检索结果-内蒙古大学图书馆

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Indarti Indriyani, Novita Budi, Arief Setya Laraswati, Dewi Yusnaeni, Wina Hidayat, Arief Program Studi Sistem Informasi Fakultas Teknik Dan Informatika Universitas Bina Sarana Informatika Sekolah Tinggi Manajemen Informatika Dan Komputer Nusa Mandiri Indonesia

Pear is a kind of fruits which has a lot of varieties. One of the way to differ pear varietiesis by looking at the color, size and shape. This research is aimed at giving assistance to classifytwo varieties of pear i.e. Monster Pear and Williams Pear. In order to get the purpose, pear image processing is done to ease the classification process of pear varieties. Research method used consists of RGB Color Room to l*a*b, image segmentation, characteristics extraction with K-Means Clustering. Besides, K-Nearest Neighbor (KNN) is used to know the distribution and the classification. The use of practice data will increase the accuracy of pear varieties classification. The data used here are 88 pear images which cover 44 image practice data of Monster pear and 44 image practice data of Williams pear. Meanwhile, the test data taken are 10 images of each variety. The result of this research shows that the pear classification accuracy level is 95% which is very good. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Motorized Vehicle Security System with Master and Slave Key Models 1

Motorized Vehicle Security System with Master and Slave Key ...

引用

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Sriyadi Maruloh Susila, Mochamad Nandi Andriansah Nawawi, Imam Sri Sulistyawati, Meiva Eka Alawiyah, Sufi Program Studi Sistem Informasi Fakultas Teknik and Informatika Universitas Bina Sarana Informatika Sekolah Tinggi Manajemen Informatika Dan Komputer Nusa Mandiri Jakarta Indonesia

Indonesian are carrying out routines both for personal gain, groups can use a variety of transportation in land, sea and air. Kinds of transportation that are available in the middle of the community that is affordable to have, are vehicles both two-wheeled and four-wheeled. The tendency of motorized vehicles to increase due to various factors including more economical compared to other transportation, simplify and speed up the duration of the trip, encourage the economy of the motorcycle taxi online or traditional. In the midst of people enjoying transportation, there are threats made by certain elements who take advantage of the owner's carelessness and of course the security weaknesses of the vehicle. The threat that occurs is motor vehicle thief. Based on data from the Central Statistics Agency for motor vehicle theft in Indonesia in January 2016 was 3,468 cases, February 3,595 cases, March 3,518 cases), meaning that in 2016 for three months as many as 10,581 people were disadvantaged because of the crimes. Di era industrial revolution 4.0 marked the massive implementation of information technology in all fields including transportation. Collaboration of information technology with the field of automation, in this case the workshop can create new innovationsin securing motor vehicles so that they are not vulnerable to deception. The innovation is SIKEMO, works based on inputs known by the sensor, if the sensor recognizes the input data, the process ca be continued and the motor can be used (starter is on status), and vice versa if the sensor does not recognize the input data, then SIKEMO will reject and of course the vehicle cannot be used ( starter in the off status). The Security System developed at SIKEMO uses data input with two models, namely the master key and the slave key. The master key input data has the highest authority besides being recognized by SIKEMO, the master key can be used to grant access rights to the slave key to be recognized by the SIKEM

关键词： Software prototyping

来源：评论

学校读者我要写书评

暂无评论

Partial Business Process Re-engineering in New Generation Cooperatives Enterprise Architecture Implementation 2

Partial Business Process Re-engineering in New Generation Co...

引用

2nd International Conference on Computer, Science, Engineering, and Technology, ICComSET 2019

作者： Dwipriyoko, Estiyan Widjajani Program Studi Informatika Fakultas Teknik Universitas Langlangbuana Bandung Indonesia Program Studi Teknik Industri Fakultas Teknik Universitas Langlangbuana Bandung Indonesia

Every enterprise have Business Process Re-engineering (BPR) phase in their Enterprise Architecture implementation transitions. One of best practices in BPR is to have not more then 5 years for transitions phase. Enterprise that in transitions phase condition would experience gradual change in BPR, from partial unit change, until it has fully structural and functional change. But there was one successful New Generation Cooperatives Enterprise, that has experiencing more than 5 years time for Partial BPR phase. This research problem is about a case of successful Malaysian Collective Investment Cooperatives, that implemented SAP information systems and technology, but not change their business structure and only partially change their business strategies, for more than 5 years time. Enterprise historical data and annual reports were used for quantitative research data collections, to be analyzed in this research. Enterprise Architecture Planning framework approach were used as qualitative research method to describe and analyzed the New Generation Cooperatives Enterprise Architectures and the implementation process. This research found that Partial BPR in New Generation Cooperatives Enterprise Architecture were proven successful, because of the business profit were substantial, and the cooperatives enterprise still has the same expenditures for their remaining unchanged business structure. This Research concluded that Partial BPR could be noneffective successful, depends on how high the profit that been generated from the business process. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Profitability

