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
 8   9   10   11   12 
Sentiment Analysis on IMDB Movie Reviews using BERT Rani Puspita; Cindy Rahayu
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24239

Abstract

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.
Data Sharing Technique for Electronic Health Record (EHR) Classification using Support Vector Machine Algorithm Moh. Erkamim; Said Thaufik Rizaldi; Sepriano Sepriano; Khoirun Nisa; Sulhatun Sulhatun; Zilrahmi Zilrahmi; Winalia Agwil
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.24794

Abstract

The Electronic Health Record (EHR) integrates information about medical history in patients, complications, and history of drug use efficiently, which demands optimality and speed of service for efficiency and effectiveness of services, especially in determining outpatient and inpatient services on accurate patient history data. In efforts to improve data accuracy, this study combined the c, γ, and degree kernels in the Linear, Polynomial, and Radial Basis Function (RBF) kernels as well as data sharing techniques 10-fold cross-validation, k-medoids, and Hold- out (70 % 30%) resulted in superior K-Medoids data sharing techniques for each Polynomial kernel with an accuracy of 75.76% and a Radial Basis Function (RBF) kernel with an accuracy of 75.56% so that it can be said that the combination of K-Medoids and Polynominal kernel in the algorithm Support Vector Machine (SVM) can be used in this research case
Optimizing Malware Detection Using Back Propagation Neural Network and Hyperparameter Tuning Annisa Arrumaisha Siregar; Sopian Soim; Mohammad Fadhli
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24731

Abstract

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.
Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images Agus Ambarwari; Emir Mauludi Husni
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.25263

Abstract

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.
Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method Akhmad Mustolih; Primandani Arsi; Pungkas Subarkah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24864

Abstract

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%.
Website User Interface Design Using Data Mining Task Centered System Design Method At National Private Humanitarian Institutions Dendy K. Pramudito; Tanti Widia Nurdiani; Bambang Winardi; Arief Yanto Rukmana; Kraugusteeliana Kraugusteeliana
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.25814

Abstract

Humanitarian organizations that support social communities by providing work opportunities for employees. In actuality, a website is required to serve as a channel for contacting new contributors and publicizing the organization. In order to create websites for humanitarian organizations using the task-centered system design methodology, research was done based on these issues. Identification, requirements, design as a scenario, and walkthrough evaluation are the four stages of this process. The PACT framework is used to identify users and necessary tasks during the identification phase. The tasks that are actually required are then chosen at the requirements stage. The task-based design is then completed using the Figma program during the design as scenario stage. The workflow and usability of the website, which was developed utilizing cognitive walkthrough and SUS, are also evaluated at this point. Based on the findings of the assessment, it can be said that cognitive walkthrough testing can be used to assess the components of an interface that are easy to learn, effective, and efficient, and that SUS can be used to assess the usability of the design outcomes. Based on the findings of the cognitive testing, a learnability and effectiveness score of 95% with the predicate "very good" and an average efficiency value of 0.1 goals/second with the predicate "very fast" were obtained. The SUS test then yielded an acceptable predicate and a SUS rating of 83
Javanese Script Letter Detection Using Faster R-CNN Muhammad Helmy Faishal; Mahmud Dwi Sulistiyo; Aditya Firman Ihsan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24641

Abstract

The Javanese script is now rarely used, and some people no longer recognize it. The construction of a Javanese script recognition system based on digital image processing is one of its preservation efforts. This study proposes a model capable of detecting and recognizing Javanese characters using Faster R-CNN to help people who are not familiar with the Javanese script. Faster R-CNN was chosen because it does not require additional processing compared to the previous method and Faster R-CNN has better accuracy and the ability to detect small objects. Faster R-CNN shows good results in text detection, but the use of Faster R-CNN in detecting Javanese script has not been found which makes its performance unknown, so this study will show how Faster R-CNN performs in detecting Javanese script. In this study, Faster R-CNN was able to show good performance by obtaining mean average precision (mAP) values up to 0.8381, accuracy up to 96.31%, precision up to 96.53%, recall up to 96.38 %, and F1-Score up to 96.41%. These results indicate that Faster R-CNN has better results than the previous method and can detect Javanese characters well.
Prediction Model of Revenue Restaurants Business Using Random Forest Erfan Ainul Yakin; Ririen Kusumawati; Usman Pagalay
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v%vi%i.24984

Abstract

This research was conducted to predict the level of revenue from the Soto Kwali Pak Wasis restaurant business using Machine Learning. The Random Forest method was chosen because it can predict optimal and fast results with low hardware requirements. Prediction Model results using the Random Forest method resulted in an average accuracy value of 75.4% from a combination of 4 experiments. Thus, the Random Forest method is one of the flexible algorithms and is very suitable for predicting revenue in the Soto Kwali Pak Wasis restaurant business because of its good speed, high accuracy, and requires lower costs.
Optimizing WiFi Signal Quality Through Access Point Placement Using Genetic Algorithm Method Nurmala Dewi Lubis; Novery Lysbetti Marpaung
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.25277

Abstract

The quality of WiFi signal is one of the critical factors that affects the performance of wireless networks in dense and complex environments. Proper placement of access points (APs) in an area can enhance network coverage and optimize signal quality. However, determining the optimal location for Access Points in a complex environment often presents a complicated and intricate challenge. PT. Globalriau Data Solusi is a company operating in the internet service provider sector. Within this company, there are still several areas with poor signal coverage, which can hinder the work processes of the staff in this office. Therefore, this research aims to optimize the WiFi signal quality by strategically placing Access Points using the Genetic Algorithm (GA) method. This will extend the signal coverage to areas that currently lack proper signal reception and improve the signal quality for overall enhancement. The Genetic Algorithm method proves effective in optimizing WiFi signal quality through the appropriate placement of APs. The results indicate the potential of applying this method in designing and managing efficient and reliable wireless networks in complex environments. This research demonstrates an increase in coverage area from an initial 60% to 80.5%, with signal quality reaching -65 dBm / -45 dBm.
Implementation of Data Mining for Predicting Student Graduation Using the K-Nearest Neighbor Algorithm at Jambi Muhammadiyah University Amandha, Shandy; Rohayani, Hetty; Kurniawansyah, Kevin
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.v7i1.26150

Abstract

Graduation is one of the assessment items in the accreditation process of a tertiary institution. So that if students graduate on time, it will help assess the accreditation of a tertiary institution. The method used is K-Nearest Neighbor (K-NN). This method is used to classify objects based on learning data closest to the object. This research aims to predict the graduation of Jambi Muhammadiyah University students, whether it is worth graduating on time or not graduating on time. In research using K-NN to predict student graduation, the results were that the K-NN approach in this study produced an accuracy value of 93.33%. The result is testing with a value of K = 5 using 50 training data for Jambi Muhammadiyah University students who have graduated in 2022; then data testing is tested with training data that has been tested before, 4 students who graduated not on time and who graduated on time were 46 students.

Page 10 of 24 | Total Record : 233

 8   9   10   11   12 

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