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All Journal Jambura Journal of Mathematics JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
I GUSTI BAGUS NGURAH DIKSA
Institut Teknologi Sepuluh November
Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Mathematics Social Sciences

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ANALISIS KETERKAITAN ANTAR KELOMPOK PENGELUARAN INFLASI MENGGUNAKAN VECTOR AUTOREGRESSIVE MODEL I GUSTI BAGUS NGURAH DIKSA
Jambura Journal of Probability and Statistics Vol 2, No 1 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i1.7763

Abstract

In this study, testing steps were carried out, namely the stationarity test, determining the optimum lag, hypothesis testing and the formation of the VAR model, the Granger causality test and classical assumptions. The data used are month to month inflation data for each inflation expenditure group in Indonesia for the period January 2013 to December 2019. The inflation expenditure group is foodstuffs; processed food, beverages, cigarettes and tobacco; housing, water, electricity, gas and fuel; clothing; health; education, recreation and sports; and transportation, communication, and financial services. However, in this study only five inflation expenditure groups were used, namely foodstuffs; processed food, beverages, cigarettes and tobacco; housing, water, electricity, gas and fuel; clothing; as well as transportation, communication and financial services. The purpose of this study is to analyze the relationship between inflation expenditure groups and to find a forecasting model for inflation expenditure groups in Indonesia. After the Granger causality test was carried out, all probability values between endogenous variables, namely the five groups of inflation expenditures were less than 0,05 or rejected H0. Therefore, it can be concluded that there is a causal relationship between endogenous variables.
Peramalan Gelombang Covid 19 Menggunakan Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing di Indonesia dan Prancis I Gusti Bagus Ngurah Diksa
Jambura Journal of Mathematics Vol 3, No 1: January 2021
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (624.967 KB) | DOI: 10.34312/jjom.v3i1.7771

Abstract

ABSTRAKIndonesia dan Prancis adalah dua Negara yang mengalami Covid 19 dengan pola pergerakan kasus Covid 19 yang berbeda. Kondisi Indonesia masih mengalami siklus one wave namun Prancis sudah masuk pada pola second wave. Makna second wave adalah kondisi epidemi Covid 19 yang baru muncul setelah epidemi sebelumnya dianggap selesai. Dalam peramalan kasus Covid 19 baik itu terkait informasi puncak dari terjadinya kasus Covid 19 serta ramalan terkait akan berakhirnya pandemi kasus Covid 19 suatu negara merupakan hal penting bagi pemerintah suatu Negara. Model hybrid meningkatkan akurasi ramalan dibandingkan model time series yang dilakukan secara terpisah. Tujuan penelitian ini adalah melakukan peramalan kasus Covid 19 di Indonesia dan Prancis dengan menggunakan metode hybrid dan membandingkan dengan peramalan dengan salah satu metode tunggal. Metode yang digunakan adalah metode tunggal yaitu Nonlinear Regression Logistic dan metode Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing. Hasilnya adalah model peramalan Hybrid Nonlinear Regression Logistic and Doubel Exponential Smoothing lebih bagus digunakan dalam peramalan kasus Covid 19 di Indonesia dan Prancis. Terlihat bahwa nilai MAPE model Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing jauh lebih kecil dibandingkan model peramalan Nonlinear Regression Logistic. ABSTRACTIndonesia and France are two countries that have experienced Covid 19 with different patterns of movement of Covid 19 cases. Indonesia's condition is still experiencing a one wave cycle but France has entered into the second wave pattern. The meaning of the second wave is the condition of the Covid 19 epidemic which only emerged after the previous epidemic was considered over. In forecasting the Covid 19 case, whether it is related to the peak information on the occurrence of the Covid 19 case and predictions regarding the end of the pandemic of the Covid 19 case in a country, it is important for the government of a country. The hybrid model improves forecast accuracy compared to the time series model which is carried out separately. The purpose of this study is to forecast the cases of Covid 19 in Indonesia and France using the hybrid method and comparing with forecasting with one single method. The method used is a single method, namely Nonlinear Logistic Regression and Hybrid Nonlinear Regression Logistic-Double Exponential Smoothing methods. The result is that the Hybrid Nonlinear Regression Logistic and Double Exponential Smoothing forecasting model is better used in forecasting the Covid 19 cases in Indonesia and France. It can be seen that the MAPE value of the Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing model is much smaller than the Nonlinear Regression Logistic forecasting model.
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