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Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Published by Universitas Udayana
ISSN : 20881541     EISSN : 25415832     DOI : 10.24843/LKJITI
Core Subject : Science,
Computer Science & IT
Lontar Komputer [ISSN Print 2088-1541] [ISSN Online 2541-5832] is a journal that focuses on the theory, practice, and methodology of all aspects of technology in the field of computer science and engineering as well as productive and innovative ideas related to new technology and information systems. This journal covers research original of paper that has not been published and has been through the double-blind reviewed journal. Lontar Komputer published three times a year by Research institutions and community service, University of Udayana. Lontar Komputer already indexing in Scientific Journal Impact Factor with impact Value 3.968. Lontar Komputer already indexing in SINTA with score S2 and H-index 5.
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Articles 6 Documents
 1 
Search results for , issue "Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023" : 6 Documents clear
Real-time Face Recognition System Using Deep Learning Method Ayu Wirdiani; I Ketut Gede Darma Putra; Made Sudarma; Rukmi Sari Hartati; Lennia Savitri Azzahra Lofiana
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p06

Abstract

Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.
Business Process Analysis with Business Process Improvement Method Case Study: University Integrated Registration Management System Oka Sudana; I Made Suwija Putra; Pradita Dewi
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p05

Abstract

The university's integrated registration management system is a system to facilitate the registration of prospective new students at University X. A good registration system should be able to provide accurate, and relevant information to improve the quality of the information system. The quality improving can be done with business process analysis. In this research business process analysis is done using business process improvement methods (BPI) up to phase 3, namely streamlining. The data to be analyzed is obtained from the results of questionnaires distributed to stakeholders. Determination of quality factor indicators on questionnaire questions using the McCall framework. The questionnaire results showed a business process that is categorized as critical, namely Study Program Transfer with an average value of 80%, Quality factors categorized as critical are Correctness 80% and Integrity 51% and Scholarship Application Management with an average value of quality factor 78%, quality factors categorized critically are Correctness 80% and Integrity 53%. Recommendations for business process improvement in the form of draft Standard Operating Procedures (SOP) also flowchart using streamlining with bureaucracy elimination and upgrading simplification tools in the process of moving management program and management of waiver submissions.
Optimization Strategy on Deep Learning Model to Improve Fruit Freshness Recognition I Gusti Agung Indrawan; Putu Andy Novit Pranartha; I Wayan Agus Surya Darma; I Putu Eka Giri Gunawan
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p01

Abstract

The high fruit production during the harvest season is a challenge in the process of sorting fresh fruit and rotten fruit in plantations. Automatic fruit freshness classification based on deep learning can speed up the sorting process. However, building a model with high accuracy requires the right strategy based on the dataset's characteristics. This research aims to apply optimization strategies to deep learning models to improve model performance. The optimization strategy is implemented by optimizing the model using fine-tuning strategy by selecting the best parameters based on learning rate, optimizers, transfer learning, and data augmentation. The transfer learning process is applied based on the dataset's characteristics by training some parameters with a size of 30% and 60%, which were tested in four scenarios. The fine-tuning strategy is applied to three Deep Learning models, i.e., MobileNetv2, ResNet50, and InceptionResNetV2, which have various parameter sizes. Based on test results, fine-tuning strategy produces the best performance up to 100% with a learning rate of 0.01, the SGD optimizers on the InceptionResNetV2 model are trained on 60% of the parameters.
Nowcasting the Number of Airplane Passengers at Ngurah Rai Airport Using Google Trends Data I Putu Juni Adi Widianata; Nori Wilantika
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p02

Abstract

Data on the number of aircraft passengers is essential to airport managers and the government's policies. The policy relates to improving the facilities and capacity of airports and other affected sectors, such as the transportation and tourism industries. A policy taken will be better if the data used is very close to the time of policy decision-making. Therefore, a technique is needed to forecast very close to the current condition of the number of aircraft passengers, namely nowcasting. One of the data sources that can be used for nowcasting is Google Trends data. In this study, the identification of relevant keywords used for nowcasting, the formation of nowcasting models, and the search for the best model for nowcasting the number of aircraft passengers was carried out. The nowcasting methods used are SARIMAX and multilayer perceptron. In this study, five relevant keywords were generated for domestic departures and two for international departures. In the nowcasting modeling, the best model for nowcasting domestic departures is produced, namely the multilayer perceptron with MAPE and MAE values of 11.194% and 28.048 respectively, while for departures Internationally, the best model was produced, namely SARIMAX with MAPE and MAE values of 8,641% and 50,205 respectively.
The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims I Made Sukarsa; Ni Kadek Dwi Rusjayanthi; Made Srinitha Millinia Utami; Ni Wayan Wisswani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p04

Abstract

Traffic accidents are still significant contributors to a fairly high death. Denpasar’s resort police record every traffic accident in the form of a daily report. The stored data can generate valuable information to improve policies and propagate better traffic practices. This research utilizes the classification technique with the XGBoost, random forest algorithm, and SMOTE method. The study shows that the SMOTE technique can increase the model's accuracy. Using the classification method with the two algorithms produces factors that affect the severity of traffic accident victims with feature importance. The feature importance obtained using the XGBoost model by counting the weight value for testing using the original dataset, the dataset for the type of two-wheeled vehicle, and the dataset of the kind of vehicle other than two-wheeled indicate that the variables influencing the severity of victims in road accidents are the time of accident between 00.00-06.00, the type of vehicle motorcycle, the type of opponent vehicle truck and pickup car, the age of the driver between 16-25, sub-district road status and front – side type of accident.
Associative Classification with Classification Based Association (CBA) Algorithm on Transaction Data with Rshiny Alesia Arum Frederika; I Putu Agung Bayupati; Wira Buana
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2023): Vol. 14, No. 1 April 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i01.p03

Abstract

Data mining can be used for businesses with large amounts of data. One of the data mining techniques is Associative Classification. It is a new strategy in data processing that combines association and classification techniques to build a classification model. This research used an associative classification technique on sales transaction data of Frozen Food Stores, which had sales transaction data on their business activities. It would be used in sales strategies to find items often purchased by class customers, namely, members and general. This research aimed to classify based on association rules using the CBA (Classification based Association) algorithm on sales transaction data. The application used the R programming language that business owners could use. The results of the rules obtained from the trial had the value of support, confidence, coverage, and lift ratio, which were the best value levels of a rule. The results of the rules that had the highest lift ratio value from all the data that have been inputted can be used as a reference to be implemented in sales strategies in knowing consumer needs.

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