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All Journal Journal of Telematics and Informatics JIKO (Jurnal Informatika dan Komputer) Jurnal Sains dan Informatika J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Sains Komputer dan Teknologi Informasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Maura Widyaningsih
STMIK Palangkaraya
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics

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Identifikasi Kematangan Buah Apel Dengan Gray Level Co-Occurrence Matrix (GLCM) Maura Widyaningsih
Jurnal SAINTEKOM Vol. 6 No. 1 (2016): Maret 2016
Publisher : STMIK Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1584.457 KB) | DOI: 10.33020/saintekom.v6i1.7

Abstract

Digital image processing is part of the technological developments in the concepts and reasoning, the human wants the machine (computer) can recognize images like human vision. Recognizing the image is one way to distinguish the traits that exist in the image. Texture is one of the characteristics that distinguish the image, is the basic characteristic of the image identification. Gray Level Co-Occurrence Matrix (GLCM) is one method of obtaining characteristic texture image by calculating the probability of adjacency relationship between two pixels at a certain distance and direction. The characteristics of texture obtained from GLCM methods include contrast, correlation, homogeneity, and energy. The extracted features are then used for identification with the nearest distance calculations (Eucledian Distance). The final results analysis program to identify the category of apples raw, half-ripe or overripe. Training data used are 12 images apple, consisting of 4 is crude, 4 is half-cooked, and 4 is ripe, 7 data used for testing. Testing GLCM with 00 angle feature extraction results of the test images can be recognized by a factor Eucledian Distance to the query image. Identification of test data is information all the data can be recognized. Eucledian Distance is a method that helps the introduction of a test object data.
Dempster Shafer Untuk Sistem Diagnosa Gejala Penyakit Kulit Pada Kucing Maura Widyaningsih; Rio Gunadi
Jurnal SAINTEKOM Vol. 7 No. 1 (2017): Maret 2017
Publisher : STMIK Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1237.787 KB) | DOI: 10.33020/saintekom.v7i1.24

Abstract

Expert System which is a branch of Artifical Intelligence, who learned about the estimation or decision-making ability of an expert. Methods and concepts are still needed in solving the problem of diagnosis, with engineering calculations involve computing systems., given the level of need for information and resolving cases. The application development is aimed at implementing the knowledge of an expert into a program that can help in diagnosing the symptoms of skin health problems in cats. Dempster Shafer (DS) is a method that is non monotonous in solving the problem of uncertainty due to the addition or subtraction of new facts.The system is made to diagnose the type of skin disease in cats after applying the method of DS. The system can also perform data management if there is a data change disease, symptoms, treatment solutions, as well as the rules of the disease. The diagnosis system with DS according to analysis from experts.
Image Processing Bentuk Jarimatika dengan deteksi Canny dan Ektraksi Momen Hu: Image Processing of Jarimatic shape With Canny Detection and Moment Hu Extraction Maura Widyaningsih; Susi Hendartie
Jurnal Sains Komputer dan Teknologi Informasi Vol. 4 No. 2 (2022): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsakti.v4i2.2546

Abstract

Ilmu Processing merupakan sub keilmuan Computer vision pada Artifical Intelegence, yang membantu dalam analisis akan kebutuhan informasi dengan obyek citra. Pada konsep pembelajaran jarimatika peranan jari membantu informasi dalam mempermudah hitungan matematika. Jarimatika merupakan teknik belajar matematika praktis bagi siswa dengan menggunakan jari pada 2 belah tangan kanan dan kiri. Namun obyek citra perlu dianalisis terlebih dahulu dengan menggunakan metode dan konsep penyelesaiannya. Pengembangan dan pengujian dilakukan dan dikembangkan secara terus menerus demi kemajuan manusia khususnya pendidikan. Aplikasi pengenalan bentuk jari diproses dengan metode image processing dengan teknik filtering Gausian Blur, resize, grayscale, dan teknik segmentasi dengan menggunakan deteksi tepi Canny dan deteksi contour, dilanjutkan dengan dilation. Ekstraksi ciri menggunakan Moment Hu dari hasil citra kontur Dari hasil segmentasi dan deteksi tepi memberikan hasil obyek dapat menunjukan tepian dengan jelas dan penebalan dengan dilasi untuk memperkuat tepian citra, sehingga membantu dalam penentuan nilai kontur. Hasil citra direkomendasikan pada proses pembelajaran data sehingga memastikan apakah citra jarimatika dikenal bentuknya secara berbeda atau tidak, yang selanjutnya dapat dikembangkan pada penerapan pola jari pada machine learning untuk jarimatika.
Co-Authors Agus Harjoko Elok Faiqotul Himmah Frengklin Matatula Leo Giovanni Maysaroh Maysaroh Naela Fauzia Dini Rio Gunadi Sulistyowati Susi Hendartie Susi Hendartie Wina Wulandari