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
 12   13   14   15   16 
Recognition of Hijaiyah Letter Patterns Using The Bidirectional Associative Memory Method Nanda Jarti; Sestri Novia Rizki
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.v7i1.29192

Abstract

Artificial Intelligence has a very broad scope so it can solve very complex problems. Hijaiyah letter pattern recognition using the Bidirectional Associative Memory (BAM) method has the ability to detect maximum results for the blind who are learning to recognize hijaiyah letters in producing information using a bipolar system. The problem in this research is the difficulty for blind people in recognizing hijaiyah letters and patterns, so this research was designed to help blind people learn hijaiyah letters so that they can easily understand the patterns and types of letters using the 3x3 order matrix concept. In the BAM has the advantage of layers that are interconnected between one layer and another layer so that they can be connected from the input layer to the output layer. This research will produce a Hijaiyah letter recognition pattern using the 3x3 Matrix system to design a system for identifying Hijaiyah letter patterns. From the results of the tests that have been carried out, there are 5 Hijaiyah letter patterns, namely ا, ث, ج, ذ and ش which are processed by pattern recognition with a value of 1. [1.1] [1,-13],[-1,1][3,-11 ],[1,-1] [-9,-17],] [-1,1][ [13,9], [-1,1][-13,13]. Of the 5 Hijaiyah letter patterns, only 2 letter patterns were able to be recognized by the system, namely the letters ذ and ش, and 3 patterns were not able to be recognized by the system. Thus it can be concluded that not all patterns using the BAM method are able to recognize patterns precisely and correctly.
Predicting Urban Happiness: A Comparative Analysis of Deep Learning Models Airlangga, Gregorius
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.v7i1.28801

Abstract

This study explores the efficacy of various deep learning models in predicting urban happiness scores, a critical indicator of the quality of life in urban environments. Recognizing the complex interplay of factors contributing to urban happiness, we employed a suite of models, including Dense Neural Networks (DNN), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), Autoencoders, Multi-Layer Perceptron with Dropout (MLP Dropout), and Simple Recurrent Neural Networks (RNN), to analyze a comprehensive dataset encompassing environmental quality, socio-economic factors, and urban infrastructure. Our methodology centered on rigorous data preprocessing to ensure integrity and usability, followed by a detailed comparative analysis of model performances based on Root Mean Squared Error (RMSE) metrics. The results revealed that the CNN model outperformed others in identifying spatial patterns crucial for urban happiness, indicating its superior capability in processing complex urban data. In contrast, the LSTM model showed less accuracy, suggesting a nuanced understanding of temporal data's role in predicting urban happiness. This research not only sheds light on the potential of deep learning in urban studies but also offers valuable insights for urban planners and policymakers aiming to enhance urban living conditions. Through this comparative analysis, our study contributes to the growing discourse on leveraging advanced data analytics for urban planning and opens avenues for future research into the integration of diverse data sources and model hybridization to refine urban happiness predictions.
Development Tourism Destination Recommendation Systems using Collaborative and Content-Based Filtering Optimized with Neural Networks Fahrizal, Diki; Kustija, Jaja; Akbar, Muhammad Aqil Haibatul
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.v7i2.28713

Abstract

Tourism, a vital sector in the global economy, benefits significantly from advancements in infrastructure, accessibility, and information availability. However, the vast volume of information can overwhelm travelers, underscoring the need for efficient recommendation systems. This research aims to develop an advanced tourist destination recommendation system by integrating Collaborative Filtering (CF) and Content-Based Filtering (CBF) models with Neural Networks. This approach seeks to enhance recommendation accuracy by closely aligning with user preferences and addressing the challenge of limited data. The study utilizes data from 523 tourist destinations across West Java, along with user preference assessments, encompassing stages of data collection, labeling, pre-processing, pre-training, neural network-based training, model evaluation, and the presentation of recommendation outcomes. The optimization of the recommendation models through neural networks has notably improved the precision of tourist destination suggestions, achieving Root Mean Square Error (RMSE) values below 0.37 for both CF and CBF approaches. This research significantly contributes to increasing the search efficiency and accuracy for tourist destination information, offering a strategic solution to the prevalent issue of information overload in the tourism industry.
AI-Generated Misinformation: A Literature Review Fatimah, Rafharum; Mumtaz, Auziah; Fahrezi, Fauzan Muhammad; Zakaria, Diky
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.v7i2.26455

