cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mustakim
Contact Email
Mustakim
Phone
-
Journal Mail Official
ijaidm@uin-suska.ac.id
Editorial Address
-
Location
Kab. kampar,
Riau
INDONESIA
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
 15   16   17   18   19 
Classification of The Level of Public Satisfaction With the Use of Water Tourism Jetski in Balai Ujung Tanjung Using the Naïve Bayes Algorithm Fatwa, Nursalimah Isnaina; Kurniawan, Rakhmat
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.v7i2.32761

Abstract

Jetski water tourism is one of the attractions that is often visited by the public compared to other attractions. One of the factors causing this is because there is no fee charged to visitors. The source of funds used in this tourist attraction is from the local government budget. Be it in terms of assessment to improve facilities, or even comments on whether the Jetski Water Tourism facility is good or bad. Certainly, with the public comments, it will help the government in improving its services to the community, especially in the management of this water tourism Jetski.The sentiment data collected from visitors to this Water Tourism Jetski can be used as a benchmark for the government in improving this Water Tourism Jetski facility. Both in terms of scope and the Jetski media used. By knowing the responses and comments of the community regarding Jetski Wisata Air, the government can evaluate in order to support visitor satisfaction and so that Jetski Wisata Air can last long and compete with other tourist attractions. The Naïve Bayes Algorithm has often been used in a study in the form of sentiment analysis. The Naïve Bayes model shows that the level of public satisfaction with Jetski Water Tourism in Ujung Tanjung Hall, Tanjungbalai City can be predicted with an accuracy of 75%. This indicates that the model is quite effective in identifying the level of user satisfaction, although there is a 25% possibility of inaccuracy in prediction. With this accuracy, the model can provide useful insights for the evaluation and improvement of jetski tourism services, but it should be considered to conduct further analysis to improve accuracy and get a more comprehensive picture of community satisfaction
Data Mining on Women's Clothing Sales in Market Places with the K-Means Clustering Algorithm Dalimunthe, Rizna Fitriana; Putri, Raissa Amanda
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.v7i2.31384

Abstract

Clothing is a necessity that must be used to cover the body with the main material made of fiber or textile so that the body is completely covered without gaps. Marketplace is an application or website that provides online buying and selling facilities from various sources. On the Shopee marketplace, there are many shops selling women's clothing from various groups and types of clothing. The K-Means Clustering algorithm in the research was applied to make it easier for sellers and buyers to find out what kind of women's clothing is currently selling well in the marketplace by grouping it into 3 clusters, namely the best-selling, best-selling, and least-selling. Research data was obtained from the Shopee marketplace with 3 variables, namely product price, number of sales, and buyer assessments of 4 types of women's clothing in the form of tunics, dresses, abayas/gamis, and shirts totaling 1200 data. The results of this research make it easier for buyers to make decisions and sellers to develop shop ideas.
Unlocking the Future: EFL Students' Insights on Artificial Intelligences for Academic Writing Assapari, Muhammad Mugni; Hidayati, Rosyadi; Mukti, Siti Raudatul Warni
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.v8i1.30307

Abstract

Lately, the widely used and fiercely debated ChatGPT has attracted the interest of researchers, professors, lecturers, and administrators. Additionally, English as a Foreign Language (EFL) learners require AI feedback on scientific writing to improve their writing abilities. This quantitative study investigated Indonesian EFL learners' perceptions and challenges in a university English program using ChatGPT tools. This study investigated undergraduate students' viewpoints on using AI-powered ChatGPT tools in English academic writing. It focused on the main goal, results, and feedback to improve their second language writing skills. Data were collected from (n=80) students using questionnaires and semi-structured interviews to analyze their impressions. A survey was administered to EFL undergraduates in the English Language Education Study Program at Mataram State Islamic University, Indonesia. Writing ability was assessed online using Google Forms. However, students reported the advantages and difficulties of using ChatGPT for academic writing. These results demonstrate that AI-enabled digital tools can enhance student performance in EFL, academic writing, and other disciplines. The benefits and drawbacks of artificial intelligence must be studied and evaluated, and its implications for academic writing must be developed
Exploring New Frontiers: XCEEMDAN, Bidirectional LSTM, Attention Mechanism, and Spline in Stock Price Forecasting Kelvin, Kelvin; Sinaga, Frans Mikael; Winardi, Sunaryo; Susmanto, Susmanto
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.v7i2.29649

