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Journal : JAIS (Journal of Applied Intelligent System)

Data Mining Algorithm Testing For SAND Metaverse Forecasting Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7155

Abstract

Metaverse is a technology that allows us to buy virtual land. In the future life in the real world can be duplicated into the Metaverse to increase efficiency, effectiveness, and a world without being limited by space and time. To buy land in the Metaverse, one can be done by using SAND. SAND is a crypto asset from a game called The Sandbox which functions as a transaction tool where in that game we can buy land and build it for various purposes just like we can store our Non-Fungible Tokens there. Metaverse is a digital business that will promise in the future because it offers easy and fast transactions. This study aims to compare the exact algorithm for making predictions about the SAND cryptocurrency used to buy Metaverse land. 7 algorithms are being compared, namely Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, Generalized Linear Models, Gaussian Process, and K-Nearest Neighbors. The research method used is Knowledge Discovery in Databases. The research results show that the Support Vector Machines Algorithm has the most optimal Root Means Square Error value, root_mean_squared_error: 0.022 +/- 0.062 (micro average: 0.062 +/- 0.000). Based on this comparison, the Support Vector Machines Algorithm is suitable for predicting SAND Metaverse prices.
Opinion Mining on Chat GPT based on Twitter Users Nashrulloh, Muhammad Rikza; Julianto, Indri Tri; Muzaky, Rifky Khoerul
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.8399

Abstract

The presence of Chatbots can assist humans in their everyday lives. Chat GPT is one of the commonly used Chatbots that humans rely on to support their work, serve as an assistant, or even create artistic works or writings. The purpose of this research is to investigate opinions regarding the presence of Chat GPT. This Opinion Mining method is conducted by crawling data from Twitter, which can be categorized into three opinions: Positive, Negative, or Neutral. To calculate the accuracy level of the model created, two algorithms, Naïve Bayes and K-Nearest Neighbour, are compared. The model validation process utilizes K-Fold Cross Validation by varying the value of k (k=2, k=4, k=6, k=8, and k=10) and different sampling methods, namely Linear, Shuffled, and Stratified, to obtain optimal accuracy values. The research results indicate that the K-Nearest Neighbour Algorithm achieves the highest accuracy value of 92.40%. Based on this comparison, the K-Nearest Neighbour Algorithm is deemed suitable for modeling Opinion Mining of Chat GPT. The distribution of Twitter users' opinion percentages regarding Chat GPT is as follows: Positive 9.4%, Negative 1.4%, and Neutral 89%. Neutral opinions dominate the results of the conducted Opinion Mining.Keyword : chat GPT, opinion mining, twitter