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The Determinant Analysis of the Utilization of Google Classroom as the E-Learning Facility in Yogyakarta Nahdlatul Ulama University
Get PDFPurpose: to find out what factors cause lecturers and students to adopt and refuse to adopt Google Classroom as a means of E-Learning at the Yogyakarta Nahdlatul Ulama University.Design/methodology/approach: This research was conducted using a qualitative approach to get the meaning of a phenomenon. The Innovation Diffusion Theory is used as the basis for this research to find out how the role of Google Classroom as a means of E-Learning and how the suitability of Google Classroom as a means of E-Learning at Nahdlatul Ulama University Yogyakarta.Findings/result: the factors of adoption consisted of synchronizing the students and lecturers’ email with Google, integrating other Google features, making an efficiency of fund, time and place, finding an alternative way for e-learning, evaluating the facilities, filling the teaching and learning process, communicating between the lecturers and students, and knowing the lateness of submitting assignment. Besides, there were some factors of rejection such as the limited ownership of electronic media, limited knowledge, Internet connection, and no attendance facilityOriginality/value/state of the art: The factors of lecturers and students are adopt and refuse to adopt Google Classroom as a means of E-Learning at Nahdlatul Ulama University Yogyakarta
Data Mining for Determining The Best Cluster Of Student Instagram Account As New Student Admission Influencer
Get PDFPurpose: This study aims to apply the web data extraction method to extract student Instagram account data and the K-Means data mining method to perform clustering automatically to determine the best cluster of students\u27 Instagram accounts as influencers for new student admissions.Design/methodology/approach: This study implemented the web data extraction method to extract student Instagram account data. This study also implemented a data mining method called K-Means to cluster data and the Silhouette Coefficient method to determine the best number of clusters.Findings/result: This study has succeeded in determining the seven best student accounts from 100 accounts that can be used as influencers for new student admissions with the highest Silhouette Score for the number of influencers selected between 5-10, which is 0.608 of the 22 clusters.Originality/value/state of the art: Research related to the determination of the best cluster of students\u27 Instagram accounts as new student admissions influencers using web data extraction and K-Means has never been done in previous studies
Recurrent Neural Network With Gate Recurrent Unit For Stock Price Prediction
Get PDFStock price prediction is a solution to reduce the risk of loss from investing in stocks go public. Although stock prices can be analyzed by stock experts, this analysis is analytical bias. Recurrent Neural Network (RNN) is a machine learning algorithm that can predict a time series data, non-linear data and non-stationary. However, RNNs have a vanishing gradient problem when dealing with long memory dependencies. The Gate Recurrent Unit (GRU) has the ability to handle long memory dependency data. In this study, researchers will evaluate the parameters of the RNN-GRU architecture that affect predictions with MAE, RMSE, DA, and MAPE as benchmarks. The architectural parameters tested are the number of units/neurons, hidden layers (Shallow and Stacked) and input data (Chartist and TA). The best number of units/neurons is not the same in all predicted cases. The best architecture of RNN-GRU is Stacked. The best input data is TA. Stock price predictions with RNN-GRU have different performance depending on how far the model predicts and the company\u27s liquidity. The error value in this study (MAE, RMSE, MAPE) constantly increases as the label range increases. In this study, there are six data on stock prices with different companies. Liquid companies have a lower error value than non-liquid companies
Cluster Analysis of Hospital Inpatient Service Efficiency Based on BOR, BTO, TOI, AvLOS Indicators using Agglomerative Hierarchical Clustering
Get PDFPurpose: The research proposed an approach for grouping hospital inpatient service efficiency that have the same characteristics into certain clusters based on BOR, BTO, TOI, and AvLOS indicators using Agglomerative Hierarchical Clustering.Design/methodology/approach: Applying Agglomerative Hierarchical Clustering with dissimilarity measures such as single linkage, complete linkage, average linkage, and ward linkage.Findings/result: The experiment result has shown that ward linkage was given a quite good score of silhouette coefficient reached 0.4454 for the evaluation of cluster quality. The cluster formed using ward linkage was more proportional than the other dissimilarity measures. Ward linkage has generated cluster 0 consists of 23 members, cluster 1 consists of 34 members, while both of cluster 2 and 3 consists of only 1 member respectively. The experiment reported that each cluster had problems with inpatient indicators that were not ideal and even exceeded the ideal limit, but cluster 0 generated the ideal BOR and TOI parameters, both reached 52.17% (12 of 23 hospital inpatient) and 78.36% (18 of 23 hospital inpatient) respectively.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce more proportional, representative and quality clusters in mapping hospital inpatient service efficiency that have the same characteristics into certain clusters using Agglomerative Hierarchical Clustering Method compared to the K-means Clustering Method which is often trapped in local optima.
