http://journal.uad.ac.id/index.php/JIFO/issue/feedJurnal Informatika2023-09-06T15:51:41+07:00Andri Pranoloandri.pranolo@tif.uad.ac.idOpen Journal Systems<p><strong>Jurnal Informatika</strong> is a peer-reviewed national journal (abroad authors are welcome), aims to bring together research work in the area of Information science and technology, multimedia system, and computational intelligence, including theories, methods, tools, technologies, applications, and so on. The journal is published three times a year starting from Vol 14 No 1 January 2020 (January, May, and September)</p><hr /><table class="data" width="100%" bgcolor="f1f4fc"><tbody><tr valign="top"><td width="30%">Journal title</td><td width="70%"><strong>Jurnal Informatika</strong></td></tr><tr valign="top"><td width="30%">Initials</td><td width="70%"><strong>JIFO</strong></td></tr><tr valign="top"><td width="30%">Abbreviation</td><td width="70%"><strong>J. Inform.</strong></td></tr><tr valign="top"><td width="30%">Frequency</td><td width="70%"><strong>3 issues per year</strong></td></tr><tr valign="top"><td width="30%">DOI</td><td width="70%"><strong>prefix 10.26555</strong><strong><br /></strong></td></tr><tr valign="top"><td width="30%">Print ISSN</td><td width="70%"><strong>1978-0524 </strong></td></tr><tr valign="top"><td width="30%">Online ISSN</td><td width="70%"><strong>2528-6374</strong></td></tr><tr valign="top"><td width="30%">Editor-in-chief</td><td width="70%"><a href="https://www.scopus.com/authid/detail.uri?authorId=57163914100%20"><strong>Murinto</strong></a></td></tr><tr valign="top"><td width="30%">Managing Editor</td><td width="70%"><strong><a href="https://www.scopus.com/authid/detail.uri?authorId=56572821900"> Andri Pranolo</a></strong></td></tr><tr valign="top"><td width="30%">Publisher</td><td width="70%"><strong><a href="https://uad.ac.id/en"> Universitas Ahmad Dahlan</a></strong></td></tr><tr valign="top"><td width="30%">Citation Analysis</td><td width="70%"><strong><a href="/index.php/JIFO/pages/view/scopuscitation">SCOPUS</a></strong><strong> | </strong><strong><a href="https://scholar.google.co.id/citations?user=dJAOkfEAAAAJ&hl=id">Google Scholar</a> | <a href="https://app.dimensions.ai/discover/publication?search_mode=content&search_text=10.26555&search_type=kws&search_field=full_search&order=date&and_facet_source_title=jour.1313927">Dimensions</a></strong></td></tr><tr valign="top"><td width="30%">OAI address</td><td width="70%"><strong><a href="/index.php/JIFO/oai">OAI JIFO</a></strong></td></tr></tbody></table><hr /><p>Jurnal Informatika invites a <strong>original articles</strong> and is <strong>not simultaneously submitted to another</strong> <strong>journal or conference</strong>. Jurnal Informatika is a scientific journal publishing theories and applications with significant implications to the information system, multimedia, and computational intelligence.<strong> The papers' major substantive should apply an algorithm and mathematics implementation</strong> on the <strong>information system, multimedia, and computational intelligence</strong>. The Scope topics include, but are not limited to:</p><ul><li><strong>Information science & technology</strong>. The journal opens the topics to highlight the advanced works on information science and technology, such as theories and applications on Communication and networks, Data warehousing and mining, Enterprise system development and resource management, Management information and database systems, Security and privacy, Software engineering, System development and process management, and Usability engineering.</li><li><strong>Multimedia system. </strong>This section welcomes the excellent paper related to Multimedia software and systems integration, compression and editing, storage, fusion and embedding, Multimedia Internet of Things, Multimedia user interfaces, and New integrated media.</li><li><strong>Computational Intelligence</strong>. This section publishes the specific topics of Agent System and Multi-Agent Systems, Decision Support System, Image Processing & Computer Vision, Natural Language Processing, Explainable human-in-the-loop artificial intelligence, and Trusted machine/deep learning.</li></ul><p>A submitted paper <strong>must be written in English</strong> to get minimum criteria to the initial review stage by editors, and further review process by a minimum of two reviewers. 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If you have any queries please contact jifo@uad.ac.id.</p>http://journal.uad.ac.id/index.php/JIFO/article/view/26053Towards a Complete Kurdish NLP Pipeline: Challenges and Opportunities2023-09-06T15:42:05+07:00Karwan Jacksikarwan.jacksi@uoz.edu.krdDastan Mauluddastan.mawlud@mhe-krg.orgDastan Mauluddastan.mawlud@mhe-krg.orgIsmael Aliismael.ali@uoz.edu.krdIsmael Aliismael.ali@uoz.edu.krdWith the rapid growth of Kurdish language content on the web, there is a high demand for making this information readable and processable by machines. In order to accomplish this, the Kurdish Natural Language Processing (KNLP) pipeline is required. Computers that can process human language use the field of Natural Language Processing (NLP). In its efforts to bridge the communication gap between humans and computers, NLP draws from a wide range of fields, including computer science and computational linguistics. There have been some notable efforts made toward creating the KNLP pipeline. However, it does not support the complete NLP tasks needed to enable semantic web and text mining applications. This paper surveys the work done in the field of NLP for the Kurdish language, its applications, and linguistic challenges.2023-01-10T00:00:00+07:00Copyright (c) 2023 Karwan Jacksi, Dastan Maulud, Dastan Maulud, Ismael