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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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KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers using Dictionary Lookup and Peter Norvig Spelling Corrector Tresna Maulana Fahrudin; Ilmatus Sa’diyah; Latipah Latipah; Ibnu Zahy’ Atha Illah; Cagiva Chaedar Bey Lirna; Burhan Syarif Acarya
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.p02

Abstract

Many Indonesian spelling errors occur in research papers published to the public, closely related to academics in all institutions such as research institutions, government, schools, and universities. The spelling errors usually writing punctuation, writing letters, writing words, writing words originating from foreign or regional languages (uptake words), using affixed words, and writing ineffective sentences. The mistakes made by the academics then become a cycle in the academic environment. They usually provide guidance for writing an undergraduate thesis, thesis, dissertations to students, or the other forms of documents and scientific papers. Therefore, the research proposed the application to facilitate all authors of scientific papers in producing quality scientific works based on the General Guidelines for Indonesian Spelling published by the Agency for Development and Language Development. The application is named KEBI 1.0 Checker (Indonesian Spelling Error 1.0 Checker), a web-based application with a built-in algorithm to detect and correct Indonesian Spelling in scientific papers. The experiment result shows that the application has given the best accuracy performance to correct the non-standard words, and typographical errors reached 100% and 55,52%, respectively. The application also has been detected 209 meaningless words. The application processing time is relatively low, the average time needed to correct non-standard words is 0.016 seconds, and typo words are 14.58 seconds. KEBI 1.0 Checker is helpful for the end-user in academics but needs to improve the vocabulary of the large corpus in various fields of science for correcting typo words.
Spatial Based Deep Learning Autonomous Wheel Robot Using CNN Eko Wahyu Prasetyo; Nambo Hidetaka; Dwi Arman Prasetya; Wahyu Dirgantara; Hari Fitria Windi
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.p05

Abstract

The development of technology is growing rapidly; one of the most popular among the scientist is robotics technology. Recently, the robot was created to resemble the function of the human brain. Robots can make decisions without being helped by humans, known as AI (Artificial Intelligent). Now, this technology is being developed so that it can be used in wheeled vehicles, where these vehicles can run without any obstacles. Furthermore, of research, Nvidia introduced an autonomous vehicle named Nvidia Dave-2, which became popular. It showed an accuracy rate of 90%. The CNN (Convolutional Neural Network) method is used in the track recognition process with input in the form of a trajectory that has been taken from several angles. The data is trained using Jupiter's notebook, and then the training results can be used to automate the movement of the robot on the track where the data has been retrieved. The results obtained are then used by the robot to determine the path it will take. Many images that are taken as data, precise the results will be, but the time to train the image data will also be longer. From the data that has been obtained, the highest train loss on the first epoch is 1.829455, and the highest test loss on the third epoch is 30.90127. This indicates better steering control, which means better stability.
Sistem Pendukung Keputusan Berbasis Fitur untuk Prediksi Penyakit Jantung Berdasarkan Rasio Diskriminan Fisher dan Algoritma Backpropagation Muh Dimas Yudianto; Tresna Maulana Fahrudin; Aryo Nugroho
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.p01

Abstract

Coronary heart disease included a group of cardiovascular, and it is a leading cause of death in low and middle-income countries. Risk factors for coronary heart disease are divided into two, namely primary and secondary risk factors. The need to identify characteristics or risk factors in heart disease patients by making the classification model. The modeling of heart disease classification to know how the system can able to reach the best prediction accuracy. Fisher's Discriminant Ratio is one of the methods for feature selection, which is used to get high discriminant features. While Backpropagation is one of the classification models to recognize patterns in heart disease patients. The experiment results showed that the accuracy of the classification model using 13 original features reached 92%. By reducing the features based on the score of the feature selection, then the lowest feature was removed from original features and left there were 12 features involved in the classification model which the accuracy increased to 93%. Furthermore, the results of determining the threshold (accuracy does not decrease continuously) and consider the effect of eliminating the lowest features that are considered quite fluctuating on accuracy. The accuracy reached 90% by eliminating the five lowest features and left eight existing features.
A Practical Analysis of the Fermat Factorization and Pollard Rho Method for Factoring Integers Aminudin Aminudin; Eko Budi Cahyono
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.p04

Abstract

The development of public-key cryptography generation using the factoring method is very important in practical cryptography applications. In cryptographic applications, the urgency of factoring is very risky because factoring can crack public and private keys, even though the strength in cryptographic algorithms is determined mainly by the key strength generated by the algorithm. However, solving the composite number to find the prime factors is still very rarely done. Therefore, this study will compare the Fermat factorization algorithm and Pollard rho by finding the key generator public key algorithm's prime factor value. Based on the series of test and analysis factoring integer algorithm using Fermat's Factorization and Pollards' Rho methods, it could be concluded that both methods could be used to factorize the public key which specifically aimed to identify the prime factors. During the public key factorizing process within 16 bytes – 64 bytes, Pollards' Rho's average duration was significantly faster than Fermat's Factorization.
Design of Web Virtual Reality for Job Interview Preparation Simulation Pius Dian Widi Anggoro
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.p02

