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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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Predictive Maintenance for Electrical Substation Components Using K-Means Clustering: A Case Study Roosadi, Hizkia Raditya Pratama; Emiliano, Hughie Alghaniyyu; Astari, Satria Dina; Utama, Nugraha Priya; Kesuma, Rahman Indra
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.v7i2.26815

Abstract

PT PLN (Persero) UP2D Kalselteng aims to provide reliable electricity supply, necessitating effective substation maintenance. This study proposes a predictive maintenance approach using K-means clustering on electrical current performance data from eight components in the Amuntai main electrical substation. The data undergoes preprocessing, including mapping to absolute z-scores to address electricity fluctuations. The K-means algorithm clusters performances, and models are evaluated using Silhouette scores. Results indicate the potential for predicting maintenance needs, as clusters align with real power outage data. The proposed method provides a proactive strategy for substation maintenance, enhancing system reliability. Feature combination experiments reveal that individual models for transformers and feeders are optimal. Hyperparameter tuning refines models, showcasing silhouette scores above 0.5, indicative of high-quality clusters. Comparisons with real-world power outage data validate the model's capability to identify anomalies, reinforcing the feasibility of the predictive maintenance approach. While the study demonstrates promise, on-field implementation and additional experiments are crucial for comprehensive validation and refinement of the predictive maintenance models.
Decoding Energy Usage Predictions: An Application of XAI Techniques for Enhanced Model Interpretability Airlangga, Gregorius
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.v7i2.29041

Abstract

The growing complexity of machine learning models has heightened the need for interpretability, particularly in applications impacting resource management and sustainability. This study addresses the challenge of interpreting predictions from sophisticated machine learning models used for building energy consumption predictions. By leveraging Explainable AI (XAI) techniques, including Permutation Importance, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME), we have dissected the predictive features influencing building energy usage. Our research delves into a dataset consisting of various building characteristics and weather conditions, applying an XGBoost model to predict Site Energy Usage Intensity (Site EUI). The Permutation Importance method elucidated the global significance of features across the dataset, while SHAP provided a dual perspective, revealing both the global importance and local impact of features on individual predictions. Complementing these, LIME offered rapid, locally focused interpretations, showcasing its utility for instances where immediate insights are essential. The findings indicate that 'Energy Star Rating', 'Facility Type', and 'Floor Area' are among the top predictors of energy consumption, with environmental factors also contributing to the models' decisions. The application of XAI techniques yielded a nuanced understanding of the model's behavior, enhancing transparency and fostering trust in the predictions. This study contributes to the field of sustainable energy management by demonstrating the application of XAI for insightful model interpretation, reinforcing the significance of interpretable AI in the development of energy policies and efficiency strategies. Our approach exemplifies the balance between predictive accuracy and the necessity for model transparency, advocating for the continued integration of XAI in AI-driven decision-making processes.
Sentiment Analysis of Brand Ambassador Influence on Product Buyer Interest Using KNN and SVM Putri, Natasya Kurnia; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Yasin, Verdi
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.v7i2.29469

Abstract

In the dynamic marketing, companies usually use strategies involving celebrities or influencers to promote their products or brands. The currently popular strategy is using Korean boy bands as brand ambassadors. This collaboration certainly gets a lot of opinion responses through tweets on X app social media. This research aims to analyze sentiment to determine how the product buyer's interest responds to brand suitability, brand image management, and the influence of issues that arise in this collaboration. The research stages consist of data collection, pre-processing, labeling, weighting, and classification with K-Nearest Neighbor and Support Vector Machine and performance evaluation using a confusion matrix. The dataset used was 696 tweets taken using web scrapping techniques. This research uses the Lexicon-based method to divide the dataset into positive, negative, and neutral classes. The SVM method shows superior test results by achieving an accuracy rate of 83.34% compared to the KNN method, which produces an accuracy value of 71.2% in its calculations
Eligibility Study of Targeted Electricity Subsidies Using DBSCAN on 450 VA and 900 VA Households at PLN UP3 Bandung Suchardy, Randy Zakya; Firmansyah, Adi; Utama, Nugraha Priya; Kesuma, Rahman Indra
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.v7i2.26818

