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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 4 Documents
 1 
Search results for , issue "Vol. 17 No. 1 (2023): January 2023" : 4 Documents clear
A Hybrid Approch Tomato Diseases Detection At Early Stage Ullah, Arif; khalid, Muhammad Azeem; Sebai, Dorsaf; Alam, Tanweer
Jurnal Informatika Vol. 17 No. 1 (2023): January 2023
Publisher : Universitas Ahmad Dahlan

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Abstract

 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.
Utilization of the PROMETHEE algorithm to determine the suitability of the atmospheric environment in traditional buildings Distia Diva, Ana; Winiarti, Sri; Miksa Mardhia, Murein
Jurnal Informatika Vol. 17 No. 1 (2023): January 2023
Publisher : Universitas Ahmad Dahlan

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Abstract

The 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%.
Hyper Parameter Tuning of Multilayer Convolutional Network and Augmentation Method for Classification Motive of Batik Nursikuwagus, Agus; hartono, tono; Nurwicaksono, M A; Choir, M M; Saputri, M A
Jurnal Informatika Vol. 17 No. 1 (2023): January 2023
Publisher : Universitas Ahmad Dahlan

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Abstract

The 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.
Towards a Complete Kurdish NLP Pipeline: Challenges and Opportunities Jacksi, Karwan; Maulud, Dastan; Ali, Ismael
Jurnal Informatika Vol. 17 No. 1 (2023): January 2023
Publisher : Universitas Ahmad Dahlan

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Abstract

With 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.

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