Konstruksi Forecasting System Multi-Model untuk pemodelan matematika pada peramalan Indeks Pembangunan Manusia Provinsi Nusa Tenggara Barat

Lalu Sucipto
Syaharuddin Syaharuddin

Abstract


Penelitian ini bertujuan untuk mengembangkan produk Forecasting System Multi-Model (FSM) guna menentukan metode terbaik dalam sistem peramalan (forecast) dengan mengkonstruksi beberapa metode dalam bentuk Graphical User Interface (GUI) Matlab dengan menghitung semua indikator tingkat akurasi guna menemukan model matematika terbaik dari data time series pada periode tertentu. Pada tahap simulasi, tim peneliti menggunakan data Indeks Pembangunan Manusia (IPM) Provinsi Nusa Tenggara Barat (NTB) tahun 2010-2017 guna memprediksi IPM NTB tahun 2018. Adapun metode yang diuji adalah Moving Average (SMA, WMA dan EMA), Exponential Smoothing Method (SES, Brown, Holt, dan Winter), Naive Method, Interpolation Method (Newton Gregory), dan Artificial Neural Network (Back Propagation). Kemudian model dievaluasi untuk melihat tingkat akurasi masing-masing metode berdasarkan nilai MAD, MSE, dan MAPE. Berdasarkan hasil simulasi data dari 10 metode yang diuji diketahui bahwa metode Holt paling akurat dengan hasil prediksi tahun 2018 sebesar 67,45  dengan MAD, MSE, dan MAPE berturut-turut sebesar 0,22654; 0,075955 dan 0,34829.

 

 

 

The purpose of this research is to develop a product was called Forecasting System Multi-Model (FSM) to determine the best method in the forecasting system by constructing several methods in the form of Graphical User Interface (GUI) Matlab. It was done by all indicator accuration to find the best mathematical model of time series data in a certain period. In the simulation phase, this research used the Human Development Index (HDI) data of West Nusa Tenggara (NTB) Province in 2010 - 2017 to predict the HDI data of NTB in 2018. The methods tested were Moving Average (SMA, WMA and EMA), Exponential Smoothing Method (SES, Brown, Holt, and Winter), Naive Method, Interpolation Method (Newton Gregory), and Artificial Neural Network (Back Propagation). Then the models/methods were evaluated to see the level of accuracy of each method based on the value of MAD, MSE, and MAPE. Based on data simulation result from 10 tested method known that Holt method is most accurate with prediction result of 2018 equal to 67,45 with MAD, MSE, and MAPE respectively equal to 0.22654, 0.075955 and 0.34829.


Keywords


Exponential Smoothing; Forecasting System Multi-Model; FSM; Human Development Index; IPA; Indeks Pembangunan Manusia; mathematical model; NTB; Nusa Tenggara Barat; pemodelan matematika; West Nusa Tenggara

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References


Andriani, Y., Silitonga, H., & Wanto, A. (2018). Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 4(1), 30-40.

Ayub, A. F., Sembok, T. M., & Luan, W. S. (2008). Teaching and Learning Calculus Using Computer. Retrieved from http://atcm.mathandtech.org/ep2008/papers_full/2412008_15028.pdf

Guangpu, L., & Yuchun, G. (2012). The Application of MATLAB in Communication Theory. Procedia Engineering, 29, 321-324.

Irawan, M. I., Syaharuddin, S., Utomo, D. B., & Rukmi, A. M. (2013). Intelligent Irrigation Water Requirement System Based on Artificial Neural Networks and Profit Optimization for Planning Time Decision Making of Crops in Lombok Island. Journal of Theoretical and Applied Information Technology, 58(3), 657-671.

Sudarsono, A. (2016). Jaringan Syaraf Tiruan Untuk Memprediksi Laju Pertumbuhan Penduduk Menggunakan Metode Bacpropagation (Studi Kasus Di Kota Bengkulu). Jurnal Media Infotama, 12(1), 61-69.

Sugiono, S. (2016). Metode Penelitian Kuantitatif, Kualitatif, dan R & D. Bandung: Alfabeta.

Suhaedi, S., Febriana, E., Syaharuddin, S., & Negara, H. (2017). Ann Back Propagation For Forecasting And Simulation Hydroclimatology Data. International Journal Of Scientific & Technology Research, 6(10), 110-114.

Surihadi, A. A. (2009). Penerapan Metode Single Moving Average Dan Exponential Smoothing Dalam Peramalan Permintaan Produk Meubel Jenis Coffee Table Pada Java Furniture Klaten. Surakarta: Universitas Sebelas Maret.

Suryani, I., & Wahono, R. S. (2015). Penerapan Exponential Smoothing untuk Transformasi Data dalam Meningkatkan Akurasi Neural Network pada Prediksi Harga Emas. Journal of Intelligent Systems, 1(2), 67-75.

Syaharuddin, S., Negara, H. R., Mandailina, V., & Sucipto, L. (2017). Calculus Problem Solution And Simulation Using GUI Of Matlab. International Journal of Scientific & Technology Research, 6(9), 275-279.

Thiagarajan, S., Semmel, M., & Semmel, D. (1974). Instructional development for training teachers of exceptional children: A sourcebook. Bloomington: Indiana University. Retrieved from https://files.eric.ed.gov/fulltext/ED090725.pdf




DOI: https://doi.org/10.26594/register.v4i2.1263

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