cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota tangerang,
Banten
INDONESIA
Tech-E
Published by Universitas Buddhi Dharma
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Tech-E dikembangkan dengan tujuan menampung karya ilmiah Dosen dan Mahasiswa, baik hasil tulisan ilmiah maupun penelitian yang berupa hasil studi kepustakaan.
Arjuna Subject : -
Articles 7 Documents
 1 
Search results for , issue "Vol. 5 No. 2 (2022): Tech-E" : 7 Documents clear
Pengembangan Sistem Arsip Digital dalam Meningkatkan Pelayanan Publik Menggunakan Extreme Programming Arisantoso Arisantoso; Jefri Rahmadian; Harriansyah Harriansyah; Dwi Sidik Permana; Imam Ahmad
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.941

Abstract

The process of filing a letter is usually done by recording it in an archive book, then the letter is stored in a place or filing cabinet that has been provided. This is considered not optimal, in providing correspondence services that are needed by the community. Due to the limited staff working in the village office, the process takes a long time. This research discussed how to create and design digital archive applications and correspondence starting from data collection methods (interviews, observations and documentation) using extreme programming development methods, system design using UML with Use case Diagram design model, and CRC Card. TRITAM model test results that have been conducted involving 16 respondents showed results of 82.56% with good criteria. From the results of these tests showed good results for prototype users for the mail filing system.
The Stocks Saving Simulation based on Historical Data Web-based Mesakh Septiadi Simijaya; Aditiya Hermawan
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.622

Abstract

This study aims to simulate the calculation of saving stocks based on historical data for the past 10 years for the period January 2010 - January 2020, because saving simulations based on historical data are still very rare, so a simulation application for saving stock calculations based on historical data is made that can help customers. investors in simulating the calculation of saving stocks so that it can be used as learning to determine how to save the right way that can produce a good return. This application is created using a waterfall model. From the application made, it is expected to know the good return results of the 10 issuers used in this study, and in making basic applications on user requirements obtained through several respondents through online questionnaires and processed with requirement elicitation techniques. With the application of a stock saving calculation simulation application, it is hoped that it can be a lesson for investors in saving stocks and can also be a lesson for potential investors and can also make it easier for investors to do calculations because the calculation process is computerized.
Comparison of Seven Machine Learning Algorithms in the Classification of Public Opinion Sri Redjeki; Setyawan Widyarto
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.1046

Abstract

Sentiment analysis is one way that is widely used to identify the beginning of public opinion in various fields of life which are associated with very massive and a lot of information through social media. This study aims to compare several algorithms in machine learning to see the best ability in sentiment classification. The research dataset uses a dataset of public opinion related to tourism in Indonesia. The number of datasets used is 10,228 twitter data that have been cleaned and labelled. The machine learning algorithm used is Logistic Regression, KNN, AdaBoost, Decision Tree, SVM, Random Forest and Gaussian. The seven algorithms for sentiment classification from the Twitter public opinion each produce a Gaussian accuracy of 0.52; SVM 0.78; KNN 0.98; Logistic Regression, Random Forest, Decision Tree, AdaBoost of 0.99. This study shows that the selection of the right machine learning algorithm will have a very good impact on the classification of public opinion through social media
Perancangan Wifi cerdas untuk destinasi wisata yang terintegrasi di wisata cerdas Ruci Meiyanti; Muhammad Yusuf Bagus Rasyiidin; Fachroni Arbi Murad; Riri Fajriah
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.766

Abstract

The use of public wireless networks and not integrated between tourist destinations is an obstacle for visitors when moving to other tourist destinations. This is an obstacle to internet access in the tourism era 4.0. To overcome this, smart wifi is needed. Smart wifi is an integrated internet network access among other tourist destinations so as to expand the connectivity and interaction of tourist visitors. This study used the NDLC method. The result of this research was the design of smart wifi which was built on a wireless network with captive portal technology and barcode scanning. Smart wifi contributes to the development of digitalization in smart tourism, making it easier for visitors to get internet access in various tourist destinations.
Integrating Analysis of Quality Management of Higher Education: Analytical Hierarchy Process and Multiple Linear Regression Satria Abadi; Citrawati Jatiningrum; Samsurijal Hasan; Riki Riki
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i2.1114

