Publisher: Universitas Udayana (Total: 68 journals)   [Sort alphabetically]

Showing 1 - 68 of 68 Journals sorted by number of followers
E-J. of Tourism     Open Access   (Followers: 11)
E-Jurnal Manajemen Universitas Udayana     Open Access   (Followers: 10)
Matrik : Jurnal Manajemen, Strategi Bisnis dan Kewirausahaan     Open Access   (Followers: 7)
Sport and Fitness J.     Open Access   (Followers: 5)
Advances in Tropical Biodiversity and Environmental Sciences     Open Access   (Followers: 5)
e-J. of Linguistics     Open Access   (Followers: 4)
Buletin Studi Ekonomi     Open Access   (Followers: 4)
Buletin Veteriner Udayana     Open Access   (Followers: 3)
J. of Marine and Aquatic Sciences     Open Access   (Followers: 2)
Agrotrop : J. on Agriculture Science     Open Access   (Followers: 2)
J. of Food Security and Agriculture     Open Access   (Followers: 2)
Majalah Ilmiah Peternakan     Open Access   (Followers: 2)
Simbiosis : J. of Biological Sciences     Open Access   (Followers: 2)
E-Jurnal Medika Udayana     Open Access   (Followers: 2)
Jurnal Master Pariwisata (J. Master in Tourism Studies)     Open Access   (Followers: 2)
Jurnal Ergonomi Indonesia (The Indonesian J. of Ergonomic)     Open Access   (Followers: 2)
E-J. of Cultural Studies     Open Access   (Followers: 2)
Majalah Ilmiah Teknologi Elektro : J. of Electrical Technology     Open Access   (Followers: 2)
J. of Veterinary and Animal Sciences     Open Access   (Followers: 1)
Linguistika : Buletin Ilmiah Program Magister Linguistik Universitas Udayana     Open Access   (Followers: 1)
E-Jurnal Ekonomi dan Bisnis Universitas Udayana     Open Access   (Followers: 1)
COPING (Community of Publishing in Nursing)     Open Access   (Followers: 1)
itepa : Jurnal Ilmu dan Teknologi Pangan     Open Access   (Followers: 1)
Humanis : J. of Arts and Humanities     Open Access   (Followers: 1)
E-Jurnal Agroekoteknologi Tropika (J. of Tropical Agroecotechnology)     Open Access   (Followers: 1)
J. of Health Sciences and Medicine     Open Access   (Followers: 1)
E-Jurnal Ekonomi Pembangunan Universitas Udayana     Open Access   (Followers: 1)
Jurnal Ilmiah Akuntansi dan Bisnis     Open Access   (Followers: 1)
Archive of Community Health     Open Access   (Followers: 1)
Jurnal Matematika     Open Access   (Followers: 1)
Jurnal Ekonomi Kuantitatif Terapan     Open Access   (Followers: 1)
Indonesia Medicus Veterinus     Open Access   (Followers: 1)
Jurnal Veteriner     Open Access   (Followers: 1)
Jurnal Magister Hukum Udayana (Udayana Master Law J.)     Open Access   (Followers: 1)
Jurnal Spektran     Open Access   (Followers: 1)
Jurnal Ilmu dan Kesehatan Hewan (Veterinary Science and Medicine J.)     Open Access   (Followers: 1)
Public Health and Preventive Medicine Archive     Open Access   (Followers: 1)
Bumi Lestari J. of Environment     Open Access  
E-Jurnal Akuntansi     Open Access  
Jurnal Biologi Udayana     Open Access  
Jurnal Farmasi Udayana     Open Access  
Jurnal BETA (Biosistem dan Teknik Pertanian)     Open Access  
Jurnal Rekayasa dan Manajemen Agroindustri     Open Access  
Piramida     Open Access  
Jurnal Kimia (J. of Chemistry)     Open Access  
Indonesian J. of Legal and Forensic Sciences     Open Access  
Kertha Patrika     Open Access  
Jurnal Destinasi Pariwisata     Open Access  
Lingual : J. of Language and Culture     Open Access  
Jurnal Arsitektur Lansekap     Open Access  
Buletin Fisika     Open Access  
Intl. J. of Engineering and Emerging Technology     Open Access  
Jurnal Analisis Pariwisata     Open Access  
Udayana J. of Law and Culture     Open Access  
J. of Electrical, Electronics and Informatics     Open Access  
Jurnal IPTA     Open Access  
Jurnal Kepariwisataan dan Hospitalitas     Open Access  
Jurnal Ilmu Komputer     Open Access  
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi     Open Access  
JBN (Jurnal Bedah Nasional)     Open Access  
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi)     Open Access  
Intisari Sains Medis     Open Access  
Jurnal Energi Dan Manufaktur     Open Access  
Jurnal Ilmiah Mahasiswa SPEKTRUM     Open Access  
Jurnal Udayana Mengabdi     Open Access  
Ecotrophic : J. of Environmental Science     Open Access  
Ruang-Space: Jurnal Lingkungan Binaan (J. of The Built Environment)     Open Access  
Cakra Kimia (Indonesian E-J. of Applied Chemistry)     Open Access  
Similar Journals
Journal Cover
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2088-1541 - ISSN (Online) 2541-5832
Published by Universitas Udayana Homepage  [68 journals]
  • QSAR Study for Prediction of HIV-1 Protease Inhibitor Using the
           Gravitational Search Algorithm–Neural Network (GSA-NN) Methods

