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Indonesian Journal of Artificial Intelligence and Data Mining
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : 26143372     EISSN : 26146150     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) is an electronic periodical publication published by Puzzle Research Data Technology (Predatech) Faculty of Science and Technology UIN Sultan Syarif Kasim Riau, Indonesia. IJAIDM provides online media to publish scientific articles from research in the field of Artificial Intelligence and Data Mining. IJAIDM will be published 2 (two) times a year, in March and September, each edition contains 7 (seven) articles. Articles may be written in English or Indonesia.
Arjuna Subject : -
Articles 233 Documents
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Interactive Real-Time Weight Management Platform Using Machine Learning Methods Haksa, Febrina Rosadah; Endri, Jon; Aryanti, Aryanti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36874

Abstract

This research develops an interactive and real-time web-based weight management platform that integrates machine learning methods using decision tree algorithms to detect the risk of weight-related diseases. The platform features an automatic Body Mass Index (BMI) calculator as well as a risk prediction system for diseases such as obesity and cardiovascular disorders. The data used includes the user's weight, height, eating habits, and physical activity level parameters collected through a live user interface. Based on the data, a decision tree algorithm is used to classify the health risk level and provide personalized recommendations to the user to help with preventive weight management. Initial testing showed that the decision tree model applied was able to achieve a prediction accuracy rate of 97%, demonstrating reliable performance in identifying health risks based on lifestyle data. This platform is expected to be an accessible technology solution to increase public awareness of the importance of weight management and disease prevention independently.
Ideal Temperature Classification of Meeting Rooms Using You Only Look Once Architecture Version 8 and Multilayer Perceptron Based on Human Density Image Data Ridwan, Naufal Taufiq; Yulita, Winda; Kesuma, Rahman Indra; Ramadhani, Uri Arta; Bagaskara, Radhinka
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.34230

Abstract

Indonesia, located along the equator, experiences a tropical climate that results in high indoor temperatures. Elevated temperatures can affect health, making air conditioning (AC) necessary to regulate indoor environments. However, improper use of AC systems, such as leaving them on even when a room is unoccupied, can lead to significant energy waste. This research focuses on the efficient use of AC systems through the integration of sensors and cameras, combining two distinct technologies. The first technology is object detection using You Only Look Once (YOLOv8), which was chosen for its superior performance in terms of speed, accuracy, and computational efficiency. The second is the classification of optimal AC temperatures using the Multilayer Perceptron (MLP) algorithm, selected for its high performance in accuracy, sensitivity, and speed. In addition, the study takes into account human density in the room to optimize temperature regulation. The integration of object detection and temperature classification technologies enables the system to operate in real time and automatically adjust temperature settings based on dynamic room conditions. The research successfully implemented YOLOv8 for object detection and Multilayer Perceptron for optimal room temperature classification. Test results showed precision, recall, and F1-score values of 0.82, 0.92, and 0.86, respectively.
Fresh Fruit Bunches Forecasting with the Double Exponential Smoothing Method Anggraini, Yuli; Manurung, Nuriadi; Yuma, Febby Madona
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36184

Abstract

PTPN IV Regional 1 Huta Padang Plantation is an oil palm plantation unit in North Sumatra that manages the cultivation, harvesting, and processing of Fresh Fruit Bunches (FFB) to produce palm oil. During the harvest process to produce Crude Palm Oil (CPO), a lot is often wasted due to unstable market demand. This research aims to implement the double exponential smoothing method for the production of fresh fruit bunches to be used as CPO. The problem faced is that production uncertainty often causes an imbalance between crop yields and market demand. If production is excessive, the company risks a spike in storage costs and potential product spoilage. Conversely, if production is too low, the company may miss out on market opportunities. This forecasting uses historical data from the previous year. The results of research at PTPN IV Regional 1 Kebun Huta Padang show that palm oil production forecasting for February 2025 amounted to 982.97 Kg with an error rate of 7.44%. This value is obtained using an alpha constant of 0.8 which gives the smallest forecasting error rate.
Internet of Things-Based Charity Box Security With Automatic Money Detection And Location Tracking Anggraini, Nur Septi; Salamah, Irma; Aryanti, Aryanti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37254

