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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.
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Articles 233 Documents
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Prediction of Heart Disease Using K-Nearest Neighbor and Particle Swarm Optimization Algorithm Saputra, Elin Panca; Saryoko, Andi; Kusumo, Aryo Tunjung; Priyono, Priyono
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.v8i1.33373

Abstract

One of the leading causes of death nowadays is heart disease, so more has to be done to avoid it, such as by making prediction models work better. Among the machine learning algorithms is K-Nearest Neighbor (K-NN) is among the best methods for predicting heart disease based on several risk factors, including smoking, high blood pressure, diabetes, age, and so on. To get accurate values and attribute selection features, we tested them with K-NN, and to improve the results of our research predictions, we combined them using the Particle Swarm Optimization (PSO) algorithm. The results are very interesting after we do the calculations, the algorithm that uses PSO-based K-NN gets a higher weight compared to using only the K-NN algorithm. The predicted value of the weight resulting from the PSO-based K-NN is 97.67%. while the results only use K-NN of 64.92%. The advantages of PSO can also select attributes that can affect it, namely age, diabetes, and ejection fraction. So gathering information through data mining. The PSO-Based K-NN method, which is the primary machine learning technique used in this computation, yields the greatest results in terms of accuracy for heart disease when applied to the data assets. Using the K-NN - PSO algorithm can provide promising results for predicting symptoms that cause heart disease with very good accuracy. PSO is Used to choose features and optimize k values on the K-NN dataset, after which the accuracy is output on the K-NN.
Tomato Pest and Disease Identification Based on Improved Deep Residual Network and Transfer Learning Linli, Peng; Sen, Tjong Wan; Fahmi, Hasanul; Roestam, Rusdianto
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.v8i1.34038

Abstract

Tomatoes are a vital global crop, but their yield can be severely impacted by various diseases like leaf mold and spotted wilt. Early and accurate diagnosis of these diseases is crucial for implementing timely treatments, thereby reducing crop loss. Traditional manual diagnosis often suffers from low accuracy, high costs, and time consumption. To address these issues, this study introduces a method for identifying tomato pests and diseases using an improved residual network and transfer learning. A dataset comprising images of seven common tomato diseases and healthy leaves was created. This study introduces an improved residual network and transfer learning method to accurately identify tomato pests and diseases. The enhanced ResNet50 model, with an attention mechanism and focal loss, achieved 98.10% recognition accuracy. This research not only facilitates early disease detection, reducing crop loss but also minimizes pesticide use, thereby enhancing environmental sustainability and agricultural productivity worldwide.
Automatic Detection of Acne Types Using the YOLOv5 Method Pinasty, Salsabila; Hakim, Raden Bagus Fajriya
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.v8i1.35617

Abstract

Acne is very common due to several factors such as hormones, hygiene, and environmental exposure. This research aims to develop an automatic detection system for facial skin problems using the You Only Look Once v5 (YOLOv5) algorithm, focusing on the problem of acne types on acne-prone faces, and this research is the latest research that has never been done before. The research methodology was carried out by taking datasets directly on acne faces, with a sample of 1230 images. The research process includes data collection, labeling using the Roboflow platform, dividing the dataset into training, testing, and validation data, and implementing the YOLOv5 algorithm using Google Colab. The research stages include data input, object labeling, dataset configuration, YOLOv5 preparation, modeling, model testing, hyperparameter tuning, and model performance evaluation. The results of this study resulted in an accuracy rate seen based on the mapped value of 87.6%, so this can be considered that the model is considered good in detecting the type of acne on facial skin problems in accordance with testing on data, and this model can be implemented to automatically detect facial skin problems, especially on faces with acne, in the future.
Enhancing Image Quality for the Detection of Underwater Debris with Adaptive Fuzzy Filter Halim, Apriyanto; Ulina, Mustika; Tanti, Tanti; Sinaga, Frans Mikael
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.v8i1.33010

