Journal of Applied Intelligent System最新文献

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Automatic Power-up Items Placement on Shooter Game using Convolutional Neural Network 基于卷积神经网络的射击游戏自动升级道具放置
Journal of Applied Intelligent System Pub Date : 2020-12-31 DOI: 10.33633/JAIS.V5I1.4213
A. Nugraha, Abas Setiawan, Wijanarto Wijanarto
{"title":"Automatic Power-up Items Placement on Shooter Game using Convolutional Neural Network","authors":"A. Nugraha, Abas Setiawan, Wijanarto Wijanarto","doi":"10.33633/JAIS.V5I1.4213","DOIUrl":"https://doi.org/10.33633/JAIS.V5I1.4213","url":null,"abstract":"- A shooter game is a popular game genre with various components. To make a shooter game more attractive, some power-ups items can support players to achieve their goals. Power-ups items provide more power to players, some of which include ammo, extra lives, and invulnerability. The location of power-ups items should be in a special place so that it neither too easy to find nor too difficult to find. Item placement could be done manually by a human or a technical artist. It will need a relatively long time and high cost. In this paper, we try to mimic technical artist vision when placing an item. Visual images have been collected by scanning spatially the forest terrain by using a virtual camera on top. Each image data comply with the item placement rules according to the Tomb Raider and Uncharted 4 games. Convolutional Neural Network (CNN) is used to find out which images can be occupied by power-up items or not. From several experimental scenarios, the use of the Global Average Pooling layer is proven to produce a model that is not overfitting. The best CNN models are developed and got an accuracy of 90.5% with an architecture that includes the Global Average Pooling layer. That model is applied to the new forest terrain so that power-up items can automatically be placed in an appropriate location.","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121877684","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
Sequential Model for Mapping Compound Emotions in Indonesian Sentences 印尼语句子复合情绪映射的顺序模型
Journal of Applied Intelligent System Pub Date : 2020-12-31 DOI: 10.33633/JAIS.V5I1.4264
Aripin, Wisnu Agastya, Hanny Haryanto
{"title":"Sequential Model for Mapping Compound Emotions in Indonesian Sentences","authors":"Aripin, Wisnu Agastya, Hanny Haryanto","doi":"10.33633/JAIS.V5I1.4264","DOIUrl":"https://doi.org/10.33633/JAIS.V5I1.4264","url":null,"abstract":"This research proposes mapping Indonesian sentences with single and multiple structures into emotion classes based on a multi-label classification process. The result of this research can apply in various fields, including the development of facial expressions in virtual character animation. Applications in other fields are facial expression analysis, human-computer interaction systems, and other virtual facial character system applications. In previous research, the classification process used for emotion mapping was usually based only on the frequency of occurrence of adjectives. The resulting emotion classes are less representative of sentence semantics. In this research, the proposed sequential model can take into account the semantics of the sentence so that the results of the classification process are more natural and representative of the semantics of the sentence. The method used for the emotion mapping process is multi-label text classification with continuous values between 0-1. This research produces the tolerant-method that utilizes the error value to deliver accuracy in the model evaluation process. The tolerant-method converts the predicted-label, which has an error value less than or equal to the error-tolerant value, to the actual-label for better accuracy. The model used in the classification process is a sequential model, including one-dimensional Convolution Neural Networks (CNN) and bidirectional Long Short-Term Memory (LSTM). The CNN model generates feature maps of each input in a partial way. Meanwhile, bidirectional LSTM captures information from input data in two directions. Experiments were performed using test data on Indonesian sentences. Based on the experimental results, bidirectional LSTM can produce an accuracy of 91% in the 8: 2 data portion and error-tolerant of 0.09.Keywords : Sequential Model, Mapping Compound Emotions, Sentence Semantics, Indonesian Sentences","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122167550","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
Aspect Based Sentiment Analysis: A Systematic Literature Review 基于面向的情感分析:系统的文献综述
Journal of Applied Intelligent System Pub Date : 2020-09-24 DOI: 10.33633/JAIS.V5I1.3807
S. Suhariyanto, R. Sarno, C. Fatihah, Edi Faisal
