2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)最新文献

ENT Randomness Test on DM-PRESENT-80 and DM-PRESENT-128-based Pseudorandom Number Generator 基于DM-PRESENT-80和dm - present -128伪随机数发生器的ENT随机性检验

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702862

B. H. Susanti, Jimmy Jimmy, Mareta W. Ardyani

{"title":"ENT Randomness Test on DM-PRESENT-80 and DM-PRESENT-128-based Pseudorandom Number Generator","authors":"B. H. Susanti, Jimmy Jimmy, Mareta W. Ardyani","doi":"10.1109/ISRITI54043.2021.9702862","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702862","url":null,"abstract":"In cryptography, random numbers hold a special importance in which they can be utilized as keys, to generate challenges, or merely as a value. The Pseudorandom Number Generator (PRNG), as the name suggests, makes the generation of random numbers possible. It is widely accepted that there are two different categories of PRNG algorithms, namely the specifically built and those based on existing cryptography algorithms. The block cipher-based hash function scheme is one of the mostly used algorithms to generate outputs of a PRNG. In this study, we performed the ENT randomness test on PRNG which is based on hash function based on block cipher. Since the hash function produces a fixed output, the data set will be used to match the required sample. The hash function scheme used is Davies-Meyer with block cipher construction, namely the PRESENT-80 and PRESENT-128 algorithms. The results showed that the output sequences produced by one iteration and two iterations of DM-PRESENT-80 and DM-PRESENT-128 as a whole passed the ENT test, which means that the PRNG has a random output.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126135691","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

Mining User Reviews for Software Requirements of A New Mobile Banking Application 挖掘新的移动银行应用软件需求的用户评论

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702813

Andika Elok Amalia, Muhammad Zidny Naf’an

{"title":"Mining User Reviews for Software Requirements of A New Mobile Banking Application","authors":"Andika Elok Amalia, Muhammad Zidny Naf’an","doi":"10.1109/ISRITI54043.2021.9702813","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702813","url":null,"abstract":"Migration to the new system or application is very challenging, especially if the users have to adapt to a new application that is implemented with direct conversion technique. It triggers many user reactions, one of them is their opinions and rate about the application in play store (Google Play Store for example). Application reviews can be used to elicit user requirements or to verify requirements. This paper demonstrated the result of mining application reviews to support software requirements elicitation. It motivated by research area natural language processing (NLP) for requirement engineering (RE). Training and testing conducted to a dataset contains about 1200 application reviews of a new mobile banking application by classifying them into two classes (req and other) using Multinomial Naïve Bayes algorithm. Req is for opinions that contain requirement such as feature addition or user interface (UI) request while other is label for opinions/reviews contain non-requirements. The classification performance measured are accuracy score 0,8220 and one of class that has higher classifier performance is “other” class with value precision 0.83, recall 0.94 and F1 0.99. Even though, the result is not optimal yet, especially for “req” class, this research already implemented all categories of NLP technologies such as NLP techniques, NLP tools, and NLP resources.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125363490","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

Odor Source Localization in Low Computational Controller Micro Quadrotor 低计算控制器微型四旋翼的气味源定位

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702805

Nurrizal Alqindi, Muhammad Rausyan Fikri, D. W. Djamari

引用次数: 1

Input Feature Selection in ECG Signal Data Modelling using Long Short Term Memory 基于长短期记忆的心电信号数据建模中的输入特征选择

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702810

Ahmad Saikhu, C. V. Hudiyanti, Arya Yudhi Wijaya

引用次数: 0

An Enhanced Classification of Bacteria Pathogen on Microscopy Images Using Deep Learning 利用深度学习增强显微镜图像上细菌病原体的分类

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702809

S. A. Akbar, K. Ghazali, H. Hasan, Z. Mohamed, W. S. Aji

引用次数: 5

Design of an IoT-based Body Mass Index (BMI) Prediction Model 基于物联网的身体质量指数(BMI)预测模型设计