Abstract

The Attention Mechanism is acknowledged as a machine learning method proficient in managing relationships within sequential data, surpassing traditional models in this regard. However, the unique characteristics of stock data, including substantial volatility, multidimensionality, and non-linear patterns, present challenges in attaining accurate forecasts of stock prices. This research aims to tackle these hurdles by enhancing a prior model through the incorporation of an Attention Mechanism, resulting in an enhanced model. The forecasted data are standardized and prepared for analysis before undergoing signal decomposition into high and low-frequency components. Subsequently, the Attention Mechanism processes the high-frequency signals. Evaluation entails comparing the performance of the proposed model with that of the previous model using identical parameters. The findings indicate that the proposed model achieves a reduced RMSE value of 0.5708777053 compared to the previous model's average RMSE value of 0.5823726212, indicating enhanced accuracy in stock price prediction. This approach is anticipated to make a substantial contribution to the advancement of more dependable and effective stock price prediction models, addressing the limitations of prior methodologies
Classification of Big Data Stunting in North Sumatra Using Support Vector Regression Method Simanullang, Maradona Jonas; Rosnelly, Rika; Riza, Bob Subhan
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.v8i1.32177

Abstract

Stunting in children is a serious issue in society, especially in areas with high levels of malnutrition like North Sumatra. Therefore, it is important to develop an effective approach to identify the factors contributing to stunting and predict its risks in children, considering the high prevalence of stunting in this region. The high rate of stunting in North Sumatra indicates the urgency of this problem, making research on Big Data classification using Support Vector Regression (SVR) methods highly important. This study aims to offer profound understanding into factors influencing stunting in the region, thus enabling the development of more effective and targeted intervention strategies. The objective of this research is to categorize Big Data related to stunting in North Sumatra using SVR methods, taking into account factors such as wasting and malnutrition. The main focus of this research is to identify patterns related to stunting, predict the risk of stunting in children, and design more effective intervention strategies while addressing the issues of wasting and malnutrition. The research process encompasses several steps including data collection, pre-processing to handle missing values and outliers, normalization, and the application of Support Vector Regression (SVR). The final outcomes were achieved using a Voting Classifier that integrates Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), resulting in an accuracy rate of 91.78%. This method effectively pinpoints the main factors contributing to stunting, which supports clinical decision-making and intervention strategies. The study highlights the potential of big data and machine learning in the healthcare sector, offering a model for enhancing health services and tracking children’s health conditions.
Application of Canny Method to Detect Vehicle License Plate in Tanjung Balai City Government Mess Area Aini, Siti Nurul; Kurniawan, Rakhmat
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.v7i2.32426

Abstract

Vehicles have a license plate that serves to be the identity of a vehicle. The shape of the plate is in the form of a piece of metal mounted on the vehicle as an official identity. Making a license plate or Motor Vehicle Number Sign in Indonesia is regulated in Government Regulation No. 60 of 2016 with a validity period of 5 years. The regulation is about the type and tariff of Non-Tax State Revenue (BNBP), and has been officially enacted on January 6, 2017, by replacing Government Regulation No.50 of 2010, quoted from the Kompas newspaper website. Image is one of the components of multimedia that plays an important role because it contains information in visual form. Images have more information that can be conveyed than in the form of text. An image is a collection of image elements (pixels) that as a whole record a scene through a visual sensory (camera). Canny edge detection can detect edges with a minimum error rate, canny edge detection has a difference with other operators because it uses a Gaussian Derivative Kernel that can refine the appearance of the image. Good location can minimize the distance of edge detection produced by processing, so that the location of the edge can be detected similar to the real edge. The accuracy value of applying this method reaches 99.88%-100%. And lastly, one response to single edge that can produce a single edge, not giving false edges.
A Hybrid Traditional and Machine Learning-Based Stacking-Based Ensemble Forecasting Approach for Coal Price Prediction Yaqin, Alvin Muhammad 'Ainul; Hamdi, Rafisal; Zamzani, Muhammad Imron; Hertadi, Christopher Davito Prabandewa; Nabiha, Hilwa Dwi Putri
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.v7i2.30547

Abstract

Accurate coal price forecasts are crucial, as volatility in coal prices significantly impacts company performance and profitability. Traditional time series forecasting methods, such as exponential smoothing, are known for their simplicity and low data requirements. In contrast, machine learning techniques, such as random forest and neural network, offer higher accuracy in predictions. However, very few attempts have been made to combine the simplicity of traditional methods with the accuracy of machine learning techniques. This paper presents a novel stacking-based model that integrates both traditional statistical methods and machine learning techniques to enhance coal price predictions. Using Indonesian coal price data from January 2009 to October 2021, we trained the models on various combinations of predictors to generate new predictions. Our findings demonstrate that our stacking-based model outperforms other models, with RMSE and MAPE values of 6.44 and 5.97%, respectively. These results indicate that the model closely forecasts actual coal prices, capturing 94.03% of the price movements. The main contribution of this study is the application of stacking-based models to coal price forecasting in Indonesia, which has not been previously explored, thus enriching the literature on this topic.
Identification of Little Tuna Species Using Convolutional Neural Networks (CNN) Method and ResNet-50 Architecture Pusparani, Diah Ayu; Kesiman, Made Windu Antara; Aryanto, Kadek Yota Ernanda
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.v8i1.31620

