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All Journal Perfecting a Video Game with Game Metrics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika Technomedia Journal Indonesian Journal of Computer Science El-Mujtama: Jurnal Pengabdian Masyarakat
iwan Syarif
Politeknik Elektronika Negeri Surabaya
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : Jurnal Ilmiah Kursor

 1 
INTELLIGENT SYSTEM FOR AUTOMATIC CLASSIFICATION OF FRUIT DEFECT USING FASTER REGION-BASED CONVOLUTIONAL NEURAL NETWORK (FASTER R-CNN) Hasan Basri; Iwan Syarif; Sritrusta Sukaridhoto; Muhammad Fajrul Falah
Jurnal Ilmiah Kursor Vol 10 No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v10i1.187

Abstract

In 2018, the Indonesian fruit exports increased by 24% from the previous year. The surge in demand for tropical fruits from non-tropical countries is one of the contributing factors for this trend. Some of these countries have strict quality requirements – the poor level quality control of fruit is an obstacle in achieving greater export yield. This is because some exporters still use manual sorting processes performed by workers, hence the quality standard varies depending on the individual perception of the workers. Therefore, we need an intelligent system that is capable of automatic sorting according to the standard set. In this research, we propose a system that can classify fruit defects automatically. Faster R-CNN (FRCNN) architecture proposed as a solution to detect the level of defect on the surface of the fruit. There are three types of fruit that we research, its mangoes (sweet fragrant), lime, and pitaya fruit. Each fruit divided into three categories (i) Super, (ii) middle, (iii) and fruit defects. We exploit join detection and video tracking to calculate and determine the quality fruit in real-time. The datasets are taken in the field, then trained using the FRCNN Framework using the Tensorflow platform. We demonstrated that this system can classify fruit with an accuracy level of 88% (mango), 83% (lime), and 99% (pitaya), with an average computation cost of 0.0131 m/s. We can track and calculate fruit sequentially without using additional sensors and check the defect rate on fruit using the video streaming camera more accurately and with greater ease.
SEGMENTATION OF LUNG CANCER IMAGE BASED ON CYTOLOGIC EXAMINATION USING THRESHOLDING METHOD Rulisiana Widodo; Tessy Badriyah; Iwan Syarif; Willy Sandhika
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.277

Abstract

Lung cancer is the most dangerous cases which mostly attacks the man with the biggest causes of smoking. This cancer threatens the second largest death after heart attack, lung cancer cases increase significantly every year in various countries. Several methods have been established to detect lung cancer, including Computed Tomography of the thorax, sputum examination and cytology examination. The most decisive examination is through cytologic examination of the pleural fluid. However, the current state of biopsy performed by doctors does not always get a lot of specimens, making it difficult to determine the presence of cancer cells in the lungs. Cytological examination through the pleural fluid has difficulty in detecting cell images. The image of pleural fluid that has a high density between cells will produce an image with low detail, while an image with a low density will produce an image with high detail. Image segmentation is an important part in determining the cellular anatomy of pleural fluid to characterize images with cancer or normal categories. We propose the methodology of research by using group images to separate objects from other objects by highlighting important parts using image segmentation on pleural fluid of patients suspected of having lung cancer. Thresholding method used to see the comparison is Adaptive Thresholding, binary thresholding and Otsu Thresholding. The classification results of the three methods show a high accuracy of 99% on binary thresholding, then 97% accuracy on otsu thresholding and the lowest accuracy of 96% on adaptive thresholding, the three methods are considered to increase in proportion to the addition of the epoch parameter.
Co-Authors Adam Prugel-Bennett Agung Muliawan Agus Indra Gunawan Ahmad Syauqi Ahsan Aidil Saputra Kirsan Ali Ridho Barakbah Amalia Wirdatul Hidayah Andhik Ampuh Yunanto Arna Fariza Assodiky, Hilmy Ayu Ahadi Ningrum Bagas Dewangkara Binti Kholifah Daisy Rahmania Syarif Desy Intan Permatasari Deyana Kusuma Wardani Dian Neipa Purnamasari Dimas Bagus Santoso Dona Wahyudi Edi Satriyanto Entin Martiana Kusumaningtyas Fabyan Kindarya Fachrul Rozie Fahrudin, Tresna Maulana Ferry Astika S Ferry Astika Saputra Fitri Setyorini Gary Wills Grezio Arifiyan Primajaya Hasan Basri Hilmy Assodiky Hisyam, Masfu Idris Winarno Irsal Shabirin M Udin Harun Al Rasyid, M Udin Harun Masfu Hisyam Maulana, Yufri Isnaini Rochmat Mufid, Mohammad Robihul Muhammad Fajrul Falah Muhlis Tahir Mustika Kurnia Mayangsari Nadila Wirdatul Hidayah Nana Ramadijanti Nur Sakinah Nur Ulima Rusmayani Rabiatul Adawiyah Reesa Akbar Rengga Asmara Rengga Asmara Renovita Edelani Rosmaliati, Rosmaliati Rulisiana Widodo S, Ferry Astika Selvia Ferdiana Kusuma Stritusta Sukaridhoto Sumarsono, Irwan Tessy Badriyah, Tessy Tresna Maulana Fahrudin Umi Sa'adah Utomo, Agus Priyo Willy Sandhika Yufri Isnaini Rochmat Maulana