Abstract

The study focus on to determine factors of the quality management on the higher education and analysis the effect of important factors of quality management. Factors of quality management in this study which covering of human resources, facilities and infrastructure, leadership, and organization. Sample study using students from several private universities in Lampung Province. Analysis method using integrating analysis by Analytical Hirarchy Method (AHP) and Multiple Regression Linear (MLR). Correlation test using the product moment stated quality management of higher education have a strong relationship to human resources, has a moderate relationship with infrastructures, and a weak relationship to the leadership and organizing. The result by multiple regression linear method reveal that significant effect on human resources, facilities and infrastructure, leadership and organizational on Quality Management in higher education. While, AHP method suggestion the result that the most important in Quality Management of Higher Education is a human resources owned by a higher education. This evidence contribute to the decision makers in universities which is priority and have to improve the quality of higher education management
Classification of Mint Leaf Types Based on the Image Using Euclidean Distance and K-Means Clustering with Shape and Texture Feature Extraction Trinugi Wira Harjanti; Hari Setiyani; Joko Trianto; Yuri Rahmanto
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.940

Abstract

Mint is a plant that has many benefits and uses. However, some people are not familiar with the types of mint leaves because they cannot tell the difference. Actually, if you look closely, mint leaves have their own characteristic shape and texture. However, most people judge mint leaves to have a shape similar to other leaves so it is difficult to tell them apart. This paper aims to classify the types of mint leaves using the Euclidean distance algorithm and K-Means clustering with shape and texture feature extraction. The K-Means Clustering Algorithm functions as a segmentation so that the image to be classified can be separated from other objects. In the feature extraction process, metric and eccentricity parameters are used. Meanwhile, for texture feature extraction, use the parameters in the Gray Level Co-occurence Matrix (GLCM). Furthermore, the classification process uses the Euclidean Distance algorithm which has a function to represent the level of similarity between two images by taking into account the distance value from the identified image. Based on the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, the precision value is 82%, recal is 84% ​​and accuracy is 83%.
Application of Data Mining for Student Department Using Naive Bayes Classifier Algorithm Yohana Tri Utami; Debby Alita; Ade Dwi Putra
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.1012

Abstract

SMAN 02 Negeri Agung does not have a system that can assist schools in determining majors. The problem that occurs is that SMAN 02 Negeri Agung, when doing majors, still uses existing data, for example, using a majoring interest questionnaire, there are questions about the interests that students want, and the values of their junior high school report cards, which consist of Indonesian, Mathematics, Science, Social Studies, and English. However, there are still many students who choose majors not based on their interests or historical grades, such as following friends' choices. This can hinder student academic activities in the future, which will affect the value and development of student potential. With this major system, it is hoped that it can help schools and students minimize errors in determining and choosing a major. Based on the problems described above, the authors want to apply the Naïve Bayes method, which will produce a high level of accuracy in determining new student majors more effectively and efficiently.

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol. 9 No. 1 (2025): The Tech-E Journal Vol. 9 No. 1 publishes research papers in such informatics: Vol. 8 No. 2 (2025): The Tech-E Journal Vol. 8 No. 2 publishes research papers in such informatics: Vol. 8 No. 1 (2024): The Tech-E Journal Vol. 8 No. 1 publishes research papers in such informatics: Vol. 7 No. 2 (2024): The Tech-E Journal Vol. 7 No. 2 publishes research papers in such informatics: Vol. 7 No. 1 (2023): The Tech-E Journal Vol. 7 No. 1 publishes research papers in such informatics: Vol. 6 No. 2 (2023): The Tech-E Journal Vol. 6 No. 2 publishes research papers in such informatics: Vol. 6 No. 1 (2022): Tech-E Vol. 5 No. 2 (2022): Tech-E Vol 5 No 1 (2021): Tech-E Vol 4 No 2 (2021): Tech-E Vol 4 No 1 (2020): Tech-E Vol 3 No 2 (2020): Tech-E Vol 3 No 1 (2019): Tech-E Vol 2 No 2 (2019): Tech-E Vol 2 No 1 (2018): Tech-E Vol 1 No 2 (2018): Tech-E Vol 1 No 1 (2017): Tech-E Vol 1 No 1 (2017): Tech-E More Issue