Geographic Information System Design for Bridge Management in Brebes Regency
Get PDFPurpose: geographic information system (GIS) design to monitoring and management of bridges that have geographic references, as well as a tool for planning activity programs (maintenance, rehabilitation, strengthening or replacement) of bridges.Design/methodology/approach: waterfallFindings/result: web-based geographic information system (GIS) for bridge management in Brebes RegencyOriginality/value/state of the art: this research does not only focus on site search as the main strength of GIS but maximizes bridge inspection activities as an important part of the bridge management system as a tool for planning bridge construction and maintenance activitie
Automated Website Monitoring System Using Web Scraping and Raspberry Pi
Get PDFPurpose: Create a system to monitor website availability automatically using web scraping and raspberry piDesign/methodology/approach: This system successfully checks website availability using various ISPs with an accuracy of more than 90%.Findings/result: This system successfully checks website availability using various ISPs with an accuracy of more than 90%.Originality/value/state of the art: The contribution of this research is to create systems and agents that collaborate automatically to check website availability. Tujuan: Membuat sebuah sistem untuk melakukan pemantauan ketersediaan situs web secara otomatis menggunakan web scraping dan raspberyy piPerancangan/metode/pendekatan: Pada penelitian ini dibuat sebuah sistem utama sebagai pusat data dan beberapa agent menggunakan raspberry pi. Sistem utama dibangun menggunakan codeigniter dan web scraping di raspberry pi dilakukan menggunakan node js serta REST API untuk komunikasi antara agent dan sistem utama.Hasil: Sistem ini berhasil melakukan pengecekan ketersediaan situs web menggunakan berbagai ISP dengan keakuratan lebih dari 90%.Keaslian/ state of the art: Kontribusi penelitian ini adalah membuat sistem dan agen yang berkolaborasi secara otomatis untuk mengecek ketersediaan situs web.
K-Means Algorithm and Binary Search on FiBuSI
Get PDFPurpose: Create an application called FiBuSI (Find Business and Stock Investment) using the k-means algorithm and binary search for data search features. This application is intended for entrepreneurs and investors where they can interact with each other to build a joint business.Method: Using the RAD (Rapid Application Development) Method which focuses on system testing based on user experience related to Blackbox Testing using the Katalon Studio tools for testing functions on the FiBuSI application.Result: Based on the results of testing the FiBuSI application which focuses on the success of application functions and algorithm implementation, that each application function is successfully executed (PASSED) based on testing using the Katalon Studio tools. Meanwhile, testing the k-means algorithm (data filter) and binary search (search for letter data) was also successfully carried out by testing it directly by the user on the FiBuSI application and also using the results from the Katalon Studio tools.State of the art: Based on several studies that have been done previously related to the use of the k-means algorithm and binary search that this algorithm is carried out on 2 different features but in 1 application for business data search. In concept, the FiBuSI application focuses on bringing together entrepreneurs and investors in one platform
Evaluation Of Jogja Application Success From User\u27s Perspective Using Development of Delone And Mclean Models To Support The Realization Of The Smart Province
Get PDFPurpose: This study aims to measure success and determine the factors that support or hinder the success of the Jogja Istimewa application.Methodology: This study uses a modified DeLone and McLean Model 2003. The data used are primary data obtained from interviews with the DISKOMINFO and answers to 125 users of the Jogja Istimewa application as respondents in a distributed questionnaire. The results of the questionnaire were processed using SPSS to test the validity, reliability and normality of the data. After that, the data is processed using Structural Equation Modeling (SEM) to test the inner model and outer model which includes hypothesis testing.Result There are nine hypotheses tested using the SEM model. Nine hypotheses were proposed, it was stated that five hypotheses were accepted and four other hypotheses were rejected. the Jogja Istimewa application has a high success rate. The factors that are stated to influence the success of the Jogja Istimewa application are Information Quality, Service Quality, System Quality and User Satisfaction. The factors that are stated to hinder the success of the Jogja Istimewa application are Format of Output and Reliability in the Information Quality variable, the System Quality variable in the Language indicator, and the Charges for System Use indicator on the Intention to Use variable.Value: Based on previous research, this study has a fairly similar reference but different case studies, indicators, and conceptual models to test hypotheses in addition to knowing the factors that hinder and support the success of the Jogja Istimewa application
The Design of an Android-Based Integrated Islamic Boarding School Information System as an Impact of Covid-19
No full textPurpose: This study aims so that all information that is spread through various information systems in Islamic boarding schools can be known through an integrated systemDesign/methodology/approach: The method of developing the system using the prototype methodFindings/result: Android-based integrated information systemOriginality/value/state of the art: System integration in Islamic boarding schools that is carried out is the integration process of various systems that previously existe
Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network
Get PDFOrange is a type of fruit that is easily found in Sambas Regency. The types that are widely sold are Siam oranges, madu susu and susu. Each type of orange has a different quality and a different price. The price difference often results in fraud committed by traders against buyers to the detriment of the buyer. This is because differentiating types of oranges based on the appearance of the fruit does not have a standard. Therefore, in this study, a citrus fruit classification system was created based on images by implementing deep learning. The method of deep learning used in this research is Convolutional Neural Network (CNN) with AlexNet architecture. The types of oranges that will be observed are madu oranges, madu susu, and siam. The data used are 2250 images of oranges with each class totaling 750 images with a size of 227x227 pixels. The training data is 1575 images and the test data is 675 images. The training is carried out with a total of 10 epochs and each epoch will produce a model. System testing is carried out based on the model generated in the training process. Each model will be observed results in the form of accuracy which is calculated using a confusion matrix. The most optimal model was generated from training in epoch the 9th which resulted in an accuracy of 94.81%