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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 5 Documents
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Search results for , issue "Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022" : 5 Documents clear
Balinese Script Recognition Using Tesseract Mobile Framework Gede Indrawan; Ahmad Asroni; Luh Joni Erawati Dewi; I Gede Aris Gunadi; I Ketut Paramarta
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p03

Abstract

One of the main factors causing the decline in the use of Balinese Script is that Balinese people are less interested in reading Balinese Script because of their reluctance to learn Balinese Script, which is relatively complicated in the recognition process. The development of computer technology has now been used to help by performing character recognition or known as Optical Character Recognition (OCR). Developing the OCR application for Balinese Script is an effort to help preserve, from the technology side, as a means of education related to Balinese Script. In this study, that development was conducted by using a Tesseract OCR engine that consists of several stages, i.e., the first one is to prepare the dataset, the second one is to generate the dataset using the Web Scraping method, the third one is to train the OCR engine using the generated dataset, and finally, the fourth one is to implement the generated language model into a mobile-based application. The study results prove that the dataset generation process using the Web Scraping method can be a better choice when faced with a training dataset that requires a large dataset compared to several previous studies of non-Latin character recognition. In those studies, the jTessBox tools were used, which took time because they had to select per character for a dataset. The best result of the language model is a combination of character, word, sentence, and paragraph datasets (hierarchical combination of character, word, sentence, and paragraph datasets) with a coincidence rate of 66.67%. The more diverse and structured hierarchical datasets used, the higher the coincidence rate.
Metode Boosting Untuk Prediksi Angka Kejadian Demam Berdarah Dengue di Kabupaten Bandung Fhira Nhita; Didit Adytia; Aniq Atiqi Rohmawati
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p05

Abstract

Dengue infections are among the top 10 diseases that cause the most deaths worldwide. Dengue is a severe global threat and problem, especially in tropical countries like Indonesia. The Indonesian Ministry of Health also stated that dengue is as dangerous as COVID-19. One of the preventive actions that can be taken is by controlling vectors (the Aedes aegypti mosquito) where weather factors influence their breeding. In this study, the prediction of dengue incidence rate is carried out using three boosting methods i.e., Extreme Gradient Boosting, Adaptive Boosting, and Gradient Boosting. The data used are monthly data of dengue incidence rate and weather data. The case study used is Bandung district, West Java Province, Indonesia. The important issues that is investigated in this study is to find the weather parameters that have the most influence on IR and gradually improve the prediction model through three test scenarios. From the test results, the weather parameter that has the most influence on the next month's IR is temperature. Meanwhile, the best training data length is five years (2016-2020). Finally, the best prediction model achieved by AdaBoost method with value of Root Mean Square Error and Correlation Coefficient for testing data (January-December 2021) are 0.55 and 0.95, respectively.
Embryo Grading after In Vitro Fertilization using YOLO Dewi Ananta Hakim; Ade Jamal; Anto Satriyo Nugroho; Ali Akbar Septiandri; Budi Wiweko
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p01

Abstract

In vitro fertilization is an implementation of Assistive Reproductive Technology. This technology can produce embryos outside the mother's womb by manipulating gametes outside the human body. The success rate of in vitro fertilization is the selection of good-grading embryos. In this study, the authors used Yolo Version 3 to perform object detection objectively by introducing grades for each embryo image. The author uses an embryo image sourced from the Indonesian Medical Education and Research Institute with information on the quality of the embryo. In this study, the author separated the data into two schemes. The first scheme separates data into training data of 70%, 15% validation data, and 15% for testing data. The second scheme uses a Stratified K-Fold Cross-Validation with a fold value =3. In training, the writer configures the values ??of Max Batches=6000, Steps=4800,5400, Batch=64, and Subdivision=16 by doing image augmentation (saturation=1.5, exposure=1.5, hue=0.1, jitter=0.3, random=1). For each of the obtained mAP (Mean Average Precision) values ??for data separation schemes, one is 100.00% in the 6000th iteration, while for the two-data separation scheme, the highest mAP is 97.33%.% in the fold=3 and 5000th iteration. It means that both separation schemes are sufficient in terms of mAP.
HiT-LIDIA: A Framework for Rice Leaf Disease Classification using Ensemble and Hierarchical Transfer Learning Oddy Virgantara Putra; Niken Trisnaningrum; Niken Sylvia Puspitasari; Agung Toto Wibowo; Ema Rachmawaty
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p06

Abstract

Rice is one of the global most critical harvests, and a great many people eat it as a staple eating routine. Different rice plant diseases harm, spread, and drastically reduce crop yields. In extreme situations, they may result in no grain harvest at all, posing a severe threat to food security. In this paper, to amplify the recognition ability for rice leaf disease (RLD) classification, we proposed hierarchical transfer learning (HTL) methods incorporating ensemble models containing two-step. In the first step, an ensemble combining MobileNet and DenseNet was addressed to tackle the diseased leaf problem. Consequently, DenseNet and XceptionNet were fused to identify three RLDs. Here, we compare our models with state-of-the-art deep learning models such as ResNet, DenseNet, InceptionNet, Xception, MobileNet, and EfficientNet. Our framework at top-notch with 89 % and 91 % for accuracy. In future works, RLD segmentation is suggested to pinpoint the illness and quantify the afflicted region.
Sentiment Analysis on Product Reviews from Shopee Marketplace using the Naïve Bayes Classifier Emil R. Kaburuan; Yunita Sartika Sari; Ika Agustina
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 3 (2022): Vol. 13, No. 3 December 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i03.p02

Abstract

Online shopping has become a popular shopping method ever since the number of internet users increased. Online shopping activities have become very easy and flexible because they can be completed anywhere and anytime. The products provided are also complete. The products sold often do not always match the actual conditions because the product can only be seen through pictures. Users who have purchased a product can share their opinions using the review feature. However, the products purchased thousands or millions of times have many reviews. To take an overview of the product, it is essential to go through every positive and negative review, which takes a lot of time and effort. Reviews of products from the Shopee marketplace will be classified into positive or negative sentiments towards women's home wear clothing or house dress in this study. The research starts with data crawling, text preprocessing, training data, testing, and evaluation model and then concludes with a general description based on the most frequently discussed topics in the reviews for each sentiment class. Classification is done using the Naïve Bayes Classifier algorithm. The accuracy obtained is 90,03%. The total dataset is 2907 data.

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