Abstract

Indonesia is home to a vast array of biodiversity, including various species of little tuna. However, the process of identifying little tuna species is still challenging due to their diversity. The Indonesian Society and Fisheries Foundation (MDPI), which has the task of collecting fisheries data manually, is prone to significant identification errors. Therefore, the author proposes the utilization of Deep Learning, a Machine Learning method due to its ability to model various complex data such as images or pictures and sounds. This approach can facilitate the identification process of little tuna. In this research, the Resnet-50 architecture is utilised in the modelling process with the original dataset of 500 images. In this study, several test scenarios were also applied. The best results obtained are global accuracy of 91% and matrix accuracy value of 95%. These results were obtained using an augmented dataset with some parameter adjustments to the model built. With these good accurate identification, the MDPI Foundation is expected to better manage fisheries data and use it to support sustainable fisheries management.
Comparison Analysis of HSV Method, CNN Algorithm, and SVM Algorithm in Detecting the Ripeness of Mangosteen Fruit Images Anam, M. Khairul; Sumijan, Sumijan; Karfindo, Karfindo; Firdaus, Muhammad Bambang
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.v7i2.29739

Abstract

Mangosteen contains a substance known as Xanthone, a phytochemical compound with the distinctive red component in mangosteen that is known for its properties as an anticancer, antibacterial, and anti-inflammatory agent. Additionally, Xanthone has the potential to strengthen the immune system, promote overall health, support mental well-being, maintain microbial balance in the body, and improve joint flexibility. The mangosteen fruit is consumable when it reaches maturity, displaying a dark purplish-black color. Besides the edible part of the fruit, the peel also possesses remarkable medicinal properties. To detect whether the fruit is ripe or not, this research employs image processing techniques. The study utilizes the HSV (Hue, Saturation, and value) color space method, CNN (Convolutional Neural Network) algorithm, and SVM (Support Vector Machine) algorithm. These methods and algorithms are chosen for their relatively high accuracy levels. The dataset used in this research is obtained from mangosteen datasets available on Kaggle. The results of this study indicate that the HSV method achieved an accuracy of 86.6%, SVM achieved an accuracy of 87%, and CNN achieved an accuracy of 91.25%. From the achieved accuracies, it is evident that the CNN algorithm yields higher accuracy compared to the others.
Algorithm Comparison of Hierarchical and Non-Hierarchical Clustering Method in Grouping Regional Poverty Variables Maulana, Farhan; Wijayanto, Arie Wahyu
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.v8i1.29393

Abstract

One of the objectives of the main Sustainable Development Goals (SDGs) is to end poverty in all forms. Although West Sumatera Province occupies ranking seventh lowest national in poverty, there is an increase amounting to 0.11 percent in September 2022 compared to March 2022. This shows the complexity of the poverty problem in the region. The Provincial Government needs to understand the poverty situation by grouping it based on characteristics in each region. This is a strategic step so that poverty reduction policies can be developed on target and efficiently according to the conditions of each region. This study aims to investigate Clustering methods, namely a non-hierarchical method represented by K-means, Fuzzy C-means, and K-medoids also the hierarchical method, represented by Divisive Analysis (DIANA) and Agglomerative Nesting (AGNES) with complete linkage, average linkage, single linkage, and Ward’s method, to group regencies/cities and compare the performance of the Clustering methods used, to get the best method using Davies Bouldin Index and Dunn index. The results of this research indicate that the divisive analysis method and agglomerative nesting, especially in complete linkage, single linkage, and Ward’s method is the best Clustering method. This method works optimally when the number of clusters is equal to 3. It is hoped that our findings can support policies that are right on target and efficient in efforts to overcome poverty in West Sumatera.

Page 17 of 24 | Total Record : 233

 15   16   17   18   19 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol 8, No 3 (2025): November 2025 Vol 8, No 2 (2025): July 2025 Vol 8, No 1 (2025): March 2025 Vol 7, No 2 (2024): September 2024 Vol 7, No 1 (2024): March 2024 Vol 6, No 2 (2023): September 2023 Vol 6, No 1 (2023): Maret 2023 Vol 5, No 2 (2022): September 2022 Vol 5, No 1 (2022): March 2022 Vol 4, No 2 (2021): September 2021 Vol 4, No 1 (2021): March 2021 Vol 3, No 2 (2020): Spetember 2020 Vol 3, No 1 (2020): March 2020 Vol 2, No 2 (2019): September 2019 Vol 2, No 1 (2019): March 2019 Vol 1, No 2 (2018): September 2018 Vol 1, No 1 (2018): March 2018 More Issue