Indonesian Journal of Artificial Intelligence and Data Mining
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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<![CDATA[Virtual Assistant for Thesis Technical Guide Using Artificial Neural Network ]]>
<![CDATA[Mohammad Ovi Sanjaya]]>;
<![CDATA[Saiful Bukhori]]>;
<![CDATA[Muhammad `Ariful Furqon]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.23473
<![CDATA[This study focuses on finding best practice for Artificial Neural Network (ANN) implementation in the information system for student’s thesis technical instructions. The machine learning model applied sequential model, it means ANN only use 1 input layer, a hidden/dense layer and 1 output layer. The Stochastic Gradient Decent (SGD) method was applied into data training process. The results of this study are chatbot applications, and model testing using the confusion matrix. The result of model evaluation are 99,49% accuracy and 91% in F-1 score.]]>
<![CDATA[Hybrid Machine Learning Techniques for Comparative Opinion Mining ]]>
<![CDATA[Bernard Omoi Ondara]]>;
<![CDATA[Stephen Waithaka]]>;
<![CDATA[John Kandiri]]>;
<![CDATA[Lawrence Muchemi]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.22644
<![CDATA[Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains. From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.]]>
<![CDATA[The Use of Large Databases for Diagnosing Human Diseases at Early Stage ]]>
<![CDATA[Abdullah M. Al Al-Ansi]]>;
<![CDATA[Vladimir Ryabtsev]]>;
<![CDATA[Tatyana Utkina]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.24525
<![CDATA[The purpose of this article is to demonstrate the ability of the Eidos intellectual system to recognize human diseases at an early stage by processing large databases containing signs of diseases. To study the signs of diseases, it is proposed to use an automated system-cognitive analysis implemented in the Eidos intellectual system. Automated system-cognitive analysis extracts information from large databases and forms knowledge from them that makes it possible to recognize human diseases. In the process of forming models, the amount of information is calculated in the value of the factor by which the modeling object will pass under its influence to a certain state corresponding to the class. This allows for comparable and correct processing of heterogeneous information about observations of the object of modeling, presented in different types of measuring scales and different units of measurement. The results of recognition of the following diseases were obtained with high reliability: chronic kidney disease, lung cancer, breast cancer, liver disease, risks of developing diabetes and stroke. The results of the study can be applied in medical institutions in many countries, since the Eidos system is freely available on the Internet.]]>
<![CDATA[Fuzzy Tsukamoto-Based Detection of Ping of Death Attacks: Advancing Network Security with Precise Classification ]]>
<![CDATA[Muhammad Adam Hawari]]>;
<![CDATA[Wahyu Adi Prabowo]]>;
<![CDATA[Rifki Adhitama]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.23858
<![CDATA[Internet services have the potential to be targeted by hackers using various DDoS (Distributed Denial of Service) attack techniques, including the ping of death attack. This attack involves multiple machines launching simultaneous attacks on the database server and File Transfer Protocol (FTP), resulting in severe consequences for computer networks. To effectively classify such attacks, the Fuzzy Tsukamoto method is employed, which represents each IF-THEN rule as a Fuzzy set with a corresponding membership function. Fuzzy logic offers great flexibility, tolerance for imprecise data, and the ability to model highly complex and nonlinear functions. By implementing this classification technique, it becomes easier to differentiate and analyze network traffic captured by Wireshark, enabling the detection of ping of death attacks against the server with maximum accuracy through the Fuzzy Tsukamoto method in the classification process.]]>
<![CDATA[Harnessing the Power of Stacked GRU for Accurate Weather Predictions ]]>
<![CDATA[Mohammad Diqi]]>;
<![CDATA[Ahmad Wakhid]]>;
<![CDATA[I Wayan Ordiyasa]]>;
<![CDATA[Nurhadi Wijaya]]>;
<![CDATA[Marselina Endah Hiswati]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.24769
<![CDATA[This research proposed a novel approach using Stacked GRU (Gated Recurrent Unit) models to address the problem of weather prediction and aimed to improve forecasting accuracy in sectors like agriculture, transportation, and disaster management. The key idea involved leveraging the temporal dependencies and memory management capabilities of Stacked GRU to model complex weather patterns effectively. Comprehensive data preprocessing ensured data quality and fine-tuning of the model architecture and hyperparameters optimized performance. The research demonstrated the Stacked GRU model's effectiveness in accurately forecasting temperature, pressure, humidity, and wind speed, validated by low RMSE and MAE scores and high R2 coefficients. However, challenges in forecasting humidity and a percentage discrepancy in wind speed predictions were observed. Overfitting and computational complexity were identified as potential limitations. Despite these constraints, the study concluded that the Stacked GRU model showed promise in weather forecasting and warranted further refinement for broader applications in time-series prediction tasks.]]>
<![CDATA[Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering ]]>
<![CDATA[Rufus Kinyua Gikera]]>;
<![CDATA[Jonathan Mwaura]]>;
<![CDATA[Elizaphan Maina]]>;
<![CDATA[Shadrack Mambo]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.22718
<![CDATA[Clustering is one of the tasks performed during exploratory data analysis with an extensive and wealthy history in a variety of disciplines. Application of clustering in computational medicine is one such application of clustering that has proliferated in the recent past. K-means algorithms are the most popular because of their ability to adapt to new examples besides scaling up to large datasets. They are also easy to understand and implement. However, with k-means algorithms, k-hyperparameter tuning is a long standing challenge. The sparse and redundant nature of the high-dimensional datasets makes the k-hyperparameter tuning in high-dimensional space clustering a more challenging task. A proper k-hyperparameter tuning has a significant effect on the clustering results. A number of state-of-the art k-hyperparameter tuning techniques in high-dimensional space have been proposed. However, these techniques perform differently in a variety of high-dimensional datasets and data-dimensionality reduction methods. This article uses a five-step methodology to investigate the trends and advances on the state of the art k-hyperparameter tuning techniques in high-dimensional space clustering, data dimensionality reduction methods used with these techniques, their tuning strategies, nature of the datasets applied with them as well as the challenges associated with the cluster analysis in high-dimensional spaces. The metrics used in evaluating these techniques are also reviewed. The results of this review, elaborated in the discussion section, makes it efficient for data science researchers to undertake an empirical study among these techniques; a study that subsequently forms the basis for creating improved solutions to this k-hyperparameter tuning problem.]]>
