JUITA: Jurnal Informatika最新文献

Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction 最小均方自适应算法在Covid-19预测中的实现

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.11963

S. Prasetyowati, Munaf Ismail, Badieah Badieah

引用次数: 0

Fraud Detection Using Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier Algorithms on Credit Cards 信用卡欺诈检测使用随机森林分类器、逻辑回归和梯度增强分类器算法

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.12050

Muhamad Sopiyan, Fauziah Fauziah, Yunan Fauzi Wijaya

{"title":"Fraud Detection Using Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier Algorithms on Credit Cards","authors":"Muhamad Sopiyan, Fauziah Fauziah, Yunan Fauzi Wijaya","doi":"10.30595/juita.v10i1.12050","DOIUrl":"https://doi.org/10.30595/juita.v10i1.12050","url":null,"abstract":"The following credit card records were used in this study of 284.807 transactions made by credit card holders in Europe for two days from the Kaggle dataset. This is a very poor data set, having 492 transactions, an imbalance of only 0.172% of the 284.807 transactions. The purpose of this study is to obtain the best model and then simulate it by electronically detecting unauthorized financial transactions in bank payment systems. The dataset for this study is unbalanced class data with 99.80% for the major class and 0.2% for the minor class. This type of class-imbalanced data problem is solved by applying method a combination of minority oversampling techniques using Synthetic Minority Oversampling Technique (SMOTE). To determine the most appropriate and accurate classification in solving class balance problems, comparisons were made with the Random Forest Classifier (RFC), Logistic Regression (LGR), and Gradient Boosting Classifier (GBC) algorithms. The test results in this study are the Random Forest Classifier (RFC) algorithm is better than other algorithms because it has the highest accuracy the percentage of data-train is 100% and data-test is 99.99% and the evaluation of the AUC score as a result of algorithm testing is 0.9999.","PeriodicalId":174460,"journal":{"name":"JUITA: Jurnal Informatika","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127740654","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

Implementation of Principal Component Analysis and Learning Vector Quantization for Classification of Food Nutrition Status 主成分分析与学习向量量化在食品营养状况分类中的应用

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.11104

Jasman Pardede, Hilwa Athifah

引用次数: 0

Alphabet Recognition with Augmented Reality Technology Based on Android Using Extreme Programming Model 基于Android的基于增强现实技术的字母识别

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.12125

Fitri Yanti, Jaka Sutresna

{"title":"Alphabet Recognition with Augmented Reality Technology Based on Android Using Extreme Programming Model","authors":"Fitri Yanti, Jaka Sutresna","doi":"10.30595/juita.v10i1.12125","DOIUrl":"https://doi.org/10.30595/juita.v10i1.12125","url":null,"abstract":"Lack of literacy or interest in reading is the cause of children having difficulty recognizing the letters of the alphabet and assembling them into words or sentences because the letters are similar. Early childhood students have difficulty learning letters because there are too many letters of the alphabet that must be memorized, the number of letters of the alphabet there are 26 letters that must be memorized. In addition, early childhood students complain that reading is very difficult to pronounce because the way the teacher conveys reading techniques for students is difficult to understand so that it is boring for early childhood students. This makes it difficult for young children to pronounce letters. In software development using Extreme Programming (XP) where writing programs in pairs, two programming people work together to write programs. Currently, computer vision technology has been used in various industries, including trade, medicine, education, and so on. Augmented reality is one of the computer vision technologies. The technology of computer vision is to combine synthetic images into the real world or vice versa. By making an android application with an extreme programming method that utilizes Augmented Reality technology that can display 3D, animation, and sound so that it looks real, it makes early childhood interested and makes it easier for them to learn the letters of the alphabet.","PeriodicalId":174460,"journal":{"name":"JUITA: Jurnal Informatika","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019367","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

Classification of Customer Loans Using Hybrid Data Mining 基于混合数据挖掘的客户贷款分类

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.12521

E. Mandala, Eva Rianti, Sarjon Defit

{"title":"Classification of Customer Loans Using Hybrid Data Mining","authors":"E. Mandala, Eva Rianti, Sarjon Defit","doi":"10.30595/juita.v10i1.12521","DOIUrl":"https://doi.org/10.30595/juita.v10i1.12521","url":null,"abstract":"At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the \"Not Feasible\" cluster, 15 customers into the \"Feasible\" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%.","PeriodicalId":174460,"journal":{"name":"JUITA: Jurnal Informatika","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122990480","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

