Indonesian Journal of Artificial Intelligence and Data Mining最新文献_第5页

Spam Classification on 2019 Indonesian President Election Youtube Comments Using Multinomial Naïve-Bayes 利用多项式对2019年印尼总统选举Youtube评论进行垃圾邮件分类Naïve-Bayes

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2019-07-08 DOI: 10.24014/ijaidm.v2i1.6445

Jonathan Radot Fernando, R. Budiraharjo, Emeraldi Haganusa

引用次数: 1

Prediction Of Amount Of Use Of Planning Family Contraception Equipment Using Monte Carlo Method (Case Study In Linggo Sari Baganti District) 基于蒙特卡罗方法的计划生育避孕器具使用量预测(以林戈沙里巴甘提区为例)

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2019-04-24 DOI: 10.24014/ijaidm.v2i1.5825

Rani Yunima Astia, Julius Santony, S. Sumijan

引用次数: 17

Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization 基于粒子群优化选择支持向量机的活动日志在线学习成功预测

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2019-03-31 DOI: 10.24014/IJAIDM.V2I1.6500

E. Saputra, Sukmawati Angreani Putri, Indriyanti Indriyanti

引用次数: 3

Prediction of Student Graduation Time Using the Best Algorithm 用最优算法预测学生毕业时间

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2019-03-31 DOI: 10.24014/IJAIDM.V2I1.6424

V. Riyanto, A. Hamid, Ridwansyah Ridwansyah

引用次数: 14

Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification 基于样本自举和粒子群优化的信贷审批分类数据挖掘优化

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2019-03-31 DOI: 10.24014/IJAIDM.V2I1.6299

Andre Alvi Agustian, Achmad Bisri

{"title":"Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification","authors":"Andre Alvi Agustian, Achmad Bisri","doi":"10.24014/IJAIDM.V2I1.6299","DOIUrl":"https://doi.org/10.24014/IJAIDM.V2I1.6299","url":null,"abstract":"Credit approval is a process carried out by the bank or credit provider company. Where the process is carried out based on credit requests and credit proposals from the borrower. Credit approval is often difficult for banks or credit providers. Where the number of requests and classifications must be made on various data submitted. This study aims to enable banks or credit card issuing companies to carry out credit approval processes effectively and accurately in determining the status of the submissions that have been made. This research uses data mining techniques. This study uses a Credit Approval dataset from UCI Machine Learning, where there is a class imbalance in the dataset. 14 attributes are used as system inputs. This study uses the C4.5 and Naive Bayes algorithms where optimization is needed using Sample Bootstrapping and Particle Swarm Optimization (PSO) in the algorithm so that the results of the research produce good accuracy and are included in the good classification. After using the optimization, it produces an accuracy rate of C4.5 which is initially 85.99% and the AUC value of 0.904 becomes 94.44% with the AUC value of 0.969 and Naive Bayes which initially has an accuracy value of 83.09% with an AUC value of 0.916 to 90 , 10% with an AUC value of 0.944.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131236163","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: 2

The Application Of Fuzzy K-Nearest Neighbour Methods for A Student Graduation Rate 模糊k近邻法在学生毕业率计算中的应用

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2018-11-25 DOI: 10.24014/IJAIDM.V1I1.5654

I. Ahmad, H. Sulistiani, Hendri Saputra

{"title":"The Application Of Fuzzy K-Nearest Neighbour Methods for A Student Graduation Rate","authors":"I. Ahmad, H. Sulistiani, Hendri Saputra","doi":"10.24014/IJAIDM.V1I1.5654","DOIUrl":"https://doi.org/10.24014/IJAIDM.V1I1.5654","url":null,"abstract":"The absence of prediction system that can provide prediction analysis on the graduation rate of students becomes the reason for the research on the prediction of the level of graduation rate of students. Determining predictions of graduation rates of students in large numbers is not possible to do manually because it takes a long time. For that we need an algorithm that can categorize predictions of students' graduation rates in computing. The Fuzzy Method and KNN or K-Nearest Neighbor Methods are selected as the algorithm for the prediction process. In this study using 10 criteria as a material to predict students' graduation rate consisting of: NPM, Student Name, Semester 1 achievement index, Semester 2 achievement index, Semester 3 achievement index, Semester 4 achievement index, SPMB, origin SMA, Gender , and Study Period. Fuzzyfication process aims to change the value of the first semester achievement index until the fourth semester achievement index into three sets of fuzzy values are satisfactory, very satisfying, and cum laude. Make predictions to improve the quality of students and implement KNN method into prediction, where there are some attributes that have preprocess data so that obtained a value, and the value is compared with training data, so as to produce predictions of graduating students will be on time and graduating students will be late. This study produces a prediction of student pass rate and accuracy.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134522067","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: 5

Classification of Pineapple Fruit Comosus Merr (Nanas) Quality Using Learning Vector Quantization Method 基于学习向量量化方法的菠萝果实质量分类

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2018-11-25 DOI: 10.24014/ijaidm.v2i2.4621

M. Efendi, Sarjon Defit, G. W. Nurcahyo

{"title":"Classification of Pineapple Fruit Comosus Merr (Nanas) Quality Using Learning Vector Quantization Method","authors":"M. Efendi, Sarjon Defit, G. W. Nurcahyo","doi":"10.24014/ijaidm.v2i2.4621","DOIUrl":"https://doi.org/10.24014/ijaidm.v2i2.4621","url":null,"abstract":"The demands of publics for these fruits Ananas Comosus Merr (Pineapple) became higher years to years because of the fruit has so many virtues for human healthy and the taste of this fruit is sweet and fresh. Therefore the pineapple farmers have to protect the quality and quantity of this plant in order to get high produce. This research help the pineapple farmers to classify to quality of pineapple fruits by using neural network with Learning Vector Quantization method which has 2 classes, such as: First quality (1st) and Second quality (2nd) quality. This method has 2 process they are : training process and testing process. To input data in the training and testing process are using uniformity, characteristic of varieties, the rate of aging, hardness, size, stem, crown, manure, destroyer, spoilage, rotten and the total solid content of the least was taken by observed the crop of pineapple farmers in the Teluk Batil village Sungai Apit district Siak Riau province. Learning Vector Quantization method automatically will classify the pineapple into their class. The result of the testing classification has gotten the accuracy 65.56% for the first (1st) quality and 34.44% for the second (2nd) quality. At the second testing has gotten 66.67% the accuracy for the first (1st) quality and 33.33% for the second (2nd) quality. At the third (3rd) testing has gotten 64.44% the accuracy for first (1st) quality and 35.56% for the second (2nd) quality.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121829293","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

Texture Features Extraction of Human Leather Ports Based on Histogram 基于直方图的人体皮革端口纹理特征提取

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2018-11-15 DOI: 10.24014/ijaidm.v1i2.6084

A. S. Sinaga

引用次数: 7

Fuzzy Logic Implementation to Control Temperature and Humidity in a Bread Proofing Machine 面包打样机温湿度控制的模糊逻辑实现

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2018-11-13 DOI: 10.24014/ijaidm.v1i2.5664

Aulia Ullah, Oktaf Brillian Kharisma, I. Santoso

{"title":"Fuzzy Logic Implementation to Control Temperature and Humidity in a Bread Proofing Machine","authors":"Aulia Ullah, Oktaf Brillian Kharisma, I. Santoso","doi":"10.24014/ijaidm.v1i2.5664","DOIUrl":"https://doi.org/10.24014/ijaidm.v1i2.5664","url":null,"abstract":"Factors that need to be considered of producing good quality bread are raw materials, balance formulas (recipes) and production processes. The bread dough that cannot proof perfectly has become a problem in the process of bread production. Therefore, the temperature and humidity of the room must be controlled at a certain temperature range. The solution of this problem is proposing a controller that uses Fuzzy logic to control temperature and humidity in the bread examination room. A bread proofing machine is added a controller such as evaporator that it is can controlled the temperatur and humidity automatically. The heat and steam produced are regulated using a Fuzzy logic algorithm embedded in the microcontroller with a predetermined set point of temperature and humidity is 35 oC and 80%. The test is done by determining the percentage error from the temperature and humidity test results, that is when the machine is free of load obtained the percentage error to set points is 0,429 % and 0,937 %. While the engine is loaded. It gives the results are 0,024 % and 0,015%. The results of this test prove that controlling temperature and humidity in a bread proofing machine using Fuzzy logic can provide good results compared to conventional controllers. as a result, the bread mixture can expand uniformly.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131306216","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: 5

Apriori Algorithm through RapidMiner for Age Patterns of Homeless and Beggars 基于RapidMiner的无家可归者和乞丐年龄模式Apriori算法

Indonesian Journal of Artificial Intelligence and Data Mining Pub Date : 2018-11-13 DOI: 10.24014/ijaidm.v1i2.5670

Wirta Agustin, Yulya Muharmi

{"title":"Apriori Algorithm through RapidMiner for Age Patterns of Homeless and Beggars","authors":"Wirta Agustin, Yulya Muharmi","doi":"10.24014/ijaidm.v1i2.5670","DOIUrl":"https://doi.org/10.24014/ijaidm.v1i2.5670","url":null,"abstract":"Homeless and beggars are one of the problems in urban areas because they can interfere public order, security, stability and urban development. The efforts conducted are still focused on how to manage homeless and beggars, but not for the prevention. One method that can be done to solve this problem is by determining the age pattern of homeless and beggars by implementing Algoritma Apriori. Apriori Algorithm is an Association Rule method in data mining to determine frequent item set that serves to help in finding patterns in a data (frequent pattern mining). The manual calculation through Apriori Algorithm obtaines combination pattern of 11 rules with a minimum support value of 25% and the highest confidence value of 100%. The evaluation of the Apriori Algorithm implementation is using the RapidMiner. RapidMiner application is one of the data mining processing software, including text analysis, extracting patterns from data sets and combining them with statistical methods, artificial intelligence, and databases to obtain high quality information from processed data. The test results showed a comparison of the age patterns of homeless and beggars who had the potential to become homeless and beggars from of testing with the RapidMiner application and manual calculations using the Apriori Algorithm.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121934951","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: 3