Indonesian Journal of Artificial Intelligence and Data Mining最新文献_第3页

An Ensemble Voting Approach for Dropout Student Classification Using Decision Tree C4.5, K-Nearest Neighbor and Backpropagation 使用决策树 C4.5、K-近邻和反向传播对辍学学生进行分类的集合投票法

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-07-22 DOI: 10.24014/ijaidm.v6i1.23412

Daffa Nur Cholis, Nurissaidah Ulinnuha

{"title":"An Ensemble Voting Approach for Dropout Student Classification Using Decision Tree C4.5, K-Nearest Neighbor and Backpropagation","authors":"Daffa Nur Cholis, Nurissaidah Ulinnuha","doi":"10.24014/ijaidm.v6i1.23412","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i1.23412","url":null,"abstract":"Many factors cause drop out in students. This study classified active students and drop out students using 1092 student data consisting of 557 active student data and 535 drop out student data. The independent variables used are Semester, Semester Credit Units (SKS), Semester Grade Point Average (IPS), Grade Point Average (IPK), admission pathways and Single Tuition Fee (UKT). Classification is carried out using the Ensemble Voting method where the method will combine the Decision Tree C4.5, KNN and Backpropagation methods as a single method. In addition to knowing the classification of active students and drop out students, this study aims to prove whether the Ensemble Voting method is able to get better results than the single method. This classification using a comparison of training and testing data of 90:10 to build model. Classification results from a single method will be included in the Ensemble Voting method. The Decision Tree C4.5 method gets 95.45% accuracy, 98.03% precision and 92.59% recall. KNN gets 96.36% accuracy, 100% precision and 92.59% recall. Backpropagation gets 90.90% accuracy, 95.83% precision and 95.18% recall. Meanwhile, the Ensemble Voting rule used is Ensemble Soft Voting with a weight of (2,1,1). Ensemble Voting with Ensemble Soft Voting rules is able to improve the accuracy, precision and recall values with 98.18% accuracy, 100% precision and 96.29% recall.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356478","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

Small Timescaled Data for Covid-19 Prediction with RNN-LSTM in Tangerang Regency 利用 RNN-LSTM 对坦格朗地区 Covid-19 进行预测的小时间尺度数据

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-07-22 DOI: 10.24014/ijaidm.v6i1.21676

Sagita Sasmita Wijaya, Marlinda Vasty Overbeek

{"title":"Small Timescaled Data for Covid-19 Prediction with RNN-LSTM in Tangerang Regency","authors":"Sagita Sasmita Wijaya, Marlinda Vasty Overbeek","doi":"10.24014/ijaidm.v6i1.21676","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i1.21676","url":null,"abstract":"Throughout the pandemic, many people have become familiarised with the new type of virus that has been spreading throughout the world, called the Coronavirus. On the 2nd of March, the year 2020, the Indonesian government had announced the identification of first Covid-19 case in Indonesia. With the arrival of Covid-19, and its spreading across all the provinces of Indonesia, the number of positive cases keeps growing even in the present day. Tangerang Regency is one of the areas that has opaqued citizens in the Banten Province. The purpose of this research is to discuss how to predict the sum of Covid-19 cases in the Tangerang Regency using the RNN-LSTM method. Although this method is very eloquent if used to perform a sequential task, its complexity and loss of gradient can make this model difficult to be trained, hence resulting in the use of the Long Short-Term Memory (LSTM) to reduce these weaknesses and help the RNN to look back on past data. This research uses Python as the programming language and Jupyter Notebook for the visualization of the results of the prediction. Therefore, the prediction model has been evaluated using various computational methods, such as RMSE with its error percentage of 0.05, and MSE and MAE with the same error percentage of 0.03 with the loss of their models being 9.6793e-04.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356519","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

Watermarking Study on The Vector Map 矢量图水印研究

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-07-13 DOI: 10.24014/ijaidm.v6i1.22211

Hartanto Tantriawan, Rinaldi Munir

引用次数: 0

Forecasting The Value of Indonesian Oil-Non-Oil and Gas Imported Using The Gated Recurrent Unit (GRU) 利用门控循环单元（GRU）预测印尼石油、非石油和天然气进口价值

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-07-13 DOI: 10.24014/ijaidm.v6i1.20651

Dian Kurniasari, Sulistian Oskavina, W. Wamiliana, W. Warsono

引用次数: 0

Method of Application of Support Vector Regression In Predicting The Number of Visits of Foreign Tourists to The Province of Maluku 支持向量回归在预测马鲁古省外国游客访问量中的应用方法

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-07-13 DOI: 10.24014/ijaidm.v6i1.19803

Wahyuni Aprilya, Marlon S. N. Van Delsena, M. Y. Matdoana, Article Info, Studi Statistika

