1 10 9 https://repository.horizon.ac.id/files/original/5b6a9ea916b1869024602a130c233b7a.pdf f19f11a7336e5a70d5e182767b0ff38d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting Airline Passenger Satisfaction with Classification Algorithms Subject The topic of the resource Classification, Airlane, Satisfaction, Predicting, Random Forest Description An account of the resource Airline businesses around the world have been destroyed by Covid-19 as most international air travel has been banned. Almost all airlines around the world suffer losses, due to being prohibited from carrying out aviation transportation activities which are their biggest source of income. In fact, several airlines such as Thai Airways have filed for bankruptcy. Nonetheless, after the storm ends, demand for air travel is expected to spike as people return for holidays abroad. The research is aimed at analyzing the competition in the aviation industry and what factors are the keys to its success. This study uses several classification models such as KNN, Logistic Regression, Gaussian NB, Decision Trees and Random Forest which will later be compared. The results of this study get the Random Forest Algorithm using a threshold of 0.7 to get an accuracy of 99% and an important factor in getting customer satisfaction is the Inflight Wi-Fi Service. Creator An entity primarily responsible for making the resource B.Herawan Hayadi 1,*, Jin-Mook Kim 2, Khodijah Hulliyah 3, Husni Teja Sukmana 4 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/2cd5ea3fa50d0d738506650cc9514d5e.pdf b52ef20628ea4f18b0feef1ca6f39cf4 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of the Effect of Website Sales Quality on Purchasing Decisions on e-commerce Websites Subject The topic of the resource CatModel, Hyperparameter, E-Commerce, Sales Quality, RMSE Description An account of the resource Both Web-based information infrastructure and marketing activities are dealt with by business-to-consumer electronic commerce. Centered on information systems and marketing literature, this review suggests a research model to explain the effect on consumer loyalty of the dimensions of website quality (system quality, information quality, and service quality). In order to verify the validity of the calculation model, confirmatory factor analysis was performed, and the structural model was also examined to investigate the correlations hypothesized in the study model. In this study, by comparing the Hyperparameter class & Catboost class we can find a number of distributions of individual absolute errors which can be considered as a fairly important factor in the analysis of sales quality on e-commerce websites. Creator An entity primarily responsible for making the resource Deden Hardan Gutama 1,*, Izzatul Umami 2, Pujo Hari Saputro Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/da91041805076efec6062f51785d66db.pdf f385b93805a5d164891a7bc208b1e6c7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource With topological data analysis, predicting stock market crashes Subject The topic of the resource TDA, Market Crashes, Stock Detection, Topology Description An account of the resource We are investigating the evolution of four big US stock market indexes' regular returns after the 2000 technology crash and the 2007-2009 financial crisis. Our approach is based on topological data processing (TDA). To identify and measure topological phenomena occurring in multidimensional time series, we use persistence homology. We obtain time-dependent point cloud data sets using a sliding window, which we connect a topological space for. Our research indicates that a new method of econometric analysis is offered by TDA, which complements the traditional statistical tests. The tool may be used to predict early warning signs of market declines that are inevitable Creator An entity primarily responsible for making the resource Nugroho Agung Prabowo 1,* , R Arri Widyanto 2 , Mukhtar Hanafi 3 , Andi Widiyanto 4 , Bambang Pujiarto 5 , Meidar Hadi Avizenna Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/d85a2c8f8ecc86cb3e12bf4e6fc92bdb.pdf 7ff5d1f805422b36cb3a63624fcb37d0 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Modelling Customers Lifetime Value For Non-Contractual Business Subject The topic of the resource CLV, CRM, Business, E-Commerce, Customer Transaction Prediction Description An account of the resource Because customer equity is becoming increasingly important in today's business environment, many companies are focusing on customer loyalty and profitability as a way to grow market share. A company's successful Customer Relationship Management (CRM) begins with identifying true value and customer loyalty, as customer value can provide basic information that can be used to spread more targeted and personalized marketing. Customer lifetime value (CLV) is used in this document to segment consumers in non-contracted companies. The findings of this research are very promising. CLV has successfully analyzed and produced a fairly strong assumption about the value possessed by each customer whether they will make a return transaction or not. Creator An entity primarily responsible for making the resource Riyanto 1,* , Abdul Azis Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLIST Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/993d22ca363eeb7991f00b03ee873293.pdf 87999faed4c1e6b80ca217d5e8105dc9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Implementing the Expected Goal (xG) Model to Predict Scores in Soccer Matches Subject The topic of the resource xG model, Soccer match, Distance, Predict scores. Description An account of the resource Football is a sport that has the most fans in the world. What makes sebak patterns so popular are their uncertain and unpredictable results. There are many factors that affect the outcome of a football match, including strategy, skill, and even luck. Therefore, guessing the results of a soccer match is an interesting problem. All shots are grouped into sections on the playing field and theoretical goal scores are applied to each area. The factors analyzed are: distance of shot from goal and angle of shot in relation to goal. When calculating xG, it is