Article Per Year (5 Year)
p-Index From 2020 - 2025
4.465
P-Index
This Author published in this journals
All Journal Jurnal Pekommas Jurnas Nasional Teknologi dan Sistem Informasi SEEIJ (Social Economics and Ecology International Journal) ILKOM Jurnal Ilmiah Jurnal Ilmiah Realtech Jurnal Peduli Masyarakat Journal of Electrical Engineering and Computer (JEECOM) JOINTER : Journal of Informatics Engineering Vivabio : Jurnal Pengabdian Multidisiplin Jurnal Pengabdian Informatika (JUPITA) Journal of Computing Theories and Applications Jurnal Pengabdian Masyarakat Tekno
Junaidy Budi Sanger
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Katolik De La Salle Manado
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Electrical Engineering and Computer (JEECOM)

 1 
Pelembutan Citra dengan Metode Filter Gaussian Junaidy B Sanger; Immanuela P. Saputro P. Saputro; Yunita Komalig
Journal of Electrical Engineering and Computer (JEECOM) Vol 5, No 1 (2023)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v5i1.5894

Abstract

Image is a multimedia component rich in information and has an essential role as an information provider. However, the images encountered often experience a decrease in quality, such as defects or noise, causing the information conveyed from these images less clear. Noise causes the image to be too contrasted, blurry, or not sharp enough. One type of noise is contrast noise. Image Smoothing here is one of the operations to improve quality which aims to smooth out images with unbalanced contrast noise. Disturbances in the image are generally in the form of variations in the intensity of a pixel that is not correlated with neighboring pixels. Contrasting images are caused by uneven lighting, which can cause the information in the image to be reduced and difficult to interpret. For this reason, quality improvement must be made to get a better image. In this study, the softening of contrasting images uses the Gaussian Filter method. This filter has the effect of equalizing the gray distance to make the image obtained smoother. Based on the results of the tests, it got an accuracy of 83.3%, meaning that the application's performance is suitable for image smoothing.
Classification of LPG Gas Usage Satisfaction Level Using The Naïve Bayes Algorithm Adrian, Angelia Melani; Patras, Bella Alisia; Sanger, Junaidy B.
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.11064

Abstract

LPG gas is a very important energy source in everyday life for cooking activities. Although the importance of LPG gas in supporting everyday life has been widely recognized, satisfaction with the use of LPG gas is an issue that should not be ignored. Often products or services that do not meet customer expectations can cause dissatisfaction. This can be caused by low quality, prices that do not match the quality received, or not in accordance with user expectations.This study aims to classify the level of satisfaction of LPG gas usage using the Naïve Bayes algorithm. The data obtained from the survey results are 250 data using 5 attributes, namely meeting needs, good quality, affordable prices, repurchasing, and recommending products. And using 2 classes, namely satisfied and dissatisfied.The model achieved an accuracy of 89.3% with a 70:30 training-to-test data split, 91.2% with an 80:20 split, and 94.0% with a 60:40 split, indicating that performance varied based on the proportion of training and test data used.
Co-Authors Adrian, Angelia M. Agnessya Mapalie Angelia Adrian Angelia Adrian Angelia Adrian Angelia Melani Adrian Bangala, Ari Lesmono Daniel Nusalawo Debby Paseru Debby Paseru Dino Rahardiyan Emma M Moko Fransiskus Piay Immanuel, Joshua H. Immanuela P. Saputro P. Saputro Immanuela Puspasari Saputro Immanuela Saputro Iyan Mulyana Jaya Saleh Johanis Sahambangung Joseph, Hizkia R.M. Joshua Immanuel Kawatak, Steven Y. Lanny Sitanayah Lianly Rompis Lohonauman, Charlie A. P. Mapalie, Agnessya Maria Pantouw Meirio Tengor Michael George Sumampouw Mohamad Iqbal Suriansyah Moko, Emma Moningkey, Clara Nusalawo, Daniel Pantouw, Maria L. Patras, Bella Alisia Pether Romony Phang, Alvaro D. Rahardiyan, Dino Rinaldi Munir Rinaldi Munir Rinaldi Munir Romony, Pether V B Sahambangung, Johanis R. Sandi Winata Saputro, Immanuela P. Saputro, Immanuela Puspasari Taulu, Angelica Tengor, Meirio R. Wikarsa, Liza Yunita Komalig