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INDONESIA
Jurnal Ilmu Komputer
Published by Universitas Udayana
ISSN : 19795661     EISSN : 2622321X     DOI : -
Core Subject : Science, Education,
Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science
JIK is a peer-reviewed scientific journal published by Informatics Department, Faculty of Mathematics and Natural Science, Udayana University which has been published since 2008. The aim of this journal is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of computer science. JIK is consistently published two times a year in April and September. This journal covers original article in computer science that has not been published. The article can be research papers, research findings, review articles, analysis and recent applications in computer science.
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Articles 8 Documents
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Search results for , issue "Vol 14 No 1 (2021): Jurnal Ilmu Komputer" : 8 Documents clear
Peningkatan Akurasi Pembobotan Attribute importance weights Pada Deteksi Fraud Erba Lutfina; Solichul Huda
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p01

Abstract

Kerugian miliaran dollar setiap tahunnya dialami oleh bank yang disebabkan oleh Fraud. Salah satu solusi untuk mengatasi kasus fraud yang dialami dunia perbankan dapat dilakukan dengan proses deteksi fraud. Pada proses deteksi Fraud, terdapat berbagai atribut PBF (Process Based Fraud) yang setiap atributnya memiliki dampak yang berbeda dalam mendeteksi fraud. Untuk menentukan bobot setiap atribut PBF digunakan metode MDL (Modified Digital Logic). Metode MDL menghasilkan attribute importance weights yang sesuai dengan dampak atribut PBF. Namun peran pakar masih sangat signifikan dalam menilai setiap attribute importance weights. Penelitian ini bertujuan untuk mengubah prosedur penentuan bobot attribute importance weights dalam metode MDL dengan menambahkan metode Multiple Linear Regression (MLR). Dengan mengganti inputan yang sebelumnya diberikan oleh pakar menjadi perbandingan bobot atribut secara otomatis. Kemudian hasil dari kedua metode dievaluasi menggunakan confusion matrix. Berdasarkan hasil eksperimen, metode MLR menunjukkan persentase klasifikasi menggunakan semua attribute importance weights menunjukkan hasil yang lebih baik dengan akurasi sebesar 99,5%.
Analisis Sentimen Opini Pemindahan Ibu Kota Pada Twitter Dengan Metode Support Vector Machine Tezza Fazar Tri Hidayat; Garno Garno; Azhari Ali Ridha
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p06

Abstract

The relocation of the capital city of Indonesia has now been inaugurated by President Joko Widodo on August 26, 2019, to Kalimantan, it is a new history in Indonesian history because it has never happened before, giving rise to many opinions or responses from the public. Sentiment analysis is an activity used to analyze a person's opinions or opinions on a topic. Twitter is a social media used to express user's opinions and united them on a topic. Support Vector Machine is a method of text mining which includes classification methods and Term Frequency - Inverse Document Frequency is a method of weighting characters. SVM and TF-IDF can be used to analyze public opinion sentiments on the topic of moving the Indonesian capital. The purpose of this study is to classify public opinion on the topic of moving the Indonesian capital from thousands of tweets that have been collected and filtered. Tweets that have been processed are 992 tweets from March 22-29, 2020 and, consist of 221 data with positive labels and 771 negative data. And using the SVM method which has an accuracy of 77.72% and combined with TFIDF which increases its accuracy to 78.33%.
Perbandingan Performa Routing Protocol antara Mixed Routing Protocol dengan Single Routing Protocol pada Aplikasi VoIP Menggunakan Riverbed Modeler Academic Edition 17.5 Dewa Nyoman Suartama Ariawan; Gede Saindra Santyadiputra; I Ketut Resika Arthana
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p02

Abstract

VoIP is a communication technology device that is used by companies and homes to communicate. So far, there are several shortcomings of VoIP that may impact the real conditions in the field, so it is necessary to have a combination of routing protocols that can cover the shortcomings of both the VoIP application and the routing protocol. This study aims to conduct a comparative study of the performance of mixed routing protocols with single routing and to test IPv4 and IPv6 using semi-mesh topology and mesh wireless network topology in VoIP applications. This study uses the OPNET Riverbed Modeler Academic Edition 17.5 simulation method with a design and analysis model that includes the stages of create network model,modeling, application, profile and failure / recovery configuration, choose statistics, run simulation, view and analyze the results. The results showed that the combination of EIGRP-IGRP-ISIS-OSPF-RIPv2 and RIPv2-ISIS-EIGRP provided good research results and covered the weaknesses of RIPv2, IS-IS and EIGRP compared to single routing protocols, both in semi-mesh and wireless mesh topologies. network.mixed routing protocol. The addition of the variance of the comparison between IPv4 and IPv6 based on a single routing protocol also shows significant results, where both IPv4 and IPv6 in the semi-mesh topology and the mesh wireless network are in a range partly in good and very good ranges with the rest being sufficient, bad and very bad.
User Loyalty Prediction Using Naive Bayes Method in "Udatari" an art Performance Marketplace Ngurah Agus Sanjaya ER; I Gusti Agung Gede Arya Kadyanan
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p07