{"title":"Aspect Based Sentiment Analysis: A Systematic Literature Review","authors":"S. Suhariyanto, R. Sarno, C. Fatihah, Edi Faisal","doi":"10.33633/JAIS.V5I1.3807","DOIUrl":"https://doi.org/10.33633/JAIS.V5I1.3807","url":null,"abstract":"Aspect based sentiments can provide more detailed information about the sentiment (positive, negative, and neutral) based on an aspect in a review. It can provide better recommendations to users in decision making process. A number of previous studies have been conducted on aspect-based sentiment analysis indicating that survey is needed to provide an overview of the method available in aspect-based sentiment analysis. The survey method has been implemented since the last 5 years to obtain novelty from existing methods. The Systematic Literature Review (SLR) method is used to review a collection of 34 papers from various academic databases which focus on the aspect of extraction, sentiment analysis, and aspect aggregation. The papers will be sorted based on the focus of the method used. For each analysis, a detailed analysis is described on the contribution of the method to the aspect-based sentiment analysis alongside a comparison with other methods as well as advantages and disadvantages. The last section discusses the method commonly used in this study as well as future challenges in the study focusing on aspect-based sentiment analysis.","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127908873","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
5th Hyphotesis Consideration of UTAUT for IOT By Exploiting ACO based Classification 基于蚁群分类的物联网UTAUT的第5个假设考虑
Journal of Applied Intelligent System Pub Date : 2020-09-24 DOI: 10.33633/JAIS.V5I1.3523
R. Ariyanto, V. A. Lestari, S. E. Sukmana
{"title":"5th Hyphotesis Consideration of UTAUT for IOT By Exploiting ACO based Classification","authors":"R. Ariyanto, V. A. Lestari, S. E. Sukmana","doi":"10.33633/JAIS.V5I1.3523","DOIUrl":"https://doi.org/10.33633/JAIS.V5I1.3523","url":null,"abstract":"Internet of things (IoT) application needs to be evaluated to gain better improvement and innovation. The evaluation can be examined from user acceptance. The unified theory of acceptance and use of technology (UTAUT) can be used as a model to identify user acceptance in using technology, including IoT application. However, the ease of use of technology must be included, so the determining of easy of use from negative aspects must be included, so the 5th hypothesis of UTAUT (hindering condition) must be included. Before this hypothesis is formulated and included in evaluation by the user, obtaining data to identify the real condition of the user is performed using forensic analysis and ACO based classification. To evaluate this activity, this 5th hypothesis is measured by reliability and validity test, also hypothesis testing itself.","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122482082","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
Diagnosis Of Heart Disease Using K-Nearest Neighbor Method Based On Forward Selection 基于正向选择的k近邻方法诊断心脏病
Journal of Applied Intelligent System Pub Date : 2020-03-06 DOI: 10.33633/jais.v4i2.2749
Junta Zeniarja, Anisatawalanita Ukhifahdhina, A. Salam
{"title":"Diagnosis Of Heart Disease Using K-Nearest Neighbor Method Based On Forward Selection","authors":"Junta Zeniarja, Anisatawalanita Ukhifahdhina, A. Salam","doi":"10.33633/jais.v4i2.2749","DOIUrl":"https://doi.org/10.33633/jais.v4i2.2749","url":null,"abstract":"Heart is one of the essential organs that assume a significant part in the human body. However, heart can also cause diseases that affect the death. World Health Organization (WHO) data from 2012 showed that all deaths from cardiovascular disease (vascular) 7.4 million (42.3%) were caused by heart disease. Increased cases of heart disease require a step as an early prevention and prevention efforts by making early diagnosis of heart disease. In this research will be done early diagnosis of heart disease by using data mining process in the form of classification. The algorithm used is K-Nearest Neighbor algorithm with Forward Selection method. The K-Nearest Neighbor algorithm is used for classification in order to obtain a decision result from the diagnosis of heart disease, while the forward selection is used as a feature selection whose purpose is to increase the accuracy value. Forward selection works by removing some attributes that are irrelevant to the classification process. In this research the result of accuracy of heart disease diagnosis with K-Nearest Neighbor algorithm is 73,44%, while result of K-Nearest Neighbor algorithm accuracy with feature selection method 78,66%. It is clear that the incorporation of the K-Nearest Neighbor algorithm with the forward selection method has improved the accuracy result. Keywords - K-Nearest Neighbor, Classification, Heart Disease, Forward Selection, Data Mining","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124060176","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: 4
Implementation of Fuzzy Logic Controller for Wall Following and Obstacle Avoiding Robot 墙体跟随避障机器人模糊控制器的实现
Journal of Applied Intelligent System Pub Date : 2019-07-16 DOI: 10.33633/JAIS.V4I1.2168
A. Soetedjo, M. I. Ashari, Cosnas Eric Septian
