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

Authors

  • Lalu Sucipto Universitas Islam Negeri Mataram
  • Syaharuddin Syaharuddin Universitas Muhammadiyah Mataram

DOI:

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

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

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.

Author Biographies

Lalu Sucipto, Universitas Islam Negeri Mataram

Tadris Matematika

Syaharuddin Syaharuddin, Universitas Muhammadiyah Mataram

Pendidikan Matematika

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Published

2018-07-01

How to Cite

[1]
L. Sucipto and S. Syaharuddin, “Konstruksi Forecasting System Multi-Model untuk pemodelan matematika pada peramalan Indeks Pembangunan Manusia Provinsi Nusa Tenggara Barat”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 4, no. 2, pp. 114–124, Jul. 2018.

Issue

Vol. 4 No. 2 (2018): July

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Article

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