Decision Making Support System for Prediction of Prices in Agricultural Commodity

Vohra, Aman; Pandey, Nitin; Khatri, Sunil Kumar

doi:10.1109/aicai.2019.8701273

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…Ilustrasi tersebut merupakan pengembangan dari model (Kumar & Iyengar, 2018) dalam komoditas beras. Beberapa fitur kecerdasan buatan yang dapat ditambahkan meliputi prediksi harga (Vohra, Pandey & Khatri, 2019) dan prediksi hasil panen (Liakos et al, 2018). Perangkat IOT juga dapat ditambahkan dalam sistem tersebut berupa sensor dan perangkat penunjang lainnya.…”

Section: Usulan Pengembanganunclassified

Blockchain dan Kecerdasan Buatan dalam Pertanian : Studi Literatur

Wihartiko

Nurdiati

Buono

et al. 2021

JTIIK

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Dewasa ini teknologi blockchain dan kecerdasan buatan (artificial intelligence/AI) telah diimplementasikan dalam bidang pertanian. Teknologi blockchain menjanjikan keamanan dan peningkatan kepercayaan untuk pengguna. Teknologi kecerdasan buatan menjanjikan berbagai kemudahan bagi pengguna. Perpaduan kedua teknologi tersebut dapat meningkatan kepercayaan terhadap sistem kecerdasan buatan (blockchain for AI) atau dapat juga digunakan untuk meningkatkan kinerja sistem blockchain (AI for blockchain). Tujuan penelitian ini mengulas kedua teknologi tersebut dalam studi literatur serta memberikan tantangan riset ke depan terkait implementasinya di bidang pertanian. Metodologi yang digunakan adalah Systematic Literature Review (SLR) dan text mining. Text mining digunakan untuk memberikan deskripsi riset yang ada berdasarkan kata-kata di setiap artikel terpilih. SLR digunakan untuk memberikan ulasan yang komprehensif terkait riset Blockchain dan kecerdasan Buatan dalam pertanian. Hasil penelitian menunjukan bahwa terdapat 10 % penelitian terkait penerapan blockchain dan AI dalam pertanian. Riset tersebut memiliki potensi besar untuk berkembang terlihat dari peningkatan jumlah publikasi dalam 2 tahun terakhir. Kontribusi penelitian ini meliputi posisi riset terkini dan usulan riset ke depan dengan mempertimbangkan kondisi pertanian Indonesia. Posisi riset tersebut didominasi komunitas peneliti dari negara-negara di Asia seperti India (33%), Pakistan (33%), China (14%) dan Korea (14%). Originalitas penelitian ini terletak pada studi literatur dari integrasi teknologi blockchain dan kecerdasan buatan dalam bidang pertanian menggunakan SLR dan text mining. AbstractArtificial intelligence and blockchain technology are being developed and implemented in Agriculture. Blockchain technology promises security and trust for users. Moreover, artificial intelligence technology promises convenience for users. The combination of these two technologies will increase trust in artificial intelligence systems. Besides, this combination can also increase security on the blockchain system through the application of artificial intelligence. This paper summarizes the application of both technologies and reviews them in a systematic literature review, presents a description of articles based on text mining, and provides future research challenges related to the implementation of blockchain and artificial intelligence in agriculture. The methodologies used are Systematic Literature Review (SLR) and text mining. Text mining is used to describe a description of existing research based on the words in each selected article. SLR is used to provide a comprehensive review of Blockchain research and Artificial intelligence in agriculture. The results showed that there were 10% of research related to the application of blockchain and AI in agriculture. This research has great potential for growth as seen from the increase in the number of publications in the last 2 years. The contribution of this research includes the latest research positions and future research proposals taking into account the conditions of Indonesian agriculture. The research position is dominated by the research community from countries in Asia such as India (33%), Pakistan (33%), China (14%) and Korea (14%). The originality of this research is a literature study on the integration of blockchain and artificial intelligence in agriculture using SLR and text mining.

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Section: Usulan Pengembanganunclassified

Blockchain dan Kecerdasan Buatan dalam Pertanian : Studi Literatur

Wihartiko

Nurdiati

Buono

et al. 2021

JTIIK

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“…This also enables the farmers to ensure distinct commodities prior prices. This model offers maximum options to the farmer so they can plan and estimate for different crops and finally they can go with beneficiary one [13].…”

Section: Related Workmentioning

confidence: 99%

Developing an Agricultural Product Price Prediction Model using HADT Algorithm

Rȧjeswari¹,

Suthendran²

2019

IJEAT

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Big Data Predictive Analytics and Data mining are emerging recent research field to analyse the agricultural crop price. The applications and techniques of data mining as well as Big Data using agriculture data is considered in this paper. In particular, the farmers are more concern about estimating that how much profit they are about to expect for the chosen crop. As with many other sectors the amount of agriculture data are increasing on a daily source. In this work, agriculture crop price dataset of Virudhunagar District, Tamilnadu, India is considered and for the price prediction model based on data mining decision tree techniques. The main goal is to establish the new predictive model based on Hybrid Association rule-based Decision Tree algorithm (HADT). The outcome for the suggested HADT forecast model is heartening and precise to predict agricultural product prices than other current decision tree models.

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“…The price for the commodity decreases after the cultivation of crops, and so they are not able to get the anticipated amount. The severe impact of this problem increases the suicide rate every year [5].…”

Section: Introductionmentioning

confidence: 99%

An extrapolative model for price prediction of crops using hybrid ensemble learning techniques

2023

IJATEE

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Agriculture is the basis for food and the backbone of a country's economy. In India, around 70% of the population is actively involved in growing crops for food or providing direct raw materials to a variety of industries, including textile, food processing, and non-agricultural sectors. The development of technology aids the agricultural sector in forecasting a variety of factors, including crop quality, disease detection, and soil quality, to increase crop yield. However, increased agricultural yield may not always result in a profit due to price reductions. Thus, price forecasting is crucial before choosing the crop to plant since it aids in making informed choices that reduce the risk and loss associated with market price instabilities. This study provides a hybrid model for price prediction that combines an autoregressive integrated moving average (ARIMA) model, a linear statistical analysis for time series data, and an ensemble machine learning approach using support vector regression (SVR). The work has three models: 1) a statistical model is applied over the input features related to crop price, and the residuals are evaluated using SVR, 2) SVR is applied over the predicted price from the statistical model along with the other input features, 3) SVR is applied to the results obtained from the statistical model and its residuals, in addition to the input features. After analysing the results, the model for price forecasting that produced better outcomes was finally chosen. The experimental and result analysis reveals that model 3 has improvedresults with a 13.37% deviation from actual observation compared to models 1 and 2, which have resulted with deviations of 14.68% and 16.48%, respectively. Additionally, compared to other models, the suggested model has the lowest average prediction errors and average divergence from actual values. Thus, the proposed model is suitable for reliable price forecasting and optimal performance.

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Decision Making Support System for Prediction of Prices in Agricultural Commodity

Cited by 15 publications

References 6 publications

Blockchain dan Kecerdasan Buatan dalam Pertanian : Studi Literatur

Blockchain dan Kecerdasan Buatan dalam Pertanian : Studi Literatur

Developing an Agricultural Product Price Prediction Model using HADT Algorithm

An extrapolative model for price prediction of crops using hybrid ensemble learning techniques

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