Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Computer Engineering, Electronics and Information Technology

Machine Diagnosis based Multi-Agent Technology by Autonomous Sensor with Energy Harvesting Munawir Munawir; Devi Rimadhani Agustini; Rahmawati Rahmawati; Abdullah Muadz Nadzir Anzhar
Journal of Computer Engineering, Electronics and Information Technology Vol 1, No 1 (2022): COELITE: Volume 1, Issue 1, 2022
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (487.024 KB) | DOI: 10.17509/coelite.v1i1.43818

Abstract

A machine diagnosis system based on multi-agent technology is proposed. We mainly focus on developing a multi-agent system for rotating machinery fault diagnosis by vibration sensor with energy harvesting. To estimate the inner rotation of machines in plant frequency analysis is frequently used. Our approach for diagnosis is agent-based, where vibration data is analyzed by a set of software agents coming from distributed servers to the user side. Another feature of this study is the development of autonomous vibration sensors. It earns electric power from vibration so that we are free from battery maintenance, and continuous online monitoring is enabled. Based on the implementation results of the existing multi-agent system design prototype, the harvesting sensor working process can produce total energy of 205µW with a working cycle of about 6.5 minutes, the energy harvester works, and the power accumulates continuously.
Sentiment Analysis of Twitter Users’ Opinion Data Regarding the Use of ChatGPT in Education Jezzy Putra Munggaran; Ahmad Ali Alhafidz; Maulana Taqy; Devi Aprianti Rimadhani Agustini; Munawir Munawir
Journal of Computer Engineering, Electronics and Information Technology Vol 2, No 2 (2023): COELITE: Volume 2, Issue 2, 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/coelite.v2i2.59645

Abstract

This article presents a sentiment analysis of Twitter users' opinions regarding the use of ChatGPT in education. ChatGPT, an AI chatbot developed by OpenAI, has gained significant attention for its ability to provide detailed responses across various knowledge domains. However, concerns have been raised about its occasional inclusion of inaccurate information. This study aims to analyze the sentiment of Twitter users' opinions towards ChatGPT in education and evaluate its accuracy. The sentiment analysis process involves data crawling, labelling, preprocessing, sentiment analysis, and evaluation. Data is collected from Twitter using the RapidMiner Studio tool and labelled as positive or negative sentiment based on the presence of positive or negative words. Preprocessing techniques are applied to standardize and reduce the volume of words in the data. The sentiment analysis classification is performed using machine learning algorithms, specifically Naive Bayes and Support Vector Machine (SVM). The accuracy, precision, and recall of the classification models are evaluated. The sentiment analysis results provide insights into Twitter users' overall sentiment towards ChatGPT in education. This study contributes to understanding Twitter users' opinions and sentiments regarding using ChatGPT in education. The findings can be valuable for educators and policymakers in assessing the potential impact of ChatGPT on academic integrity and the educational landscape.