Predicting GDP of Indonesia Using K-Nearest Neighbour Regression

Priambodo, Bagus; Rahayu, S. Norin; Hazidar, Al Hamidy; Naf’an, Emil; masril, Mardhiah; Handriani, Inge; Putra, Zico Pratama; Nseaf, Asama Kudr; Setiawan, Deni; Jumaryadi, Yuwan

doi:10.1088/1742-6596/1339/1/012040

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Other research on Micro and Small Business (MSB) indicated that e-commerce will potentially bring positive impact on national economic productivity [6]. The prediction of Indonesia Gross Domestic Product (GDP) showed positive trend using K-Nearest Neighbour Regression [7]. To show up the government target, we then explain it in table 1.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Indonesian Digital Economy based on Available New Start-up

Hidayat¹,

Mahardiko²,

Rosyad³

2023

IJIEEB

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Since the last 5 years, digital economy is growing steadily in Indonesia. Right now, the digital economy faces some potential problems and Covid-19 pandemic. This paper presents current data of the national Gross Domestic Product (GDP) and other GDPs (billion IDR) and the number of start-up, and predicts near some categories of future GDP and numbers of available new start-up for the next few years. The forecast will use Markov chain analysis. The results indicate that, while there are problems faced by the digital economy industry, the GDP and numbers of start-up are significantly increasing.

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Section: Introductionmentioning

confidence: 99%

Forecasting of Indonesian Digital Economy based on Available New Start-up

Hidayat¹,

Mahardiko²,

Rosyad³

2023

IJIEEB

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“…Many studies in economic field of macro economy, linear regression model has been widely used as prediction methods in predicting the GDP and inflation (Priambodo, Rahayu, Hazidar et al, 2019). When looking for a mathematical representation of the GDP data, exponential function can be implied, as it discovers the simplest description of the data and well known for mathematical-defines distributions (Nielsen, 2015).…”

Section: Introductionmentioning

confidence: 99%

Mathematical Approach for Predicting the Gross Domestic Product of Malaysia

Ayob

Rohni

2020

MJSSH

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Gross domestic product (GDP) is a monetary measure of the market value of overall final goods and services produced in a given year, and serves as a gauge of the economy’s overall health and size. The GDP prediction is significant, as it can capture and understand the future developments of a country’s economy. In this paper, three different mathematical models have been used to predict Malaysia’s gross domestic product using regressions. The models discussed in this paper are linear, exponential and parabolic regressions. In developing the models, data from year 1970 to 2014 has been employed and data from year 2015 to 2019 has been used to examine the models' accuracy. The models are then observed to identify the most appropriate to express the relationship between the years and Malaysia’s gross domestic product. In this study, it is found that the parabolic regression model is more accurate compared to the linear and exponential regression models. The parabolic regression model is also the most appropriate since it is adjusted to the real conditions of Malaysia's gross domestic product which is the main subject of this paper. Finally, it is obtained that the prediction values of GDP in Malaysia will increase for the next ten years (2020 - 2029). Keywords: Gross domestic product, Linear regression, Exponential regression, Parabolic regression

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Prospects of Machine Learning With Blockchain in Healthcare and Agriculture

Singh

Singh²,

Deka

2021

Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications

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Presently, machine learning (ML) techniques have gained considerable attention, with growing interest in various areas and applications. Healthcare, agriculture, and bioinformatics are the most identified areas to study with the help of ML. This chapter introduces about the basic principle of ML such as data, model, basic mathematical details of ML, and types of learning. The important aspect of ML is “how to teach a machine.” This chapter focuses on the types of learning: supervised, unsupervised, semi-supervised, and reinforcement learning. Some commonly used ML algorithms such as decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes, k-mean, q-learning, etc. are briefly discussed for understanding. Finally, the author offers the application of ML with blockchain that is reforming the traditional healthcare and agricultural sector to a more reliable means.

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Predicting GDP of Indonesia Using K-Nearest Neighbour Regression

Cited by 4 publications

References 4 publications

Forecasting of Indonesian Digital Economy based on Available New Start-up

Forecasting of Indonesian Digital Economy based on Available New Start-up

Mathematical Approach for Predicting the Gross Domestic Product of Malaysia

Prospects of Machine Learning With Blockchain in Healthcare and Agriculture

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