Geographically Weighted Regression (GWR) method was used to model the rice production of West Java Provincial regencies/municipalities by accounting for spatial heterogeneity. The GWR model employs the fixed bi-square kernel function as its weighting function. This model includes five explanatory variables, such as number of agricultural labor, number of used rice seed, number of two-wheel tractor, number of water pump, and number of farmer groups, with rice production as the response variable. GWR model has greater coefficient determination (96.8 percent) and smaller AIC values (920.76) than global regression. During the period of 2018-2020, the number of two-wheel tractors and the number of water pumps had the greatest impact on rice production in West Java and the number of two-wheeled tractors and the number of farmer groups variables has an effect on rice production in most regencies/municipalities in West Java. There are 11 groups of areas which has the similarity of significant predictor variables
Pengaruh Ketidakpastian Pendapatan Terhadap Status Kepemilikan Rumah di Indonesia
Full text linkPersentase kepemilikan rumah pada rumah tangga di Indonesia menujukan trend yang semakin menurun dari tahun ke tahun. Keterbatasan penyediaan rumah dan tingginya harga rumah membuat sebagian besar rumah tangga menempuh skema kredit untuk dapat memiliki rumah. Penelitian ini bertujuan untuk menyelidiki bagaimana hubungan antara ketidakpastian pendapatan (income uncertainty), kendala pembiayaan (credit constraint) dan preferensi risiko (risk preference) rumah tangga terhadap status kepemilikan rumah di Indonesia. Studi ini menggunakan data IFLS Tahun 2007 dan 2014. Estimasi menggunakan variabel dengan metode probit dengan data panel dan probit OLS (ordinaryleast square) pada tahun 2014 dilakukan untuk mengetahui hubungan kausalitas antara ketidakpastian pendapatan (income uncertainty), kendala pembiayaan (credit constraint) dan preferensi risiko (risk preference) dengan status kepemilikan rumah di Indonesia. Hasil penelitian menunjukkan bahwa Indoensia sebagai negara berkembang menunjukan pengaruh negatif dari ketidakpastian pendapatan (income uncertainty) terhadap status kepemilikan rumah di Indonesia lebih besar bila dibandingkan dengan negara maju lainnya. Sedangkan kendala kredit memiliki pengaruh negatif terhadap status kepemilikan rumah di Indonesia, terutama kendala yang bersumber dari keterbatasan kekayaan yang digunakan sebagai jaminan pinjaman
Prediksi Penjualan Emas di PT. Pegadaian Area Jambi Menggunakan Fuzzy Time Series Cheng
Full text linkSetiap bulan berat emas yang terjual di PT. Pegadaian Area Jambi tidak bisa dipastikan. Data hasil penjualan emas setiap bulannya tidak stabil yang mengalami kenaikan dan penurunan penjualan. Hal tersebut menyebabkan data penjualan emas bersifat fluktuasi. Penelitian ini dilakukan untuk meramalkan jumlah penjualan emas pada bulan mendatang.
Fuzzy Time Series (FTS) Cheng merupakan metode peramalan untuk memprediksi data time series yang pola datanya tidak tetap atau berubah-ubah mengalami penurunan dan kenaikan di setiap periode. Sistem yang digunakan untuk memprediksi data dengan menangkap pola dari data sebelumnya atau data historis disebut sistem Fuzzy Times Series. Dalam penentuan interval, metode Cheng memiliki cara yang agak berbeda dengan membentuk Fuzzy Logical Relationship (FLR) berdasarkan pada urutan dan perulangan FLR yang sama dimasukan semua hubungan dengan pemberian bobot.
Penelitian ini menggunakan data Time Series. Data Time Series adalah data yang disusun berdasarkan urutan waktu atau data yang dikumpulkan dari waktu ke waktu atau disebut historis. Waktu yang digunakan pada data penelitian ini yaitu bulanan. Data yang digunakan adalah data penjualan emas di PT. Pegadaian Area Jambi dari bulan Januari 2020 hingga Oktober 2022. Penelitian ini memberikan hasil peramalan terhadap data historis menghasilkan nilai error Mean Absolute Percentage Error (MAPE) <10% dan menghasilkan nilai error Mean Absolute Deviation (MAD) sebesar 368 gram. Sehingga peramalan untuk bulan November 2022 diprediksi sebesar 4.521 gram. Sehingga metode Fuzzy Time Series Cheng sangat baik diguakan untuk meramalkan rata-rata penjualan emas di PT. Pegadaian Area Jambi
Identifikasi Data Outlier (Pencilan) dan Kenormalan Data Pada Data Univariat serta Alternatif Penyelesaiannya
Full text linkPenelitian ini bertujuan mengindentifikasi outlier (pencilan) dan kenormalan data pada univariat data. Adapun data yang digunakan berupa data persentase kemiskinan di Indonesia tahun 2022 yang berasal dari Badan Pusat Statistik. Metode pengujian outlier dilakukan dengan menggunakan grafik box plot, histrogram dan uji Grubbs. Sedangkan pengujian kenormalan data menggunkan uji SK Test dan Shapiro Wilk. Hasil penelitian menunjukkan terdapat data outlier yaitu pada observasi Provinsi Papua, dan data tidak berdistribusi normal. Selanjutnya dilakukan berbagai alternatif dalam menangani data outlier. Hasil menunjukkan menggunakan teknik tranformasi box cox, winsorizing dan trimming data, dapat menyelesaikan masalah outlier data. Metode box cox dan trimming sekaligus mampu mengatasi masalah kenormalan data, sedangkan metode winsorizing belum dapat mengatasi masalah kenormalan data
