2022 International Seminar on Application for Technology of Information and Communication (iSemantic)最新文献

Systematic Literature Review on Solving Personalization Problem in Digital Marketing using Machine Learning and Its Impact 利用机器学习解决数字营销中的个性化问题及其影响的系统文献综述

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920387

Aryo Bhaskaraputra, Febriana Sutojo, Adji Nouvaldi Ramadhan, Alexander Agung Santoso Gunawan, Anderies

{"title":"Systematic Literature Review on Solving Personalization Problem in Digital Marketing using Machine Learning and Its Impact","authors":"Aryo Bhaskaraputra, Febriana Sutojo, Adji Nouvaldi Ramadhan, Alexander Agung Santoso Gunawan, Anderies","doi":"10.1109/iSemantic55962.2022.9920387","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920387","url":null,"abstract":"Nowadays, the online shopping cycle has experienced a rapid increase. Customers are spending more time on social media, which leads to them leaving digital footprints on the internet and generating massive amounts of data. With the help of machine learning, the process of gathering and analysing data becomes faster and easier. However, a recent survey shows that most marketing firms lack an effective personalization strategy for reaching their target market. Therefore, the purpose of this study is to find out how machine learning can be used to solve the personalization problem in digital marketing and its impact on future businesses. The authors would like to conduct a Systematic Literature Review (SLR) on machine learning and big data in digital marketing based on previous studies related to this topic. Several previous studies have tried to provide effective ways to improve personalization strategies in digital marketing. These studies show that machine learning can speed up the marketing process with the right target. This is because machine learning can automate, optimize, then collect data, analyse it, and store data from each user. This allows a promotion system that is right on target according to the users' needs. In general, the authors conclude that by using big data, machine learning can help marketing companies to create more effective personalized marketing strategies so that they can be directed to the right consumers. The authors also believe that this topic of personalization should be further researched for future businesses.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126644833","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

Prediction of Nutritional Requirements for Children’s Growth and Adolescents using Machine Learning 利用机器学习预测儿童成长和青少年的营养需求

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920443

A. Hermanto, Agustinus Bimo Gumelar, Evelyn Ongkodjojo, Wilson Christianto Khudrati, Andre Young, Maria Magdalena Ano Djoka, Alvin Julian, Dewa Ayu Liona Dewi, Paul L Tahalele

{"title":"Prediction of Nutritional Requirements for Children’s Growth and Adolescents using Machine Learning","authors":"A. Hermanto, Agustinus Bimo Gumelar, Evelyn Ongkodjojo, Wilson Christianto Khudrati, Andre Young, Maria Magdalena Ano Djoka, Alvin Julian, Dewa Ayu Liona Dewi, Paul L Tahalele","doi":"10.1109/iSemantic55962.2022.9920443","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920443","url":null,"abstract":"In many countries, malnutrition and stunting in children and adolescents are on the rise. They pose a substantial threat to current and near-future health care systems since they are associated with a number of comorbidities. Predictive models for children's and adolescent nutritional needs and outcomes are essential to better understanding its origins and creating suitable prevention approaches. Machine learning models are becoming increasingly useful in this field because of their predictive strength, their ability to model complex, nonlinear interactions between variables, and their capacity to handle high-dimensional data. For non-binary classification problems, the Decision Tree 4.5 machine learning algorithm is a good fit. Decision Tree 4.5 has advantages over similar systems when it comes to handling data in a range of formats. This study examined the nutritional needs of primary school-aged children. Using a decision tree, 7 until 12-year-old elementary school students were tested with a total population of 360 students, and the results showed that 79% of them had normal weight, 12.5% were underweight, and 7.8% were overweight.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122817434","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

An Improved Handwritten Javanese Script Recognition using Adaptive Threshold and Multi-Feature Extraction 基于自适应阈值和多特征提取的改进手写爪哇文字识别

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920462

A. Susanto, Ibnu Utomo Wahyu Mulyono, Christy Atika Sari, E. H. Rachmawanto, De Rosal Ignatius Moses Setiadi

{"title":"An Improved Handwritten Javanese Script Recognition using Adaptive Threshold and Multi-Feature Extraction","authors":"A. Susanto, Ibnu Utomo Wahyu Mulyono, Christy Atika Sari, E. H. Rachmawanto, De Rosal Ignatius Moses Setiadi","doi":"10.1109/iSemantic55962.2022.9920462","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920462","url":null,"abstract":"Image quality greatly affects the object recognition process in the image. If the image quality is not good, the recognition process becomes more difficult. Preprocessing, feature extraction, and classifier are the most important parts of the object recognition process in the image. This process will determine object recognition accuracy, precision, and recall. The preprocessing section plays an important role in carrying out a kind of quality improvement so that objects can be easily identified before feature extraction is carried out. This study proposes using an adaptive thresholding method to enhance recognition accuracy in machine learning-based Javanese scripts. The use of adaptive thresholding is carried out in the image binarization process. By using adaptive thresholding, complement, median filter, and dilation operations can be performed to produce a more natural form and pattern of Javanese script writing. Thus, more accurate feature extraction is obtained. Classification is done with the KNN classifier. With a value of K=3, an increase in accuracy of 5% is obtained compared to the previous method.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114435343","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

