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All Journal Jurnal Informatika Jurnal Pekommas JURNAL PENGABDIAN KEPADA MASYARAKAT JOIV : International Journal on Informatics Visualization Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Realtech EPI International Journal of Engineering Jurnal Peduli Masyarakat Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal of Electrical Engineering and Computer (JEECOM) JOINTER : Journal of Informatics Engineering Vivabio : Jurnal Pengabdian Multidisiplin Jurnal Pengabdian Kepada Masyarakat Abdi Putra Journal of Computing Theories and Applications Jurnal Pengabdian Masyarakat Tekno
Lanny Sitanayah
Department Of Informatics Engineering, Faculty Of Engineering, Universitas Katolik De La Salle, Manado
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Journal of Computing Theories and Applications

 1 
Sentiment Analysis for Political Debates on YouTube Comments using BERT Labeling, Random Oversampling, and Multinomial Naïve Bayes Angdresey, Apriandy; Sitanayah, Lanny; Tangka, Ignatius Lucky Henokh
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11668

Abstract

The 2024 Indonesian Presidential Election marked the fifth general election in the country, aimed at electing a new President and Vice President for the 2024–2029 term. Candidates competed to succeed the outgoing president, who had served two constitutional terms. A key aspect of this election was the candidate debates, where each candidate presented their vision, allowing the public to assess their policies. These debates were broadcast on platforms like YouTube, giving the public a space to comment. However, analyzing YouTube comments presents challenges due to the volume of data, language diversity, and informal expressions. Sentiment analysis, crucial for understanding public opinion, uses algorithms such as Naïve Bayes, which is based on Bayes' Theorem and assumes feature independence. Naïve Bayes is widely used in text analysis for its speed and simplicity. When applied to YouTube comments from the 2024 debates, the algorithm demonstrated its effectiveness, especially with a balanced dataset through random oversampling. It achieved 85.155% accuracy, high precision, recall, and an AUC of 96.8% on an 80:20 data split. Its fast classification time (0.000998 seconds) makes it suitable for real-time sentiment analysis, validating its use for political events. Future applications may incorporate advanced techniques like BERT for more sophisticated analysis.
A Low-Cost Hydroponic Monitoring System with Internet of Things and Fuzzy Logic Sitanayah, Lanny; Joseph, Hizkia R.M.; Sanger, Junaidy B.
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12059

Abstract

The need for urban communities to consume vegetables is increasing. This has caused people to start cultivating vegetables using hydroponic techniques. However, due to their busy activities, they do not have enough time to monitor and control hydroponics, which must always be in ideal conditions. This paper designs and implements an Internet of Things-based monitoring system to help hydroponic owners monitor their hydroponics anywhere and anytime. The built system requires a monitoring device assembled using a NodeMCU ESP8266 microcontroller, a pH detection detector sensor, and a DHT22 temperature and humidity sensor. This system uses the Mamdani Fuzzy Logic algorithm to determine warnings to be displayed on the application interface when the water pH, temperature, and humidity are in certain conditions. The Mamdani Fuzzy Logic algorithm can interpret environmental data into a warning that humans can easily understand, even if they lack technical expertise. In addition to being able to help monitor, this system also allows owners to find out what elements need to be added or changed for their hydroponic place. Our evaluation results show that the defuzzification stage in the application has high accuracy, which is 99.92%, compared to Matlab’s results.
IoT-Based Home Electricity Monitoring and Consumption Forecasting using k-NN Regression for Efficient Energy Management Angdresey, Apriandy; Sitanayah, Lanny; Rumpesak, Zefanya Marieke Philia; Ooi, Jing-Quan
Journal of Computing Theories and Applications Vol. 3 No. 1 (2025): JCTA 3(1) 2025 - in progress
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 Abraham Abast Aminuyati Anasthasya Banurea Anatje Lihiang Angelia Melani Adrian Anthoni Pulakiang Anthoni R. Pulakiang Apriandy Angdresey Billy Fernando Oentomo Dejan Kalengkongan Dino Rahardiyan Elisabeth Y. Yolasb Emma M Moko Evander Kristalino Fino C. Colling Immanuela Puspasari Saputro Indah Suryanto Jeri Wahyu Utama Johanna Zusye wa Joseph, Hizkia R.M. Junaidy Budi Sanger Junaidy Sanger Kalengkongan, Dejan A. Kawatak, Steven Y. Kumenap, Vivie Deyby Lahama, Artquito Beltsazar Livana Dethris Rawung Lohonauman, Charlie A. P. Micael Liem Michael George Sumampouw Noula J. Mokorimban Ooi, Jing-Quan Pether Romony Phang, Alvaro D. Rahardiyan, Dino Rampengan, Mercy Maggy Franky Rayuni A.F Kansil Romony, Pether V B Rumpesak, Zefanya Marieke Philia Steven Pandelaki Tangka, Ignatius Lucky Henokh Vandri Josua Abram Sampul Vivie D Kumenap Vivie D. Kumenap Vivie Kumenap Vivie Kumenap