P PREDIKSI PERTUMBUHAN EKONOMI KOTA MALANG DENGAN MODEL ARIMA
Abstract
This research aims to apply data mining methods with the ARIMA model to predict the rate of economic growth in Malang City by using techniques in data mining to obtain varied and long-term predictions. The problem arises due to the use of conventional methods that are less able to provide projections for economic growth in the next few years. To overcome this problem, research using the ARIMA model is expected to solve the problem by producing predictions for the next few years and a good level of accuracy. The data used in this study were obtained from the Central Bureau of Statistics and Malang City Government for the period 2004 to 2023. The ARIMA (0,0,1) model is used as the main model in predicting the economic growth rate in Malang City along with five variables that may have an influence on the main variable. The ARIMA (0,0,1) model has a better level of accuracy when compared to the ARIMA (1,0,1) and ARIMA (1,1,1) models. The results showed that the ARIMA model built was able to predict the rate of economic growth quite well. This study concludes that the ARIMA model created is more varied in predicting economic growth and needs to be used parameters or methods, so that the prediction results can be used as a reference for the government in planning a more effective and efficient economic development strategy
References
[2] R., & Suryansah Habibi, "Aplikasi Prediksi Jumlah Kebutuhan Perusahaan.," Bandung: Kreatif Industri Nusantara., 2020.
[3] R., Fauzan, M. N., & Rahayu, W. I. Roza, "Tutorial Sistem Informasi Prediksi Jumlah Pelanggan Menggunakan Metode Regresi Linier Berganda Berbasis Web Menggunakan Framework CodeIgniter," Bandung: Kreatif Industri Nusantara, 2020.
[4] A. K., Israwan, L. F., Hardiansyah, A., Setiawan, J., S, W., Khikmah, L., et al. Wardhani, "Teknik Peramalan Pada Teknologi Informasi," Padang: PT Global Eksekutif Teknologi, 2022.
[5] A., & Ade Irma Purnamasari, I. A Julkarnaen, "Analisis Penjualan Roti pada Distributor My Roti menggunakan Metode Regresi Linier berdasarkan Nilai RMSE," Jurnal Mahasiswa Teknik Informatika, pp. 3225-3229, 2024.
[6] A. D., & Nainggolan, A. G. Tambunan, "Analisis Time Series untuk Prediksi Polusi Udara dengan Model Prophet Facebook dan SVR," Universitas Mikroskil, 2023.
[7] Michel, "Perbandingan Metode Prophet dan Long Short Term Memory (LTSM) dalam Peramalan Kualitas Udara (Studi Kasus Kualitas Udara Kota Bandar Lampung)," Universitas Lampung, 2024.
[8] I., & Megasari, R. T. Sungkawa, "Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Voulme Penjualan PT Satriamandiri Citramulia," Journal of Communications Technology and Electronics, pp. 636-645.
[9] Aghaamintha, Akbarghanadian, & Weckman Fallahtafti, "Forecasting ATM Cash Demand Before and During the COVID-19 Pandemic Using an Extensive Evaluation of Statistical and Machine Learning Models," SN Computer Science, pp. 1-19, 2022.
[10] Cakranegara, Pesik, Yusuf, & Sutaguna Pudjowati, "The Influence of Employee Competence and Leadership on the Organizational Commitment of PERUMDA Pasar Juara Employees," Jurnal Darma Agung, pp. 606-613, 2022.
[11] A. T., Saddewisasi, W., & Prasetyo, A. Adriyanto, "Pelatihan Pembukuan Sederhana Berbais Microsoft Excel Pada Usaha Kecil dan Menengah (UMKM) Kota Semarang," Jurnal Pengabdian Masyarakat Radiasi, pp. 46-52, 2023.
[12] 606-613, "erbandingan Metode Prophet dan Long Short Term Memory (LTSM) dalam Peramalan Kualitas Udara (Studi Kasus Kualitas Udara Kota Bandar Lampung)," Universitas Lampung, 2021.
[13] Primadona & Fauzi, "Penerapan Data Mining pada Penjualan Produk Elektronik. Computer and Science Industrial Engineering (COMASIE)," pp. 463-472, 2023.
[14] N., & Nopriadi Azwanti, "Analisis Pola Belanja Konsumen menggunakan Algoritma Apriori pada Raffa Photocopy," Jurnal Teknologi dan Open Source, pp. 63-73, 2020.
[15] Achmad, & Payu, Ajunu, "Perbandingan Metode Autoregressive Integrated Moving Average dan Metode Double Exponential Smoothing dari Holt dalam Meramalkan Nilai Impor di Indoensia," Journal of Probability and Statistics, pp. 37-46, 2020.
[16] Ezzine, Aman, Moussami, & Lachhab Fattah, "Forecasting of Demand using ARIMA Model," International Journal of Engineering Business, pp. 1-9, 2018.
Copyright (c) 2024 husni mochamad

This work is licensed under a Creative Commons Attribution 4.0 International License.