cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ramdan Satra
Contact Email
Ramdan Satra
Phone
-
Journal Mail Official
ramdan@umi.ac.id
Editorial Address
-
Location
Kota makassar,
Sulawesi selatan
INDONESIA
ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
Arjuna Subject : -
Articles 24 Documents
 1   2   3 
Search results for , issue "Vol 12, No 2 (2020)" : 24 Documents clear
Perbandingan Metode Sobel, Prewitt, Robert dan Canny pada Deteksi Tepi Objek Bergerak Wahyu Supriyatin
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.541.112-120

Abstract

Computer vision is one of field of image processing. To be able to recognize a shape, it requires the initial stages in image processing, namely as edge detection. The object used in tracking in computer vision is a moving object (video). Edge detection is used to recognize edges of objects and reduce existing noise. Edge detection algorithms used for this research are using Sobel, Prewitt, Robert and Canny. Tests were carried out on three videos taken from the Matlab library. Testing is done using Simulik Matlab tools. The edge and overlay test results show that the Prewitt algorithm has better edge detection results compared to other algorithms. The Prewitt algorithm produces edges whose level of accuracy is smoother and clearer like the original object. The Canny algorithm failed to produce an edge on the video object. The Sobel and Robert algorithm can detect edges, but it is not clear as Prewitt does, because there are some missing edges.
Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store Lutfi Budi Ilmawan; Muhammad Aliyazid Mude
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.597.154-161

Abstract

In this research, the performance of SVM classification method will be compared with other classification methods, by using the Naïve Bayes classification method. Naïve Bayes classification method is a light classification method and has a high accuracy if applied to the text classification according to some previous studies. The accuracy of the classifier is measured using the K-fold cross validation method whose results will be tabulated in a confusion matrix table, with a value of K = 3. In this study, the data processed are textual reviews of applications in the Indonesian language Google Play Store obtained from previous research. The test results obtained from the 3-fold cross-validation method produce that SVM Classifier has a higher value of accuracy when compared with the accuracy of the Naïve Bayes classifier, the SVM classifier gets an accuracy of 81.46% and Naïve Bayes classifier by 75.41%.
Performa Klasifikasi K-NN dan Cross Validation pada Data Pasien Pengidap Penyakit Jantung Huzain Azis; Purnawansyah Purnawansyah; Farniwati Fattah; Inggrianti Pratiwi Putri
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.507.81-86

Abstract

Globally, the number one cause of death each year is cardiovascular disease. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels, such as coronary heart disease, heart failure or heart failure, hypertension and stroke. The purpose of this study was to measure the performance of accuracy, precision, recall and f-measure of the K-NN and Crossvalidation methods on a dataset of cardiovascular patients. The dataset used was 1000 records consisting of 11 attributes (age, gender, height, etc.) cardiovascular and non cardiovascular patient data, the dataset was obtained from the UCI Machine Learning Repository managed by the Hungarian Institute of Cardiology Budapest: Andras Janosi, MD, University Hospital, Zurich, Switzerland. The steps taken are: dividing the simulation ratio of the dataset to 20:80, 50:50 and 80:20, applying crossvalidation (k-fold = 10) and classification using the K-NN method (k = 2 to K = 900). The research results from the simulation of the dataset ratio 50:50 obtained an accuracy value of 82%, 82% precision, 82% recall and 80% f-measure at a value of K = 13, then the research results from the simulation of the dataset ratio 20:80 obtained an accuracy value of 87%, 87% precision, 97% recall and 92% f-measure at the value of K = 3, and the results of research from the simulation of the dataset ratio 80:20 obtained an accuracy value of 91%, 92% precision, 60% recall and 72% f-measure at the value K = 5.
Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa Muhammad Rafi Muttaqin; Meriska Defriani
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.542.121-129

Abstract

In helping to develop technology in the field of education as well as bringing about a major change in competitiveness between individuals and groups, to be able to do so requires sufficient information and data to be analyzed further. In this case STT Wastukancana Purwakarta is under the auspices of Bunga Bangsa Foundation, seeing that STT Wastukancana Purwakarta students have several obstacles in their final project, one of which is difficult in determining the topic of the thesis title to be made so that sometimes the topic of the thesis title taken is not in accordance with their abilities each student. This problem can be overcome by applying the clustering method. The analytical method used is Knowledge Discovery in Database (KDD). The method of grouping students uses the clustering method and the K-Means algorithm as a clustering calculation where the Clustering aims to divide students into clusters based on grades obtained from semester 1 to 7, so as to produce student recommendations in taking thesis topics. The tool used to implement the algorithm is Rapidminer. The results of this study are grouping students according to their expertise, which is obtained based on the cluster that has the highest score and is dominated by the most subjects according to the subjects that have been grouped by each expertise. So, the results of this cluster are used as a reference for students to take the thesis title topic.

Page 3 of 3 | Total Record : 24

 1   2   3 

Filter by Year

2020 2020


Reset
Filter By Issues
All Issue Vol 17, No 3 (2025) Vol 16, No 3 (2024) Vol 16, No 2 (2024) Vol 16, No 1 (2024) Vol 15, No 3 (2023) Vol 15, No 2 (2023) Vol 15, No 1 (2023) Vol 14, No 3 (2022) Vol 14, No 2 (2022) Vol 14, No 1 (2022) Vol 13, No 3 (2021) Vol 13, No 2 (2021) Vol 13, No 1 (2021) Vol 12, No 3 (2020) Vol 12, No 2 (2020) Vol 12, No 1 (2020) Vol 11, No 3 (2019) Vol 11, No 2 (2019) Vol 11, No 1 (2019) Vol 11, No 1 (2019) Vol 10, No 3 (2018) Vol 10, No 3 (2018) Vol 10, No 2 (2018) Vol 10, No 2 (2018) Vol 10, No 1 (2018) Vol 10, No 1 (2018) Vol 9, No 3 (2017) Vol 9, No 3 (2017) Vol 9, No 2 (2017) Vol 9, No 2 (2017) Vol 9, No 1 (2017) Vol 9, No 1 (2017) Vol 8, No 3 (2016) Vol 8, No 3 (2016) Vol 8, No 2 (2016) Vol 8, No 2 (2016) Vol 8, No 1 (2016) Vol 8, No 1 (2016) More Issue