Abstract

Udatari is the first traditional dance platform in Indonesia which provides information about traditional events such as, dance tutorials, group dancer and dance attributes. The tight competition in the startup world, requires Udatari as a new startup to manage application users optimally. Knowing loyal users will help startups determine the right marketing strategy. In this study, the method used for clustering is the K-Means method where this method seeks to classify existing data into several groups provided that the data in one group have the same characteristics as each other. The model used for the clustering process is RFM, namely recency, frequency and monetary. The purpose of this clustering is to get the segmentation of users who have different Customer Lifetime Value. The second method for conducting classification is the Naïve Bayes method, where this method predicts future opportunities based on past experiences. The purpose of this classification is to predict new users into the user segmentation obtained from the clustering results. From the results of this study, the optimum k value for K-Means are 3 clusters with the largest CLV value in the second cluster where testing on this method uses the Silhouette Index. Furthermore, for the test results of the Naïve Bayes method, the average accuracy value is 97.44% where the accuracy of each class is 92.31% for cluster 0 (first cluster), 100% for the second cluster and 100% for the third cluster. Keywords: K-Means, Naïve Bayes, Loyalty, Segmentation, RFM
Impact of Machine Learning Applications For Price Prediction Of Onion In India Sayed Mannan Ahmad; Ankush Kumar Gaur; Anil Kumar
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p04

Abstract

Conceptual India is one of the second-biggest maker of onion on the planet. Onion is the most extravagant wellspring of nutrients and cell reinforcements. Furthermore, it contains malignant growth battling mixes, assists with keeping up heart more advantageous and lifts the bone thickness, additionally controls the glucose. Natural just as Climate changes sway on the rural economy of any nation. The creation of onion typically relies upon factors like science, atmosphere, economy, and topography, these elements impact on agriculture. It is been recorded that significant expense chance in the horticulture part constantly bound ranchers to endeavor suicide. The value figures are helpful for homesteads, policymakers, and agribusiness ventures. Utilizing time arrangement information in horticulture, nonstop endeavors are made by the analysts to foresee the costs utilizing different straight and nonlinear anticipating models. These days, Artificial knowledge/Machine learning models are utilized as traditional measurable models in the gauging exercise. Utilizing AI strategies in this way, utilizing authentic cost and information factors. Henceforth this exploration will assume a significant job to anticipate the cost of onion. Just as help to agriculturists to turn out to be financially well and has a more advantageous existence.
c Jurnal Ilmu Komputer I Gede Santi Astawa
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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Analisis Perubahan Ukuran Citra Medis Menggunakan Algoritma Interpolasi Bikubik Bambang Krismono Triwijoyo; Ahmat Adil
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p03

Abstract

Image interpolation is the most basic requirement for many image processing tasks such as medical image processing. Image interpolation is a technique used in resizing an image. To change the image size, each pixel in the new image must be remapped to a location in the old image to calculate the new pixel value. There are many algorithms available for determining the new pixel value, most of which involve some form of interpolation between the closest pixels in the old image. In this paper, we use the Bicubic interpolation algorithm to change the size of medical images from the Messidor dataset and then analyze it by measuring it using three parameters Mean Square Error (MSE), Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR), and compare the results with Bilinear and Nearest-neighbor algorithms. The results showed that the Bicubic algorithm is better than Bilinear and Nearest-neighbor and the larger the image dimensions are resized, the higher the degree of similarity to the original image, but the level of computation complexity also increases.
Implementasi Algoritma Naïve Bayes Berbasis Particle Swarm Optimization Untuk Memprediksi Penyakit Hepatitis Hilda Farida Husniah; Toni Arifin
Jurnal Ilmu Komputer Vol 14 No 1 (2021): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2021.v14.i01.p05

Abstract

Hepatitis disease is an inflammatory disease of the liver cells, caused by infections (viruses, bacteria, parasites), medicines (including traditional medicines), consuming alcohol, excessive fats and autoimmune diseases. The cause of hepatitis is often caused by Hepatitis B and C Virus. The Hepatitis prevalence in Indonesia in 2013 amounted to 1.2% increased twice compared to the year 2007 Riskesdas of 0.6%. East Nusa Tenggara is the province with the highest prevalence of Hepatitis in 2013 of 4.3%. Researchers are trying to make a breakthrough by making research for the prediction classification of Hepatitis patients with data mining technique. Naïve Bayes is a method used to predict the probability of the future based on past experience and proved to have a high level of accuracy and high speed of calculation. Particle Swarm Optimization is used to improve the accuracy of the method. The research aims to determine if the Naïve Bayes-based Particle Swarm Optimization method can improve the accuracy of the good. The results of using Naïve Bayes-based Particle Swarm Optimization has a confusion matrix accuracy of 91.90% and an AUC of 0946 proved that has good results than Naïve Bayes has a confusion matrix accuracy of 88.52% and AUC 0896.

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