Abstract

The expansion of artificial intelligence (AI) technologies has signaled an entirely new era in which the creation and sharing of information, both correct and misleading, are becoming increasingly automated. This research of the literature explores the landscape of AI-generated misinformation, including its various manifestations, underlying technology, societal impact, and detection tools. This paper reviews articles from the Google Scholar database related to AI-Generated Misinformation focusing on the following research questions: the types, content distribution, detector variations, differences among the various tools, and strategies for developing AI-based tools. The result is to provide an absolute comprehension of this topic, underlining the importance of interdisciplinary collaboration, robust detection methods, and media literacy with the intention to solve the ethical and societal issues it poses in the age of digital technology.
Named Entity Recognition Using Conditional Random Fields for Flood Detection In Gerbang Kertosusila Based Twitter Data Ulumiyyah, Ikrimatul; Rolliawati, Dwi; Izzuddin, Andik; Khalid, Khalid; Khunaefi, Anang; Ridwan, Mujib
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.v7i2.27062

Abstract

The national strategic area Gerbang Kertosusila East Java should be aware of floods. One of the existing efforts is to place flood sensors at several flood-prone points. However, that way is constrained by the need for more equipment to handle the many needy areas. So it is necessary to develop technology for the dissemination of flood information. Dissemination of flood information was quickly obtained from social media Twitter. One way is to use Twitter's text data source for a Named Entity Recognition model to help detect flood events and their locations. The Named Entity Recognition (NER) model was constructed using the Conditional Random Fields (CRFs) method to achieve research objectives. This research adds slang word handling at the preprocessing stage to improve model performance and the use of the BIO format in the labeling process and POS Tagging in the Feature Extraction process. Evaluation results with five Kfolds, 80% training data, and 20% test data show that the NER CRFs model performs excellently with a Precision of 0.981, Recall of 0.926, and f-measure of 0.950 so that these results can help the community and government regarding the information on the distribution of floods.
Sentiment Analysis Towards the Film Dirty Vote on Twitter Social Media Using the K-Nearest Neighbor Algorithm Fadillah, Annisa; Sriani, Sriani
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.v7i2.32471

Abstract

The appearance of the dirty vote film has received public attention and went viral on social media after being released and watched by millions of people in a short time. The dirty vote film has become a topic of discussion, one of which is on the social media platform Twitter. This research was conducted to determine the views or tendencies of public opinion regarding dirty vote films on Twitter social media using K-Nearest Neighbor which will be classified into positive, neutral and negative sentiment. The sentiment data that was collected in the data crawling process was 4000 pieces of data. Then after preprocessing there were 3978 data. Labeling was carried out using text blob, it was found that the negative sentiment class was 3470 superior to the positive sentiment class of 451 and the neutral sentiment class was 57. The 10-fold cross validation test produced an average accuracy value of 87.5%. Testing was carried out with 80% training data consisting of 3182 data and 20% test data consisting of 796 test data. The results of sentiment analysis show that the K-Nearest Neighbor method can be used for sentiment analysis. The accuracy value obtained was 87%, precision was 87%, recall was 100%, and f1-score was 93%.
Fish Detection and Classification using YOLOv8 for Automated Sorting Systems Kuswantori, Ari; Suroso, Dwi Joko
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.v7i2.30967