来源：评论

学校读者我要写书评

暂无评论

Data mining for cryptocurrencies price prediction 1

Data mining for cryptocurrencies price prediction

引用

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Fatah, Haerul Anggraini, Recha Abriana Supriadi, Deddy Pertiwi, Melisa Winda Warnilah, Ai Ilah Ichsan, Nurul Program Studi Sistem Informasi Universitas Bina Sarana Informatika STMIK Nusa Mandiri Indonesia

Electronic payments is something that is currently in high demand by investors today, but transactions are often constrained due to various problems, especially from third parties. For this reason, cryptocurrency emerged, which is one of the solutions for conducting electronic payment transactions. Some types of cryptocurrency that are most in demand by investors are bitcoin, ethereum, and ripple. The fluctuation value of cryptocurrency is very difficult to predict so that investors often experience losses when making transactions. This study aims to predict cryptocurrency prices such as bitcoin, ethereum and ripple using data mining algorithms. The data mining algorithm used in this prediction process is K-NN, Neural Network, SVM, Linear Regression, Random Forest and DecisionTree. Data mining modeling is done by dividing the dataset into each type of commodity and then analyzed using each algorithm. The results of this study indicate that the accuracy value obtained from some data mining algorithms is good enough to predict cryptocurrency prices © 2020 Published under licence by IOP Publishing Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Improved Genetic Algorithm for Flow Shop Scheduling Problem at PT. XYZ 2

Improved Genetic Algorithm for Flow Shop Scheduling Problem ...

引用

2nd International Conference on Computer, Science, Engineering, and Technology, ICComSET 2019

作者： Widyawati Waliadi, G. Program Studi Teknik Informatika Fakultas Ilmu Komputer Universitas Banten Jaya Banten Indonesia

Scheduling according to Takeshi Yamada was an allocation of limited resources in order to do compete activity which usually applied on the operational research field where one of the examples was machine scheduling. Machine scheduling is an activity consisting of job (represent the activity) and machines (represent the resource) where each machine can process only one activity at a time. the purpose of scheduling is to perform all of the process from beginning to end with the objective to minimize makespan time. PT. XYZ is a manufacturing company that produces component for steam turbine component products in power plants. Within a year the company obtained an average of 9-10 project orders for each type of product. Because of the large number of projects that must be done, the scheduling process is becoming one of critical process in order to achieve the fastest makespan time. One way to find the fastest makespan time is by rescheduling using the methods applied in flow shop scheduling. One algorithm that can be used is the genetic algorithm. Based on the results of the research, it was found that for the process of scheduling project "A" with six machines and five jobs is makepan time of 53 days. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

App Review Sentiment Analysis Shopee Application in Google Play Store Using Naive Bayes Algorithm 1

App Review Sentiment Analysis Shopee Application in Google P...

引用

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Pratmanto, Dany Rousyati, Rousyati Wati, Fanny Fatma Widodo, Andrian Eko Suleman, Suleman Wijianto, Ragil Program Studi Sistem Informasi STMIK Nusa Mandiri Universitas Bina Sarana Informatika Jakarta Indonesia

An online marketplace site is a shopping place that is currently popular with the community because it offers a variety of convenience and one of the marketplace apps is Shopee. Some people aresatisfied with the service provided by the Shopee app. But unisex some people who give complaints about this application. User-provided response to Shopee app in the Comments field of Shopee Google Play Store can be analyzed for negative and positive sentiments. This research aims to assist Shopee's management of the positive or negative opinions of application users and can provide empirical evidence for related theories so that it can be used as a donation of thought for the development of theories Next. With the number of reviews shown, you need an analysis that can classify these reviews into positive or negative classes. The method used for the sentiment analysis of Shopee app reviews is the Naive Bayes algorithm obtaining an accuracy yield of 96,667% © 2020 Published under licence by IOP Publishing Ltd.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Mobile-Assisted Language Learning (MALL): Students' Perception and Problems towards Mobile Learning in English Language 1

Mobile-Assisted Language Learning (MALL): Students' Percepti...

引用

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Nuraeni, Cicih Carolina, Irmawati Supriyatna, Adi Widiati, Wina Bahri, Syamsul Program Studi Bahasa Inggris Fakultas Komunikasi Dan Bahasa Universitas Bina Sarana Informatika Indonesia

Mobile-Assisted Language Learning (MALL) is the latest learning way in the language education where applications or websites are used to facilitate students learning activities. Mobile phone has been studied years by researchers in its connection with education-related activities. This research will focus on its main purposes, they are: 1) students' perceptions in using mobile phone in English language learning classroom activities;2). the problem of using mobile phone to support classroom activities, especially in English language learning. The method of the research was quantitative method which used 70 students as research object. The data were collected through a 5-point Likert Scale questionnaire. The research found;first, the students' majority had positive perception on the usage of MALL to support classroom activities, especially in learning English language. Second, the problem in internet connectivity is the biggest problems that students faced in terms of using MALL in English language classroom. But, It is hoped that MALL will be used as one of the teaching aids that could assist students in learning English as a Foreign Language (EFL) more effectively. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Comparison of Logistic Regression, K-Nearest Neighbour, and decision tree (C4.5) on parameter optimization to increase prediction of breast cancer