Abstract

PT PLN (Persero), a State-Owned Enterprise (SOE), is mandated by Law No. 30/2007 on Energy and Law No. 30/2009 on Electricity to provide subsidy funds for the poor. The objective of this study is to analyze eligibility criteria for electricity subsidy recipients for customers using 450 VA and 900 VA power groups, to target the electricity subsidy program better. The data used is postpaid customer data from UP3 Bandung in September 2023. The variables used are the amount of electricity consumption, the number of bills, late fees, installment fees, and 50 other variables. The method used in this research is DBScan Clustering which is applied to each power group. Within each group, we analyzed two normalized versions of the dataset standard version and the minmax version. Furthermore, to assess the optimal clustering results, we integrated various metrics, including the Davies-Bouldin Index and Silhouette Score with visual assessment. After that, the best factor suggestions were sought through decision trees, by performing different decision tree classifiers for each power group, using normalized versions of cluster labels. The results showed that among the 50 features available in the raw dataset, it was successful in identifying key features, such as late fees, installment fees, electricity consumption, and bill charges to be important criteria
Modeling The Prediction of Hard Drive Capacity Usage on Server Computers Based on Linear Regression Wahyuni, Wahyuni; Adytia, Pitrasacha; Astin, Siti Namira Rizqi; Sussolaikah, Kelik; Kasim, Fadly
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28851

Abstract

Bank of XYZ has a server computer that is used to run several information technology application services such as ATMs and others. Because the server computer uses a hard drive, the full hard drive can cause problems with the service not operating properly. Full hard drives occur without being noticed. So that this makes the computer server problematic, resulting in customer dissatisfaction and decreased customer loyalty to Bank XYZ. To solve the problem at XYZ Bank, one of the machine learning algorithms can be used to predict hard drive capacity. The method used to predict hard drive storage or usage. The machine learning algorithm used is Multiple Linear Regression. The results of this study show that the linear regression model successfully predicts the use of hard drive capacity on server computers with a sufficient level of accuracy.But it is still not optimal because only a few servers can be predicted. For further research, may consider using the LSTM (Long Short-Term Memory) algorithm. LSTM is an algorithm that is well-suited for sequence prediction problems, including time series forecasting.
Optimizing Performance Random Forest Algorithm Using Correlation-Based Feature Selection (CFS) Method to Improve Distributed Denial of Service (DDoS) Attack Detection Accuracy Soim, Sopian; Sholihin, Sholihin; Subianto, Cahyo Bayu
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.v7i2.24783

Abstract

In the ever-evolving digital era, Distributed Denial of Service (DDoS) attacks have become a major threat to the security of networks and online services, making it important to develop effective strategies to detect and overcome such attacks.This research aims to improve the performance of Random Forest algorithm in dealing with DDoS attacks by using Correlation-Based Feature Selection method. This method can identify and select the most relevant features from the dataset used, in this case the CIC-DDoS2019 dataset, with respect to accuracy, precision, recall, and F1-score as evaluation metrics, so that this research achieves the best results in effectively detecting and preventing DDoS attacks, making an important contribution in strengthening the security of networks and online services.The results show that the application of the Correlation-Based Feature Selection method is able to improve DDoS attack detection in a complex network context using the Random Forest algorithm, increasing the detection accuracy rate to 99.89%. These findings highlight the potential of using the Random Forest algorithm with the CFS method in improving DDoS attack detection in complex network environments.This study recorded a significant improvement compared to the previous study, which only achieved an accuracy rate of 99.7% using the feature importance method. 
Digital Image Processing to Detect Sumba Woven Fabric Contour Using Gray Level Co-occurrence Matrix and Self Organizing Map Mone, Bintang Vieshe; Kaesmetan, Yampi R; Meo, Meliana O.
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28355