Abstract

Automation plays a crucial role in scaling up freshwater fish cultivation to address the future threat of food scarcity and meet growing nutrition needs. The fish industry, in particular, develops automation in the sorting and selection processes. However, research on this technology's development is still very limited. In this work, we propose an approach for detecting and classifying fish running on conveyors. We use YOLOv8, which is the most popular and newest deep learning model for object detection and classification. We conducted our test using the KMITLFish dataset, a moving conveyor belt recording that encompasses common cultivated freshwater fish in Thailand along with some endemic species. As a result, our proposed method was able to accurately detect and classify eight types of fish at a conveyor speed of 505.08 m/h. Moreover, we developed this work using a ready-to-use AI platform, intending to directly contribute to the advancement of automatic fish sorting system technology in the fish industry.
Evaluation of Ensemble and Hybrid Models for Predicting Household Energy Consumption: A Comparative Study of Machine Learning Approaches Airlangga, Gregorius
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.v8i1.32819

Abstract

Accurately predicting household energy consumption is critical for efficient energy management, particularly as global energy demands rise. This study explores the predictive performance of various machine learning models, including linear regression, Ridge regression, Lasso regression, Random Forest, Gradient Boosting, XGBoost, CatBoost, and a hybrid model combining Long Short-Term Memory (LSTM) networks with Random Forest regression. The models were evaluated on a dataset consisting of minute-level energy readings over a 350-day period. Key performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (?2) were used to assess model accuracy. The results demonstrate that ensemble models, particularly Random Forest and CatBoost, outperformed traditional regression models in terms of error minimization. CatBoost achieved the lowest MSE among all models, highlighting its effectiveness in handling non-linearities and categorical data. However, none of the models achieved a positive (?2) score, indicating their limitations in fully explaining the variance within the dataset. The hybrid LSTM + Random Forest model, despite its expected strength in capturing temporal dependencies, performed worse than simpler models, suggesting issues with feature extraction and model integration.These findings suggest that while ensemble methods are well-suited for energy consumption prediction, more advanced modeling techniques or enhanced feature engineering are needed to improve performance. Future research could explore deeper neural networks or time-series models such as ARIMA to better capture the temporal patterns in household energy consumption.
Optimizing Image Classification Performance with MnasNet Model on Blurred Images Puspita, Rani; Izdihar, Zahra Nabila
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.v7i2.29571

Abstract

In this era, the development of fashion in clothing is increasing. Over the last 30 years, the fashion industry has experienced significant improvements, causing its growth and development to increase. Fashion has many types and variants, but blurry images can also make it difficult for people to classify whether this is a shirt, t-shirt, or something else. Because of that, we proposed image classification. By classifying images, we can help the fashion industry to separate categories and types of various fashion. The approach uses MnasNet which is included in the deep learning approach. The data used is 70,000 which is divided into 60,000 training data and 10,000 testing data. The MnasNet architectural model produces an accuracy of 89% and a loss of 0.4426. It can be seen that MnasNet is the right method for image classification so that the problems described in the background have been successfully solved.
Implementation of Apriori Algorithm in Determining the Layout of Items Putry, Yollanda; Andri Agus, Raja Tama; Sena, Maulana Dwi
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.v8i1.31206

Abstract

Shopping at mini markets is now becoming popular as people's shopping orientation changes. This results in consumers preferring to shop in modern markets rather than to stalls or traditional shops. Because consumers want to shop comfortably and practically or in terms of finding goods in an easy way and knowing the location of the desired item. Risaga Jaya is a retail business that sells various needs such as staples, cosmetics, stationery, various snacks, During its operation, many goods are not in demand and there is a buildup of goods in the warehouse because there is no strategy for placing the position of goods that are more attractive to consumers. The purpose of this research is to create a strategy for placing goods to maximize sales using data mining with the apriori algorithm. By using the apriori algorithm, it can organize and organize the layout of an item by bringing related items closer together so that it can increase sales at the store based on consumer purchasing patterns, namely if consumers buy chitato then buy snack candy with a support given of 10% and a confidence given of 75%.

Page 14 of 24 | Total Record : 233

 12   13   14   15   16 

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