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Arsyil Hendra Saputra
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ANALISIS CLUSTER PADA KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN PRODUKSI PALAWIJA Safitri, Diah; Widiharih, Tatik; Wilandari, Yuciana; Saputra, Arsyil Hendra
MEDIA STATISTIKA Vol 5, No 1 (2012): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (213.891 KB) | DOI: 10.14710/medstat.5.1.11-16

Abstract

Production of palawija, namely maize, cassava, sweet potato, peanut, soybean, and green bean is an important food crop in Central Java. In this article, districts/cities in Central Java are grouped into three groups based on the production of palawija so as to know which group have high potential the production of maize, cassava, sweet potato, peanut, soybean or green bean by using k-means cluster analysis. Cluster 1 consists of District Cilacap, Wonosobo, Magelang, Karanganyar, Semarang, Temanggung, Kendal, and Batang that have a high potential in maize production. Cluster 2 consists of District Banyumas, Purbalingga, Banjarnegara, Kebumen, Purworejo, Boyolali, Klaten, Sukoharjo, Sragen, Blora, Rembang, Pati, Kudus, Jepara, Demak, Pekalongan, Pemalang, Tegal, Brebes, Magelang City, Surakarta City, Salatiga City, Semarang City, Pekalongan City, and Tegal City  that have a high potential in peanut production. Cluster 3 consist of District Wonogiri and Grobogan that have a high potential in soybean production, green bean production, cassava production, and sweet potato production
ANALISIS DATA RUNTUN WAKTU DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Saputra, Arsyil Hendra; Tarno, Tarno; Warsito, Budi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (460.124 KB) | DOI: 10.14710/j.gauss.v1i1.570

Abstract

One popular method of time series analysis is ARIMA. The ARIMA method requires some assumptions; residual of model must be white noise, normal distribution and constant variance. The ARIMA model tends to be better for time series data which is linear. Whereas for the nonlinear time series data have been widely studied by nonlinear methods, one of that is Adaptive Neuro Fuzzy Inference System or ANFIS. The ANFIS method is a method that combines techniques Neural Network and Fuzzy Logic. In this thesis discussed the ANFIS method specifically for the analysis of time series data that have characteristics such as stationary, stationary with outlier, non stationary and non stationary with outlier, and the data of Indonesian palm oil prices is used as a case study. The ANFIS results which were obtained are compared with the results of ARIMA method by the value of RMSE. Based on the analysis and discussion, it is obtained that the results of ANFIS method are better than the results of ARIMA method.
Co-Authors Budi Warsito Diah Safitri Tarno Tarno Tatik Widiharih Yuciana Wilandari