Abstract

This research designs and develops an Internet of Things (IoT)-based charity box security system aimed at improving transparency, security, and efficiency in the management of donation funds. The system integrates a TCS3200 color sensor to detect banknotes, a Neo-6M GPS module for real-time location tracking, and 4x4 keypad as PIN-based access security. The ESP32 microcontroller acts as the control center, and the Telegram Bot API is used to send notifications directly to the user when suspicious activity or device movement occurs. Additional features such as buzzers, speakers, and solenoid doorlocks function as warning systems and automatic locks to prevent unauthorized access. Test results show that all features work well, with a high success rate in money detection, accurate location tracking, and fast notification delivery. Despite limitations in the accuracy of detecting some banknotes due to the similarity of RGB values, the overall system proved to be reliable and has the potential to be implemented in public facilities or places of worship. Further developments could include integration with cloud storage, the addition of cameras for visual identification, and mobile applications for more interactive remote control and monitoring.
Food Stock Analysis with the Utilization of the Single Moving Average Method Febrianti, Intan; Hambali, Hambali; Sena, Maulana Dwi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36134

Abstract

A grocery store is a store that sells daily necessities. Nine basic ingredients or better known as basic necessities are the type of business that anyone needs. The community needs a grocery store that sells daily necessities at affordable prices and close to the neighborhood. Sahriul Grosir is a business engaged in the sale of rice where uncertain demand makes this business to determine the stock for the following month. This research aims to implement the single moving average method for rice stock forecasting to support proper decision making. By using historical sales data from January 2024 to January 2025 to forecast the next 6 months. Forecasting results for the next 6 months, from February 2025 to July 2025.  With a forecasting value obtained of 223, MAD value of 23.32, MSE value of 1203.20 and MAPE value of 9.43.
Comparative Analysis of Deep Learning Methods for Predicting the Value of the Standard & Poor's Global Supply Chain Intelligence (S&P GSCI) Nickel Stock Index Rahmansyah, Ragada; Vitianingsih, Anik Vega; Hamidan, Rusdi; Lidya Maukar, Anastasia; Budi Suprio, Yoyon Arie
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36129

Abstract

The development of information technology has opened up new opportunities in stock market forecasting, especially in nickel commodities, which are increasingly strategic in the global energy transition. This study uses a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a Gated Recurrent Unit (GRU) to forecast the movement of the S&P GSCI Nickel stock index value. Yahoo Finance time series data for the years 2018–2024 are used in the dataset. The study's findings are used to evaluate each model's capacity to forecast changes in nickel stock prices. The RNN model is used in this study because it can work with sequential information, while LSTM works with three memory gates (input, forget, output), and GRU works with 2 gates, namely update and reset. Mean Absolute Percentage Error (MAPE) presents the results of open and closed variable forecasting errors with the lowest average for the RNN model of 2.08%, the LSTM model of 2.505%, and the GRU model of 1.505%. This study is expected to contribute to investor decision-making and the identification of the most accurate forecasting model for the nickel stock index
Development of a CNN-Based Mental Health Consultation Application Integrating Facial Expressions and DASS-42 Questionnaire Salsabila, Meidita; Lindawati, Lindawati; Fadhli, Mohammad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37525

Abstract

Early detection of psychological disorders such as Depression, stress, and anxiety is still limited due to a lack of awareness and inadequate access to mental health consultation services. This study aims to develop a mental health consultation application that utilizes facial expressions and the Depression, Anxiety, and Stress Scale (DASS-42) questionnaire, employing a Convolutional Neural Network (CNN) algorithm. The CNN algorithm is used to detect and classify facial expressions into emotional categories, such as anger, sadness, disgust, and fear,  as early indicators of mental conditions. In addition, the DASS-42 questionnaire provides a structured psychological assessment to determine the severity of Depression, anxiety, and stress. This combination offers a more comprehensive and accurate evaluation, thus bridging the gap in early detection methods for mental health. Based on the development and testing results, a mental health consultation app utilizing facial expressions and the DASS-42 questionnaire was successfully created by using the CNN algorithm as a facial expression detector. The system can identify facial expressions such as sadness, anger, disgust, and fear with an accuracy of 81%, showing excellent performance in detecting early signs of mental disorders.
Analyzing Student Cognitive Engagement in AI-Based Learning using Prompting Techniques Ramadana, Muhammad Rifqy; Ulfa, Saida; Soepriyanto, Yerry
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36160