recommended that the distance and angle of the shot are important. The combination of the two xG factors is better calculated than each variable only. In addition, this xG check has been able to relatively accurately identify the mid-table teams that score and concede goals. Creator An entity primarily responsible for making the resource Izzatul Umami 1,* , Deden Hardan Gutama 2 , Heliza Rahmania Hatta Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/508d7a3427f3a1607724f155edd59e5b.pdf 1ffdf77d9e6e76b80392674db65c422e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Implementation of the Convolutional Neural Network Method to Detect the Use of Masks Subject The topic of the resource Facemask Detection, CNN, OpenCV, COVID-19 Description An account of the resource The planet has been taken seriously by Coronavirus disease since the end of 2019. Wearing a mask in public is one of the key means of security for people. Furthermore, certain public service vendors only require clients to use the service if they wear masks correctly. However, based on image processing, there is relatively little study into the discovery of face masks. Almost everybody appears to wear a mask in order to shield themselves from the COVID-19 Pandemic. Monitoring whether people in the crowd wear face masks at the most public place, such as malls, museums, parks, has become increasingly important. The development of an AI approach to deal with if the person wears a face mask and their entrance would significantly assist society. In this article, we will use a deep learning model that is then combined with Keras / TensorFlow & OpenCV, respectively CNN or Confusional Neural Network. The accuracy of the research results obtained from this model is more than 96%. Creator An entity primarily responsible for making the resource Arbiati Faizah 1,* , Pujo Hari Saputro 2 , Augusta Jannatul Firdaus 3, Raden Nur Rachman Dzakiyullah Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/b88e4fb775fe696b8fd47c6e101e5ea0.pdf 73dd922bc6e4922653da58833c8abc5e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of K-Means Clustering & Logistic Regression on University data to differentiate between Public and Private University Subject The topic of the resource K-Means, Regression, Clustering Description An account of the resource The development of advances in educational methods has developed in the last few decades. especially at the higher education level such as college. The rising interest of students in pursuing their higher education has caused the sector to be split into two sectors, both private and public universities. This difference raises several questions recently about how the two types differ in carrying out the educational process. whether there is a difference in terms of cost, service or quality, we really can't tell exactly. For this study, we will try to use the K-Means Clustering & Logistic Regression to group the University into two groups, Private and Public and then compare the two model accuracy. The results of this study show that the results obtained from the K-Means clustering model (22%) are much lower than the Logistic Regression model (91%). Creator An entity primarily responsible for making the resource Adhien Kenya Anima Estetikha 1,* , Deden Hardan Gutama 2 , Musthofa Galih Pradana 3 , Dhina Puspasari Wijaya Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLIST Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/69e8dbac9f0443cc8721b587dc72d8e2.pdf 0d64a4021a76ba5635513833071cdf68 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer Subject The topic of the resource K-nearest neighbors, Min-Max Normalization, Z-Score Normalization, Breast Cancer Description An account of the resource The purpose of this study was to examine the results of the prediction of breast cancer, which have been classified based on two types of breast cancer, malignant and benign. The method used in this research is the k-NN algorithm with normalization of min-max and Z-score, the programming language used is the R language. The conclusion is that the highest k accuracy value is k = 5 and k = 21 with an accuracy rate of 98% in the normalization method using the min-max method. Whereas for the Z-score method the highest accuracy is at k = 5 and k = 15 with an accuracy rate of 97%. Thus the min-max normalization method in this study is considered better than the normalization method using the Z-score. The novelty of this research lies in the comparison between the two min-max normalizations and the Z-score normalization in the k-NN algorithm. Creator An entity primarily responsible for making the resource Henderi 1 , Tri Wahyuningsih 2,* , Efana Rahwanto Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLIST Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/103459c46216601d63b9bc19a4513571.pdf b9ace516c387fddc1fa8e56ecb722f04 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 4 NO 1 MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of Security Signing Data Authentication Integrity in Combination of Digest And AES Message Algorithm Subject The topic of the resource Message-digest, AES, SSO, Encryption, Security Description An account of the resource Online information systems with the Single Sign-On (SSO) model are currently widely used by many companies. Single Sign-On (SSO) is an independent authentication model. This system runs on the Hypertext Transfer Protocol (HTTP) protocol. Sending data or information without security is at risk of eavesdropping on information by the authorities. This study aims to compare the combination of Message-Digest and Advanced Encryption Standard (AES) algorithms to improve data security by modifying dynamic keys. The test results show that in each execution the user name and password with the MD5 algorithm are always the same while in the AES algorithm the results are always different so it is safe from replay attacks. So that the Advanced Encryption Standard (AES) algorithm can secure data through the Single-Sign-On authentication process with high-security accuracy. Creator An entity primarily responsible for making the resource Rasna 1,*, Irjii Matdoan 2, Sitti Nur Alam Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT