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ANALISIS K-MEANS CLUSTER PADA KABUPATEN/KOTA DI PROVINSI BANTEN BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA Rindang Ndaru Puspita
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 2 No. 3 (2021): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v2i3.85

The Human Development Index (HDI) is one of the parameters of success in the development of the quality of human life, besides that at the regional level, the HDI is an indicator of the primary performance measurement and allocation of Regional Incentive Funds in promoting the welfare of the people in the area. In 2020 the Banten Province Human Development Index 72.45 only rose 0.01% compared to 2019, lower than the growth in 2019, which reached 0.68% and is still stuck in the high category (70≤HDI≤80), this indicates the progress of human development in Banten experienced a slowdown, In addition, when compared to the growth of the HDI-forming indicators in 2019, all components that make up the HDI experienced a slowdown in growth except for RLS which experienced growth acceleration of 0.33% from 1.39% in 2019 to 1.72% in 2020. So it is necessary to do a deeper analysis to determine the characteristics of the indicators that make up the HDI in the City as a contributor to the HDI value of the Banten Province so that efforts can be made to increase human development as evidence of improving the welfare of the people in the Banten Province. The K-Means Cluster method is used to group cities in Banten Province based on similar characteristics in terms of the HDI compiler indicators, including Life Expectancy at Birth, Expected Years of Schooling, and Average Length of School in, and Expenditure per Capita. Based on the results of the analysis obtained three clusters consisting of cities with similar characteristics in each cluster. Cluster 1 is a City with a low HDI indicator consisting of Pandeglang, Lebak, Serang. Cluster 2 is a City with a medium HDI indicator consisting of Tangerang, Cilegon, Serang City. Cluster 3 has a high HDI indicator consisting of Tangerang City and South Tangerang City. After obtaining City information based on the characteristics of each cluster, then the Banten Provincial government can provide direction and policies to each City in Clusters 1 and 2 to be able to develop activity programs with more attention to the HDI compiler indicators so that the Human Development Index in the City can increase

PERBANDINGAN METODE ANALISIS CLUSTER HIRARKI PADA DATA MARGIN PERDAGANGAN DAN PENGANGKUTAN (MPP) KOMODITAS STRATEGIS DI INDONESIA Rindang Ndaru Puspita
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 3 No. 1 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v3i1.115

Trade and Transport Margins is the difference between the sales value and th e purchase value of the commodity as compensation for the trader who is the distributor of the commodity. The higher Trade and Transport Margins indicates the longer and less efficient distribution pattern, so that it can harm producers and consumers. Data is taken from the 2020 Poldis survey with strategic commodities of Rice, Eggs, Sugar, and Cooking Oil. The value of the National Trade and Transport Margins in 2020 for rice and sugar has decreased but is not followed by an increasing number of provincial Trade and Transport Margins which have decreased. It is necessary to conduct an analysis to determine the characteristics of commodities in each province, hierarchical cluster analysis by comparing the application of the average, complete, single, ward's, and centroid methods to obtain the best method. Based on the results of hierarchical cluster analysis for all methods, then compared the cophenetic correlation values and the highest correlation on the average method with a correlation of 0.8318939. Then cluster profiling was carried out using the average method, it was concluded that Cluster 1 was dominated by low Trade and Transport Margins for all commodities, so that the distribution pattern of all commodities was efficient in 28 provinces in Indonesia. Cluster 2 has the characteristics of high Trade and Transport Margins of Rice and Cooking Oil as well as eggs and moderate sugar, so the distribution pattern is still not efficient for rice and cooking oil commodities in 4 provinces in Indonesia. Cluster 3 has the characteristics of high Trade and Transport Margins of eggs and sugar as well as medium of rice and cooking oil, so the distribution pattern is still not efficient for eggs and sugar in 2 provinces in Indonesia. Based on the results of the cluster analysis, it is hoped that it can be used as a reference in formulating and establishing policies as an effort to reduce the Trade and Transport Margins value of strategic commodities in provinces in clusters 2 and 3 according to the conditions of commodity characteristics so that the distribution pattern of strategic commodities becomes more efficient.

PERAMALAN TINGKAT PENGANGGURAN TERBUKA PROVINSI BANTEN DENGAN METODE TRIPLE EXPONENTIAL SMOOTHING Rindang Ndaru Puspita
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 3 No. 2 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v3i2.138

The Unemployment Rate is the main indicator to measure the unemployment in the labor force. The higher Unemployment Rate will have an impact on the decline in people's welfare. Based the results of SAKERNAS in February 2022, Banten Province's Unemployment Rate is the highest in Indonesia, besides that, since the Covid-19 pandemic, the Banten Province's Unemployment Rate has increased. There needs an effort to reduce the Unemployment Rate so that it can reduce the number of unemployed, one of which is by knowing the predicted value of the Unemployment Rate in the future. From the results of historical data analysis, it is known that the data pattern of the Banten Province's Unemployment Rate is seasonal, so the Triple Exponential Smoothing Method is suitable to be used because it can stabilize seasonal patterns. Forecasting is carried out to determine the Unemployment Rate in Banten Province in the next 7 periods, from Semester 2 of 2022 to Semester 2 of 2025. From the forecasting results, the MAPE value is 8.858859%, which is smaller than 10%, that shows very accurate predictions. Furthermore, the results of this research are expected to be used as consideration in determining strategies to reduce the Banten Province's Unemployment Rate

PERBANDINGAN METODE CENTROID DAN WARD DALAM PENGELOMPOKKAN TINGKAT PENYELESAIAN PENDIDIKAN DI INDONESIA Rindang Ndaru Puspita
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 3 No. 3 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v3i3.159

Education is foundation in building the nation and state, besides that education is a right for all citizens. Educational achievements in Indonesia can be seen from the achievement of educational graduation rates for all levels of education from elementary to high school. The government needs to pay more attention to education in Indonesia, so there needs to be a grouping of provinces according to similar characteristics, so that the government can focus more on preparing programs and activities to improve the quality of education. The right method for grouping provinces is the best method from the results of cluster analysis by comparing the centroid and ward methods as seen from the value of the cophenetic correlation coefficient. Based on the research, the best method was obtained, namely the centroid method with cluster profiling: cluster 1 is a cluster with a moderate level of completion of education consisting of 31 provinces, cluster 2 with a level of completion of higher education consists of 2 provinces, and cluster 3 with a low level of completion of education consists of 1 province

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