Indonesian Journal of Artificial Intelligence and Data Mining最新文献

Lepidoptera Classification Using Convolutional Neural Network EfficientNet-B0 使用卷积神经网络 EfficientNet-B0 进行鳞翅目昆虫分类

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-11-16 DOI: 10.24014/ijaidm.v7i1.24586

Hilmi Syamsudin, Saidatul Khalidah, Jumanto Unjung

{"title":"Lepidoptera Classification Using Convolutional Neural Network EfficientNet-B0","authors":"Hilmi Syamsudin, Saidatul Khalidah, Jumanto Unjung","doi":"10.24014/ijaidm.v7i1.24586","DOIUrl":"https://doi.org/10.24014/ijaidm.v7i1.24586","url":null,"abstract":"Butterflies and moths are insects that have many different species. Butterflies and moths have considerable aesthetic, ecosystem, health, economic, health, and scientific values. However, because there are so many different varieties and patterns, it is vital to divide them by type for better identification. By creating a Convolutional Neural Network (CNN) algorithm that produces accurate results, a deep learning approach can be used to classify the types of butterfly and moth species. This paper offer an Lepidoptera including butterfly and moth classification model based on convolutional neural networks. 3390 images of 25 different butterfly and moth species were acquired with various images orientations, angles, distance, and background. Using the EfficientNet-B0 CNN architecture, different types of butterflies and moths are classified and input into the EfficientNet-B0 model. EfficientNet-B0 performs feature extraction on the image, so that it can be used to perform classification and then combined through a pooling process and connected to the final layer to produce a classification probability. The probability indicates how likely the image is to belong to a particular type or class of butterfly or moth. In comparison to earlier studies, the test results indicate an improvement in butterfly and moth classification. Increased accuracy was seen with values of 97.91% accuracy, 97% recall, 97% precision, and 97% F1-Score. This paper novelty is the enhancement of the CNN architecture EfficientNet-B0 used in image classification, which results in improved image classification accuracy.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"32 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139270007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Support Vector Machine, Naive Bayes, Decision Tree, and Gradient Boosting Algorithms for Sentiment Analysis on ChatGPT Twitter Dataset 在 ChatGPT Twitter 数据集上对支持向量机、奈夫贝叶斯、决策树和梯度提升算法进行情感分析的评估

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-11-16 DOI: 10.24014/ijaidm.v7i1.24662

Salsabila Rabbani, Dea Safitri, Farida Try Puspa Siregar, Rahmaddeni Rahmaddeni, Lusiana Efrizoni

{"title":"Evaluation of Support Vector Machine, Naive Bayes, Decision Tree, and Gradient Boosting Algorithms for Sentiment Analysis on ChatGPT Twitter Dataset","authors":"Salsabila Rabbani, Dea Safitri, Farida Try Puspa Siregar, Rahmaddeni Rahmaddeni, Lusiana Efrizoni","doi":"10.24014/ijaidm.v7i1.24662","DOIUrl":"https://doi.org/10.24014/ijaidm.v7i1.24662","url":null,"abstract":"ChatGPT is a language model employed to produce text and engage in conversation with users. It serves as a tool for generating text and facilitating interactions in a conversational manner. The model was designed to provide relevant and useful responses based on the context of the ongoing conversation. By the increasing popularity of using ChatGPT, it makes it difficult for users to classify responses about the use of ChatGPT. Therefore, sentiment classification of ChatGPT is carried out. The dataset used is sourced from the kaggle website with a total of 20,000 data. The classification methods used in this research include Support Vector Machine (SVM), Naïve Bayes, Decision Tree, and Gradient Boosting. Through the research results, the Support Vector Machine algorithm had the highest accuracy value with 80% compared to other methods, when the data is divided by a ratio of 90:10. This research is expected to help developers and service providers to improve ChatGPT and understand user responses better.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"25 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139266713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Convolutional Neural Network for Classification of Density Scale and Transparency of Needle Leaf Types 卷积神经网络在针叶密度和透明度分类中的应用

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-11-16 DOI: 10.24014/ijaidm.v7i1.26258

Diah Adi Sriatna, Rico Andrian, Rahmat Safei

{"title":"Implementation of Convolutional Neural Network for Classification of Density Scale and Transparency of Needle Leaf Types","authors":"Diah Adi Sriatna, Rico Andrian, Rahmat Safei","doi":"10.24014/ijaidm.v7i1.26258","DOIUrl":"https://doi.org/10.24014/ijaidm.v7i1.26258","url":null,"abstract":"Crown density and transparency are among the parameters in determining forest health using magic card. This is still less effective because it only relies on direct vision. Therefore, a more sophisticated and accurate application using digital image technology is needed. Convolutional Neural Network (CNN) is designed to help recognize objects in images with various positions. There are 1000 images of needle leaf types with ten classes of crown density and transparency for every kind of needle leaf, including araucaria heterophylla, cupressus retusa, pine merkusii, and shorea javanica, which are classified using AlexNet. AlexNet is a CNN architecture that has eight feature extraction layers. The AlexNet model succeeded in classifying coniferous trees on the scale of density and crown transparency with an accuracy level of 87.00% for araucaria heterophylla, cupressus retusa 96.00%, merkusii pine 86.00%, and shorea javanica 95.00%. Although some errors were still found in classification, this was caused by similar patterns and similar image positions. It is hoped that the results of this research will be used in monitoring forest health in the future.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139267337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Fuzzy Logic Method to Get Estimation of Fluid Depletion on Smart Infusion 采用模糊逻辑法估算智能输液的液体消耗量

