| dc.contributor |
Fakultas Matematika dan Ilmu Pengetahuan Alam |
|
| dc.creator |
Andini, Gita |
|
| dc.creator |
Hajarisman, Nusar |
|
| dc.date |
2020-01-25 |
|
| dc.identifier |
http://karyailmiah.unisba.ac.id/index.php/statistika/article/view/19746 |
|
| dc.identifier |
10.29313/.v0i0.19746 |
|
| dc.description |
Abstract. The main indicator of economic stability is the inflation rate as measured by the development of the Consumer Price Index (CPI). Picturing the value of CPI in the coming periods are very much needed. One of them is forecasting through the Box-Jenkins ARIMA method which was introduced and developed by G.E.P Box and G.M. Jenkins in the 1960s. Forecasting with this method does not ignore the rules in time series data. The ARIMA method has a constant average value so it is not good enough to predict data over a long period of time. In contrast to ARIMA, Fuzzy Time Series (FTS) is a concept used to predict problems where actual data is formed in linguistic values. The FTS method was first discovered by Song and Chissom (1993). FTS is focused on forecasting with uncertainty conditions, in the sense that this method does not pay attention to the rules of time series data such as stationarity testing, parameter reaction, and diagnostic models. In this thesis the ARIMA and Fuzzy Time Series Saxena-Easo methods are applied. The data used are monthly CPI data in Indonesia from January 2014 to December 2019. From the comparison of the MAPE values of the two methods, it was obtained that the Saxena-Easo FTS method produced a MAPE value smaller than ARIMA in predicting CPI in Indonesia, which amounted to 0.0140%. These results indicate that the Saxena-Easo FTS method is very good in perfecting the ARIMA method.Keywords: Time Series Analysis. ARIMA, Fuzzy Time Series, Fuzzy Time Series Saxena-Easo, Consumer Price IndexAbstrak. Indikator pokok dari stabilitas ekonomi adalah laju inflasi yang diukur oleh perkembangan Indeks Harga Konsumen (IHK). Sangat diperlukan gambaran mengenai IHK pada periode yang akan datang. Salah satunya peramalan melalui metode ARIMA Box-Jenkins yang dikenalkan dan dikembangkan oleh G.E.P Box dan G.M. Jenkins pada tahun 1960-an. Peramalan dengan metode ini tidak mengabaikan kaidah-kaidah pada data deret waktu. Metode ARIMA memiliki nilai rata-rata yang konstan sehingga tidak cukup baik untuk meramalkan data dalam jangka waktu yang panjang. Berbeda dengan ARIMA, Fuzzy Time Series (FTS) merupakan suatu konsep yang digunakan untuk meramalkan masalah dimana data aktual dibentuk dalam nilai-nilai linguistik. Metode FTS pertama kali ditemukan oleh Song dan Chissom (1993). FTS difokuskan pada peramalan dengan kondisi ketidakpastian, dalam artian metode ini tidak memperhatikan kaidah-kaidah data deret waktu seperti pengujian stasioneritas, penaksiaran parameter, dan diagnostik model. Pada skripsi ini diterapkan metode ARIMA dan Fuzzy Time Series Saxena-Easo. Data yang digunakan adalah data bulanan IHK di Indonesia periode Januari 2014 sampai Desember 2019. Dari perbandingan nilai MAPE kedua metode diperoleh bahwa metode FTS Saxena-Easo menghasilkan nilai MAPE yang lebih kecil daripada ARIMA dalam meramalkan IHK di Indonesia, yaitu sebesar 0,0140%. Hasil tersebut menunjukkan bahwa metode FTS Saxena-Easo sangat baik dalam menyempurnakan metode ARIMA.Kata Kunci: Analisis deret waktu, ARIMA, Fuzzy Time Series, Fuzzy Time Series Saxena-Easo, Indeks Harga Konsumen. |
|
| dc.language |
id |
|
| dc.publisher |
Universitas islam Bandung |
|
| dc.rights |
Copyright (c) 2020 Prosiding Statistika |
|
| dc.source |
Prosiding Statistika; Vol 6, No 1, Prosiding Statistika (Februari, 2020) |
|
| dc.source |
Prosiding Statistika; Vol 6, No 1, Prosiding Statistika (Februari, 2020) |
|
| dc.source |
2460-6456 |
|
| dc.source |
10.29313/.v0i0 |
|
| dc.subject |
Statistika |
|
| dc.subject |
Analisis deret waktu, ARIMA, Fuzzy Time Series, Fuzzy Time Series Saxena-Easo, Indeks Harga Konsumen |
|
| dc.title |
Penyempurnaan Metode ARIMA melalui Fuzzy Time Series Saxena-Easo untuk Meramalkan Indeks Harga Konsumen (IHK) di Indonesia (Berdasarkan Data Bulan Januari 2014 sampai Desember 2019) |
|
| dc.type |
info:eu-repo/semantics/article |
|
| dc.type |
info:eu-repo/semantics/publishedVersion |
|
| dc.type |
Peer-reviewed Article |
|
| dc.type |
Kuantitatif |
|