Abstract

The image quality improvement process plays a very important role. This is because the process can increase the clarity and accuracy of image detection. One type of image detection that exists is the detection of garbage found under the sea. One of the image quality improvement processes is related to noise removal. Noise is a sudden increase in pixel intensity in an image. This can cause various problems that occur such as in medical photos, satellites, and photography. One method used to remove noise from images is using Adaptive Fuzzy Filter (AFF). This method is carried out by first finding the average value of the mean fuzzy set and the gray level fuzzy. After that, the value comparison process is carried out. From the results of the research conducted on 689 images from the dataset obtained, there is a decrease in the amount of noise of around 96,23% of the total noise obtained previously. This can certainly provide good results in terms of changes in noise that have been made.
Natural Language Processing for Enhancing Anamnesis Documentation in Typhoid Fever Cases Putri, Tacyah Kholifah; Nurmalasari, Mieke; Hosizah, Hosizah; Krismawati, Dewi; Panutun, Satria Bagus
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.v8i1.33325

Abstract

The implementation of Natural Language Processing (NLP) is crucial for enhancing the quality of medical records. This study aimed to develop an NLP model to improve the accuracy of documenting disease anamnesis for typhoid fever. The problem addressed by this research is the difficulty in analyzing and classifying patient complaints recorded in electronic medical records, which can affect the accuracy of diagnosis and treatment. The urgency of this study lies in ensuring that documented medical information is used accurately to support diagnosis and patient management. A quantitative approach was used, focusing on electronic medical records of patients who underwent anti-salmonella IgM tests in 2023, involving 424 individuals. The study assessed the performance of three models: Support Vector Machines (SVM), Naive Bayes Bernoulli, and Logistic Regression. The SVM model achieved the highest accuracy at 81.4%, compared to 76.7% for Naive Bayes Bernoulli and 79.1% for Logistic Regression. Additionally, four topic models were identified, highlighting common complaint words and their impacts. The most frequently occurring symptoms in the anamnesis of typhoid fever were "defecation," "nausea," "vomiting," "fever," "diarrhea," "heartburn," "weakness," "loss of appetite," "abdominal pain," "cough," and "cold." This study demonstrates that the SVM model provides superior accuracy in analyzing medical records compared to other models.
Enhancing Stego Image Quality With SIUN Post-Processing of Image Steganography Without Embedding DCGAN Outputs Forenziana, Jessica; Sen, Tjong Wan
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.v8i1.35640

Abstract

In digital steganography, hiding information seamlessly within images is key. This study merges Deep Convolutional Generative Adversarial Networks (DCGAN) with Scale-Iterative Upscaling Networks (SIUN) to craft high-quality stego images swiftly and enhance the DCGAN image training period. Eschewing length DCGAN training, SIUN refines post-generation images, ensuring detailed visuals and increased data storage. Using the MNIST dataset, findings show that SIUN not only accelerates the process but also improves the stego image quality, suggesting a significant leap forward for secure communication efficiency. This research found that by using SIUN can enhance the quality of stego images with just 50 epochs of DCGAN training. After this initial training, the images are sent to SIUN for further quality upgrades with more efficient time.
Predicting Catfish Growth and Feed Efficiency in Using Decision Tree and Support Vector Regression Kurniawan, Zaqi; Tiaharyadini, Rizka
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.v8i1.32889

Abstract

Catfish farming has a key part in maintaining the economy of Poris Plawad Utara, Cipondoh, Tangerang where many farmers depend on it as their primary source of income. However, poor feed management creates considerable obstacles as overfeeding leads to higher expences and enviromental issues while underfeeding inhibits fish growth. Traditional methods for identifiying ideal feed amounts rely on manual observation, which often leads in irregular growth rates and feed loss. Despite the necessity of effective feed utilization, there is a paucity of powerful predictive techniques available to enable farmers accurately forecast feed demands and fish growth. There, we employ machine learning approaches including Decision Tree and Support Vector Regression (SVR), to predict catfish development and feed efficiency based on several environmental parameters such as water temperature, pH levels, and oxygen concentration. The algorithm we used was trained using data acquired from catfish farm in Poris Plawad Utara, comprising 3 month of feeding and growth records. The results of the analysis demonstrate that while Support Vector Regression (SVR) and Decision Trees perform well in stable environments, they have trouble handling environmental changes. Accuracy is impacted by feed management and environmental stability. More variables and an intricate machine learning strategy are required for better performance. While SVR works well in stable systems, complicated dynamics require adaptations. These results show that feed efficiency and fish development may be grately increased by incorporating machine learning into catfish farming operations. This methodology provides farmers with data-driven solutions that maximizes the efficiency of aquaculture and sustainability.
Random Forest Optimization Using Recursive Feature Elimination for Stunting Classification Marpaung, Sophya Hadini; Sinaga, Frans Mikael; Rambe, Khairul Hawani; Simamora, Fandi Presly; Kelvin, Kelvin
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.v8i1.35295

