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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal Ilmiah KOMPUTASI J I M P - Jurnal Informatika Merdeka Pasuruan JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Ekonomi Pembangunan International Journal of Economics, Business and Accounting Research (IJEBAR) G-Tech : Jurnal Teknologi Terapan JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Ekonomi Pembangunan
Mohamad As'ad
STMIK PPKIA Pradnya Paramita
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Public Health Social Sciences

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Model Neural Network Autoregressive untuk Prediksi Inflasi Bulanan di Kota Yogyakarta Hari Prapcoyo; Mohamad As'ad
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 11, No 2 (2023)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v11i2.54370

Abstract

AbstrakYogyakarta sebagai kota pelajar, kota pariwisata ataupun kota budaya sangatlah ramai aktifitas ekonominya karena banyak sekolah, universitas, tempat wisata dan juga tempat budaya yang tentunya banyak mahasiswa, wisatawan dalam negeri maupun luar negeri yang berkunjung ke kota tersebut. Aktifitas mahasiswa dan wisatawan di kota Yogyakarta ini bisa meningkatkan aktifitas perekonomian seperti tempat kost, penginapan atau hotel serta tidak ketinggalan tempat makan, tempat belanja dan lain sebagainya. Penelitian ini mempunyai tujuan untuk memprediksi inflasi bulanan di kota Yogyakarta yang ramai tersebut. Data sekunder inflasi bulanan untuk kota Yogyakarta diperoleh dari BPS kota Yogyakarta dan BPS pusat.  Data yang digunakan yaitu data inflasi bulanan mulai dari Januari 2006 sampai dengan Desember 2021, sebanyak 192 data. Penelitian ini menggunakan model peramalan jaringan syaraf tiruan (JST) atau artificial neural network (ANN). Model JST atau ANN yang digunakan yaitu model neural network autoregressive (NNAR). Model NNAR ini menggunakan algoritma backpropogation dengan fungsi aktifasi sigmoid biner. Pengolahan data pada penelitian ini menggunakan R package statistics yang merupakan open source program. Hasil kesimpulan dari penelitian ini adalah diperoleh model terbaik yaitu NNAR(12,8) artinya  model NNAR ini mempunyai input berupa lag-1 sampai dengan lag-12 inflasi bulanan koya Yogyakarta dengan single hiden layer mempunyai 8 neuron. Akurasi model NNAR(12,8) di ukur dengan root mean square error (RMSE, sebesar 0.05962758), mean absolute square error (MASE, sebesar 0.1011443), mean absolute percentage error (MAPE, sebesar 28.32449). Saran dari penelitian ini untuk penelitian lanjutan, model NNAR(12,8) hendaknya dibandingkan dengan model ANN yang lain atau model yang berbasis sistem cerdas (artificial intelegent, AI).
Forecasting Performance of Double Exponential Smoothing Model and ETS Model for Predicting Crude Oil Prices Hari Prapcoyo; Mohamad As'ad; Sujito Sujito; Sigit Setyowibowo; Eni Farida
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8104

Abstract

Purpose: This study aims to predict the price of monthly crude oil quickly and accurately by using an easy model and with easily available software.Design/methodology/approach: This study compares the DES-Holts and ETS models to predict price of monthly crude oil.Findings/result: The results of this study recommend the ETS(M,N,N) model to predict the price of monthly crude oil which produces an accuracy value of RMSE and MAPE of 4.385812 and 6.499007 %, respectively.Originality/value/state of the art: This study implements the DES_Holt's and ETS models to predict price of monthly crude oil with an RMSE and MAPE forecasting accuracy that has never been done in previous studies. 
Forecasting of Daily Gold Price using ARIMA-GARCH Hybrid Model Sigit Setyowibowo; Mohamad As'ad; Sujito Sujito; Eni Farida
Jurnal Ekonomi Pembangunan Vol. 19 No. 2 (2021): Jurnal Ekonomi Pembangunan
Publisher : Department of Development Economics, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29259/jep.v19i2.13903