    • Authors: Isman Kurniawan, Reina Wardhani, Maya Rosalinda, Nurul Ikhsan
      Pages: 62 - 77
      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.  
      PubDate: 2021-07-12
      DOI: 10.24843/LKJITI.2021.v12.i02.p01
      Issue No: Vol. 12, No. 2 (2021)
       
  • KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers
           using Dictionary Lookup and Peter Norvig Spelling Corrector

    • Authors: Tresna Maulana Fahrudin, Ilmatus Sa’diyah, Latipah Latipah, Ibnu Zahy’ Atha Illah, Cagiva Chaedar Bey Lirna, Burhan Syarif Acarya
      Pages: 78 - 90
      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.  
      PubDate: 2021-08-03
      DOI: 10.24843/LKJITI.2021.v12.i02.p02
      Issue No: Vol. 12, No. 2 (2021)
       
  • The The Classification of Acute Respiratory Infection (ARI) Bacteria Based
           on K-Nearest Neighbor

    • Authors: Zilvanhisna Emka Fitri, Lalitya Nindita Sahenda, Pramuditha Shinta Dewi Puspitasari, Prawidya Destarianto, Dyah Laksito Rukmi, Arizal Mujibtamala Nanda Imron
      Pages: 91 - 101
      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.
      PubDate: 2021-08-03
      DOI: 10.24843/LKJITI.2021.v12.i02.p03
      Issue No: Vol. 12, No. 2 (2021)
       
  • Offline Signature Identification Using Deep Learning and Euclidean
           Distance

    • Authors: Made Prastha Nugraha, Adi Nurhadiyatna, Dewa Made Sri Arsa
      Pages: 102 - 111
      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.
      PubDate: 2021-08-16
      DOI: 10.24843/LKJITI.2021.v12.i02.p04
      Issue No: Vol. 12, No. 2 (2021)
       
  • Dempster Shafer Algorithm For Expert System Early Detection of Anxiety
           Disorders

    • Authors: Finanta Okmayura, Vitriani Vitriani, Melly Novalia
      Pages: 112 - 122
      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.
      PubDate: 2021-08-16
      DOI: 10.24843/LKJITI.2021.v12.i02.p05
      Issue No: Vol. 12, No. 2 (2021)
       
  • Classification Of Rice Plant Diseases Using the Convolutional Neural
           Network Method

    • Authors: A A JE Veggy Priyangka, I Made Surya Kumara
      Pages: 123 - 129
      Abstract: Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.
      PubDate: 2021-08-16
      DOI: 10.24843/LKJITI.2021.v12.i02.p06
      Issue No: Vol. 12, No. 2 (2021)
       
 
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