AGENTS: Journal of Artificial Intelligence and Data Science

Implementation of Fuzzy Tsukamoto in the Design of Nutrient Control and Monitoring System for Aquaponics Based on the Internet of Things

2024-12-27T07:20:41+00:00

Aquaponic is an important aquaculture technique because it is easy to apply, saves water, and allows the integration of plant roots to absorb waste nitrogen from fish waste as nutrients. However, temperature, pH, Total Dissolved Solids (TDS), and water level greatly affect plant growth. This research aims to design a control system to monitor plant nutrition and development in real-time using temperature, pH, TDS, and ultrasonic sensors and apply Tsukamoto Fuzzy model to overcome uncertainty in decision making based on sensor data. This research uses a quantitative approach with a design and development method. Data were collected through direct observation, interviews with aquaponic farmers, and related literature studies. The designed system successfully fulfills the need to control and monitor nutrients in aquaponic systems effectively. The system utilizes an ESP8266 module and various sensors (pH, TDS/PPM, temperature, and water level) to monitor water conditions in real-time and send the data to Firebase, which is then displayed on the application interface. Automatic control allows for quick adjustments to changing environmental conditions, ensuring an optimal environment for plant growth.

Implementation of Large Language Models in Sentiment Analysis for Presidential Candidate Elections

2024-12-13T05:56:25+00:00

The presidential candidate election in Indonesia is a hot topic on social media, especially Twitter. This study analyzes public sentiment regarding the 2024 presidential candidate election using the IndoBERT model, which is specifically designed for the Indonesian language, on a dataset of 8,442 tweets. This research follows the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data was collected through crawling with keywords related to the election, followed by preprocessing and manual labeling before being processed by the model. The results show that IndoBERT achieved an accuracy of 98%, with precision, recall, and F1-score also at 98% at the 10th epoch. Batch size evaluation indicated that a batch size of 4 yielded the best performance. This model is effective in classifying sentiment related to the 2024 presidential candidate election and serves as a useful tool for understanding public opinion.

The Application of the Human-Centered Design Method in Designing Psychological Services for Students of the Faculty of Science and Technology

2024-11-25T09:31:53+00:00

The Human Centered Design method is a method used to better understand the user's comfort and convenience in using a service. In this research, the case study for applying this method is psychology services for students of the Faculty of Science and Technology. This method is usually used in creating UI/UX designs to help designers get to know users better through the level of comfort and convenience that users want when using the services they create. This research aims to apply and test whether the Human Centered Design method is good or not in designing psychological services. It is hoped that the implementation of the Human Centered Design method will be able to test whether this method is suitable for use in designing psychological services for Faculty of Science and Technology students. It is hoped that the results of this research will become new knowledge regarding the application of the Human Centered Design method in case studies of psychological services for students of the Faculty of Science and Technology and can be applied in other case studies.

Designing a Flashcard Game with a Time Tracker Feature for Early Childhood on Android Platform

2024-11-25T06:09:44+00:00

Sentiment Analysis of User Comments on the Moba Game Lokapala on Google Play Store Using Support Vector Machine Algorithm

2024-10-11T23:44:41+00:00

In this modern era, games are heavily influenced by technological advancements. The development of increasingly complex and captivating games can be played online by millions of players worldwide. The gaming industry in Indonesia has shown significant progress with the emergence of various games from local developers, one of which is Lokapala, a Multiplayer Online Battle Arena (MOBA) game that highlights the uniqueness of Indonesian culture. However, this game has received various responses from users on Google Play Store. This study aims to analyze user sentiment towards the Lokapala game on Google Play Store using the Support Vector Machine (SVM) algorithm. User review data were collected and pre-processed through stages such as data cleaning, tokenization, stopwords removal, and stemming. Subsequently, features were extracted using the TF-IDF method. The analysis results show that SVM with Radial Basis Function (RBF) kernel successfully classified user sentiment with an accuracy of 90% from a total of 300 reviews analyzed. This process not only helps in understanding overall user perceptions but also identifies specific aspects of the game that receive appreciation or criticism. Thus, game developers can use the results of this analysis to improve quality and user satisfaction, and strengthen the game's competitiveness in markets.