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Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi)
Published by Universitas Udayana
ISSN : 22523006     EISSN : 26852411     DOI : https://doi.org/10.24843/JIM
Core Subject : Science,
Computer Science & IT
The journal publishes work from all disciplinary, theoretical and methodological perspectives. It is designed to be read by researchers, scholars, teachers and advanced students in the fields of Information Systems and Information Science, as well as IT developers, consultants, software vendors, and senior IT executives seeking an update on current experience and future prospects in relation to contemporary information and communications technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 8 Documents
 1 
Search results for , issue "Vol. 9, No. 1, April 2021" : 8 Documents clear
Comparison of Support Vector Machine and K-Nearest Neighbor for Baby Foot Identification based on Image Geometric Characteristics Angga Pratama Nugraha; I Nyoman Piarsa; I Made Suwija Putra
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p08

Abstract

Biometric recognition of infant identification systems is critical in security access for identification and verification systems. However, until now, hospitals or health centres in Indonesia still use conventional biometric identification, such as stamping or inking on the soles of babies' feet affixed to paper and are very vulnerable to the risk of damage or loss of data. To resolve this problem, computer vision technology can accurately identify the baby's feet' soles with the final result in the form of digital data. This study compares the classification method of baby feet using the SVM (Support Vector Machine) algorithm with the K-Nearest Neighbor algorithm. The baby's feet understudy image was taken using a cellphone camera with sample data of 3 months old babies. Comparing the SVM and KNN classification methods obtained high accuracy, precision and recall values, namely 98.80% accuracy, 89.51% precision and 88.00% recall. (for the SVM Gaussian kernel classification), with an accuracy of 99.08%, 92.65% precision and 90.75% recall (for the KNN Ecluidean Distance classification), it can be concluded that the KNN classification method using Euclidean distance is the best for applied in the baby palm identification system using the geometric image feature.
E-Readiness of Integrated Information Systems Using STOPE Framework in Udayana University Theca Difa Yulian Syahputri; Dwi Putra Githa; I Putu Agus Eka Pratama
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p02

Abstract

Many industries and institutions have switched to digital technology, an integrated information technology system, since we entered the era of the industrial revolution 4.0. In implementing new strategies, problems often occur because they do not assess technology readiness (e-readiness) to be built. Therefore, it is necessary to conduct a technology readiness assessment (e-readiness) either before or after the new system is implemented to assess how ready it is. The review can use the STOPE framework to be flexible so that this framework can be adjusted to the case study handled. IMISSU is an integrated management information system technology belonging to Udayana University. The results of the IMISSU e-readiness assessment found that the readiness of Udayana University in implementing IMISSU is at a very ready level (4 of 4). Assessment using the STOPE framework includes three levels, namely the domain, sub-domain, and sub-sub-domain levels. The study was conducted by distributing 100 questionnaires divided among various academicians in the university, adjusted to the STOPE framework domain.
Forecasting Number of COVID-19 in Bali Province Using Neural Network Algorithm Ida Ayu Utari Dewi; I Kadek Noppi Adi Jaya; Kadek Oky Sanjaya
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p07

Abstract

COVID-19 (coronavirus disease 2019) is a large family of viruses that cause mild to severe illness, such as the common cold or colds and serious illnesses such as MERS and SARS. COVID-19 has become a pandemic, meaning that there has been an increase in cases of the disease which is quite fast and there has been spread between countries and caused enormous losses in various countries. The increasing number of COVID-19 cases every day in Indonesia, including in Bali Province and the resulting losses underlie the forecasting of the number of COVID-19 in Bali Province. Forecasting is carried out using the Neural Network algorithm for time series data on the number of COVID-19 in Bali Province. The data used is data on the number of COVID-19 in the Bali Province in the form of time series data sourced from the Bali Provincial Health Office. The entire forecasting process uses the Rapidminer Studio tools starting from preprocessing, modeling, testing and validation. The results of the RMSE (Root Mean Square Error) evaluation value based on testing for the positive patients were 18.956, the patients recovered were 15.413, the patients under treatment were 5.066 and the patients who died was 0.233.
Website-Based Application for Classification of Diabetes Using Logistic Regression Method Muhamad Soleh; Naufal Ammar; Indrati Sukmadi
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p03

Abstract

Machine learning is a one of computer science field, machine-learning studies how computers are able to learn from data to improve their intelligence. Machine learning consists of many classification methods, including Neural Networks, Support Vector Machines, Logistics Regression, and others. In this study, a classification process carried out using the Logistics Regression method for cases of Diabetes. Diabetes is an increase in glucose in the bloodstream due to a lack of insulin, which is responsible for the transfer of glucose from the blood to tissues or cells. This study created with the aim of improving previous paper. The data used in this study are the same data as previous studies published by the Pima Indian Diabetes Dataset. In this study, several stages used, those are pre-processing, processing, evaluation, and website-based application development. The data in this study divided into two, 75% for training data, and 25% for testing data. This study produces an evaluation with an accuracy 80%, which means it is better than the previous paper, which is 75, 97%.
Web-Based E-Marketing Application for Balinese Art Studio Wahyu Hardinugraha; Oka Sudana; Dwi Putra Githa
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p04

