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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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Implementation of Augmented Reality of Laboratory Building and Room Using Fast Corner Detection Algorithm Ilhami, Ahmad Mika; Ikhsan, Muhammad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.31365

Abstract

The Laboratory Buildsing is one of the buildings in Campus IV UINSU which is used to carry out practicum activities, many of the lecturers students and the community do not know the location and also the rooms in the Laboratory building. This research aims to make this application can provide convenience for users such as students, lecturers and the public to obtain information about laboratory buildings. In this research, the method used is Marker Based Tracking where application users must scan the marker first in order to see 3D objects from a floor plan and laboratory building. The results of this study in the form of Augmented Reality applications using Android as an operating system, on the marker added with the Fast Corner Detection algorithm aims to speed up real-time computing time with the consequence of reducing the level of accuracy of corner detection.  The conclusion of AR and Fast Corner Detection can be implemented through a scan of the marker, the result obtained is that when scanning the marker a point from the FCD algorithm will appear indicating that the point on this marker is the result of the implementation of the FCD algorithm.
Implementation of Fuzzy Logic Method on Plantation Monitoring System in Website-Based Smart Farming Oktariani, Clara; Nurdin, Ali; Handayani, Ade Silvia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31823

Abstract

The plantation sector is a crucial component of agriculture. However, plantation management faces several challenges, including limited technological integration within agriculture. The adoption of technology in plantations, particularly IoT (Internet of Things)-based monitoring systems, has become a significant trend in recent years. This system is designed to improve the efficiency of Smart Farming, allowing for more optimized management. This study implements the Fuzzy Logic method in a corn plantation monitoring system to address uncertainties and complexities in decision-making. The system integrates hardware and software, enabling real-time monitoring of environmental conditions through a web-based interface. Testing results indicate that the developed system achieves an accuracy level of 97.42%, providing valuable and responsive data to support farmers in decision-making. With this system, farmers can more effectively monitor and manage plantation conditions, potentially increasing productivity and agricultural yield quality.
Application of the Artificial Neural Network Algorithm to Predict the Realization of the Duty Tax on the Name of Motor Vehicles in Lampung Province Kurniasari, Dian; Ramadhani, Putri Salsabila; Wamiliana, Wamiliana; Warsono, Warsono
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29456

Abstract

Regional taxes, specifically the Motor Vehicle Name Return Tax (BBNKB), provide the primary source of revenue for regions from the several forms of taxes. The BBNKB tax is crucial in funding government and regional development due to its significant annual growth, encompassing four-wheeled and two-wheeled vehicles. Furthermore, the BBNKB tax catalyzes regional economic expansion and significantly contributes to the government's income. Hence, predicting and forecasting the BBNKB Tax in Lampung Province is necessary to monitor future tax rate fluctuations. That will enable the government to devise innovative tax payment systems and establish tax revenue targets. This study utilizes the Artificial Neural Network (ANN) methodology, using many approaches for distributing training and testing data to forecast. In addition, we utilize hyper-tuning on several factors to obtain the most favourable configurations. The ideal model achieved has a training data allocation of 80% and a testing data allocation of 20%. It was trained for 50 epochs and used a batch size of 16. The model has exceptional predictability, attaining an accuracy rating of 96.51%. Additionally, it showcases a low Root Mean Square Error (RMSE) of 0.246 and a minimal Mean Absolute Percentage Error (MAPE) score of 3.48%. Therefore, it is appropriate to predict the next two-year term. As a result, the forecast for the amount of tax collected from motor vehicle name returns in Lampung has fluctuated.
Enhanced Fashion-MNIST Classification Using a Hybrid VGG-16-DenseNet121 Architecture Ananda, Gheri Febri; Risfendra, Risfendra; Wahyudi, Eko
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.32225

