p-Index From 2014 - 2019
0.408
P-Index
This Author published in this journals
All Journal Telematika
Sulistyo, Meianto Eko
Jurusan Teknik Informatika

Published : 2 Documents
Articles

Found 2 Documents
Search

UTILIZATION OF MOODLE WEB SERVICE BASED SYSTEM TO SYSTEM WITH SIAKAD AND SSO UNS Saptono, Ristu; Sulistyo, Meianto Eko; Susilo, Joko
Telematika Vol 13, No 2 (2016): Telematika Edisi Juli 2016
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.402 KB)

Abstract

The development of information technology in education allows for integration between systems so every system can be optimized. Elearning, SIAKAD, and SSO UNS are education system in UNS (Universitas Sebelas Maret) but they are not integrated yet. Course data for elearning is still manual and SSO which can not be used to log into SIAKAD. In this study the integration of elearning, SIAKAD, and SSO utilizing REST web service and exchange data using JSON. As a result, the integration of additional system must use a bridge application as a customizer data between elearning and SIAKAD. While the results of the testing to include one course, 40 lecturers, and 40 students, including automatically enroll is 60.22 seconds, while the time required for unenroll lecturers and students is 2:13 seconds. To enroll course, lecturers and students when there are previously data was 28.5 seconds.
TEXT CLASSIFICATION USING NAIVE BAYES UPDATEABLE ALGORITHM IN SBMPTN TEST QUESTIONS Saptono, Ristu; Sulistyo, Meianto Eko; Trihabsari, Nur Shobriana
Telematika Vol 13, No 2 (2016): Telematika Edisi Juli 2016
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (296.118 KB)

Abstract

Document classification is a growing interest in the research of text mining. Classification can be done based on the topics, languages, and so on. This study was conducted to determine how Naive Bayes Updateable performs in classifying the SBMPTN exam questions based on its theme. Increment model of one classification algorithm often used in text classification Naive Bayes classifier has the ability to learn from new data introduces with the system even after the classifier has been produced with the existing data. Naive Bayes Classifier classifies the exam questions based on the theme of the field of study by analyzing keywords that appear on the exam questions. One of feature selection method DF-Thresholding is implemented for improving the classification performance. Evaluation of the classification with Naive Bayes classifier algorithm produces 84,61% accuracy.