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INDONESIA
(JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Published by Universitas Udayana
ISSN : 23015373     EISSN : 26545101     DOI : https://doi.org/10.24843/JLK
Core Subject : Science,
Computer Science & IT
Aim and Scope: JELIKU publishes original papers in the field of computer science, but not limited to, the following scope: Computer Science, Computer Engineering, and Informatics Computer Architecture Parallel and Distributed Computer Computer Network Embedded System Human—Computer Interaction Virtual/Augmented Reality Computer Security Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods) Programming (Programming Methodology and Paradigm) Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Network Traffic Modeling Performance Modeling Computer Security IT Governance Networking Technology Robotic Instrumentation Information Search Engine Multimedia Security Information Retrieval Mobile Processing Natural Language Processing Artificial intelligence & soft computing and their applications Neural networks Machine Learning Reasoning and evolution Intelligence applications Computer vision and speech understanding Multimedia and cognitive informatics Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 488 Documents
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Application of the C4.5 Algorithm to Predict the Types of Disease in Pigs Based on Android Indryaswari, I Gusti Ayu Purnami; Made Mahendra, Ida Bagus
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i01.p14

Abstract

Many Indonesian people, especially in Bali, make pigs as livestock. Pig livestock are susceptible to various types of diseases and there have been many cases of pig deaths due to diseases that cause losses to breeders. Therefore, the author wants to create an Android-based application that can predict the type of disease in pigs by applying the C4.5 Algorithm. The C4.5 algorithm is an algorithm for classifying data in order to obtain a rule that is used to predict something. In this study, 50 training data sets were used with 8 types of diseases in pigs and 31 symptoms of disease. which is then inputted into the system so that the data is processed so that the system in the form of an Android application can predict the type of disease in pigs. In the testing process, it was carried out by testing 15 test data sets and producing an accuracy value that is 86.7%. In testing the application features built using the Kotlin programming language and the SQLite database, it has been running as expected.
Implementation of K-Nearest Neighbor Algorithm in Heart Disease Classification Putri Rahayu, Ni Kadek Sukma; Mogi, I Komang Ari
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i01.p05

Abstract

The heart is an important organ that exists in the human body. The main function of the heart is to pump blood throughout the body through blood vessels. The WHO states that as many as 7.3 million people die from heart disease. In this study heart disease will be classified using the K-Nearest Neighbor algorithm. K-Nearest Neighbor algorithm is a classification algorithm based on the distance from data testing against training data with a pre-defined number of k. The results were obtained from performance measurements for the classification of heart disease with the K-Nearest Neighbor algorithm measured using the K-Fold Cross Validation algorithm, from an accuracy rate of 65.89%, a precision level of 66.27%, and a recall of 74.67%.
The Effect of Feature Selection on Music Genre Classification Giri, I Nyoman Yusha Tresnatama; Putri, Luh Arida Ayu Rahning
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 4 (2021): JELIKU Volume 9 No 4, Mei 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v09.i04.p13

Abstract

One of the things that affects classification results is the correlation of features to the class of a data. This research was conducted to determine the effect of the reduction of features (independent variable) that have the weakest correlation or have a distant relationship with the class (dependent variable). Bivariate Pearson Correlation is used as a feature selection method and K-Nearest Neighbor is used as a classification method. Results of the test showing that, 75.1% average accuracy was obtained for classification without feature selection, while using feature selection, average accuracy was obtained in the range of 75% - 79.3%. The average accuracy obtained by the selection of features tends to be higher compared to the accuracy obtained without selection of features.
Perancangan dan Implementasi Data Warehouse Penjualan (Studi Kasus: Northwind Sample Database) Ardiyanti, Ni Putu Novia; Zulkarnain, Muhammad Firdaus; Sandi, I Wayan Wijaya Kusuma; Hendrawan, I Dewa Ngurah Tri; Mahendra, Ida Bagus Made
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i01.p20

Abstract

Complex analysis is essential for corporate decision-making. The data warehouse is considered more effective to support the analysis process, design, and business decision-making. Usually, a company will build a data warehouse to store operational data that is useful in the business analysis process, so the information that the company wants can be prevalent more easily. The study will arrange a design and implementation of the data warehouse, which uses the Northwind database as its source. For the warehouse data storage, a nine-step design technique and the purification software are used to implementing the process. The design and implementation will form a sales fact chart containing information that use to help the company's business analyses and decisions, which in this study is visualized using a Microsoft Power Business Intelligence application.
Augmented Reality Application Development for Elementary School Purpose Wisnawa, Agus; Wibawa, I Gede Arta
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 4 (2021): JELIKU Volume 9 No 4, Mei 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v09.i04.p04

Abstract

The world is being hit by a pandemic due to the COVID-19 virus outbreak. The changes brought by this virus are huge, one of which is school activities that are transformed into online learning. Online learning causes students to not do their own classroom learning. This causes students to become un familiar with their school properly, such as the layout of classrooms and school facilities. By using Augmented Reality the problem can be solved. Augmented Reality (AR) is the merging of real and virtual objects in a real environment with interactive results and presented in real time. AR can be used to modeling the entire shape of the school, making it easier for users to get information about the building from the school instead of walking manually. Users only need to install the app on their smarthphone and scan the specified QR code in order to be able to bring up the building object along with the information. The result of this research is an AR application which can provide information about the rooms and buildings at SDN 1 Padangsambian.
Souvenir Sales Analysis using Apriori Algorithm (Case Study: Ubud-Market Transaction in March 2020) Rizky, Muhammad Firyanul; Arya Kadyanan, I Gusti Agung Gede; Made Mahendra, Ida Bagus
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i01.p10