Ali, Ismael Alihttp://journal.uad.ac.id/index.php/JIFO/article/view/25799Utilization of the PROMETHEE algorithm to determine the suitability of the atmospheric environment in traditional buildings2023-09-06T15:42:05+07:00Ana Distia Divaana1800018340@webmail.uad.ac.idSri Winiartisri.winiarti@tif.uad.ac.idMurein Miksa Mardhiamurein.miksa@tif.uad.ac.idThe village house is an example of a traditional building. The village house is currently used as a community residence. The life of the village house still protects and maintains ancestral customs, including in the form of house architecture. In the process of building a village house, there is one aspect that is used, such as an appropriate environmental atmosphere. Where the building must maintain the beauty of the environment. The importance of paying attention to the atmospheric environment is because the characteristics of these traditional buildings are not lost, especially in the atmospheric environment as historical evidence. Thus, to develop village house buildings, there is a lack of information related to the traditional buildings themselves, such as the past environmental conditions around traditional buildings, road conditions around buildings, beauty and distance between buildings, and building models around conventional houses, to achieve the goal, documentation of the necessary knowledge related to the atmospheric environment for traditional buildings that still maintains or provides an atmosphere like the old days. The research will be conducted using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) algorithm. The research method is by conducting a literature study, observation, and documentation. Observations and documentation were carried out to collect data in the form of 300 photos of Kampung Rumah in Borobudur, from the data in the form of 300 photos, 9 (nine) were collected. System testing using System Usability Scale (SUS), Black-box, and Expert Judgment. The result of the research is a system for determining the suitability of the environmental atmosphere for village houses in Borobudur using the PROMETHEE algorithm. This research is expected to help determine the village house environment in Borobudur by displaying the final results in the form of outranking and the system test value of 85%.2023-01-10T00:00:00+07:00Copyright (c) 2023 Ana Distia Diva, Sri Winiarti, Murein Miksa Mardhiahttp://journal.uad.ac.id/index.php/JIFO/article/view/25823Hyper Parameter Tuning of Multilayer Convolutional Network and Augmentation Method for Classification Motive of Batik2023-09-06T15:42:05+07:00Agus Nursikuwagusanursikuwagus@gmail.comtono hartonotono.hartono@email.unikom.ac.idM A Nurwicaksonoagy@agyson.comM M Choirmugia.miftahul@gmail.comM A Saputrimeyliaanggraenisaputri@gmail.comThe purpose of this research is to create a batik motive image classification system to make it easier for the public to know the name of a type of batik motive. In carrying out this research, a quantitative method was used with seven kinds of batik motives that were augmented first, where 70% of the dataset was used for training and 30% for testing so that the accuracy and precision of the system were obtained. The result of this research is that the accuracy and precision of the system in classifying batik motive images is 0.985 or 98.5%. This high accuracy and precision were obtained because the quality of the previous dataset was improved by augmenting geometric and photometric. The machine learning method used was a Convolutional Neural Network which in previous studies also provided the highest accuracy and precision. The results of this study can be used for various purposes such as marketing, cultural reservation, and science.2023-01-10T00:00:00+07:00Copyright (c) 2023 Agus Nursikuwagus, tono hartono, M A Nurwicaksono, M M Choir, M A Saputrihttp://journal.uad.ac.id/index.php/JIFO/article/view/24759A Hybrid Approch Tomato Diseases Detection At Early Stage2023-09-06T15:51:41+07:00Arif Ullaharifullahms88@gmail.comMuhammad Azeem khalidarifullahms88@gmail.comDorsaf Sebaiarifullahms88@gmail.comTanweer Alamarifullahms88@gmail.com<p class="AbstractText" align="left"> </p><p>In traditional farming practice, skilled people are hired to manually examine the land and detect the presence of diseases through visual inspection, but the visual inspection method is ineffective. High accuracy of disease detection is one of the most important factors in crop production and reducing crop losses. Meanwhile, the evolution of deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. Previous tomato detection methods based on faster region convolutional neural network (RCNN) are less efficient in terms of accuracy. Researchers have used many methods to detect tomato leaf diseases, but their accuracy is not optimal. This study presents a Faster RCNN-based deep learning model for the detection of three tomato leaf diseases (late blight, mosaic virus, and leaf septoria). The methodology presented in this paper consists of four main steps. The first step is pre-processing. At the second stage, segmentation was done using fuzzy C Means. In the third step, feature extraction was performed with ResNet 50. In the fourth step, classification was performed with Faster RCNN to detect tomato leaf diseases. Two evaluation parameters precision and accuracy are used to compare the proposed model with other existing approaches. The proposed model has the highest accuracy of 98.6% in detecting tomato leaf diseases. In addition, the work can be extended to train the model for other types of tomato diseases, such as leaf mold, spider mites, as well as to detect diseases of other crops, such as potatoes, peanuts, etc.</p>2023-01-13T00:00:00+07:00Copyright (c) 2023 asif khan