Abstract

This study aims to explore the effectiveness of a hybrid model combining the VGG16 and DenseNet121 architectures for image classification tasks on the Fashion MNIST dataset. This model is designed to leverage the advantages of both architectures to produce richer feature representations. In this study, the performance of the hybrid model is compared with several other architectures, including LeNet-5, VGG-16, ResNet-20, ResNet-50, EfficientNet-B0, and DenseNet-121, using various optimizers such as Adam, RMSProp, AdaDelta, AdaGrad and SGD. The test results indicate that the Adam and SGD optimizers deliver excellent results. The VGG16 + DenseNet121 hybrid model achieved perfect training accuracy 100%,  the highest validation accuracy 94.65%,  and excellent test accuracy 94.16%. Confusion matrix analysis confirms that this model is capable of correctly classifying the majority of images, although there is some confusion between classes with visual similarities. These findings affirm that a hybrid approach and the appropriate selection of optimizers can significantly enhance model performance in image classification tasks.
Automatic Classifier of Road Condition and Early Warning System for Potholes Manurung, Jeremia; As, Mansur; Nasution, Hamidah; Al Idrus, Said Iskandar; Saputra S, Kana
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31866

Abstract

Damaged roads can have a negative impact on road users and can fatally cause accidents. One sign of a damaged road is the presence of holes in the road. This research aims to develop an Android application that can display the location of potholes and provide early warning to driver in Simalungun Regency - North Sumatra. This research implements the Convolutional Neural Network (CNN) algorithm using the transfer learning techniques on the pre-trained MobileNetV3 model for automatic classification of road conditions. The dataset used in the research consisted of 22.538 images which were divided into two classes, namely pothole and normal. This research uses dataset with a ratio of 60:20:20, 70:20:10 and 80:10:10. MobileNetV3 large variant with a dataset ratio of 60:20:20 shows the best value with an F1-Score of 0,9035. The model was further converted to Tensorflow Lite with an F1-Score of 0.8985. This research succeeded in implementing the trained and evaluated model along with early warning of potholes via audiovisual in Android application. Application functionality testing that is carried out using black box testing, showing that the application can run well.
Sentiment Analysis on Application X on the Use of Red Oil Using the Naïve Bayes Method Tanjung, Tajuddin; Hasibuan, Muhammad Siddik
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.35422

Abstract

Red oil, as an alternative to traditional cooking oil, has gained public attention through reviews on App X. However, questions arise about how public sentiment is towards red oil and how the Naïve Bayes algorithm can classify positive and negative sentiments. This study aims to analyze user sentiment towards red oil using the Naïve Bayes method. The dataset used consists of 1,200 comments collected through the scrapping technique in 2024. After going through the process of removing duplicate comments, the number of data becomes 1,189. Before running the Naïve Bayes algorithm, the data is divided into test data and training data, with 238 data as test data and 951 data as training data. The analysis process involves pre-processing stages such as text cleaning, tokenization, and normalization, followed by word weighting with the TF-IDF method. The Naïve Bayes algorithm is applied for the classification of positive and negative sentiments. The results showed that 1,147 comments were positive sentiment, while 42 comments were negative sentiment with a total accuracy of 88.66%, then precision of 95.41%, recall of 92.44% and F1- 93.91% and it was found that the sentiment comments on the use of red oil had a greater positive polarity than negative polarity. This analysis provides important insights for producers and stakeholders regarding public perception of red oil, which is useful for strategic decision making, such as improving product quality and marketing campaigns. This method is expected to be a reference for further studies in the field of text classification and natural language processing.
Comparison of Recurrent Neural Network and Naive Bayes Algorithms in Identifying Stunting in Toddlers Sujayanti, Forentina Kerti Pratiwi; Via, Yisti Vita; Haromainy, Muhammad Muharrom Al
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33946

