2020 Fifth International Conference on Informatics and Computing (ICIC)最新文献_第3页

Double Layered Text Encryption using Beaufort and Hill Cipher Techniques 使用博福特和希尔密码技术的双层文本加密

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288538

Muhammad Fadlan, Suprianto, Muhammad, Yusni Amaliah

{"title":"Double Layered Text Encryption using Beaufort and Hill Cipher Techniques","authors":"Muhammad Fadlan, Suprianto, Muhammad, Yusni Amaliah","doi":"10.1109/ICIC50835.2020.9288538","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288538","url":null,"abstract":"Data is critical, especially in the current cyber era. Maintaining data confidentiality and security is one thing that must be done so that the data owned cannot be used by other parties who are not interested. Cryptography is one solution that can be utilized. In cryptography, there are various methods, including the Beaufort Cipher. The Beaufort cipher method is a cryptographic method using simple calculations. This method's weakness is to use mathematical calculations that are quite simple and include methods that are already quite old. Despite this fact, this method is still one of the popularly used in securing data. One way to improve data security by using this method is through integration with other cryptographic methods, one of them is the hill cipher. In this research, the encryption process is known as Double Layered Encryption. The encryption process will be done in two layers, namely using Beaufort cipher and Hill cipher. The quality of the proposed encryption process is measured using an Avalanche Effect (AE) value. The results show that the proposed method has a higher Avalanche Effect than the Beaufort cipher only and Hill cipher only methods.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"691 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123826517","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

Factors Affecting Acceptance of E-marketplace Based On Hybrid Model of Modified TAM-TRI 影响电子市场接受度的因素——基于修正TAM-TRI混合模型

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288626

K. C. Dewi, N. W. D. Ayuni

引用次数: 1

Maturity Grading of Oil Palm Fresh Fruit Bunches Based on a Machine Learning Approach 基于机器学习方法的油棕鲜果束成熟度分级

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288603

Anindita Septiarini, H. R. Hatta, H. Hamdani, Ana Oktavia, A. A. Kasim, S. Suyanto

{"title":"Maturity Grading of Oil Palm Fresh Fruit Bunches Based on a Machine Learning Approach","authors":"Anindita Septiarini, H. R. Hatta, H. Hamdani, Ana Oktavia, A. A. Kasim, S. Suyanto","doi":"10.1109/ICIC50835.2020.9288603","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288603","url":null,"abstract":"Grading maturity oil palm fresh fruit bunches (FFB) is an essential issue in the agriculture sector because the quality of palm oil determines based on the maturity level. Recently, the production of high-quality palm oil has increased continually. Therefore, the implementation of computer vision in agriculture for grading the maturity of oil palm FFB is required to avoid subjectivity in determining the maturity level. This study develops a classification method for grading the maturity level of FFB. Generally, this classification method performed using the color feature. In this study, the color feature is used to distinguish the maturity level of oil palm FFB. The mean value extracted as the color features in L*a*b color space is followed by implementing a machine learning method: Linear Discriminant Analysis (LDA), in the classification process. The experiment used a dataset of 150 images with three different classes: raw, under-ripe, and ripe. The dataset is applied in two stages: training and testing of 60 images and 90 images, respectively. The performance evaluation of the method used successfully achieved an accuracy value of 98.89% using a testing dataset of 90 images.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127655359","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: 9

A Model of Requirements Engineering on Agriculture Mobile Learning System Using Goal-Oriented Approach 基于目标导向的农业移动学习系统需求工程模型

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288536

R. Delima, Argo Wibowo, Antonius Rachmat Chrismanto, Halim Budi Santoso

{"title":"A Model of Requirements Engineering on Agriculture Mobile Learning System Using Goal-Oriented Approach","authors":"R. Delima, Argo Wibowo, Antonius Rachmat Chrismanto, Halim Budi Santoso","doi":"10.1109/ICIC50835.2020.9288536","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288536","url":null,"abstract":"Requirements engineering (RE) is an essential initial step in the software engineering process. This step requires good interaction and communication between stakeholders and system analysis. A user-oriented approach is very supportive in defining the requirements of a system. This paper contributes to the application of the goal-oriented method in RE process. Goal-oriented is applied through four steps such as elicitation, requirements definition, analysis, and specification. The elicitation step was carried out by involving fourteen stakeholders from the elements of farmers, agricultural extension workers, village government, and nongovernment employees. RE produces a specification list of functional and non-functional requirements, use case diagrams, and class diagrams. RE also creates a model design for applying the pear-to-pear interface architecture model for agriculture mobile learning. Through this research, it is known that the goal-oriented approach strongly supports a structured, systematic, and participatory RE process, making it easier to prepare requirements specifications that will become a reference at the system design and development step.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133216254","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