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-11-16 DOI: 10.24014/ijaidm.v7i1.25589

Mira Permata Sari, Ahmad Taqwa, ade Silvia Handayani

引用次数: 0

Sentiment Analysis and Topic Modelling on Crowdsourced Data 众包数据的情感分析和主题建模

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-11-16 DOI: 10.24014/ijaidm.v7i1.24777

Maria Angelika H Siallagan, Arie Wahyu Wijayanto

{"title":"Sentiment Analysis and Topic Modelling on Crowdsourced Data","authors":"Maria Angelika H Siallagan, Arie Wahyu Wijayanto","doi":"10.24014/ijaidm.v7i1.24777","DOIUrl":"https://doi.org/10.24014/ijaidm.v7i1.24777","url":null,"abstract":"Data analysis plays a crucial role in enhancing the decision-making process by uncovering concealed patterns within the data. One valuable form of crowdsourced data is user reviews on applications, which can effectively capture the satisfaction levels of application users. Application developers can utilize these reviews to identify and assess areas of the application that require evaluation or improvement. This study focuses on the classification of application reviews by utilizing sentiment analysis and employs various classification algorithms, including logistic regression, Support Vector Machines, and Random Forest. Additionally, to address negative sentiment labels, topic modeling is conducted using Latent Dirichlet Allocation (LDA). This study demonstrates that the best sentiment classification model is logistic regression, achieving an average accuracy of 0.925 and an average F1-score of 0.763. Furthermore, the LDA analysis successfully generates topic models for negative reviews, revealing three key topics: price-related issues, accessibility concerns, and application accuracy, all of which demand reevaluation and potential improvement","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"24 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139269013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Website User Interface Design Using Data Mining Task Centered System Design Method At National Private Humanitarian Institutions 使用数据挖掘任务中心系统设计法在国家私人人道主义机构设计网站用户界面

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-10-07 DOI: 10.24014/ijaidm.v6i2.25814

Dendy K. Pramudito, Tanti Widia Nurdiani, Bambang Winardi, A. Rukmana, Kraugusteeliana Kraugusteeliana

{"title":"Website User Interface Design Using Data Mining Task Centered System Design Method At National Private Humanitarian Institutions","authors":"Dendy K. Pramudito, Tanti Widia Nurdiani, Bambang Winardi, A. Rukmana, Kraugusteeliana Kraugusteeliana","doi":"10.24014/ijaidm.v6i2.25814","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.25814","url":null,"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","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"300 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139321982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing WiFi Signal Quality Through Access Point Placement Using Genetic Algorithm Method 使用遗传算法通过接入点布局优化 WiFi 信号质量

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-10-01 DOI: 10.24014/ijaidm.v6i2.25277

Nurmala Dewi Lubis, Novery Lysbetti Marpaung

{"title":"Optimizing WiFi Signal Quality Through Access Point Placement Using Genetic Algorithm Method","authors":"Nurmala Dewi Lubis, Novery Lysbetti Marpaung","doi":"10.24014/ijaidm.v6i2.25277","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.25277","url":null,"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.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139328574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images 研究机器学习如何在中等分辨率卫星图像中绘制红树林地图

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-10-01 DOI: 10.24014/ijaidm.v6i2.25263

Agus Ambarwari, Emir Mauludi Husni

{"title":"Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images","authors":"Agus Ambarwari, Emir Mauludi Husni","doi":"10.24014/ijaidm.v6i2.25263","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.25263","url":null,"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.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139328297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Javanese Script Letter Detection Using Faster R-CNN 使用更快的 R-CNN 检测爪哇语字母

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-16 DOI: 10.24014/ijaidm.v6i2.24641

Muhammad Helmy Faishal, M. D. Sulistiyo, Aditya Firman Ihsan

{"title":"Javanese Script Letter Detection Using Faster R-CNN","authors":"Muhammad Helmy Faishal, M. D. Sulistiyo, Aditya Firman Ihsan","doi":"10.24014/ijaidm.v6i2.24641","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.24641","url":null,"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.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139350411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method 使用应用 Naive Bayes 分类器方法对 Motorku X 进行情感分析

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-08-09 DOI: 10.24014/ijaidm.v6i2.24864

Akhmad Mustolih, Primandani Arsi, Pungkas Subarkah

{"title":"Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method","authors":"Akhmad Mustolih, Primandani Arsi, Pungkas Subarkah","doi":"10.24014/ijaidm.v6i2.24864","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i2.24864","url":null,"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%.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"133 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139351277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0