{"title":"Implementation of Fuzzy Logic Controller for Wall Following and Obstacle Avoiding Robot","authors":"A. Soetedjo, M. I. Ashari, Cosnas Eric Septian","doi":"10.33633/JAIS.V4I1.2168","DOIUrl":"https://doi.org/10.33633/JAIS.V4I1.2168","url":null,"abstract":"This paper presents the development of wall following and obstacle avoiding robot using a Fuzzy Logic Controller. The ultrasonic sensors are employed to measure the distances between robot and the wall, and between the robot and the obstacle. A low cost Raspberry Pi camera is employed to measure the left/right distance between the robot and the obstacle. The Fuzzy Logic Controller is employed to steer the mobile robot to follow the wall and avoid the obstacle according to the multi sensor inputs. The outputs of Fuzzy Logic Controller are the speeds of left motor and right motor. The experimental results show that the developed mobile robot could be controlled properly to follow the different wall positions and avoid the different obstacle positions with the high successful rate of 83.33%.","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132313690","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
Upload File Security on the Server Using LSB and Hill Cipher 使用LSB和Hill密码在服务器上上传文件安全性
Journal of Applied Intelligent System Pub Date : 2019-07-16 DOI: 10.33633/JAIS.V4I1.2331
L. Handoko, Chaerul Umam, Adelia Syifa Anindita
{"title":"Upload File Security on the Server Using LSB and Hill Cipher","authors":"L. Handoko, Chaerul Umam, Adelia Syifa Anindita","doi":"10.33633/JAIS.V4I1.2331","DOIUrl":"https://doi.org/10.33633/JAIS.V4I1.2331","url":null,"abstract":"The rapid development of technology not only has a positive impact, but also can have a negative impact such as the development of cyber crime that can cause messages to be unsafe. Message security can be protected using cryptography to convert messages into secret passwords. Steganography is a technique of hiding messages by inserting messages into images that are used to increase message security. In this study, it discusses a combination of hill cipher and LSB algorithms to secure messages. The message used is a 3-bit grayscale image for steganography and text messages with 32, 64 and 128 characters for cryptography. The measuring instruments used in this study are MSE, PSNR, Entropy and travel time (CPU time). Test results prove an increase in security without too damaging the image. This is evidenced by the results of the MSE trial which has a value far below the value 1, the PSNR is> 65 dB with a range of entropy values of 5 to 7, and travel times are almost the same.","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124859311","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
Improvement accuracy of recognition isolated Balinese characters with Deep Convolution Neural Network 利用深度卷积神经网络提高孤立巴厘文字识别的准确率
Journal of Applied Intelligent System Pub Date : 2019-07-16 DOI: 10.33633/JAIS.V4I1.2289
I. B. T. T. Murti
{"title":"Improvement accuracy of recognition isolated Balinese characters with Deep Convolution Neural Network","authors":"I. B. T. T. Murti","doi":"10.33633/JAIS.V4I1.2289","DOIUrl":"https://doi.org/10.33633/JAIS.V4I1.2289","url":null,"abstract":"The numbers of Balinese script and the low quality of palm leaf manuscripts provide a challenge for testing and evaluation for character recognition. The aim of high accuracy for character recognition of Balinese script,we implementation Deep Convolution Neural Network using SmallerVGG (Visual Geometry Group) Architectur for character recognition on palm leaf manuscripts. We evaluated the performance that methods and we get accuracy 87,23% .","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125212607","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
A Study on Named Entity Recognition with OpenNLP at English Texts 基于OpenNLP的英语文本命名实体识别研究
Journal of Applied Intelligent System Pub Date : 2019-07-16 DOI: 10.33633/JAIS.V4I1.2096
Metin Bilgin
{"title":"A Study on Named Entity Recognition with OpenNLP at English Texts","authors":"Metin Bilgin","doi":"10.33633/JAIS.V4I1.2096","DOIUrl":"https://doi.org/10.33633/JAIS.V4I1.2096","url":null,"abstract":"Named entity recognition is a subject, inside of information retrieval which is a subdomain of natural processing. It pertains to identifying and labeling of location, person, organization, etc., inside of text content. Named entity recognition provides identifying and classifying of person, area, etc. inside of formal and informal text content and it can be used for different purposes as question answering systems and removal of the relation between events. In this work, named entity recognition is performed and one method is suggested and results are discussed for assignment to unlabeled name entities by using OpenNLP library with the help of KNIME program in the data set.","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114147633","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
A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor 基于k近邻的蜡染激光图像纹理特征提取分类
Journal of Applied Intelligent System Pub Date : 2019-02-07 DOI: 10.33633/JAIS.V3I2.2151
Cahaya Jatmoko, Daurat Sinaga
{"title":"A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor","authors":"Cahaya Jatmoko, Daurat Sinaga","doi":"10.33633/JAIS.V3I2.2151","DOIUrl":"https://doi.org/10.33633/JAIS.V3I2.2151","url":null,"abstract":"In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data.","PeriodicalId":289013,"journal":{"name":"Journal of Applied Intelligent System","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129755950","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
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