Analisis Prediksi Jumlah Penduduk Di Kota Pasuruan Menggunakan Metode Arima

Mardiyah, Ilmiatul; Utami, Wika Dianita; Novitasari, Dian Candra Rini; Hafiyusholeh, Moh.; Sulistiyawati, Dewi

doi:10.30598/barekengvol15iss3pp525-534

Cited by 3 publications

(3 citation statements)

References 2 publications

(2 reference statements)

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“…While the Moving Average is a model that measures the autocorrelation between residual values which can be formulated as follows (Mardiyah et al, 2021) :…”

Section: Methodsmentioning

confidence: 99%

Analysis of Competitiveness and Forecasting of Indonesian Tea Exports to Main Destination Countries

Zuhdi

Rambe

Rahmadona

2022

mem

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<p>Indonesia as a tea exporter in the world has not maximized tea export opportunities as indicated by the decline in the value of tea exports from 2016 to 2020. This study aims to further analyze the competitiveness of Indonesian tea exports to major importing countries and predict the value of exports that will occur until 2025. This study used time series data from 2001-2020 which was analyzed with Revealed Comparative Advantage (RCA) and Autoregressive Integrated Moving Average (ARIMA). The results of the analysis show that the competitiveness of Indonesian tea exports to the main destination countries, namely Russia, Malaysia, China, Taiwan, and the United States, does not have a comparative advantage (RCA<1). However, based on forecasting results, the value of tea exports to Russia, Malaysia, and China is expected to increase until 2025. This increase in tea export value also has an impact in the form of strengthening the competitiveness of Indonesian tea exports in these countries even though the RCA index value is smaller than one. Thus, although the competitiveness of Indonesian tea exports is still weak in destination countries, Indonesia can optimize tea exports to countries that have increased competitiveness, such as Russia, Malaysia, and China.</p>

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“…While the Moving Average is a model that measures the autocorrelation between residual values which can be formulated as follows (Mardiyah et al, 2021) :…”

Section: Methodsmentioning

confidence: 99%

Analysis of Competitiveness and Forecasting of Indonesian Tea Exports to Main Destination Countries

Zuhdi

Rambe

Rahmadona

2022

mem

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“…(Astutiningtyas, 2023) pernah melakukan penelitian untuk memprediksi harga saham perusahaan Jasa Marga yang tidak berfluktuasi di sekitar rata-rata atau tidak stasioner dengan metode Moving Average dan Single Exponential Smoothing. Prediksi jumlah penduduk kota Pasuruan menggunakan metode ARIMA pernah dilakukan oleh (Mardiyah et al, 2021) yang menghasilkan MAPE 1,52% yang berarti sangat akurat. Penelitian lainnya mengenai metode ARIMA pernah dilakukan oleh (Farida et al, 2022) terkait jumlah penduduk kota Madiun yang menghasilkan MAPE 8,42%.…”

Section: Pendahuluanunclassified

The ARIMA-GARCH Method in Case Study Forecasting the Daily Stock Price Index of PT. Jasa Marga (Persero)

Amri,

Sari,

Widyasari

et al. 2024

EMJ

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PT Jasa Marga merupakan perusahaan besar di Indonesia yang berperan dalam pengembangan dan pengoperasian jalan tol yang dikenal sebagai salah satu perusahaan blue chip dengan saham LQ45. Namun, harga sahamnya memiliki volatilitas tinggi atau naik dan turun dengan cepat sehingga nilainya selalu berubah-ubah. Oleh karena itu, diperlukan adanya peramalan guna memprediksi harga saham PT Jasa Marga di masa yang akan datang guna mengetahui pergerakan harga sahamnya. Metode Autoregressive Integrated Moving Average (ARIMA) merupakan salah satu metode yang dapat meramalkan data dengan volatilitas tinggi, namun memiliki kekurangan pada residual yang mengandung heteroskedastisitas. Sehingga, dilakukan penambahan model Generalized Autoregressive Conditional Heteroskedasticity (GARCH) guna mengatasi masalah heteroskedastisitas yang awalnya, ditimbulkan model ARIMA sehingga dapat meramalkan data dengan volatilitas tinggi secara lebih optimal. Oleh karena itu, penelitian ini menerapkan metode ARIMA-GARCH guna menemukan model terbaik untuk meramalkan indeks harga saham harian PT Jasa Marga. Data yang digunakan berasal dari indeks penutupan harga saham harian PT Jasa Marga (Persero) periode Januari 2015 hingga Mei 2023. Pengukuran ketepatan peramalan menggunakan Mean Absolute Percentage Error (MAPE). Hasil peramalan menunjukkan model terbaik menggunakan ARIMA (2,1,1) - GARCH (1,3) dengan nilai MAPE sebesar 6,825728% yang mengindikasikan hasil peramalan sangat baik karena nilai MAPE < 10%.

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“…Next, a study that indicated the population of residents in East Kalimantan using Exponential Smoothing by Pakpahan, Basani, and Hariani that yielded a MAPE of 14.81% [10]. On the other, there are studies related to the prediction of population prediction using the ARIMA method conducted by Mardiyah et al in Pasuruan City [11], Nyoni, Mutongi, and Munyaradzi in the Gambia [12], and Nyoni in Zimbabwe obtained MAPE < 3.94% [13]. Based on the previous studies, the authors are interested in the ARIMA method, which results in an excellent level of accuracy in some cases of population forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Population of Madiun Regency Using ARIMA Method

Farida

Farmita

Ulinnuha

et al. 2022

CAUCHY

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The high population growth of the Madiun Regency can cause population density that can have implications for other problems, both in terms of social, economic, welfare, security, land availability, availability of clean water, and food needs. This study aims to predict the population growth of Madiun Regency using the ARIMA method. The ARIMA (Autoregressive Integrated Moving Average) method is popular for forecasting time series data, which is reliable because the calculation process is done gradually. This study uses annual population data of Madiun Regency from 1983 to 2021 and produces an ARIMA forecasting model (0,2,1) with a MAPE value of 8.42%. The results of this study are expected to be used as information from the Madiun Regency government in anticipating the emergence of problems caused by the population level of Madiun Regency in the future.

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Analisis Prediksi Jumlah Penduduk Di Kota Pasuruan Menggunakan Metode Arima

Cited by 3 publications

References 2 publications

Analysis of Competitiveness and Forecasting of Indonesian Tea Exports to Main Destination Countries

Analysis of Competitiveness and Forecasting of Indonesian Tea Exports to Main Destination Countries

The ARIMA-GARCH Method in Case Study Forecasting the Daily Stock Price Index of PT. Jasa Marga (Persero)

Forecasting Population of Madiun Regency Using ARIMA Method

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