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All Journal Journal of Information Technology and Computer Science Journal of Computing Theories and Applications
Apriandy Angdresey
Universitas Katolik De La Salle, Manado
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Sentiment Analysis of Visitor Reviews on Star Hotels in Manado City Jeniver Petronela Matrutty; Angelia Melani Adrian; Apriandy Angdresey
Journal of Information Technology and Computer Science Vol. 8 No. 1: April 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202381403

Abstract

Sentiment analysis is a technique of extracting the text data to analyze the opinions and evaluate to obtain the information. Sentiment analysis is performed by internet users on social media or online applications or websites to provide assessments or personal opinions. Tourism in North Sulawesi has grown by 600% in the past four years, and the rise of tourism has sent tourists flocking to the city of Manado. These travelers need a hotel that satisfies their desires, so they need to read about the hotel in the reviews on the hotel reservation service website. This takes a lot of time. To overcome existing problems, sentiment analysis applications were developed to make it easier for potential hotel users to find previous user responses. Additionally, data mining classification techniques are used to help hotel managers determine the satisfaction of previous hotel users using a Naive Bayes algorithm. The five tests performed gave the best result, 76.20% accuracy, with an average of 70.55%. While the average precision is 70.57% and 99.85% for the recall.
IoT-Based Home Electricity Monitoring and Consumption Forecasting using k-NN Regression for Efficient Energy Management Apriandy Angdresey; Lanny Sitanayah; Zefanya Marieke Philia Rumpesak; Jing-Quan Ooi
Journal of Computing Theories and Applications Vol. 3 No. 1 (2025): JCTA 3(1) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.13602

Abstract

Electricity has emerged as an essential requirement in modern life. As demand escalates, electricity costs rise, making wastefulness a drain on financial resources. Consequently, forecasting electricity usage can enhance our management of consumption. This study presents an IoT-based monitoring and forecasting system for electricity consumption. The system comprises two NodeMCU micro-controllers, a PZEM-004T sensor for collecting real-time power data, and three relays that regulate the current flow to three distinct electrical appliances. The data gathered is transmitted to a web application utilizing the k-Nearest Neighbor (k-NN) algorithm to forecast future electricity usage based on historical patterns. We evaluated the system's performance using four weeks of electricity consumption data. The results indicated that predictions were most accurate when the user’s daily consumption pattern remained stable, achieving a Mean Absolute Error (MAE) of approximately 1 watt and a Mean Absolute Percentage Error (MAPE) ranging from 1% to 1.7%. Additionally, predictions were notably precise during the early morning hours (3:00 AM to 8:00 AM) when k=6 was employed. This study demonstrates the effectiveness of integrating IoT-based systems with machine learning for real-time energy monitoring and forecasting. Furthermore, it emphasizes the application of data mining techniques within embedded IoT environments, providing valuable insights into the implementation of lightweight machine learning for smart energy systems.
Co-Authors Angelia Melani Adrian Jeniver Petronela Matrutty Jing-Quan Ooi Lanny Sitanayah Zefanya Marieke Philia Rumpesak