Abstract

Gold is a multifunctional precious metal. Apart from being jewelry, gold is a form of investment. For this reason, the public or investors need to know the estimated daily gold price for transactions for the public or investors who want to invest or also want to sell their gold, so they do not lose. This is the aim of this study. Many forecasting methods can be used to predict the daily gold price, but this study uses the ARIMA-GARCH hybrid model because this model can predict econometric models such as the daily gold price which usually contains high volatility. Daily gold price data was secondary data obtained from the investing.com website. The data was for the period March 12, 2016, to December 31, 2020. The results of this study are obtained for the ARIMA (1,1,1) -GARCH (2,1) hybrid model with a root mean square error (RMSE) forecasting accuracy value is 2.375454, the mean absolute error (MAE) is 1.702908, and the mean absolute percentage error (MAPE) is 0.001168113. From the results of this study, long-term investment is very profitable because there is an upward trend from the model obtained. For short-term investments, the public or investors have to update the research result model because the current gold price is influenced by the gold price only one period ago, so that when trading does not lose.
THE FORECASTING OF MONTHLY INFLATION IN MALANG CITY USING AN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE Eni Farida; Mohamad As'ad
International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 5, No 2 (2021): IJEBAR, VOL. 05 ISSUE 02, JUNE 2021
Publisher : LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijebar.v5i2.2328

Abstract

Abstract: Malang is known as a student city since there are a lot of schools and universities that can be found in Malang Indonesia. Malang is also an attractive tourist place with many tourist attractions in the city of Malang. Public transportation in the city of Malang is also very varied, ranging from conventional and based online. Access to the city of Malang is varied, namely trains, buses, and planes. Thus economic growth in the city of Malang is getting better, this can be seen from the economic activity in the increasingly crowded city of Malang. A good economy is usually followed by stable inflation. For this reason, it is necessary to examine how the monthly inflation rate in Malang city. This study aims to forecast inflation in the coming periods using the Autoregressive Integrated Moving Average (ARIMA) model. Secondary monthly inflation data is obtained from BPS Malang. From this research, the ARIMA model (2,0,3) is obtained. The accuracy model is used in this research namely root means square error (RMSE), mean absolute error (MAE), and mean absolute square error (MASE). The accuracy value is RMSE equal 0.2645467, MAE equal 0.2013898, and MASE equal 0.6047399. Keywords: Monthly inflation forecasting, BPS Malang city, ARIMA model.
THE FORECASTING OF MONTHLY INFLATION IN YOGYAKARTA CITY USES AN EXPONENTIAL SMOOTHING-STATE SPACE MODEL Hari Prapcoyo; Mohamad As'ad
International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 6, No 2 (2022): IJEBAR, Vol. 6 Issue 2, June 2022
Publisher : LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijebar.v6i2.4853

Abstract

Abstract: Yogyakarta is known as a student city, tourist city, and also a city of culture. Yogyakarta is an interesting tourist and cultural place with many beautiful tourist attractions in the city of Yogyakarta. Public transportation in the city of Yogyakarta is also varied, ranging from conventional and online-based. Access to the city of Yogyakarta varies, namely trains, buses, and planes. Thus, the economic growth in the city of Yogyakarta is getting better, this can be seen from the economic activity in the city of Yogyakarta which is getting busier. A good economy is usually always followed by stable inflation. This study aims to predict inflation in the future period using the Exponential Smoothing-State Space (ETS) model. Secondary monthly inflation data was obtained from BPS Yogyakarta City. From this research, the Exponential Smoothing-State Space Model / ETS (A, N, A) is obtained, which means that the monthly inflation data for the city of Yogyakarta does not contain trends, but contains additive seasonality and has additive errors. The results of this study indicate that inflation in the next three months is relatively stable, namely, the decline in inflation and the increase in inflation is still below 10%. Keywords: BPS Yogyakarta City, Monthly Inflation Forecasting, Exponential Smoothing-State Space ETS
Co-Authors Eka Yuniar Eni Farida Evy Sophia Frans Richard Kodong Hari Prapcoyo Sigit Setyo Wibowo Sigit Setyowibowo Sujito Sujito