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702866

Glorious Musangi Mark, Pierre Bakunzibake, C. Mikeka

{"title":"Design of an IoT-based Body Mass Index (BMI) Prediction Model","authors":"Glorious Musangi Mark, Pierre Bakunzibake, C. Mikeka","doi":"10.1109/ISRITI54043.2021.9702866","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702866","url":null,"abstract":"Overweight and obesity have become a major health concern associated with diseases such as cardiac arrest, type 2 diabetes, stroke, high blood pressure, and other non-communicable diseases (NCD) and are the leading risks for deaths globally, killing more people than underweight. Body Mass Index (BMI) is a measure that uses weight and height to work out a person's nutrition status. Research throughout to calculate BMI is based on traditional manual methods which are time consuming, error prone and they are not cloud-based. Very few systems have incorporated machine learning yet with low accuracy. This research presents the design and development of a IoT based body mass index prediction model. This system consists of a NodeMCU microcontroller for computations with an inbuilt ESP8266 WiFi module, human load cell sensor for body weight measurement, a HX711 load cell amplifier module and HC-SR04 ultrasonic sensor for height measurement. Values are displayed on a 16x2 LCD and send to ThingSpeak for storage and analysis. ThingSpeak is integrated with MATLAB Machine Learning to make the prediction based on height and weight sensory data. This research uses Supervised Exponential Gaussian Process Regression algorithm to predict whether a person is underweighted, normal weight, overweight or obese. The designed IoT Based BMI computation system achieves an accuracy of 99.18% with a time reduction of 1.1 % per person while the ML model achieves an accuracy of 98%.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134453428","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

Analysis of Fuzzy Logic Algorithm for Load Balancing in SDN SDN中模糊逻辑负载均衡算法分析

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702876

Ian Agung Prakoso, S. N. Hertiana, Favian Dewanta

{"title":"Analysis of Fuzzy Logic Algorithm for Load Balancing in SDN","authors":"Ian Agung Prakoso, S. N. Hertiana, Favian Dewanta","doi":"10.1109/ISRITI54043.2021.9702876","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702876","url":null,"abstract":"Server Resource limitations are generally an obstacle affecting the quality of service (QoS) due to increased traffic levels. Therefore, Load Balancing is needed to manage service requests to the optimal application server. Software-Defined Network (SDN) has advantages in controlling the network which can be exploited with various load balancing strategies that are used to distribute traffic loads to improve overall system performance. The Performance for load balancing can be improved by selecting the server with minimum load using the Fuzzy Logic Algorithm. Traditional load balancing lacked the usage of device state data. In this study, an SDN-based Server Load Balancing method using fuzzy logic methods has been performed. Fuzzy Algorithm successfully delivers HTTP requests to lowest load server based on Distribution Server Index. In the testing, the server load must be directed at the lowest server weight so that each server should not be overloaded. In testing with a request load ranging from 100 - 500, the Fuzzy algorithm imposes more traffic distribution on the 3rd Server with the lowest server load. In the CPU usage test, the fuzzy logic algorithm has the lowest average value namely 39%. In the RAM usage test, the fuzzy logic algorithm has the lowest average value namely 54%. In the throughput test, the fuzzy logic algorithm has the highest average value namely 2KBps. The Fairness Index of Fuzzy Logic is 0.45 while Round Robin's fairness index is 0.99. Round Robin Algorithm can outperform other algorithms in terms of Fairness Index, as the fairest algorithm.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131839960","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

Unmanned Surface Vehicle Autopilot and Guidance System Design with Disturbance Using Fuzzy Logic Sliding Curve 基于模糊逻辑滑动曲线的干扰无人水面车辆自动驾驶与制导系统设计