Determinan Produk Domestik Bruto di Provinsi Bali Tahun 2014-2019
Full text linkThis study aims to analyze the components of the Human Development Index on Gross Regional Domestic Product (GRDP) in Bali during the 2014-2019 period. The data used are time series and cross section obtained and processed from the Central Bureau of Statistics. Through a quantitative descriptive approach, this study uses multiple linear regression methods on panel data with the selected estimation model being the Fixed Effect Model. The results showed that the dependent variable of GRDP could be explained by independent variables by 92.03 percent and 7.97 percent of it was influenced by other variables outside this research model. Mean Years School and Life Expectancy variable have a positive effect on GRDP while the Labor Force Participation Rate variable has a negative and insignificant effect on GRDP. Government policies are needed to support equitable distribution of human development in each regio
Leading Sector in Banyumas Regency During The Covid-19 Pandemic Using Location Quotient and Shift-Share
Full text linkThe economic growth rate is an indicator used to measure the achievement of development. In 2020, the economic growth rate of Banyumas Regency contracted by -1.65 percent but this figure is still above the economic growth rate of Central Java Province which is -2.65 percent. The economic growth of a region is driven by the leading sectors in the region. This research aims to find out the leading sectors in Banyumas Regency that experienced progressive, growing, sluggish and backward movement during the Covid-19 outbreak compared to Central Java Province using Location Quotient and Shift-Share (LQ Shift- Share) analysis. The results of the analysis showed that the sectors that contributed the two largest to the Gross Regional Domestic Product (GRDP) in Banyumas Regency in 2020 at the same time including the progressive sector were trade sector (G) and construction sector(F)
Keterkaitan Antarlapangan Usaha di Provinsi Kepulauan Riau dan Hub-ungan Ekonomi dengan Provinsi Lain: Analisis IO Dan IRIO 2016
Full text linkSalah satu alternatif menggerakkan dan memacu pembangunan wilayah adalah menentukan lapangan usaha kunci atau unggulan. Sebagai salah satu daerah kepulauan di Indonesia, Provinsi Kepulauan Riau memiliki potensi keanekaragaman kondisi dan sumber daya alam. Studi ini bertujuan untuk menganalisis keterkaitan ke depan dan ke belakang (forward and backward linkages) antarlapangan usaha dalam perekonomian Provinsi Kepulauan Riau, juga hubungan ekonomi antara Provinsi Kepulauan Riau dengan provinsi lainnya. Analisis data yang digunakan adalah Inter Regional Input Output (IRIO). Tabel IRIO berukuran 17 industri x 34 provinsi yang diperoleh dari Badan Pusat Statistik (BPS). Hasil analisis menunjukkan bahwa Industri Pengolahan, Pengadaan Listrik dan Gas, Perdagangan Besar dan Eceran; Reparasi Mobil dan Sepeda Motor, Penyediaan Akomodasi dan Makan Minum, serta Informasi & Komunikasi adalah beberapa lapangan usaha unggulan di Provinsi Kepulauan Riau. Lapangan usaha Pengadaan Listrik & Gas memiliki keterkaitan ke depan dan keterkaitan ke belakang yang sangat tinggi dibandingkan dengan lapangan usaha lainnya. Analisis antarwilayah menunjukkan bahwa shock permintaan akhir di Provinsi Kepulauan Riau memiliki dampak output yang besar ke provinsi-provinsi di Pulau Sumatera (Jambi dan Riau) serta provinsi-provinsi di Pulau Jawa (Jawa Barat, DKI Jakarta, Jawa Timur, dan Banten). Di sisi lain, perekonomian Provinsi Kepulauan Riau sangat dipengaruhi oleh shock permintaan akhir di Provinsi Riau
Comparison Of Normal-Based and Beta-Based Regression Models on Ratio/ Proportion Data: Case Study: Gini Ratio Modeling in 34 Provinces in Indonesia in 2021
Full text linkThis study compares the regression using the assumption of a normal distribution with a beta distribution on ratio/proportion data. The data used is the Gini ratio data as the dependent variable and the percentage of the poor, economic growth and unemployment as independent variables in 2021. The data used is sourced from the Central Statistics Agency. The criteria for selecting the best model are based on the smallest AIC and BIC criteria. The results obtained by the beta regression model are better than the model based on the normal distribution. This result is reflected by the probability value of the model suitability test and the error value which the smaller AIC and BIC reflect. The poverty variable has a significant effect on the Gini ratio. On the other hand, there is not enough evidence that the variables of economic growth and open unemployment affect the Gini ratio. From the results obtained, it is hoped that the government will be able to implement appropriate policies in overcoming inequality so that every level of society can feel welfare without exception