Tarno Tarno
Departemen Statistika, FSM, Universitas Diponegoro, Jl. Prof Soedharto SH Tembalang, Semarang

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Journal : Media Statistika

KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART) Dwi Ispriyanti; Alan Prahutama; Mustafid Mustafid; Tarno Tarno
MEDIA STATISTIKA Vol 12, No 1 (2019): 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 (360.866 KB) | DOI: 10.14710/medstat.12.1.63-72

Abstract

Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.
PEMODELAN HYBRID ARIMA-ANFIS UNTUK DATA PRODUKSI TANAMAN HORTIKULTURA DI JAWA TENGAH Tarno Tarno; Agus Rusgiyono; Budi Warsito; Sudarno Sudarno; Dwi Ispriyanti
MEDIA STATISTIKA Vol 11, No 1 (2018): 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 (506.342 KB) | DOI: 10.14710/medstat.11.1.65-78

Abstract

The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually.Keywords: Time Series, Potato production, hybrid, ANFIS, ARIMA, LM-test