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Journal : Jurnal TAM (Technology Acceptance Model)

ANDROID-BASED APPLICATION OF LPPM STMIK PRINGSEWU ARCHIVES TO IMPROVE DATA INTEGRATION OF LECTURER PERFORMANCE Trisnawati Trisnawati; Sodikin Sodikin; M. Agus Badruzaman Al Khoir; Muhamad Muslihudin
Jurnal TAM (Technology Acceptance Model) Vol 12, No 2 (2021): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v12i2.1059

Abstract

In the era of globalization, advances in information and communication technology affect human activities which have undergone many changes and developments. The development of science in the field of technology today has created new applications with more efficient technology productivity and costs. The rapid development of technology today has affected the work in various organizations. In order to support the performance in an organization, it is not only required the quality of reliable human resources but also required appropriate technological advances. All of this aims to provide convenience for technology users in carrying out their work. Applications that are built can be used via mobile phones or laptops based on browsers to display information in the form of text and images which are accessed using the internet. This application development uses waterfall model information system design and research flow uses Fishbone Mapping Chart model. The development of an Android-based LPPM STMIK Pringsewu Archive Data Application will be integrated with Lecturer Data so that it will facilitate monitoring and archiving Lecturer Data and increasing the Lecturer Performance Index at STMIK Pringsewu. Integrated data focuses on activities related to research and community service.
THE NAÏVE BAYES METHOD AS A MEASUREMENT MODEL EFFECTIVENESS OF ONLINE LEARNING Siti Mukodimah; Muhamad Muslihudin; Suyono Suyono; Trisnawati Trisnawati
Jurnal TAM (Technology Acceptance Model) Vol 13, No 2 (2022): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v13i2.1283

Abstract

The rapid development of technology requires the world of education to be able to take advantage of its positive impact, making various new innovations by utilizing technology to support education such as online learning in the learning process amid the Covid-19 pandemic. Changes in learning methods which occur suddenly from conventional learning methods or directly face-to-face switching to distance learning methods or using online learning media greatly impact and influence students who come from underprivileged families and students who are in remote areas where internet access and inadequate infrastructure. This study aims to create a classification model for measuring the effectiveness of online learning in Pringsewu using the classification method. The classification method is used to classify data based on the nature of the data which each class already recognizes. There are various methods which can be used to classify data using the Naïve Bayes method. The results of the research conducted are a classification for measuring the effectiveness of online learning in Pringsewu. The feasibility of the model obtained is supported by the results of the analysis of the Naïve Bayes model which has an accuracy rate of 98.48%, an AUC value of 0.995, a precision level of 98.17% and a 100% recall. In this study, the level of accuracy of the performance of the model used reached values above 90%. In addition, the AUC value of the two methods used is also more than 90% which is a value that is categorized as Excellent Classification. Further research can be carried out using other different parameters such as Economic Capability, Regional Location, Connectivity Mode, Digital Literacy, and others. In addition, this research was conducted only from the student's point of view. Inclusion of school opinion in future research will be useful in determining the exact effectiveness of online learning.