引用

AIP Conference Proceedings 2023年第1期2714卷

作者： Harsih Rianto Omar Pahlevi Rudianto Amrin Paramita Kusumawardhani Program Studi Sistem Informasi Universitas Bina Sarana Informatika Jakarta Indonesia Program Studi Teknologi Komputer Universitas Bina Sarana Informatika Jakarta Indonesia Program Studi Bahasa Inggris Universitas Bina Sarana Informatika Jakarta Indonesia

The malignancy of breast cancer has caused many deaths in women. Breast cancer begins when a cancerous, malignant lump begins to grow from the breast cells. Over the decades machine learning has grown rapidly and is used for applications in health-related fields. In this study, we compared the Logistic Regression (LR), K-Nearest Neighbour (KNN), and C4.5 Decision Tree (DT) algorithm methods by applying parameter optimization to improve breast cancer prediction results. The dataset we use is the Wisconsin Breast Cancer Dataset (WBCD) which consists of 699 records. The results of the performance of LR, KNN and DT have increased in accuracy values after the parameter optimization method was applied. After testing using the same dataset on the three algorithms by comparing the AUC and Confusion Matrix values, the LR algorithm produces an Accuracy value of 96.9% and AUC of 99.5%. While the KNN Algorithm the resulting Accuracy value is 97.4% and AUC is 99.3%. For the DT algorithm, the test results get the Accuracy and AUC values of 96%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparation of K-Nearest Neighboor (K-NN) and Naive Bayes Algorithm for the Classification of the Poor in Recipients of Social Assistance 1

Comparation of K-Nearest Neighboor (K-NN) and Naive Bayes Al...

引用

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Firasari, Elly Khasanah, Nurul Khultsum, Umi Kholifah, Desiana Nur Komarudin, Rachman Widyastuty, Wiwiek Universitas Bina Sarana Informatika Indonesia Program Studi Sistem Informasi STMIK Nusa Mandiri Indonesia

Poverty is a problems faced by developing countries, as well as Indonesia. According to data from the Central Statistics Agency in 2018, more than half the distribution of the poor population in Indonesia is in Java, which is 13,340.15 million people. Somokerto village is one of the villagesin the district of Magelang, Central Java, which receives government assistance in an effort to reduce poverty. But in the process of classifying citizens who are entitled to receive assistance is still done manually. Manual classification is considered inaccurate in obtaining the results of social assistance recipients. In overcoming this problem, we need a systematic calculation to get accurate results. In this case, the researcher uses data mining classification calculation by comparing 2 calculation methods, namely K-NN and Naïve Bayes. The reseachers use Rapidminer tools. The research stages are identification of problems, data collection, implementation K-NN, ImplementationNaive bayes, data testing process to produce accuracy and compare the result. The results obtained are the accuracy of Naïve Bayes higher than K-NN, namely Naïve Bayes 89.04% and K-NN 87.67%. This figure is classified in the category of good classification. From the results of the studyit can be concluded the Naïve Bayes algorithm is suitable to be applied in the calculation of recipients of social assistance. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Developing countries

来源：评论

学校读者我要写书评

暂无评论

Determining the Eligibility of Providing Motorized Vehicle Loans by Using the Logistic Regression, Naive Bayes and Decission Tree (C4.5) 1

Determining the Eligibility of Providing Motorized Vehicle L...

引用

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Rianto, Harsih Amrin Rudianto Pahlevi, Omar Kusumawardhani, Paramita Hadi, Seno Sudarmono Program Studi Sistem Informasi Fakultas Teknik Dan Informatika Universitas Bina Sarana Informatika Jl. Kramat Raya No. 98 Jakarta Indonesia

Evaluating in determining the eligibility of giving credit is very important. Errors in providing credit worthiness assessments can result a bad credit risk. The problem that often occurs is not the application of the system by financial parties but more on HR when making predictions about the determination of consumer credit worthiness. Research in the field of computers has been done toreduce credit risk resulting in losses to the company. In this research a comparison of Logistic Regression (LR), Naïve Bayes (NB) and Decision Tree (C4.5) algorithms is performed to predict the feasibility of granting credit. In order to produce a prediction of the feasibility of granting credit to new consumers, credit data used by the company is used. The data used in this study consists of 481 consumer records that have been classified as consumers with current credit and bad credit. After testing using the same dataset on the three algorithms by comparing the AUC and Confusion Matrix values, it was found that the appropriate algorithm to be applied to the credit worthiness dataset was Logistic Regression with an Area Under Curve (AUC) value of 0.972 and Accuracy or Confusion Matrix of 93.14%. As for the Decision Tree Algorithm (C4.5) from the test results, the AUC value is 0.926 and the Accuracy is 90.85% and the Algortima Naïve Bayes AUC value is 0.905 and the Accuracy is 82.75%. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：