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 denpasar,
Bali
INDONESIA
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Published by Universitas Pendidikan Ganesha
ISSN : 20898673     EISSN : 25484265     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Engineering
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) is a collection of scientific articles in the field of Informatics / ICT Education widely and the field of Information Technology, published and managed by Jurusan Pendidikan Teknik Informatika, Fakultas Teknik dan Kejuruan, Universitas Pendidikan Ganesha. JANAPATI first published in 2012 and will be published three times a year in March, July, and December. This journal is expected to bridge the gap between understanding the latest research Informatika. In addition, this journal can be a place to communicate and enhance cooperation among researchers and practitioners.
Arjuna Subject : -
Articles 22 Documents
 1   2   3 
Search results for , issue "Vol. 13 No. 2 (2024)" : 22 Documents clear
A Comparative Study on the Impact of Feature Selection and Dataset Resampling on the Performance of the K-Nearest Neighbors (KNN) Classification Algorithm Gunadi, I Gede Aris; Rachmawati, Dewi Oktofa
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.82174

Abstract

This study aims to evaluate the impact of dataset balancing and feature selection on the performance of the K-Nearest Neighbors (KNN) classification algorithm. The primary objective is to determine the effect of different training data balance ratios on classification performance. Additionally, the study analyzes the contribution of feature selection methods and data balancing to the overall performance of the classification algorithm. Three datasets (Titanic, Wine Quality, and Heart Diseases) sourced from Kaggle, were utilized in this research. Following the preprocessing stage, the datasets were subjected to three resampling scenarios with balance ratios of 0.3, 0.6, and 0.9. Feature selection was performed by combining correlation test values and information gain values, each weighted at 50%. The selected features were those with positive combined values of summation, correlation, and information gain. The KNN classification algorithm was then applied to datasets with and without feature selection. The results indicate that achieving a perfectly balanced ratio (ratio = 1) is not essential for improving classification performance. A balance ratio of 0.6 yielded results comparable to those of a perfect balance ratio. Furthermore, the findings demonstrate that feature selection has a more significant impact on classification performance than data balancing. Specifically, data with a balance ratio of 0.3 and feature selection outperformed data with a balance ratio of 0.6 but without feature selection.
QoS Analysis of Implementation Elastic WLAN Mechanism for Adaptive Bandwidth Management Systems in Smart Buildings Sukadarmika, Gede; Indra ER, Ngurah; Yoga, I Putu Sudharma; Linawati; Budiastra, IN
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.83215

Abstract

The rapid growth of the Internet of Things technology has led to various innovative creations, However, effective management of data traffic generated by numerous sensors is essential to maintain network performance. This study develops and evaluates an adaptive Bandwidth Management System using Elastic WLAN to deal with the development of IoT system traffic so that network performance is maintained. Using Raspberry Pi as an Elastic WLAN device and a Hierarchical Token Bucket (HTB) running via Python script, this system manages Bandwidth allocation based on the number of visitors in the Smart Building. Evaluation was carried out in two rooms, comparing conditions before and after Elastic WLAN implementation. The results show that the implementation of Elastic WLAN improves network performance. This is indicated by improvements in the stability of upload and download rates, as well as very significant improvements in the Jitter and Latency parameters which are used as QoS parameters.

Page 3 of 3 | Total Record : 22

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 14 No. 2 (2025) Vol. 14 No. 1 (2025) Vol. 13 No. 3 (2024) Vol. 13 No. 2 (2024) Vol. 13 No. 1 (2024) Vol. 12 No. 3 (2023) Vol. 12 No. 2 (2023) Vol. 12 No. 1 (2023) Vol. 11 No. 3 (2022) Vol. 11 No. 2 (2022) Vol. 11 No. 1 (2022) Vol 11, No 1 (2022) Vol. 10 No. 3 (2021) Vol. 10 No. 2 (2021) Vol 10, No 2 (2021) Vol. 10 No. 1 (2021) Vol 10, No 1 (2021) Vol 9, No 3 (2020) Vol. 9 No. 3 (2020) Vol 9, No 2 (2020) Vol. 9 No. 2 (2020) Vol. 9 No. 1 (2020) Vol 9, No 1 (2020) Vol. 8 No. 3 (2019) Vol 8, No 3 (2019) Vol 8, No 2 (2019) Vol. 8 No. 2 (2019) Vol. 8 No. 1 (2019) Vol 8, No 1 (2019) Vol 8, No 1 (2019) Vol. 7 No. 3 (2018) Vol 7, No 3 (2018) Vol 7, No 3 (2018) Vol. 7 No. 2 (2018) Vol 7, No 2 (2018) Vol. 7 No. 1 (2018) Vol 7, No 1 (2018) Vol 7, No 1 (2018) Vol 6, No 3 (2017) Vol 6, No 3 (2017) Vol. 6 No. 3 (2017) Vol. 6 No. 2 (2017) Vol 6, No 2 (2017) Vol 6, No 2 (2017) Vol. 6 No. 1 (2017) Vol 6, No 1 (2017) Vol 6, No 1 (2017) Vol 5, No 3 (2016) Vol. 5 No. 3 (2016) Vol 5, No 3 (2016) Vol. 5 No. 2 (2016) Vol 5, No 2 (2016) Vol 5, No 2 (2016) Vol 5, No 1 (2016) Vol 5, No 1 (2016) Vol. 5 No. 1 (2016) Vol 4, No 3 (2015) Vol. 4 No. 3 (2015) Vol 4, No 3 (2015) Vol 4, No 2 (2015) Vol. 4 No. 2 (2015) Vol 4, No 2 (2015) Vol. 4 No. 1 (2015) Vol 4, No 1 (2015) Vol 4, No 1 (2015) Vol. 3 No. 3 (2014) Vol 3, No 3 (2014) Vol 3, No 3 (2014) Vol. 3 No. 2 (2014) Vol 3, No 2 (2014) Vol 3, No 2 (2014) Vol 3, No 1 (2014) Vol. 3 No. 1 (2014) Vol 3, No 1 (2014) Vol 2, No 3 (2013) Vol. 2 No. 3 (2013) Vol 2, No 3 (2013) Vol. 2 No. 2 (2013) Vol 2, No 2 (2013) Vol 2, No 2 (2013) Vol 2, No 1 (2013) Vol 2, No 1 (2013) Vol. 2 No. 1 (2013) Vol 1, No 3 (2012) Vol 1, No 3 (2012) Vol. 1 No. 3 (2012) Vol. 1 No. 2 (2012) Vol 1, No 2 (2012) Vol 1, No 2 (2012) Vol 1, No 1 (2012) Vol 1, No 1 (2012) Vol. 1 No. 1 (2012) More Issue