Abstract

With the increasing integration of AI in education, understanding how students engage cognitively in AI-assisted learning is crucial. Cognitive engagement in AI-assisted learning is important because it helps students interact meaningfully with AI tools, process information critically, and enhance their learning outcomes through effective AI-driven feedback and responses. To improve response quality in AI, one effective method is utilizing prompting techniques, which guide AI to generate more accurate, relevant, and structured responses, enhancing student learning experiences. This research investigates students' cognitive engagement when learning with AI-based tools using different prompting techniques, including Zero-Shot, Chain of Thought, Interactive Prompting, and Elaborate Prompting. A total of 54 students participated, and their engagement was assessed using a cognitive engagement questionnaire. The results, analyzed through a One-Sample T-test, reveal that students demonstrate significantly positive in cognitive engagement when using prompting techniques in AI-based learning. Furthermore, the findings suggest that effective prompting enhances the quality of AI-generated responses, positioning AI Chatbots as valuable learning assistants. This study provides important insights into optimizing AI-based learning strategies, highlighting the role of prompting in fostering deeper student interaction and engagement with AI tools.
Pest Detection on Green Mustard Plants Using Convolutional Neural Network Algorithm Arifin, Nurhikma; Rachmini, Siti Aulia; Rusman, Juprianus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.30953

Abstract

The productivity of mustard greens is vulnerable to pests and diseases that can threaten the yield and quality of the harvest. This study aims to detect pests on green mustard plants using the Convolutional Neural Network (CNN) method. The dataset used in this research consists of 450 images, with 225 images of pest-infested mustard greens and 225 images of healthy mustard greens. These 450 datasets are divided into 400 training data and 50 testing data. The testing was conducted fifteen times using CNN architectures with 2, 3 and 4 convolutional layers, having filter numbers of (64,32) (64, 32, 16) and (64, 32, 16, 8) respectively, and learning rates ranging from 0.1 to 0.00001 with the Adam optimizer. Based on the testing results of the learning rate and the number of layers, it was found that a learning rate of 0.001 provided the best performance with the highest accuracy and the lowest loss, especially in the model with 3 layers (64, 32, 16), which achieved an accuracy of 94% and a loss of 24.92%. A learning rate that is too high (0.1) or too low (0.00001) results in poor performance and instability, with low accuracy and high loss. Therefore, selecting the appropriate learning rate is crucial to achieving optimal results in model training.
Lithology Prediction Using Deep Learning Artificial Neural Network and Schlumberger Resistivity Inversion Data at Eastern Lampung Ramadhan, M Fitrah; Irianto, Suhendro Yusuf; Farduwin, Alhada
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37652

Abstract

The Schlumberger geoelectric method has been extensively employed in earth resource exploration due to its capability to identify variations in subsurface resistivity. However, the manual interpretation of geoelectric data inversion results is often subjective and time-consuming. This study aims to automate the lithology identification process by utilizing deep learning techniques, particularly Artificial Neural Networks (ANN), based on the inverted resistivity parameters obtained through the IPI2Win software. The Schlumberger configuration geoelectric data were obtained from survey reports provided by the Ministry of Public Works and Housing (Kementerian Pekerjaan Umum dan Perumahan Rakyat/ PUPR), which conducted geoelectric measurements in East Lampung Regency, Lampung Province, Indonesia. The ANN algorithm demonstrated an average accuracy of 90% in predicting lithology based on resistivity patterns resulting from Schlumberger inversion. Outperforming Support Vectorr Machine (SVM) (87%) and XGBoost (88%). These results confirm the initial hypothesis that ANN can effectively capture the complex relationships between resistivity values and rock types. The present study proposes an integrated approach between geophysics and machine learning with ANN algorithms for lithology prediction based on Schlumberger configuration geophysical inversion data. The present study proposes an integrated approach between geophysics and machine learning with ANN algorithms for lithology prediction based on Schlumberger configuration geophysical inversion data.

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