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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 226 Documents
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Dempster Shafer Algorithm For Expert System Early Detection of Anxiety Disorders Finanta Okmayura; Vitriani Vitriani; Melly Novalia
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i02.p05

Abstract

Anxiety is an excessive anxiety disorder that is often found in psychology. Some people generally do not realize that they may have symptoms of this anxiety disorder. If ignored and continued continuously, it can interfere with one's activities, reduce academic achievement, and disrupt psychological conditions that affect their lives. This expert system for early detection of anxiety disorders is carried out using forward chaining tracing techniques to explore the knowledge base, and the inference motor is the Dempster Shafer algorithm. Dempster Shafer calculation is done by combining symptom pieces to calculate the possibility of the anxiety disorder. This anxiety disorder detection system is built on the web. Then the test is carried out by comparing the value generated by the system with the value generated by two experts. The test results prove that the value generated by the system has a similarity of 85% to the value produced by the two experts. It can be concluded that implementing the Dempster Shafer algorithm for this expert system in the early detection of anxiety disorders is feasible.
Frequency Band and PCA Feature Comparison for EEG Signal Classification I Wayan Pio Pratama; Made Windu Antara Kesiman; I Gede Aris Gunadi
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 1 (2021): Vol. 12, No. 01 April 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i01.p01

Abstract

The frequency band method is popular in signal processing; this method separates EEG signals into five bands of frequency. Besides the frequency band, the recent research show PCA method gives a good result to classify digits number from EEG signal. Even PCA give a good accuracy to classify digit number from EEG signal, but there are no research shows which one yielded better accuracy between PCA and frequency band to classify digit number from EEG signals. This paper presents the comparison between those methods using secondary data from MindBigData (MDB). The result shows that the frequency band and PCA achieve 9% and 12,5% on average accuracy with the EPOC dataset. The paired Wilcoxon test produces a significant difference in accuracy between methods in the digit classification problem. Experiment with Muse dataset provides 31% accuracy with frequency band method and 24,8% with PCA method. The result is competitive compared to other experiments to classify digit numbers from EEG signals. In conclusion, there is no winner between the two methods since no method fits both datasets used in this research.
Structural and Semantic Similarity Measurement of UML Use Case Diagram Mohammad Nazir Arifin; Daniel Siahaan
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 11 No 2 (2020): Vol. 11, No. 2 August 2020
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2020.v11.i02.p03

Abstract

Reusing software has several benefits ranging from reducing cost and risk, accelerating development, and its primary purposes are improving software quality. In the early stage of software development, reusing existing software artifacts may increase the benefit of reusing software because it uses mature artifacts from previous artifacts. One of software artifacts is diagram, and in order to assist the reusing diagram is to find the level of similarity of diagrams. This paper proposes a method for measuring the similarity of the use case diagram using structural and semantic aspects. For structural similarity measurement, Graph Edit Distance is used by transforming each factor and use case into a graph, while for semantic similarity measurement, WordNet, WuPalmer,and Levenshtein were used. The experimentation was conducted on ten datasets from variousprojects. The results of the method were compared with the results of assessments from experts.The measurement of agreement between experts and method was done by using Gwet’s AC1 andPearson correlation coefficient. Measurement results with Gwet’s AC1 diagram similarity are 0,60,which were categorized as “moderate" agreement and the result of measurement with Pearsonis 0.506 which means there is a significant correlation between experts and methods. The resultshowed that the proposed method can be used to find the similarity of the diagram, so finding andreuse of the diagram as a software component can be optimized.
Kendali Kursor Mouse berbasis Elektrookulogram (EOG) menggunakan Continuous Wavelet Transform dan Fitur Statistik Triadi Triadi; Inung Wijayanto; Sugondo Hadiyoso
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 1 (2021): Vol. 12, No. 01 April 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i01.p06

Abstract

This study design a system prototype to control a mouse cursor's movement on a computer using an electrooculogram (EOG) signal. The EOG signal generated from eye movement was processed utilizing a microcontroller with an analog to the digital conversion process, which communicates with the computer through a USB port. The signal was decomposed using continuous wavelet transform (CWT), followed by feature extraction processes using statistic calculation, and then classified using K-Nearest Neighbors (k-NN) to decide the movement and direction of the mouse cursor. The test was carried out with 110 EOG signals then separated, 0.5 as training data and 0.5 as test data with eight categories of directional movement patterns, including up, bottom, right, left, top right, top left, bottom right bottom left. The highest accuracy that can be achieved using CWT-bump and kurtosis is 100%, while the time needed to translate the eye movement to the cursor movement is 1.9792 seconds. It is hoped that the proposed system can help assistive devices, particularly for Amyotrophic Lateral Sclerosis (ALS) sufferers.
Pengenalan Spesies Ikan dengan Faster R-CNN Inception-v2 menggunakan QUT FISH Dataset Yonatan Adiwinata; Akane Sasaoka; I Putu Agung Bayupati; Oka Sudana
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 11 No 3 (2020): Vol. 11, No. 03 December 2020
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2020.v11.i03.p03

Abstract

Fish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep learning methods used were Convolutional Layer or Convolutional Neural Network (CNN). This research experimented with using object detection method based on deep learning like Faster R-CNN, which possible to recognize the species of fish inside of the image without more image preprocessing. This research aimed to know the performance of the Faster R-CNN method against other object detection methods like SSD in fish species detection. The fish dataset used in the research reference was QUT FISH Dataset. The accuracy of the Faster R-CNN reached 80.4%, far above the accuracy of the Single Shot Detector (SSD) Model with an accuracy of 49.2%.
Offline Signature Identification Using Deep Learning and Euclidean Distance Made Prastha Nugraha; Adi Nurhadiyatna; Dewa Made Sri Arsa
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i02.p04

