2020 International Conference on Information Technology Systems and Innovation (ICITSI)最新文献_第7页

Digital security reference model: a survey and proposal 数字安全参考模型:调查与建议

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264912

Suhardi, Baharuddin Aziz, R. Doss, Purnomo Yustianto

{"title":"Digital security reference model: a survey and proposal","authors":"Suhardi, Baharuddin Aziz, R. Doss, Purnomo Yustianto","doi":"10.1109/ICITSI50517.2020.9264912","DOIUrl":"https://doi.org/10.1109/ICITSI50517.2020.9264912","url":null,"abstract":"This paper presents a survey and proposal of a digital security reference model. The proposed reference model consists of three layers: technology and engineering layer, management layer, and legal layer. The first layer focuses on technologies and engineering processes to build a secure digital system. Then, the second layer is all about management or organizational procedures. Finally, the last layer consists of compliance with existing laws and regulations. The correlation between each layer is, first, focusing on the technology and engineering layer to get anything to build an organization’s secure digital system. Then, create policies for all parties of the organizations. Last, make sure all of the technology used and procedures applied complied with the existing law. The last layer also has a role as if security breach success penetrates the two previous layers. The next is digital forensics to reveal the cyber incident and punish the criminals by the existing law. In general, this proposed reference model aims to guide organizations, companies, and governments in developing a secure digital system with a comprehensive perspective. In particular, the reference model has two use case actors, engineers, and researchers. For the engineer, this reference model guides to starting and developing a digital security system. There are five steps to creating a secure digital system using this reference model: preparation, specifications, system design, implementation, and evaluation. For the researcher, this reference model gives a comprehensive understanding. So the researcher gets a big picture of the secure digital system’s research field before starting the research. After it, the researcher determines which topic they want.","PeriodicalId":286828,"journal":{"name":"2020 International Conference on Information Technology Systems and Innovation (ICITSI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114925051","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

Searching for Malware Dataset: a Systematic Literature Review 搜索恶意软件数据集:系统文献综述

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264929

Luqman Muhammad Zagi, Baharuddin Aziz

{"title":"Searching for Malware Dataset: a Systematic Literature Review","authors":"Luqman Muhammad Zagi, Baharuddin Aziz","doi":"10.1109/ICITSI50517.2020.9264929","DOIUrl":"https://doi.org/10.1109/ICITSI50517.2020.9264929","url":null,"abstract":"Malware is one of the exciting topics widely discussed by both academicians and researchers, but the source list of malware rarely provided. Therefore, this paper aims to write a Systematic Literature Review (SLR) to find which datasets are commonly used by previous researchers. The three journal databases were used in this study, including IEEE, science direct, and ACM. The PRISMA statement was applied to maintain transparency during the literature review. To facilitate the search, the authors also provide limitations during the SLR process (inclusion and exclusion). The inclusion includes: (1) full article fully written in English; (2) peer-reviewed papers; (3) explicitly mentioning the name of dataset or database; and (4) explicitly mentioning the method to find malware characteristics and behavior. While the exclusion consists of: (1) articles written before 2015; (2) book and white paper; (3) article already indexed in another database journal; and (4) paper which is less than four pages. After both filter processes, there are 42 out of 245 articles eligible to answer the stated research question (RQ), which were: (1) where does the researcher usually find the malware database or dataset?; (2) what kind of methods applied by previous researchers to find the malware’s characteristics or behavior?; and (3) which platforms that malware usually attacks are? Based on the three RQs, we could conclude that RQ1 recorded for 37 datasets, RQ2 recorded for 47 methods, and RQ3 recorded for six platforms.","PeriodicalId":286828,"journal":{"name":"2020 International Conference on Information Technology Systems and Innovation (ICITSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129088035","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

Smartphone Application for Deep Learning-Based Rice Plant Disease Detection 基于深度学习的水稻病害检测智能手机应用