Abstract

Stunting in toddlers is a health issue that affects their quality of life. This study aims to predict stunting status using three classification methods: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gaussian Naive Bayes. The dataset from Kaggle was split into 70% for training and 30% for testing to ensure optimal model evaluation. The RNN model was built with three hidden layers of 64 units each, while the LSTM model had four hidden layers with the same number of units. Both models utilized hidden states to capture temporal patterns and employed the tanh activation function to detect complex data patterns. The ADAM optimizer with a learning rate of 0.001 was applied to accelerate convergence. In contrast, the Gaussian Naive Bayes model used a simple probabilistic approach without temporal patterns, making it suitable for simpler datasets. Evaluation using accuracy and RMSE showed that LSTM achieved the highest accuracy (91%), followed by RNN (90%), though both exhibited signs of overfitting. Gaussian Naive Bayes attained 72% accuracy with stable performance. While LSTM and RNN effectively capture complex temporal patterns, they are prone to overfitting, whereas Gaussian Naive Bayes is suitable for initial implementation or simpler datasets, supporting early intervention for stunted toddlers.
Fuzzy Time Series Analysis for Stock Sales Forecasting Raditya, Muhammad Ezar; Sriani, Sriani
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.34370

Abstract

Investing is often considered one of the ways to generate profits by allocating funds to a place or company with the aim of gaining profits and avoiding inflation in the future. Among the many types of investments, investing in stocks is one of the popular ones. However, investing in the stock market is not easy because it is considered very risky due to the fluctuating prices of shares. The unstable movement of share prices is an important indicator for investors in determining whether they will sell, hold, or buy certain shares. Therefore, a method is needed to forecast the movement of share prices. This research aims to implement the fuzzy time series method for forecasting stock sales to support efficient decision-making. Using historical data on stock sales at PT. Bank Mandiri (Persero) from January 2, 2024, to October 4, 2024. The results of the study show that the application of the fuzzy time series method produces a forecast of stock price sales with a fairly high accuracy, with an accuracy of 98.7261%, and an MAPE error rate of only 1.2739% of the 180 data tested. This study shows that the forecasting model applied is able to provide an optimal picture of the relevant trends in the movement of stock sales, so it can be used to help make strategic decisions, thus it can be a reference for investors, especially in the stock field, to minimize risk in making decisions before investing.
Motorcycle License Plate and Driver Face Verification Using Siamese Neural Network Model Pane, Yeremia Yosefan; S, Kana Saputra; Al Idrus, Said Iskandar; Syahputra, Hermawan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31750

Abstract

The security and efficiency of vehicle access management systems have become a primary concern for various institutions, including universities, offices, and public facilities. Effective access management not only enhances security but also improves the flow of incoming and outgoing vehicles, reduces congestion, and enhances user experience. This research aims to develop a vehicle plate detection system and driver face recognition using the Siamese Neural Network model to optimize traffic at the gate. The methods used include the application of deep learning algorithms, specifically the Siamese Neural Network, to verify the driver's face and the use of You Only Live Once (YOLO) to detect and recognize vehicle plates in real-time. Data was collected through direct capture with the researcher's camera. The model was trained and tested using a dataset containing images of vehicle license plates and driver faces. The results showed that the developed model was able to detect and recognize the vehicle plate and the driver's face with a fairly high accuracy, namely in the object detection results getting bounding box validation is 1.05 and class loss validation is 0.95, and 0.85 mAP. As well as in training using the Siamese Neural Network, the highest result is 0.82 with a learning rate of 10e-5 with 30 epochs. It is hoped that this system can be one of the innovations that can be applied in government agencies, universities, industries, etc.
Predicting Student On-Time Graduation Using Particle Swarm Optimization and Random Forest Algorithms Rahman, Arif; Mahdiana, Deni; Fauzi, Achmad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33577

Abstract

Higher education plays a crucial role in human resource development and national progress. A key indicator of educational quality is students' ability to graduate on time. Delays in graduation can lower the quality of higher education. Various academic and non-academic factors influence timely graduation rates. At Universitas Islam Syekh Yusuf, the trend of students graduating beyond the expected timeframe has risen over the past three years. However, the university lacks insight into the factors contributing to these delays. This research aims to identify factors causing delayed graduation using PSO and Random Forest to predict student graduation outcomes. The application of PSO reveals key factors influencing timely graduation, including study program, student active status, student leave of absence status, inactive status for semester 1, GPA1, and credit hours in semesters 1 and 2. Evaluation results show that using PSO and Random Forest to predict timely graduation achieves high accuracy (99.63%), precision (99.77%), recall (99.65%), and F1 score (99.71%).

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