Comparative Study of Classification Algorithms for Website Phishing Detection on Multiple Datasets 多数据集网站钓鱼检测分类算法的比较研究

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920475

W. Sarasjati, Supriadi Rustad, Purwanto, H. Santoso, Muljono, Abdul Syukur, Fauzi Adi Rafrastara, De Rosal Ignatius Moses Setiadi

{"title":"Comparative Study of Classification Algorithms for Website Phishing Detection on Multiple Datasets","authors":"W. Sarasjati, Supriadi Rustad, Purwanto, H. Santoso, Muljono, Abdul Syukur, Fauzi Adi Rafrastara, De Rosal Ignatius Moses Setiadi","doi":"10.1109/iSemantic55962.2022.9920475","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920475","url":null,"abstract":"Phishing has become a prominent method of data theft among hackers, and it continues to develop. In recent years, many strategies have been developed to identify phishing website attempts using machine learning particularly. However, the algorithms and classification criteria that have been used are highly different from the real issues and need to be compared. This paper provides a detailed comparison and evaluation of the performance of several machine learning algorithms across multiple datasets. Two phishing website datasets were used for the experiments: the Phishing Websites Dataset from UCI (2016) and the Phishing Websites Dataset from Mendeley (2018). Because these datasets include different types of class labels, the comparison algorithms can be applied in a variety of situations. The tests showed that Random Forest was better than other classification methods, with an accuracy of 88.92% for the UCI dataset and 97.50% for the Mendeley dataset.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"111 3S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122003246","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

Identification of Potato Supply Chain Network Design To Increase Farmer’s Income : Studi cases in Kejajar Village, Wonosobo, Central Java 提高农民收入的马铃薯供应链网络设计识别——以中爪哇Wonosobo Kejajar村为例

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920372

Silvia Dwi Irawati, Ratih Setyaningrum, D. N. Izzhati, R. Yusianto

{"title":"Identification of Potato Supply Chain Network Design To Increase Farmer’s Income : Studi cases in Kejajar Village, Wonosobo, Central Java","authors":"Silvia Dwi Irawati, Ratih Setyaningrum, D. N. Izzhati, R. Yusianto","doi":"10.1109/iSemantic55962.2022.9920372","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920372","url":null,"abstract":"The problem in this research is the supply chain network. This research was aimed to know 1) How was the picture of the current potato supply chain in Kejajar Village, Wonosobo, 2) How was the value chain for the potato commodity, 3) the benefits obtained at each link in the value added that have been given to the potato commodity and 4) the design of a potato supply chain that could provide value added so that it can be profitable for farmers. This research was carried out in Kejajar village, Wonosobo, Central Java. This type of research was descriptive quantitative where the method used in selecting respondents was the snow ball sampling method. The data processing method used descriptive analysis method SCM, Value Chain method, Value Added method, and simulation of chain design using SCM software. The results showed that there were four marketing channels in the potato supply chain in Kejajar, namely channel I farmer-collector-local wholesale market-retail, channel II farmer-collector-local wholesale market-wholesale market outside the province, channel III farmer-collectorwholesale market outside the province, channel IV farmer-local wholesale market-retail. The result of quantitative analysis showed that the highest income was obtained by farmers, but it needed to be underlined that they took the longest time to obtain this income due to the harvest process. Whereas the second largest income was obtained by collectors who got within three days. Value Added analysis using the Hayami method shows that collectors get the biggest profit from selling potatoes to p. local parent that is equal to 89%. The new supply chain design was implemented using powersim by cutting one of the supply chain actors, namely collectors and replacing it with agricultural cooperatives, where with this simulation it can be seen that farmers' income has increased 30%.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122023832","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

Residential Property Price Prediction Using Machine Learning: MakanSETU 基于机器学习的住宅物业价格预测:MakanSETU

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920395

Yash Y Panchal, Manan Mer, Abhiroop Ghosh

{"title":"Residential Property Price Prediction Using Machine Learning: MakanSETU","authors":"Yash Y Panchal, Manan Mer, Abhiroop Ghosh","doi":"10.1109/iSemantic55962.2022.9920395","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920395","url":null,"abstract":"MakanSETU is an emerging and advanced solution in the Real Estate industry. Real Estate Industry is at boom in the 21st century and trading Real Estate has become a great opportunity for Real Estate owners as well as others. The projection of Real Estate industry in business acquisitions is expected to reach 11 trillion USD. However, there is no proper solution to deal with inaccurate prices of properties online. The system proposed in this paper uses Native and new age Machine learning algorithms to predict and validate value of residential properties. Supervised learning is used in the system along with multiple Regressors to obtain the best result. Some of the regression algorithms used are Simple Linear regression, Decision tree regression, Random Forest regression (100 n-trees, 200 n-trees, and 500 n-trees), and Extreme Gradient Boost regression algorithm. The development of this system has followed a series of Data Collection, data handling, data processing, EDA, Feature engineering and Feature selection. The system enables investors to get a fair value of a property. The system is considered successful and ready to implement in the real work.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128556277","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