Abstract

The implementation of Virtual Reality (VR) in education is a breakthrough in using technology to support the teaching-learning system. This study will provide more knowledge about the use of VR in English classes. Students can practice answering interview questions in their own place, as often as they need. Students also can practice answering interview questions. This research is use VR technology in a web platform for job interview simulation cases. In the early stages, the evaluation to review the use of VR technology that running on low-specification smartphones (Low-Cost Device), which require a lower internet connection. The WebVR and React 360 libraries were used to develop the virtual environments and JavaScript for the language. The Web Speech API was used to convert the test into conversations by taking questions from the web service on the Moodle learning platform that was connected to PostgreSQL. The first test methods were the web application performance, then followed by Alpha Testing, a validation test by media and material experts. Than it continued in Beta Testing where a product test by 15 English class students participants. The data collection technique used a questionnaire that has to be answered by the participants. The validity and reliability tests were carried out for product usage test. The results obtained from the assessment of media experts and participant provide an assessment score of 83.10 from the experts and 77.58 from the users. The average score obtained is 80.34 which is included in the feasible category. Therefore, this learning media is ready to be used to support learning in English class.
The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Pramuditha Shinta Dewi Puspitasari; Prawidya Destarianto; Dyah Laksito Rukmi; Arizal Mujibtamala Nanda Imron
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.p03

Abstract

Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.
Modeling of Solar Radiation Using the Wavelet Neural Network Model in Mataram City Lombok Island Syamsul Bahri
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.p06

Abstract

Sunlight is a source of energy for living things in general. In reality, the intensity of solar radiation is an environmental parameter that has positive and negative impacts on human life in particular. Furthermore, the knowledge on the characteristics of solar radiation, including its distribution pattern, is considered by many circles, both policy-makers and researchers in the environmental field. This study aims to create a solar radiation model in response to meteorological factors such as wind speed, air pressure and temperature, humidity, and rainfall using the Wavelet Neural Network (WNN). The modeling of solar radiation in this study is carried out by simultaneously utilizing its advantages as a hybrid model that combines the neural network model and the wavelet method. These advantages through the learning process (supervised learning) are multiplied through the use of the wavelet transform as a pre-processing data method and two type wavelets function, B-spline and Morlet wavelets, as an activation function in the neural network learning process. The WNN model was analyzed in two cases of meteorological variables, which are with and without rainfall. The results based on the root of the mean square error (RMSE) indicator show that the WNN model in these two cases is quite accurate. Meanwhile, the other indicator shows that the interval of the data distribution from the model is within the actual range. This implies that the predicted intensity of the solar radiation will be in a safe position in its adverse effect when the model is used as a reference.
End User Satisfaction for Location Health Service Application with Analysis of Task Technology Fit Linda Perdana Wanti; Hijriah Fajar Muhammad Insan; Nur Wachid Adi Prasetya
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.p02

Abstract

There are several types of health services that provide information about health care facilities, such as pharmacies, health centers, clinics, and hospitals. Application of health service facilities location is used to facilitate users in reaching the nearest health service facility. The application of the health care facilities location has not been optimally used by the user so often. The advantage of analyzing the system is to determine its direct and indirect effect on the end-user. This research analyzes task technology fit (TTF) of application for the location of health service facilities based on measures of end-user satisfaction and knowledge management system (KMS). The research began with an exploratory study through interviews with users of health service applications. With the results of interviews, the research hypothesis model was built to integrate health service applications with the task technology fit model based on end-user satisfaction. The results obtained from this study are the impact of the performance of a good application system can increase end-user satisfaction in optimizing all the modules that exist in the application. The intended system performance is the quality of information presented by the application including the location of the health service facility and the accuracy of information needed by the end which affects the compatibility of the health service facility application which significantly increase the end-user satisfaction, and this will automatically affect the TTF performance for the better. This needs to be responded to so that the application continues to be updated in real-time to continue to provide information about the application in accordance with the development and needs of end-users. This linkage shows that the role of task technology fit has a good impact on system development that affects system relationships and end-user satisfaction in applications.
Recognition of The Baby Footprint Characteristics Using Wavelet Method and K-Nearest Neighbor (K-NN) I Made Aris Satia Widiatmika; I Nyoman Piarsa; Arida Ferti Syafiandini
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.p05

Abstract

Individual recognition using biometric technology can be utilized in creating security systems that are important in modern life. The individuals recognition in hospitals generally done by conventional system so it makes more time in taking identity. A newborn baby will proceed an identity tagging after birth process is complete. This identity using a bracelet filled with names and ink stamps on paper that will be prone to damage or crime. The solution is to store the baby's identity data digitally and carry out the baby's identification process. This system can increase safety and efficiency in storing a baby's footprint image. The implementation of baby's footprint image identification starting from the acquisition of baby's footprint image, preprocessing such as selecting ROI size baby's footprint object, feature extraction using wavelet method and classification process using K-Nearest Neighbor (K-NN) method because this method has been widely used in several studies of biometric identification systems. The test data came from 30 classes with 180 images test right and left baby's footprint. The identification results using 200x500 size ROI with level 4 wavelet decomposition get recognition results with an accuracy of 99.30%, 90.17% precision, and 89.44% recall with a test computation time of 8.0370 seconds.
Klasifikasi Kejang Epilepsi menggunakan Deep Batch Normalization Neural Network Adenuar Purnomo; Handayani Tjandrasa
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.p01

Abstract

Epilepsy is a chronic noncommunicable brain disease. Manual inspection of long-term Electroencephalogram (EEG) records for detecting epileptic seizures or other diseases that lasted several days or weeks is a time-consuming task. Therefore, this research proposes a novel epileptic seizure classification architecture called the Deep Batch Normalization Neural Network (Deep BN3), a BN3 architecture with a deeper layer to classify big epileptic seizure data accurately. The raw EEG signals are first to cut into pieces and passed through the bandpass filter. The dataset is very imbalanced, so an undersampling technique was used to produce a balanced sample of data for the training and testing dataset. Furthermore, the balanced data is used to train the Deep BN3 architecture. The resulting model classifies the EEG signal as an epileptic seizure or non-seizure. The classification of epileptic seizures using Deep BN3 obtained pretty good results compared to other architectures used in this research, with an accuracy of 53.61%.

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