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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 5 Documents
 1 
Search results for , issue "Vol 16, No 3 (2022): September 2022" : 5 Documents clear
Image processing for maturity classification of tomato using otsu and manhattan distance methods Anindita Septiarini; Hamdani Hamdani; Muhammad Sofian Sauri; Joan Angelina Widians
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a21985

Abstract

Currently, image processing-based systems have been widely applied in various fields, one of which is agriculture. The system can be used to classify fruit maturity. Tomato is one of the agricultural products consumed by the community. Therefore, the requirement for ripe tomatoes increases. In this work, the classification method based on image processing for grading the maturity level of tomato was developed to distinguish tomato into three classes: unripe, half-ripe, and ripe. Classification is carried out based on the skin color of the tomato. The method required five main processes; initially, the detection of the region of interest (ROI) applied using the Otsu method followed by the conversion of RGB to HSV color space. Afterward, segmentation with Otsu thresholding on the S channel of the HSV color space was implemented. Subsequently, the extraction of the mean, median, max, and min features on each channel from the YIQ color space; therefore, a total number of 12 features was produced. Finally, the K-nearest neighbor (KNN) method using Manhattan distance is applied with the values of k = 1, 3, 5, 7, and 9. The dataset used consists of 90 images of tomatoes (30 raws, 30 half-ripes, and 30 ripes), where the dataset is divided into two types, including 54 images as training data and 36 images as testing data. The evaluation results were able to achieve the highest accuracy value of 0.9722.
Covid-19 diagnosis and clinical symptom expression levels in a deep learning model Minh-Dat Le Chon; Thai-Ngoc Nguyen
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a23350

Abstract

In December 2019, a new strain of virus called COVID-19 (previously designated as 2019-nCoV) caused the first detected outbreak in Wuhan City, Hubei Province, China and since then spread globally. Viruses can cause several types of damage to the respiratory tract, including Tracheitis; Bronchitis; Pneumonia. It is difficult to distinguish coronavirus pneumonia from some other microbiological causes through X-ray images. However, it can be distinguished from a normal person by chest X-ray and CT-Scan, along with clinical judgment through actual symptoms. The following article provides the process and setup of an analytical machine learning model and provides some clinical comparisons between the effectiveness of the machine learning model and the level of clinical symptomatology of a statistical sample. Medical records of some patients in Ho Chi Minh City, Vietnam.
Revisiting the challenges and surveys in text similarity matching and detection methods Alva Hendi Muhammad; Kusrini Kusrini; Irwan Oyong
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i3.a23471

Abstract

The massive amount of information from the internet has revolutionized the field of natural language processing. One of the challenges was estimating the similarity between texts. This has been an open research problem although various studies have proposed new methods over the years. This paper surveyed and traced the primary studies in the field of text similarity. The aim was to give a broad overview of existing issues, applications, and methods of text similarity research. This paper identified four issues and several applications of text similarity matching. It classified current studies based on intrinsic, extrinsic, and hybrid approaches. Then, we identified the methods and classified them into lexical-similarity, syntactic-similarity, semantic-similarity, structural-similarity, and hybrid. Furthermore, this study also analyzed and discussed method improvement, current limitations, and open challenges on this topic for future research directions.
Classification of IGF1R ligand compounds for Identification of herbal extracts using extreme gradient boosting Mohammad Hamim Zajuli Al Faroby; Siti Amiroch; Bernadus Anggo Seno Aji; Avriono Aritonang
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i3.a23286

Abstract

Diabetes Mellitus is a serious disease that requires serious treatment. The cause of this disease is due to malfunctions in insulin and insulin-producing organs. One of the proteins that become insulin signaling receptors is IGF1R, which has an important role in activating and maximizing insulin performance. In this study, we aimed to obtain herbal compounds that can activate the function of the IGF1R protein by utilizing compound data in an open database and modeling it using the ensemble method, namely extreme gradient boosting. We found that this method produces the best classification model than with other algorithms. We predicted 844 data for herbal compounds, but only 15 data met the threshold of 0.6. We got one plant from the fifteen herbal compounds, namely Zostera Marine, which was confirmed to have compounds that bind to IGF1R. These compounds have the highest probability value in the classification model that we formed compared to others.
CloudIoT paradigm acceptance for e-learning: analysis and future challenges Arif Ullah; Hanane Aznaoui; Canan Batur Sahin; Ikram Daanoune; Ozlem Batur Dinle
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i3.a21744

Abstract

E-learning is the theme interrelated to the virtualized distance learning with the help of electronic communication machines, certainly with the help of Internet. CloudIoT paradigm is the combination of cloud resource and internet of thing which become prevalent now days due to the flexibility and fast access for those reason different countries used CloudIoT paradigm different purposes. E-learning is one of the best examples where virtual environment provides cost-effective alternative to physical labs as well as to run scientific applications. The world order change in education sector due to Covid-19 all activity shift in to e-learning system. In this paper we present the review about CloudIoT paradigm and it usage in e-learning system as well as we extant taxonomy of CloudIoT paradigm for e-leaning purpose. In the related work section we present the existing contribution in the field of e-learning using CloudIoT paradigm are highlighted. We also contemporaneous the most standard framework which carried out for e-leaning using CloudIoT paradigm is discuss. The contribution section of the paper present the issue being faced by in adopting CloudIoT paradigm for e-learning are discussed along with recommendation and future work.

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