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702781

M. Sahal, Briliant Rizqi Haqiqi, R. E. A. Kadir

{"title":"Unmanned Surface Vehicle Autopilot and Guidance System Design with Disturbance Using Fuzzy Logic Sliding Curve","authors":"M. Sahal, Briliant Rizqi Haqiqi, R. E. A. Kadir","doi":"10.1109/ISRITI54043.2021.9702781","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702781","url":null,"abstract":"One technology that is developing rapidly at this time is unmanned vehicles. Unmanned surface vehicle (USV) or an unmanned ship can go without a captain. USV has a variety of functions such as a vehicle and a combat tool to a means of transportation. The development of the USV into an autonomous vehicle requires a variety of configurations both in determining speed, trajectory and avoiding obstacles. The designed system certainly has external disturbances which can affect the stability of the system. The existence of environmental changes that can occur with various conditions results in the USV system that needs to be able to adapt. To overcome these problems, a stable intelligent controller can be designed. In this study, the USV autopilot and guidance system was designed using a Fuzzy Logic Sliding Curve. In testing the results of system design, simulations will be carried out using MATLAB software. This study carried out various variations to obtain an autopilot and guidance unmanned surface vehicle (USV) system method that was able to reach the waypoint in the fastest time and the shortest distance.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115366970","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

East Nusa Tenggara Weaving Image Retrieval Using Convolutional Neural Network 基于卷积神经网络的东努沙登加拉织造图像检索

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702843

Silvester Tena, Rudy Hartanto, I. Ardiyanto

{"title":"East Nusa Tenggara Weaving Image Retrieval Using Convolutional Neural Network","authors":"Silvester Tena, Rudy Hartanto, I. Ardiyanto","doi":"10.1109/ISRITI54043.2021.9702843","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702843","url":null,"abstract":"The popularity of East Nusa Tenggara (ENT) province is attributed to a variety of traditional woven fabrics with local cultural attributes. Each tribe in the province has its design and colors that differentiate the fabrics leading to diverse decorative motifs. Due to different varieties, it is challenging for users to know both the type of motif and its origins. In this research, several Convolutional neural network (CNN) architecture benchmarks were carried out for ENT weaving images retrieval. The image retrieval method was chosen for the study since it has feature extraction and similarity measurement, which make searching and selection relatively easier. Furthermore, the CNN method is often used for feature extraction due to its ability to recognize objects while hashing and hamming distance algorithms help reduce the computation time for similarity testing. This study was conducted by comparing several pre-trained CNN architectures, including VGG16, ResNet101, InceptionV3, and Discrete Wavelet Transform. The results showed that the highest accuracy is ResNet101 architecture with 100%, 88.50%, and 55% at top=1, top=5, and top=10, respectively. The pre-trained CNN model and Discrete Wavelet Transform combination provided better results in case the feature dimensions were above 16-bit. The feature dimensions are generally based on the best 6-bit hashing code, though they are computationally time-consuming.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114780442","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

Improving Clustering Method Performance Using K-Means, Mini Batch K-Means, BIRCH and Spectral 利用K-Means、Mini Batch K-Means、BIRCH和谱改进聚类方法的性能

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702823

Tenia Wahyuningrum, S. Khomsah, S. Suyanto, Selly Meliana, Prasti Eko Yunanto, W. A. Al Maki

{"title":"Improving Clustering Method Performance Using K-Means, Mini Batch K-Means, BIRCH and Spectral","authors":"Tenia Wahyuningrum, S. Khomsah, S. Suyanto, Selly Meliana, Prasti Eko Yunanto, W. A. Al Maki","doi":"10.1109/ISRITI54043.2021.9702823","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702823","url":null,"abstract":"The most pressing problem of the $k$-Nearest Neighbor (KNN) classification method is voting technology, which will lead to poor accuracy of some randomly distributed complex data sets. To overcome the weakness of KNN, we added a step before the KNN classification phase. We developed a new schema for grouping data sets, making the number of clusters greater than the number of data classes. In addition, the committee selects each cluster so that it does not use voting techniques such as standard KNN methods. This study uses two sequential methods, namely the clustering method and the KNN method. Clustering methods can be used to group records into multiple clusters to select commissions from these clusters. Five clustering methods were tested: K-Means, K-Means with Principal Component Analysis (PCA), Mini Batch K-Means, Spectral and Balanced Iterative Reduction and Clustering using Hierarchies (BIRCH). All tested clustering methods are based on the cluster type of the center of gravity. According to the result, the BIRCH method has the lowest error rate among the five clustering methods (2.13), and K-Means has the largest clusters (156.63).","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116463680","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