Systematic Review of Educational Data Mining for Student Performance Prediction using Bibliometric Network Analysis (SeBriNA) 基于文献计量网络分析(SeBriNA)的教育数据挖掘学生成绩预测系统综述

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920477

Eni Heni Hermaliani, A. Z. Fanani, H. Santoso, Affandy Affandy, Purwanto Purwanto, Muljono Muljono, Abdul Syukur, Dedy Setiadi, Fauzi Adi Rafrastara

{"title":"Systematic Review of Educational Data Mining for Student Performance Prediction using Bibliometric Network Analysis (SeBriNA)","authors":"Eni Heni Hermaliani, A. Z. Fanani, H. Santoso, Affandy Affandy, Purwanto Purwanto, Muljono Muljono, Abdul Syukur, Dedy Setiadi, Fauzi Adi Rafrastara","doi":"10.1109/iSemantic55962.2022.9920477","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920477","url":null,"abstract":"Data mining has emerged as a way of working with large amounts of data in various fields of technology that produce data types quickly and correctly. In particular, emerging technologies such as data mining (DM), machine learning (ML), and big data are utilized to predict student performance. This paper uses bibliometrics to give a complete picture of the studies that have been done on how DM technologies are used in Educational Data Mining (EDM). The study aims to determine which DM techniques are most often used to predict student performance and how the field of DM for education to predict student performance has changed over time. To investigate the topic, we used both qualitative and quantitative methods. We used the Scopus database to find relevant articles published in scientific journals, and this study includes 130 articles published between 2015 and 2021. Also, we used the bibliometric library and bibliophily features for the bibliometric analysis. Our findings show that various EDM technologies are used at each stage of student performance prediction. Several supervised ML algorithms are used for prediction. The bibliometric analysis shows that EDM for predicting student performance is a proliferating field of science. Scientists from all over the world are keen to conduct research and collaborate in this interdisciplinary scientific field.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129316456","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

CTR Prediction of Advertisements using Decision Trees based Algorithms 基于决策树算法的广告点击率预测

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920363

Mayur Rattan Jaisinghani, Chirag Lundwani, Orijeet Mukherjee, Neeharika Nagori, Prerna. B. Solanke

引用次数: 0

A Conceptual Paper: Model of Integrated Surveillance System of Tuberculosis Based on the Internet of Things (IoT) for Accelerating Indonesia Free Tuberculosis in 2030 一篇概念论文:基于物联网(IoT)的结核病综合监测系统模型，以加速印度尼西亚在2030年消除结核病

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920390

S. Handayani, R. Hinchcliff, Farrikh Al Zami, Z. Hasibuan

{"title":"A Conceptual Paper: Model of Integrated Surveillance System of Tuberculosis Based on the Internet of Things (IoT) for Accelerating Indonesia Free Tuberculosis in 2030","authors":"S. Handayani, R. Hinchcliff, Farrikh Al Zami, Z. Hasibuan","doi":"10.1109/iSemantic55962.2022.9920390","DOIUrl":"https://doi.org/10.1109/iSemantic55962.2022.9920390","url":null,"abstract":"Tuberculosis (TB) remains a public health problem in the world. Second disease causing death after Covid-19. In 2020, case findings of TB cases in Indonesia slightly decreased compared to 2019, from 568.987 to 351.936 cases. To combat the disease, Indonesia has adopted the End TB program, targeting to reduce TB incidence to 65 cases per 100,000 population by 2030. At the same time, many challenges need to be overcome, such as low coverage of TB treatment, delay of diagnosis and treatment, and other factors associated. This paper aims to propose a model of an Integrated Surveillance System of Tuberculosis Based on the Internet of Things (IoT). The research will employ the End-to-End Life Cycle Automation System approach. Data collection will use two sources of data, primary and secondary data. The various research instruments (Questionnaire, interview guidelines, checklist observation, and IoT) will be used to capture primary data in this research. Secondary data sources will use reports of TB in multilevel (district/city, province, and national level), medical records of TB patients, news of TB prevention and treatment programs, demography and geography information, and poverty level. The data will produce a model of an integrated surveillance system. The field test will be conducted on the design and continuously improved based on the result. The information provided by the system will be available on a dashboard as a data visualization that can be easily accessed. This system will provide rapid and precise analysis to help the government achieve the Free TB agenda 2030. The system will help develop an effective and efficient TB prevention program in the community for health services based on their need.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115915466","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

Optimization Of Infant Birth Predictions During The Covid-19 Pandemic Using The Particle Swarm Optimization Based K-Nn Algorithm Method 基于粒子群优化的K-Nn算法优化Covid-19大流行期间婴儿出生预测

2022 International Seminar on Application for Technology of Information and Communication (iSemantic) Pub Date : 2022-09-17 DOI: 10.1109/iSemantic55962.2022.9920468

Ayu Hernita, M. Soeleman, A. Zainul Fanani

引用次数: 1