Prediction of Student Graduation with Naive Bayes Algorithm 用朴素贝叶斯算法预测学生毕业

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288625

Hartatik, Kusrini Kusrini, Agung Budi Prasetio

{"title":"Prediction of Student Graduation with Naive Bayes Algorithm","authors":"Hartatik, Kusrini Kusrini, Agung Budi Prasetio","doi":"10.1109/ICIC50835.2020.9288625","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288625","url":null,"abstract":"The research carried out in this study is the development and analysis of student performance in the academic field using the Naive Bayes algorithm so that it can help agencies and students see early graduation predictions, and help managers to see the progress and predictions of active student graduation. The purpose of this research is to study student achievement prediction models that have model values. Referring to previous research in reference that getting student prediction results in the form of semester GPA 1,2,3,4, in this study make predictions based on training data and variables that affect the model. The prediction model optimization step by selecting the variable used in the prediction model development is IPS1,2,3,4. The data used in this study are the results of observations from universities. The result of this research is the prediction of Student Achievement Development with Naive Bayes Algorithm based on Ip semester 1.2,3,4 variable and added value are UN rate, Gender, and status stay. From the results of research conducted from the initial stage up to the testing stage the application of the naïve Bayes method for the prediction process of graduate students, it was found that: the application of the naïve Bayes algorithm for model 1 is a model prediction using variable IP students result in an accuracy of 75% and R2 = 68,2%. Model 2 for prediction are used 8 variables namely Nim, Gender, Residence, IPS 1, IPS2, IPS3, IPS4, study period and student status result accuracy of prediction 89% and with a prediction level of R2 = 71,4 %. This certainly improves the performance of the training data efficiency prediction model.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133588526","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: 5

Spatial Intelligent Decision Support System for Coconut Agroindustry Downstream Development: An Overview 椰农产业下游发展空间智能决策支持系统研究综述

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288517

S. Wardah, Marimin Marimin, M. Yani, Taufik Djatna

引用次数: 1

Basic Knowledge Construction Technique to Reduce The Volume of Low-Dimensional Big Data 减少低维大数据量的基础知识构建技术

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288550

G. Karya, B. Sitohang, Saiful Akbar, V. Moertini

{"title":"Basic Knowledge Construction Technique to Reduce The Volume of Low-Dimensional Big Data","authors":"G. Karya, B. Sitohang, Saiful Akbar, V. Moertini","doi":"10.1109/ICIC50835.2020.9288550","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288550","url":null,"abstract":"Big-data has the characteristics of high volume, velocity, and variety (3v) and continues to grow exponentially following the development of the use of world information and communication technology. The main problem in the use of big data is data deluge. The need for technology and big-data storage and processing methods to offset the exponential data growth rate is potentially unlimited, giving rise to the problem of increasing exponential technology requirements as well. In this paper, we propose a new approach in the realm of big-data analysis, through separating the basic-knowledge construction process from the original data into knowledge with much smaller velocity and volume. There are three problems to be solved, such as formulating basic-knowledge, developing a method for constructing basic-knowledge from initial data, and developing a technique for analyzing basic-knowledge into final knowledge. In this study, the technique used to build basic-knowledge is clustering-based. Analysis of basic-knowledge into final-knowledge is limited to the clustering-based analysis process. The main contributions in this paper are basic-knowledge formulation, new big-data analytic architecture, basic-knowledge construction algorithms (DSC4BKC), and analysis algorithms from basic-knowledge (BDAfBK) to final-knowledge. To test our proposed method, we use the BIRCH clustering algorithm with O(n) complexity as the baseline. We also used the artificial test-data generated from WEKA, and the IRIS4D and Diabetes data from the UCI Machine Learning Data Set for validation. Our test shows that the proposed method much more efficient in using data storage (84.69% up to 99.80%), faster in processing (20.84% up to 86.91%, and produces final-knowledge that is similar to the baseline.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123238551","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