Abstract

Stunting is still a major health problem in Indonesia, with a prevalence of 27% in toddlers in 2023, far from the WHO target of below 20%. RSU Mitra Medika Tanjung Mulia in Medan serves patients with various socio-economic backgrounds, which affects the quality of services, including stunting detection. Conventional methods are prone to bias and error. This study used the Random Forest algorithm and the Recursive Feature Elimination (RFE) feature selection method to improve the accuracy of stunting classification. After data preprocessing and feature selection, two main variables were identified, namely age and height. The initial Random Forest model achieved an accuracy of 94.38%, which increased to 94.42% after hyperparameter tuning. The results showed that this approach produced an accurate, efficient model that can be integrated into clinical systems, helping medical personnel identify children at risk of stunting quickly and accurately, increasing the effectiveness of interventions, and supporting government efforts to reduce the prevalence of stunting
Enhancing Single Nucleotide Polymorphisms Detection from Imbalanced Data: A Study of Resampling Techniques in Machine Learning Algorithms Nurhasanah, Rossy; Arisandi, Dedy; Purnamasari, Fanindia; Hayatunnufus, Hayatunnufus; Simangunsong, Daisy Sere Damara; Pulungan, Aflah Mutsanni
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.v8i1.32942

Abstract

Identifying the actual Single Nucleotide Polymorphisms (SNPs) by sourcing Next Generation Sequencing (NGS) data emerges an imbalanced problem due to the inherent high error rate of NGS technology. The imbalance problem has been found to have a negative impact on machine learning algorithms because it produces biased models and poor performance, particularly in detecting actual SNP that belong to the underrepresented class in question.   This study evaluates the effectiveness of several resampling techniques, including Borderline-SMOTE, Random Undersampling, and Tomek-Link, in enhancing the performance of machine learning algorithms, specifically Random Forest (RF) and Artificial Neural Networks (ANN). Furthermore, we compare these techniques to determine the most effective approach. Our results indicate that Borderline-SMOTE improves the F-Measure of RF from 69.72 to 91.52 (a 31.2% increase) and ANN from 79.75 to 91.32 (a 14.5% increase) and outperforms other resampling methods. These findings highlight the crucial role of resampling techniques and the careful selection of algorithms in improving classification accuracy for imbalanced datasets.
Enhancing Product Recommendation Accuracy Using Bipartite Link Prediction and Long Short-Term Memory in Retail Industry Siregar, Ivan Michael; Rosdiana, Firlie Resti
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.v8i2.36052

Abstract

As competition in the retail sector intensifies, the demand for accurate customer-product recommendation systems has grown. Traditional similarity-based approaches such as common neighbor, Jaccard, Adamic Adar, preferential attachment, and resource allocation have been widely adopted in many business applications. However, these methods often struggle with capturing complex purchasing behaviors, product heterogeneity, temporal demand variations, and scalability challenges. This study introduces a deep learning-based recommendation model that integrates bipartite link prediction networks with Long Short-Term Memory (LSTM) to improve predictive accuracy. The bipartite network represents customer-product interactions, while the LSTM model captures sequential purchasing patterns to forecast future transactions. Experimental evaluation on a real-world building materials retail dataset comprising 389,087 transactions demonstrates the effectiveness of the proposed approach, achieving a Precision of 0.8223, Recall of 0.8034, F1-score of 0.8128, NDCG of 0.8601, and overall prediction accuracy of 0.854. The results indicate that the proposed model significantly outperforms similarity-based techniques, offering a robust solution for enhancing recommendation performance in dynamic retail environments.

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