Abstract

Sumba woven cloth is one of the cultural heritages of the island of Sumba. Based on its manufacture, the classification process for Sumba woven fabrics is based on the identification of colors or motifs. However, the classification process is not an easy process. In addition to the classification process, the wider community also does not get much information about Sumba woven fabrics clearly, therefore digital image processing technology is needed to build a system that can overcome the problems faced. The image of the Sumba woven fabric sample is converted to grayscale and resized, then segmented using Sobel detection. Then extracted using Gray level co-occurrence matrix (GLCM). After extraction, it will be classified using a Self Organizing Map (SOM). Based on the results of this study, it was concluded that the accuracy of the validation test was 80%, and the program was successful.
Data Augmentation Using Test-Time Augmentation on Convolutional Neural Network-Based Brand Logo Trademark Detection Suyahman, Suyahman; Sunardi, Sunardi; Murinto, Murinto; Khusna, Arfiani Nur
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.v7i2.28804

Abstract

The detection and acknowledgment of logos holds significant importance in the corporate sphere, facilitating the detection of unauthorized logo usage and ensuring trademark uniqueness within specific industry sectors. Presently, convolutional neural networks powered by deep learning are widely utilized for image recognition. However, their effectiveness is dependent on a substantial volume of training images which may not always be readily available. This study suggests employing Test Time Augmentation to address dataset constraints by expanding the original dataset, thereby enhancing classification accuracy and preventing overfitting. Test-Time Augmentation is a method used to improve the accuracy of convolutional neural networks by creating numerous augmented variations of the test images and then merging their predictions. The research findings indicate that the application of TTA has the highest performance on the VGG16 model with 98% precision, 99% recall, and 98% F1-score, and 98.87% accuracy
Coffee Type Classification Using Backpropagation Artificial Neural Network Adytia, Pitrasacha; Wahyuni, Wahyuni; Asmaramany, Dimas; Sussolaikah, Kelik
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28853

Abstract

Coffee has several types including robusta coffee, arabica coffee and luwak coffee. Each coffee has certain characteristics of color, texture, aroma and also the quality of the taste. Coffee counterfeiting is also common. This coffee counterfeiting usually uses materials such as corn, wheat, soybeans, husks, sticks and robusta coffee beans. So that a model is needed to be able to classify the type of coffee. This research uses artificial neural network machine learning algorithms to identify and classify coffee. Quality training and testing data is needed in this method because it will affect the final results. Initial data is collected via e-nose, with this equipment data on changes in electrical voltage will be obtained from 4 sensors, namely MQ-2, MQ-3, MQ-7 and MQ-135. These 4 features will be used in the classification process. With 900 sets of training data, the test results show that the neural network is able to provide correct classification 99% of the 3 sets of testing data. The results of training and testing show that the neural network formed can identify and distinguish coffee types with good results.
Exploratory Data Analysis of Indonesian Presidential Election Candidate Campaign in 2019 on Twitter Hermawan, Fadlan Bima; Sutanto, Taufik Edy; Santoso, Ary
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.v7i2.26308

Abstract

Social media users in Indonesia are growing over time, this has caused many political actors, both individuals, and political parties, to take advantage of this. According to Hootsuite (We are Social) in the Digital 2022 report, Indonesia has 191 million active social media users, so there will be many political actors campaigning on social media. In the literature that discusses similar topics, it is rare to analyze comprehensive exploratory data analysis such as text analysis, hashtags, and social network analysis. From the data analysis conducted in the 2019 Election, the following results were obtained. In text analysis, the narratives used by the two candidates were very different, it was seen that some used other positive and negative narratives. The hashtag analysis found that consistency in campaigning had an influence on electability in the election. In the analysis of social networks, it is found that users who influence social media campaigns, users who appear are not only politicians but also ordinary people who try to support their chosen candidate. The research conducted is expected to be a basic reference in exploring data on social media in other cases, especially in the political field

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