Abstract

Hand signature is one of human characteristic that human have since birth, which can be used as identity recognition. A high accuracy signature recognition is needed to identify the right owner of signature. This study present signature identification using a combination method between Deep Learning and Euclidean Distance. 3 different signature datasets are used in this study which consist of SigComp2009, SigComp2011, and private dataset. Signature images preprocessed using binary image conversion, Region of Interest, and thinning. Several testing scenarios is applied to measure proposed method robustness, such as usage of various Pretrained Deep Learning, dataset augmentation, and dataset split ratio modifier. The best accuracy achieved is 99.44% with high precision rate.
Helmet Monitoring System using Hough Circle and HOG based on KNN Rachmad Jibril Al Kautsar; Fitri Utaminingrum; Agung Setia Budi
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 1 (2021): Vol. 12, No. 01 April 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i01.p02

Abstract

Indonesian citizens who use motorized vehicles are increasing every year. Every motorcyclist in Indonesia must wear a helmet when riding a motorcycle. Even though there are rules that require motorbike riders to wear helmets, there are still many motorists who disobey the rules. To overcome this, police officers have carried out various operations (such as traffic operation, warning, etc.). This is not effective because of the number of police officers available, and the probability of police officers make a mistake when detecting violations that might be caused due to fatigue. This study asks the system to detect motorcyclists who do not wear helmets through a surveillance camera. Referring to this reason, the Circular Hough Transform (CHT), Histogram of Oriented Gradient (HOG), and K-Nearest Neighbor (KNN) are used. Testing was done by using images taken from surveillance cameras divided into 200 training data and 40 testing data obtained an accuracy rate of 82.5%.
Improving Network Performance of IP PBX Based Telecommunication System Wardi Wardi; Zulfajri Basri Hasanuddin; Andani Andani; Jeffry Jo Salli; Andi Muhammad Syafaat
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 11 No 2 (2020): Vol. 11, No. 2 August 2020
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2020.v11.i02.p04

Abstract

IP-PBX based communication has become a human need in the era of technology. Some researchers design a wireless telecommunication system based on IP PBX using Raspberry Pi. The previous researches have small network coverage and problematic on the ability of the system to support multiple concurrent connections. Based on these problems, these research aims are to expand the coverage area and increase the number of concurrent calls. This study used Asterisk FreePBX for the media configuration of the servers. The clients: laptops and smartphone devices used Linkin and Bria softphone. The testing was conducted in terms of voice and video services in the form of signal strength, CPU performance of servers, and network performance parameters such as delay, jitter, and packet loss. The results obtained that the CPU performance of the servers for seven calls simultaneously is around 16.9% compared with previous research, at an average of 45%. Based on ETSI standards, the measurement of network performance parameters when communicating between clients outperform than previous research. The clients can communicate well up to 390 meters.
QSAR Study for Prediction of HIV-1 Protease Inhibitor Using the Gravitational Search Algorithm–Neural Network (GSA-NN) Methods Isman Kurniawan; Reina Wardhani; Maya Rosalinda; Nurul Ikhsan
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i02.p01

Abstract

Human immunodeficiency virus (HIV) is a virus that infects an immune cell and makes the patient more susceptible to infections and other diseases. HIV is also a factor that leads to acquired immune deficiency syndrome (AIDS) disease. The active target that is usually used in the treatment of HIV is HIV-1 protease. Combining HIV-1 protease inhibitors and reverse-transcriptase inhibitors in highly active antiretroviral therapy (HAART) is typically used to treat this virus. However, this treatment can only reduce the viral load, restore some parts of the immune system, and failed to overcome the drug resistance. This study aimed to build a QSAR model for predicting HIV-1 protease inhibitor activity using the gravitational search algorithm-neural network (GSA-NN) method. The GSA method is used to select molecular descriptors, while NN was used to develop the prediction model. The improvement of model performance was found after performing the hyperparameter tuning procedure. The validation results show that model 3, containing seven descriptors, shows the best performance indicated by the coefficient of determination (r2) and cross-validation coefficient of determination (Q2) values. We found that the value of r2 for train and test data are 0.84 and 0.82, respectively, and the value of Q2 is 0.81.
Propeller Speed Control System on Autonomous Quadcopter with Variations in Load Fulcrum Point Ratna Aisuwarya; Ibrahim Saputra; Dodon Yendri
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 3 (2021): Vol. 12, No. 03 December 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i03.p04

Abstract

The need for unmanned vehicles is increasingly needed in certain conditions, such as distribution of disaster supply, distribution of medicines, distribution of vaccines in the affected areas in pandemic situations. The various types of goods to be distributed require a different fulcrum. This research implemented PID control for the quadcopter balance control system to achieve stability during hovering. PID control is used to achieve a certain setpoint to produce the required PWM output for the propeller to reach a speed that can fly the quadcopter tilted until it reaches a steady state. Tests were carried out on the roll and pitch motion of the quadcopter by providing a load. The results show that PID control can be implemented for the quadcopter balance control system during hovering by determining the PID constants for each roll and pitch motion with the constanta of Kp = 0.15, Kd = 0.108, and Ki = 0.05. The quadcopter takes 3 – 6 seconds to return to the 0 degree setpoint when it is loaded.

Page 17 of 23 | Total Record : 226

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