Average Based Length Fuzzy Time Series Data Seasonal untuk Prediksi Volume Impor Migas Indonesia

Main Article Content

Adika Setia Brata
Alfian Anhar
Windy Lestari
Melda Juliza
Suci Rahmawati
M Tantry Abeng Evan Nugroho

Abstract

This study aims to predict the value of Indonesian oil and gas imports using the Fuzzy Time Series method of determining average-based intervals (Average Based Length). The data used in this research is annual periodic data from 2013-2021 which was downloaded from the bps.go.id website. The research results show that the volume value of oil and gas imports in Indonesia in January 2023 will reach 4125,802 thousand tons. This research can be concluded that the accuracy of the estimation is very good, producing a MAPE value of 8.04%. The MAPE value is defined as having very good estimation results if the MAPE value is <10%.

Article Details

How to Cite
Brata, A. S., Anhar, A., Lestari, W., Juliza, M., Rahmawati, S., & Nugroho, M. T. A. E. (2021). Average Based Length Fuzzy Time Series Data Seasonal untuk Prediksi Volume Impor Migas Indonesia. Jurnal Ekonomi Manajemen Dan Sekretari, 6(1), 15–21. https://doi.org/10.35870/jemensri.v6i1.1764
Section
Articles
Author Biographies

Adika Setia Brata, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar

Program Studi Statistik, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar, Kota Denpasar, Provinsi Bali, Indonesia

Alfian Anhar, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar

Program Studi Statistik, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar, Kota Denpasar, Provinsi Bali, Indonesia

Windy Lestari, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar

Program Studi Statistik, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar, Kota Denpasar, Provinsi Bali, Indonesia

Melda Juliza, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar

Program Studi Statistik, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar, Kota Denpasar, Provinsi Bali, Indonesia

Suci Rahmawati, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar

Program Studi Sistem Informasi, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar, Kota Denpasar, Provinsi Bali, Indonesia

M Tantry Abeng Evan Nugroho, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar

Program Studi Statistik, Institut Sains dan Teknologi Nahdlatul Ulama Bali Denpasar, Kota Denpasar, Provinsi Bali, Indonesia

References

Nopirin. (1997). Ekonomi Internasional (3rd ed.). BPFE: Yogyakarta.

Nurkhasanah, L. A., Suparti, S., & Sudarno, S. (2015). Perbandingan Metode Runtun Waktu Fuzzy-Chen Dan Fuzzy-Markov Chain Untuk Meramalkan Data Inflasi Di Indonesia. Jurnal Gaussian, 4(4), 917-926.

Pramana, M. W., Purnamasari, I., & Prangga, S. (2021). Peramalan Data Ekspor Nonmigas Provinsi Kalimantan Timur Menggunakan Metode Weighted Fuzzy Time Series Lee. J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, 14(1), 1-10.

Qi, M., & Zhang, G. P. (2008). Trend time–series modeling and forecasting with neural networks. IEEE Transactions on neural networks, 19(5), 808-816.

Ritha, N., Matulatan, T., & Hidayat, R. (2020, August). Penerapan Fuzzy Time Series Stevenson Porter pada Peramalan Pergerakan Nilai Forex. In Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) (Vol. 4, No. 3, pp. 179-184).

Solikhin, S., & Yudatama, U. (2019). Fuzzy Time Series dan Algoritme Average Based Length untuk Prediksi Pekerja Migran Indonesia. Jurnal Teknologi Informasi dan Ilmu Komputer, 6(4), 369-376.

Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series—Part II. Fuzzy sets and systems, 62(1), 1-8.

Wijaya, A. B., Dewi, C., & Rahayudi, B. (2018). Peramalan Curah Hujan Menggunakan Metode High Order Fuzzy Time Series Multi Factors. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(3), 930-939.

Yasid, A., & Satoto, B. D. (2014). Analisis Cluster Otomatis Menggunakan Algoritma Novel Modified Differential Evolution. Prosiding Semnastek, 1(1).