Development of Fuzzy-Based Smart Drip Irrigation System for Chili Cultivation 基于模糊的辣椒种植智能滴灌系统开发

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.12998

S. Wahjuni, W. Wulandari, M. Kholili

引用次数: 2

Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network 卷积神经网络的对数递减惯性权粒子群优化算法

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.12573

M. Murinto, Miftahurrahma Rosyda

引用次数: 1

Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification 预训练卷积神经网络模型在皮肤病分类中的性能评价

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.12041

Afandi Nur Aziz Thohari, L. Triyono, Idhawati Hestiningsih, B. Suyanto, Amran Yobioktobera

{"title":"Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification","authors":"Afandi Nur Aziz Thohari, L. Triyono, Idhawati Hestiningsih, B. Suyanto, Amran Yobioktobera","doi":"10.30595/juita.v10i1.12041","DOIUrl":"https://doi.org/10.30595/juita.v10i1.12041","url":null,"abstract":"Indonesia is a tropical country that has various skin diseases. Tinea versicolor, ringworm, and scabies are the most common types of skin diseases suffered by the people of Indonesia. The classification of the three skin diseases can be automatically completed by artificial intelligence and deep learning technology because the classification process using an expert will require a lot of money and time. The challenge in classifying skin diseases is in the process of collecting data. Because health data cannot be obtained freely, there must be approval from the patient or hospital. Therefore, to overcome the limited amount of data, Pre-Trained CNN is used. The Pre-Trained CNN model has many patterns from thousands of images, so we do not need many images to train the model. In this study, a comparison of five pre-trained CNN models was conducted, namely VGGNet16, MobileNetV2, InceptionResNetV2, ResNet152V2, and DenseNet201. The aim is to find out which CNN model can produce the best performance in classifying skin diseases with a limited amount of image data. The test results show that the ResNet152V2 model has the best classification ability with the highest accuracy, precision, recall, and F1 score values, namely 95.84%, 0.963, 0.96, and 0.956. As for the training execution time, the ResNet152V2 model has the fastest time to achieve 95% accuracy. That's happened because the addition of the dropout parameter is 20%.","PeriodicalId":174460,"journal":{"name":"JUITA: Jurnal Informatika","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125011891","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

Android Game-Based Learning Media Recognizes the Structure and Functions of Plant and Animal Parts for Elementary School 基于Android游戏的小学动植物器官结构与功能识别学习媒体

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.12582

E. Sudarmilah, Ikhwan Caesar Amri Pradana, Diah Priyawati

引用次数: 0

Design of Intelligent Automated Quest Control System in the Covid-19 Era 新型冠状病毒时代智能自动任务控制系统设计

JUITA: Jurnal Informatika Pub Date : 2022-05-27 DOI: 10.30595/juita.v10i1.11977

Yahfizham Yahfizham, I. Yusti

{"title":"Design of Intelligent Automated Quest Control System in the Covid-19 Era","authors":"Yahfizham Yahfizham, I. Yusti","doi":"10.30595/juita.v10i1.11977","DOIUrl":"https://doi.org/10.30595/juita.v10i1.11977","url":null,"abstract":"The rapid spread of corona virus disease 2019 (COVID-19), throughout direct of the human-to-human interaction makes the virus massively infect humans in all around the world. Until now, there has not been found the right way of healing it. This study aims to design of the intelligent automated quest control system capable for detecting COVID-19 by the body of temperature. The method approach was taken applied research, beginning with determining of the hardware using the ArduinoTM UNO microcontroller, the MLX90614 infrared thermometer, the TCRT5000 infrared reflective sensor, motor driver L293D, the output was displayed on a Liquid Crystal Display (LCD) screen, interaction control using Roller Limit Switch and instruction using the C programming language with Arduino IDE user interface. The system testing is done by comparing the temperature sensor readings infrared thermometer versus standard thermometer. Based on the results of a limited scale trial of 5 volunteers, an average error of 2.72% was obtained and the system worked well (opening or locking the door) in accordance with the temperature limits that had been set for detecting COVID-19. This research novelty that the simple and inexpensive design of the device system prevented and minimize the spread of COVID-19. The last, limitations of the system not being tested by the experts and large sample.","PeriodicalId":174460,"journal":{"name":"JUITA: Jurnal Informatika","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114042650","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