Abstract

Balinese art and culture are unique because they may not be found in other regions. This uniqueness provides a characteristic and an attraction for foreign and domestic tourists visiting the island of Bali. The art studio is a place to provide traditional performing arts. The constraints experienced by art studios generally prevail in the manual marketing of art services, while the public or consumers experience problems ordering these performing arts services. E-Marketing is an online marketing medium with the help of the internet, which aims to expand the marketing of performing arts services without being limited by space and time. The method used in this study includes five main stages, namely, requirements analysis, system design, implementation, testing, and system maintenance. This research produces an E-Marketing Application for Sanggar Seni Bali with a website as the mediator. The Black Box test results show that all the system functionality components have been running successfully. The questionnaire analysis based on the answer scale percentage results shows that this application has been running well, responds according to the data needed, and is very suitable to be applied to people, especially those who need assistance in ordering traditional performing arts services. The implementation of this application will indirectly be a means of promoting traditional performing arts.
Evaluation of IT Governance at Office X using the COBIT 5 Framework Krissna Bayu; Oka Sudana; Ayu Wirdiani; Ananda Paramartha
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p01

Abstract

The IT governance audit aims to determine the level of importance (maturity level) of a company or government agency. IT governance audit is essential because if governance is not implemented correctly, the harmony between IT is beneficial to support business processes, and standard procedures will not be achieved. One of the agencies that require the implementation of good IT governance is Office X in Badung Regency. Office X in Badung Regency has a function in developing ICT infrastructure through application development, public service content, and ICT networks to improve public services. Conditions that occur in the field are frequent errors caused by internal and external parties that negatively impact the data management of Office X. The audit process begins with observations in the agency environment related to activity data and implementation, then mapping with business goals according to COBIT 5, followed by mapping business goals with IT Goals to obtain IT processes. The obtained IT processes are then selected to obtain critical IT processes according to the Executive Office. The audit results show that four IT processes need to be given recommendations for improvement, namely MEA 01, EDM 01, APO 07, DSS02.
Augmented Reality Application with Real Object Marker of Tanah Lot Temple I Putu Yoga Pertama Yasa; I Ketut Gede Darma Putra; Dewa Made Sri Arsa
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p05

Abstract

Tanah Lot Temple is located in Beraban Village, Kediri District, Tabanan Regency. Tanah Lot Temple is a temple that is included in the Dang Kahyangan Temple. Tanah Lot Temple has buildings scattered in the Tanah Lot Temple Tourism Object. Not all of the buildings in Tanah Lot Temple are known by tourists. Augmented Reality is a visual technology that combines objects or virtual worlds into real-world views in real-time. This study used markerless method and was developed using the Wikitude library and supported by web hosting to store information data and building coordinate data. The system in this study runs dynamically, making it easier to manage data without modifying the program code. Augmented Reality application results with real object marker of Tanah Lot Temple are that it can display information in the form of text narrative and location points along with voice explanations for each building in Tanah Lot Temple.
Implementing kNearest Neighbor Methods to Predict Car Prices Deshiwa Budilaksana; I Made Sukarsa; Anak Agung Ketut Agung Cahyawan Wiranatha
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p06

Abstract

The demand for automotive in Indonesia has never subsided, considering that the human need for transportation greatly affects people's daily lives. Various attempts are made by manufacturers to produce cars of a quality that is comparable to the costs incurred and following market demand. Prediction is a process that can be done to achieve this goal. One of the prediction methods that can be used in this case is the kNearest Neighbor. The prediction process consists of a preprocessing stage that cleans and filters unnecessary variables, followed by a variable multicollinearity test stage with Variance Inflation Factor (VIF). The multicollinearity test found 4 variables that had a specific influence in predicting the VIF value of these variables, respectively 2.22, 2.08, 1.53, 1.10 for Horse Power, Car Width, Highend, and, Hatchback respectively. The four variables of the VIF test results have a positive correlation with the price variable as the dependent variable. The prediction model is made using 4 variables selected based on the VIF test, to determine the accuracy of the method used, the Linear Regression model and, the kNearest Neighbor through the validation test with Mean Absolute Error (MAE) and R2. The kNearest Neighbor method produces an MAE test of 0.06 and R2 results are 0.843. This can be concluded if the overall kNearest Neighbor method has qualified performance in making predictions with continuous value variables or in other words using the concept of regression.

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