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264942

Heri Andrianto, Suhardi, A. Faizal, Fladio Armandika

{"title":"Smartphone Application for Deep Learning-Based Rice Plant Disease Detection","authors":"Heri Andrianto, Suhardi, A. Faizal, Fladio Armandika","doi":"10.1109/ICITSI50517.2020.9264942","DOIUrl":"https://doi.org/10.1109/ICITSI50517.2020.9264942","url":null,"abstract":"An increase in the human population requires an increase in agricultural production. Generally, the most important thing in agriculture that affects the quantity and quality of crops is plant diseases. In general, a farmer knows that his plant is attacked by a disease through direct vision. However, this process is sometimes inaccurate. With the development of machine learning technology, plant disease detection can be done automatically using deep learning. In this study, we report on a deep learning-based rice disease detection system that we have developed, which consists of a machine learning application on a cloud server and an application on a smartphone. The smartphone application functions to capture images of rice plant leaves, send them to the application on the cloud server, and receive classification results in the form of information on the types of plant diseases. The results showed that the smartphone-based rice plant disease detection application functioned well, which was able to detect diseases in rice plants. The performance of the rice plant disease detection system with VGG16 architecture has a train accuracy value of 100% and a test accuracy value of 60%. The test accuracy value can be improved by adding the number of datasets and increasing the quality of the dataset. It is hoped that with this system, rice plant disease control can be carried out appropriately so that yields will be maximized.","PeriodicalId":286828,"journal":{"name":"2020 International Conference on Information Technology Systems and Innovation (ICITSI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114667725","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: 16

Fraud Detection at Self Checkout Retail using Data Mining 基于数据挖掘的自助结账零售欺诈检测

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264919

Hafid Yoza Putra

引用次数: 1

IoT-Based Smart Band For Tracking Position And Monitoring Conditions Of Children 基于物联网的儿童位置跟踪和状态监测智能手环

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264954

Lathifah Arief, Taufik Fadhlul Hadi, T. Sundara

引用次数: 0

Improving of Conditional Automatic Cover (CAC) Business Model Acceptance Services: Case Study of PT JAS 条件自动覆盖(CAC)业务模式的改进:以PT JAS为例

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264949

George Michael Samuel Hartono, Wahyu Ari Bowo, Aina Rahmah Hayah, M. R. Shihab, B. Ranti, Meisuchi Naisuty

{"title":"Improving of Conditional Automatic Cover (CAC) Business Model Acceptance Services: Case Study of PT JAS","authors":"George Michael Samuel Hartono, Wahyu Ari Bowo, Aina Rahmah Hayah, M. R. Shihab, B. Ranti, Meisuchi Naisuty","doi":"10.1109/ICITSI50517.2020.9264949","DOIUrl":"https://doi.org/10.1109/ICITSI50517.2020.9264949","url":null,"abstract":"Information Technology forces and inspires an organization to change or be left behind with increasingly competitive business competition. This also affects the business model that is run within the organization to stay afloat and compete to increase the value of the products or services offered. PT JAS is a sharia guarantee company that utilizes information technology to innovate in creating and running its business model. However, in carrying out its business operations in the CAC financing guarantee model, the company experienced a constraint that is the limited number of operational resources owned is not proportional to the number of financing guarantees proposed, so that requires a longer time in the process of acceptance with the amount of debtor data that must be inputted. For example, such as gold installment guarantee products, KPR, micro businesses, and so on. In addition, the internal guarantee core system capability is sometimes felt slow and sometimes consumes server resources. The purpose of this study is to obtain an appropriate model in improving PT JAS’s internal capabilities in providing the best guarantee acceptance services to business partners. And increase the company’s business value. The research methodology used in this study uses document studies and interviews. The results of this study are the analysis of external and internal capabilities and strengths using PESTEL analysis combined with SWOT analysis and the provision of system recommendations in the McFarlan’s quadrant. Then a connectivity model that connects each recommended application that is connected internally and the application with business partners is proposed.","PeriodicalId":286828,"journal":{"name":"2020 International Conference on Information Technology Systems and Innovation (ICITSI)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121721020","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

Trust Evaluation using Data Distribution Technique on Service Computing 基于数据分布技术的服务计算信任评估