{"title":"Method of Application of Support Vector Regression In Predicting The Number of Visits of Foreign Tourists to The Province of Maluku","authors":"Wahyuni Aprilya, Marlon S. N. Van Delsena, M. Y. Matdoana, Article Info, Studi Statistika","doi":"10.24014/ijaidm.v6i1.19803","DOIUrl":"https://doi.org/10.24014/ijaidm.v6i1.19803","url":null,"abstract":"Maluku Province is one of the areas in Indonesia that has many tourist attractions to visit, both natural and cultural heritage. The high interest of foreign tourists who want to visit various tourist objects, makes the tourism industry able to bring benefits and profits for most of the people of Maluku. However, in the last two years, 2020-2021, all countries were faced with the Covid-19 pandemic. The Covid-19 pandemic has resulted in a decrease in the number of foreign tourist visits to Indonesia. To increase marketing activities in the midst of the Covid-19 pandemic that has hit Indonesia since 2020, foreign tourist information is increasingly needed as material for evaluation and planning for future development. One of the methods used to predict the number of foreign tourist visits to Maluku Province is Support Vector Regression (SVR). Based on forecasting using test data, the RMSE value is 1.334985 and the MAPE obtained is 1.256346%, so the prediction of the number of foreign tourist visits to Maluku Province in 2022 (January-June) states that in January the number of tourist visits was 999 hundred visits. and increased until June as many as 1121 thousand visits.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139359852","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

Image Classification of Beef and Pork Using Convolutional Neural Network Architecture EfficienNet-B1 使用卷积神经网络架构 EfficienNet-B1 对牛肉和猪肉进行图像分类

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2023-06-02 DOI: 10.24014/ijaidm.v6i1.21843

Isnan Mellian Ramadhan, J. Jasril, Suwanto Sanjaya, Febi Yanto, Fadhilah Syafria

引用次数: 0

Identifying Characteristics of Households Recipient of the Government’s Social Protection Program 确定政府社会保障计划受助家庭的特征

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2022-08-11 DOI: 10.24014/ijaidm.v5i1.18579

Nofrida Elly Zendrato, B. Sartono, U. Syafitri

{"title":"Identifying Characteristics of Households Recipient of the Government’s Social Protection Program","authors":"Nofrida Elly Zendrato, B. Sartono, U. Syafitri","doi":"10.24014/ijaidm.v5i1.18579","DOIUrl":"https://doi.org/10.24014/ijaidm.v5i1.18579","url":null,"abstract":"According to Statistics Indonesia, the number of poor people increased by 1,12 million people in March 2020. In March 2021, the percentage of poor people increased by 0,36 points compared to March 2020. The percentage of poor people in Banten Province has increased in the last three years (2019-2021). One way to reduce poverty by the government is to increase social protection programs. The characteristics of households receiving social protection programs were identified by modeling the classification of households using the random forest technique, obtaining important variables using the permutation feature importance and Shapley additive explanations interpretation techniques, and analyzing the most important variables from the two interpretations methods. Handling the imbalance data on the response variables using SMOTE technique and evaluating the classification model obtained an AUC value of 0,718. The important variables were selected from the permutation feature importance and Shapley additive explanation methods based on a consistent ranking at the top. Shapley’s additive explanation was more consistent than permutation feature importance. Six important, namely capita expenditure, education of the head of household, age of head of household, source of drinking water, floor area, and the number of household members.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124932516","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 of Expedition Customer Satisfaction using BiGRU and BiLSTM 基于BiGRU和BiLSTM的探险顾客满意度情感分析

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2022-06-25 DOI: 10.24014/ijaidm.v5i1.17361

Salsabila Zahirah Pranida, A. Kurniawardhani

引用次数: 0

Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets 决策树算法机器学习在公共数据集检测Covid-19患者中的实现

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2022-06-10 DOI: 10.24014/ijaidm.v5i1.17054

Nadiah Nadiah, Sopian Soim, S. Sholihin

{"title":"Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets","authors":"Nadiah Nadiah, Sopian Soim, S. Sholihin","doi":"10.24014/ijaidm.v5i1.17054","DOIUrl":"https://doi.org/10.24014/ijaidm.v5i1.17054","url":null,"abstract":"The advancement of AI (Artificial Intelligence) technology has been widely implemented in numerous sectors of daily life. Machine Learning is one of the subfields of Artificial Intelligence. Using statistics, mathematics, and data mining, machine learning is developed so that machines may learn by assessing data without being reprogrammed. At this time the world is on alert for the spread of a popular virus, the corona virus. Coronaviruses are part of a family of viruses caused by diseases ranging from the flu. The disease caused by the coronavirus is known as Covid-19. Therefore, to help identify whether a somebody has coronavirus disease based on certain symptoms, a model is created that can classify people with the covid-19 virus using machine learning. The classification methods utilized in this study are decision trees and large-scale machine learning projects. The study employed Python 3.7 as its programming language and PyCharm as its Integrated Development Environment (IDE). Based on the results, the accuracy rate as expected after conducting various trials is 99%.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124532557","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: 1

Human Face Identification Using Haar Cascade Classifier and LBPH Based on Lighting Intensity 基于光照强度的Haar级联分类器和LBPH人脸识别

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2022-05-14 DOI: 10.24014/ijaidm.v5i1.15245

Hutama Hadi, Hasdi Radiles, R. Susanti, M. Mulyono

引用次数: 0