<![CDATA[Sentiment Analysis on IMDB Movie Reviews using BERT ]]>
<![CDATA[Rani Puspita]]>;
<![CDATA[Cindy Rahayu]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.24239
<![CDATA[Before technology existed, opinions could only be obtained from acquaintances, friends, or experts who were experts in certain fields. However, as technology develops, it turns out that opinions can be expressed through social media so that they can influence everyone who sees them. One of them is movie reviews. Human opinion about something is often not valid. So, this study aims to investigate the sentiment analysis related to IMDB Movie Reviews. The approach used is BERT. BERT is a deep learning approach. The data used in this study is the IMDB Movie Review of 50,000 data. The existing data is divided into three parts, namely training data, validation data, and testing data. The results obtained from the BERT model are 91.69% for training accuracy 0.187 for training loss, 91.85% for validation accuracy, 0.212 for validation loss, 91.78% for testing accuracy, and 0.207 for testing loss. It can be seen, that BERT is a very effective approach for sentiment analysis of IMDB Movie Review so that the research problem regarding the invalidity of one's opinion can be handled properly.]]>
<![CDATA[Optimizing Malware Detection Using Back Propagation Neural Network and Hyperparameter Tuning ]]>
<![CDATA[Annisa Arrumaisha Siregar]]>;
<![CDATA[Sopian Soim]]>;
<![CDATA[Mohammad Fadhli]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.24731
<![CDATA[The escalating growth of the internet has led to an increase in cyber threats, particularly malware, posing significant risks to computer systems and networks. This research addresses the challenge of developing sophisticated malware detection systems by optimizing the Back Propagation Neural Network (BPNN) with hyperparameter tuning. The specific focus is on fine-tuning essential hyperparameters, including dropout rate, number of neurons in hidden layers, and number of hidden layers, to enhance the accuracy of malware detection. A Back Propagation Neural Network (BPNN) with dropout regularization is trained on an extensive dataset as part of the research design. Hyperparameter optimization is conducted using GridSearchCV, with experiments varying learning rates and epochs. The best configuration achieves outstanding results, with 98% accuracy, precision, recall, and F1-score. The proposed approach presents an efficient and reliable solution to bolster cybersecurity systems against malware threats.]]>
<![CDATA[Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images ]]>
<![CDATA[Agus Ambarwari]]>;
<![CDATA[Emir Mauludi Husni]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.25263
<![CDATA[Intertidal mangrove forests are ecosystems that are extremely productive offering diverse socio-economic advantages. Preserving and appropriately using these ecosystems is crucial. However, safeguarding and restoring mangroves present challenges due to their extensive and hard-to-reach areas. Leveraging remote sensing technology and diverse image classification methods has shown promise in accurately mapping and monitoring mangroves. This study reviews the use of machine learning methods in mapping and monitoring mangroves, particularly using moderate-resolution multispectral satellite images. The literature study was conducted by systematically searching and analyzing articles published in Scopus-indexed journals from 2018 and 2023. The primary goals are to uncover methodologies for mapping mangroves with moderate-resolution imagery, identify advancements in machine learning algorithms, and assist researchers in staying updated in this field. The findings reveal that various machine-learning algorithms can be employed to map mangroves. Mangrove mapping with machine learning typically involves stages such as inputting multispectral images, image preprocessing, image classification, and assessing accuracy. Among the techniques, in the case of remote sensing data, ensemble tree-based approaches such as random forest outperform single classifiers. Potential and emerging issues for future research encompass automating the generation of training datasets for specific land cover classification, developing methods to transfer the classification model to different study areas, and making use of cloud-based technologies for processing remote sensing data.]]>
<![CDATA[Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method ]]>
<![CDATA[Akhmad Mustolih]]>;
<![CDATA[Primandani Arsi]]>;
<![CDATA[Pungkas Subarkah]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.24864
<![CDATA[The rapid development of technology has brought convenience to humans in their daily lives. The continuously evolving technology generates large amounts of data. Data can provide valuable information if processed effectively. The Motorku X application is one of the innovations created by Astra Motor to facilitate consumers or potential customers in servicing and purchasing motorcycles. The Motorku X application generates review data every day. These review data can be utilized for future application development. To make the most of the reviews, sentiment analysis is one of the techniques used to process the review data. Sentiment analysis is a method to measure consumer sentiments in terms of positive or negative reviews. The algorithm used in this research is the Naïve Bayes classifier. One of the advantages of Naïve Bayes is its ability to work quickly and efficiently in terms of computational time. The research consists of several stages: data collection, data labeling, pre-processing, data splitting, tf-idf weighting, implementation of Naïve Bayes classifier, and evaluation of the results. The data comprises 1000 reviews divided into two classes: positive class (number) and negative class (number). The research was conducted with three scenarios of training and testing data sharing: 90%:10%, 80%:20%, and 70%:30%. The best results were achieved with the 90%:10% ratio, with an accuracy of 76%, precision of 76%, and recall of 97%.]]>