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264963

Yudhi Biantoro, Suhardi, Y. Bandung, N. Kurniawan

{"title":"Trust Evaluation using Data Distribution Technique on Service Computing","authors":"Yudhi Biantoro, Suhardi, Y. Bandung, N. Kurniawan","doi":"10.1109/ICITSI50517.2020.9264963","DOIUrl":"https://doi.org/10.1109/ICITSI50517.2020.9264963","url":null,"abstract":"In this modern era, the role of service computing has grown rapidly. Almost all service sectors are computationally based. The existence of service computing depends on the trust of its users. However, user ratings are spread across multiple systems and use different formats. The history recorded in the system is a record that can be used as an evaluation of the next service computing system development decision. In general, a trust record or a trust value is determined by a certain value. There are at least two categories, namely 0 and 1 (distrust and trust), or it could be more than two. Sometimes too many or too few categories are defined, making it difficult to conduct a trust evaluation analysis. This study aims to perform a redesign calculation to determine the category of trust. The calculation process is carried out using data distribution techniques using the available data sets. The result of this research is categorization evaluation of trust in service computing system. By obtaining a simpler category result, the old category can be adjusted to the new design, so that the results are not confusing when evaluating trustworthiness.","PeriodicalId":286828,"journal":{"name":"2020 International Conference on Information Technology Systems and Innovation (ICITSI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125971864","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

Classification of Air Pollution Levels using Artificial Neural Network 基于人工神经网络的空气污染等级分类

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264910

F. Hamami, Inayatul Fithriyah

引用次数: 6

Design of Cow Cattle Weighing System Technology and Automatic Giving Feed 奶牛称重系统技术及自动给料设计

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264977

Dodon Yendri, Adrizal, Derisma, Hafni Afif

引用次数: 4

Classification Cherry’s Coffee using k-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) 基于k近邻和人工神经网络的樱桃咖啡分类

2020 International Conference on Information Technology Systems and Innovation (ICITSI) Pub Date : 2020-10-19 DOI: 10.1109/ICITSI50517.2020.9264927

S. Anita, Albarda

{"title":"Classification Cherry’s Coffee using k-Nearest Neighbor (KNN) and Artificial Neural Network (ANN)","authors":"S. Anita, Albarda","doi":"10.1109/ICITSI50517.2020.9264927","DOIUrl":"https://doi.org/10.1109/ICITSI50517.2020.9264927","url":null,"abstract":"The quality of coffee is determined from 60% when planted, 30% when roasted and 10% when brewed. This research examines more deeply the process of sorting the coffee cherries using the dry method. The technology that is possible to solve this coffee cherry fruit sorting problem is image processing, this is seen because the current conventional method uses human eyes and hands in sorting. This sorting process aims to separate superior fruit (red, half red, broken red, brown)., black, half black, orange, yellow, and green) of inferior fruit (spotted, moldy, with 1 hole, and more than 1 hole) and coffee cherries (round, oval, broken, perfect).The purpose of this study was to develop a coffee cherry sorting machine technology with faster and more accurate results so that it could replace the conventional coffee cherry sorting process. The coffee cherries are categorized into ripe, undercooked, raw, and damaged cherries using the GLCM (Gray-Level Co-Occurrence Matrix) algorithm for feature extraction and the KNN (k-Nearest Neighbor) and ANN (Artificial Neural Network) classification algorithm. newrb. The success obtained from this research is ANN accuracy of 24.41% and using the KNN method of 72.12%. With the simulation carried out, the coffee cherries classification process with an amount of 1,885 can be carried out in a total time of 356.02 seconds or the equivalent of 6 minutes. Author identifies indicators of coffee cherries as skin color (red, half red, cracked red, brown, black, half black, orange, yellow, and green), cherri shape (round, oval, broken, perfect), and cherries skin defects (speckled -button, moldy, with 1 hole, and more than 1 hole). It is hoped that the results of this study can serve as a consideration for developing a more advanced national coffee industry.","PeriodicalId":286828,"journal":{"name":"2020 International Conference on Information Technology Systems and Innovation (ICITSI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998536","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: 6