Study and Research on the Identification of the Leaves of Indonesian Herbal Medicines Using Manhattan Distance and Neural Network Algorithms 基于曼哈顿距离和神经网络算法的印尼中草药叶片鉴别研究

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288564

Trinugi Wira Harjanti, S. Madenda, J. Harlan, E. Lussiana

{"title":"Study and Research on the Identification of the Leaves of Indonesian Herbal Medicines Using Manhattan Distance and Neural Network Algorithms","authors":"Trinugi Wira Harjanti, S. Madenda, J. Harlan, E. Lussiana","doi":"10.1109/ICIC50835.2020.9288564","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288564","url":null,"abstract":"Indonesia is one country that has enormous potential in the use of medicinal plants as herbal medicines. Utilization or use of plants, especially medicinal plants as a means of healing disease has long been used. However, people in general still have difficulty knowing the types of plants that can be used as herbal medicines. This is due to the limited information and knowledge possessed by the community to identify and identify the use of medicinal plants. This study describes the development of feature extraction in leaf images for the identification of medicinal plants, where the main difficulty in the leaf identification stage is that the morphological (physical leaf shape) and physiological (leaf shape characteristics) are different for each type of leaf. There are three methods proposed in this research, namely the first is the proposed leaf feature model in the form of 16 perimeter point distances to leaf centroid points and seven median line connectors. The second is to develop leaf feature extraction methods and algorithms so that 23 leaf shape features can be generated for each type of medicinal plant. Third, making a prototype identification system or the introduction of medicinal plants based on leaf morphological characteristics. The identification process is carried out using two approaches, namely the Manhattan Distance and Artificial Neural Networksimilar. In the testing phase of the resulting software prototype, 51 types of medicinal plant leaves were used where each type consisted of 10 different leaf images. Based on the trial results, the accuracy rate of identification or recognition of medicinal plants using Manhattan Distance is 99.0196%, and when using Neural Networks, the accuracy rate reaches 100% for training data and 84.31% for testing data.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124823736","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

Gamification of the Lecturer Career Promotion System with a Recommender System 基于推荐系统的讲师职业晋升系统的游戏化

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288541

Tubagus Mohammad Akhriza, Indah Dwi Mumpuni

{"title":"Gamification of the Lecturer Career Promotion System with a Recommender System","authors":"Tubagus Mohammad Akhriza, Indah Dwi Mumpuni","doi":"10.1109/ICIC50835.2020.9288541","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288541","url":null,"abstract":"Gamification aims to bring a pleasant gaming atmosphere to the non-game system, as actors in the system complete their missions. Gamification can be applied in the educator career promotion system (ECPS) implemented in Indonesian higher education institutions (HEIs) intending to motivate educators in the lower-middle career state to move up to a more productive state in international researches and publications. This situation is caused by the ineffective implementation of ECPS in HEI, which usually only relies on extrinsic motivation (income) as an attraction for career advancement. On the other hand, gamified ECPS triggers the intrinsic motivation of educators to undertake many more productive activities voluntarily and happily. The gamified ECPS is equipped with a recommender system to provide the next career states that can be achieved by the educator. Experiments were carried out on educator career data recorded in a government office for HEI service. As a result, besides the proposed model being able to simulate the gamification approach, it is also able to recommend the next-state for educators, and interestingly, as much as 30% of government decisions for educator promotion do not match the results of the RS.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121695695","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

Improving Value-Based E-Government Towards the Achievement of Smart Government 完善基于价值的电子政务，实现智慧政府

2020 Fifth International Conference on Informatics and Computing (ICIC) Pub Date : 2020-11-03 DOI: 10.1109/ICIC50835.2020.9288609

A. Hermanto, Rosziati Binti Ibrahim, G. Kusnanto

引用次数: 4