Abstract

Ubud market is one of the largest art markets in Bali, there are many local Balinese souvenir traders and craftspeople, most of them are livelihoods depend on buying and selling local souvenirs, Since the Covid-19 pandemic entered in April 2020, Ubud market traders have started to close their business and hoping economic recoveryin future. The author tries to do a track record of souvenir sales transactions in Ubud market to find the last sales pattern before the traders closes their business to give a solution for marketing strategies in future. The sales transaction data will just become meaningless trash if it’s useless.. To get use information about the products that are most sold out at Ubud Market from the transaction database, the author uses the Apriori algorithm. This study was determined final rules on 2 itemset combination, If buying Manik-Manik Craft, Also buy Barong Shirt with the highest confidence 70% and Minimum Support 28%, and for 3 itemset a combination, If buying Celuk Silver, and Barong Shirt, Also buy Manik-Manik Craft with the highest confidence 37.5% and Minimum Support 12%, based on that there are 3 best-selling souvenir products, namely Barong Shirt, Manik-Manik Craft and Silver-Celuk in March 2020. Keywords: Apriori Algorithm, Data Mining, Sales Analysis, Association Rule Mining, Ubud Market.
Ontology-based Approach: A Smartphone Knowledge Representation Indrayasa, I Wayan Gede; Pramartha, Cokorda
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i01.p01

Abstract

The development of technology in the modern era is currently increasing very rapidly so that it can make human work easier. One of the technologies that are often used by the community to meet the needs of life is a smartphone. The rapid development of smartphones has made people's purchasing power higher with existing criteria, ranging from brands, prices to features that potential buyers must consider in buying a smartphone. The lack of public knowledge also makes people confused about choosing a smartphone product because of the many smartphone brands on the market. Ontology can be a solution to explicitly describe information about smartphones. The construction of an ontology model is carried out using the methodology methodology. The ontology that was built has 7 classes, 12 subclasses, 9 object properties, 2 data properties, and 92 instances. The ontology built using SPARQL with several search criteria on this ontology can produce the output that the user wants and can represent knowledge from a set of concepts in the knowledge domain, in this case the smartphone and its relationship between these concepts.
Classification of Pop and RnB (Rhythm and Blues) Songs with MFCC Feature Extraction and K-NN Classifier Ramadhan, Zhaqy Hikkammi Gullam; Widiartha, I Made
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 4 (2021): JELIKU Volume 9 No 4, Mei 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v09.i04.p09

Abstract

Classification is a technique for designing functions based on observations of attributes in a data so that data can be mapped that do not have a class which in this study can be called genres, into data that has been classified according to the given rules. In this research, music classification is conducted to determine whether the class or genre of music is pop or RnB (Rhythm and Blues) by using MFCC as the feature extraction method and K-NN as the classification method. The test results in this study obtained an accuracy of 77.5% with an optimal value of k = 31 as a parameter in K-NN.
Analysis of the Effect of Feature Reduction on Accuracy and Computational Time in Mushroom Dataset Classification Prayogo, Agus; Astawa, I Gede Santi
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i01.p15

Abstract

Classification is a technique to mapping the class of a certain data from its attribute or feature values. One of things that affects the classification result is the correlation of its features to the class classification results. Research conducted to determine the effect of the reduction in features that are least correlated or have a distant relationship with the classification result class (dependent variable). Because features that do not have much correlation, have no effect on the classification results. From the research, the accuracy of the reduction of each feature per test scenario has a range between 83% -88% higher than the initial accuracy without feature selection at 82% accuracy. Meanwhile, the computation time obtained does not have a significant difference in changing compared to without feature reduction, in the range of 2.3-2.7. For the data used is the Mushroom dataset obtained from the UCI Machine Learning Repository
Real Time Pitch Detection Dengan Algoritma FFT dan Spectrogram Untuk Tuning Vokal Noak, Devin Reness; Bayu Atmaja Darmawan, I Dewa Made
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 8 No 3 (2020): JELIKU Volume 8 No 3, February 2020
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2020.v08.i03.p15

Abstract

The song is a means of entertainment most often heard by humans where in the song consists of music and vocals. Good quality music and vocal singers will make a song more pleasant to hear. To make the song sound tunable and in accordance with the rhythm can be done by adjusting the vocals according to the tone of the song. From this we know that measuring sound frequencies needs to be done to determine whether a frequency or period is loud, it can also be used as a tool in vocal training, one of them for vocal tuning applications to find the harmonious sound of the sound. Moreover, it can be used as a learning need in Sound Frequency Processing. Where one of the parts to create a vocal tuning application can be through the Real-time spectrogram program. This RTS uses Pyaudio as sound recording, uses the Python 3.6 programming language and uses the Fast Fourier Transform method which will help when making real-time spectrogram and pitch detection programs. The test results obtained 75% accuracy in real-time pitch detection programs.

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