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All Journal Parameter: Journal of Statistics
Putera, Fachruddin Hari Anggara
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Clustering of Province in Indonesia Based on Aquaculture Productivity Using Average Linkage Method Putera, Fachruddin Hari Anggara; Mangitung, Septina F.; Madinawati; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2021.v2.i1.15683

Abstract

Fisheries are one of the agricultural sub-sectors that play an important role in contributing to income figures for the state and the region because most of Indonesia's territory is water so that the fisheries sector is a sub-sector that is feasible to be developed in this country, one of which is through aquaculture. One of the efforts that can increase and maintain productivity in the aquaculture sector is to classify provinces that produce aquaculture production into groups based on the similarity of characteristics possessed by each province in Indonesia. In this study, clustering was carried out using cluster analysis using the average linkage method and based on the analysis results obtained showed that cluster 1 consists of 25 provinces, cluster 2 consists of 5 provinces, cluster 3 consists of 2 provinces, cluster 4 consists of 1 province, and cluster 5 consists of 1 province with a standard deviation value within a cluster of 11,729 and a standard deviation between clusters of 118,745.
Corn Production Exploration of Central Sulawesi Using Multiplicative Winter Model Putera, Fachruddin Hari Anggara; Amelia, Rezi; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 2 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i2.15943

Abstract

Corn is a very important food ingredient after rice. Central Sulawesi corn production data is in the form of time series data which every year in certain months increases or decreases in production. Therefore, the method that can be used for forecasting is the winter multiplicative method. This study aims to build the best model for forecasting corn production in Central Sulawesi using the winter multiplicative method. The results of this study are used to explore corn production for the next period. Modeling is done by selecting the best combination of parameters and the best combination of model parameters is obtained with a mean absolute percentage error (MAPE) of 18% with a value of α = 0,5; γ = 0,1; and β = 0,1. The data plot of the forecasted corn production shows fluctuations which indicate seasonal factors and trends in it
Co-Authors Amelia, Rezi Lilies Handayani Madinawati Mangitung, Septina F.