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Journal : Journal of Mutidisciplinary Issues

FORECASTING RICE INVENTORY IN INDONESIA USING THE ARIMA ALGORITHM METHOD Kurniawan, Faisal Rizki; Sutomo, Rudi
Journal of Multidisciplinary Issues Vol 1 No 2 (2021): Journal of Multidisciplinary Issues
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (733.119 KB) | DOI: 10.53748/jmis.v1i2.15

Abstract

Objective – The global development of the world is increasingly developing, and complex accompanied by the era of globalization, making various sectors of need to follow these developments. The agricultural sector, which is the main sector, especially in the need for food for every society, especially in Indonesia, is also touched by technological developments. Planning the supply of rice needed monthly is crucial so that there is no excess or shortage of the required rice stock. Methodology – Made predictions from the amount of rice stock data using the CRISP-DM method to analyze the data and use the ARIMA Algorithm. Findings – This research predicts the amount of rice production that will be carried out in the next few months by applying the forecasting or prediction method using the CRISP - DM method and using the ARIMA algorithm. Novelty – This study predicts the amount of stock of an item using Rapidminer tools.  
Sentiment Analysis Comparative Analysis of Sentiment Analysis Using the Support Vector Machine and Naive Bayes Algorithm on Cryptocurrencies: CRISP - DM Nicholas, Nicholas; Sutomo, Rudi
Journal of Multidisciplinary Issues Vol 1 No 3 (2021): Journal of Multidisciplinary Issues
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1098.281 KB) | DOI: 10.53748/jmis.v1i3.22

Abstract

Objective – Cryptocurrency is growing overtime even being adopted as a legal money in a country out there. Besides can be used as a money, cryptocurrency also can be used as a digital goods to be trade and investment assets. To do some investing in cryptocurrency, there’s a need to evaluate the fundamental and sentiment of that cryptocurrency. This study aims to evaluate cryptocurrency based on responses of Twitter user.Methodology – The Algorithms used in this sentiment analysis study are Support Vector Machine and Naïve Bayes because it’s already proven that these 2 algorithm able to give a good accuracy and performance and using CRISP – DM framework for the study flow.Findings – This research predicts the sentiment for Bitcoin, Ethereum, Binance Coin, Dogecoin, and Ripple using the CRISP - DM method and using Support Vector Machine and Naïve Bayes algorithm.Novelty – This study calculate the sentiment on cryptocurrency using Rapidminer tools.Limitations - This study uses Bitcoin, Ethereum, Binance Coin, Dogecoin, and Ripple using tools such as rapidminerKeywords — Cryptocurrency, Naïve Bayes, Sentiment Analysis, Support Vector Machine