Articles
<![CDATA[KAJIAN TENTANG SISTEM PENGENALAN BUAH PADA PLATFORM IOS MENGGUNAKAN METODE COLOR HISTOGRAM ]]>
<![CDATA[Bryce Clement Sutedja]]>;
<![CDATA[Lina Lina]]>;
<![CDATA[Agus Budi Dharmawan]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 2 (2013): Jurnal Ilmu Komputer dan Sistem Informasi ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v1i2.3117
<![CDATA[Today the use of the mobile phone as an information retrieval system has become a frequently performed. Mobile phone that is easy and practical to carry anywhere, lightweight and increasingly advanced technology makes the device more efficient to use a mobile phone than a desktop or laptop. On basis of these, designed an application to recognize fruit using color histogram method for people affected by Alzheimer. This application is made using xcode with objective-c programming language. The application is built in the iOS platform, the operating system used on the mobile device technology company Apple, the iPhone. Keywords : Color Histogram, iOS, Xcode, Objective-c, Fruit,iPhone.]]>
<![CDATA[KAJIAN TENTANG SISTEM PENGENALAN AIR MINERAL KEMASAN GELAS BERDASARKAN LOGO DENGAN METODE HISTOGRAM OF ORIENTED GRADIENTS ]]>
<![CDATA[Yudhistira Anggara D]]>;
<![CDATA[Lina Lina]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 2 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v6i2.2661
<![CDATA[Application System for the Introduction of Mineral Water Glass Packaging Based on the Logo with the Histogram of Oriented Gradients method is an application used to carry out brand recognition or the logo of glass packaging mineral water. This game is designed on Windows operating systems and uses Python software, and OpenCV. The methods used in this design are the Histogram of Oriented Gradients as a method for feature extraction, the Euclidean Distance method as a method for measuring similarity distances, and k-Nearest Neighbor as a method of recognizing logos or brands.]]>
<![CDATA[PENDETEKSIAN SEL DARAH PUTIH DARI CITRA PREPARAT DENGAN CONVOLUTIONAL NEURAL NETWORK ]]>
<![CDATA[Danny Danny]]>;
<![CDATA[Lina Lina]]>;
<![CDATA[Arlends Chris]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v9i1.11557
<![CDATA[White Blood Cells play an important role as parts of the immune system by fighting against viruses, bacteria and potentially harmful foreign objects that enter the human body. The amount of white blood cells can indicate a certain disease or infection within the human body. This research aims to develop a system that can automatically detect and locate the location of white blood cells in a slide image that is stained or not stained. By not staining blood cell images, it can save time and resources that are normally used in white blood cell detection. This system is built using convolutional neural networks (CNN), a deep learning architecture. The CNN model is used for detecting white blood cells in stained images and is trained with 528 images and the model that is used for detecting white blood cells in unstained images is trained with 264 images. Bounding box regression is used to predict the location of white blood cells. The experiment test results show the detection accuracy for the stained images reach 53.85% and for the unstained images has 54.69% accuracy.]]>
<![CDATA[PENDETEKSIAN SEL DARAH PUTIH DARI CITRA PREPARAT DENGAN YOU ONLY LOOK ONCE ]]>
<![CDATA[Fredriek Andrianson]]>;
<![CDATA[Lina Lina]]>;
<![CDATA[Arlends Chris]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v9i1.11562
<![CDATA[Blood is a fluid that in the body, in the blood there are platelets, red blood cells and white blood cells. White blood cells play an important role in maintaining the body's defense from viruses or bacteria. Many diseases are related to white blood cells, to find out these conditions you need to do an examination to identify white blood cells. Based on this, this design aims to detect white blood cells on images of blood preparations. The method used is You Only Look Once to detect white blood cells in images of blood preparations. The test results showed that the proposed method obtained an accuracy rate of 100% for the detection of white blood cells with stained images and white blood cells without stained images obtained an accuracy rate of 76.5%.]]>
<![CDATA[KAJIAN TENTANG REKONTRUKSI OBJEK 3D MENGGUNAKAN STEREO VISION DENGAN METODE HARRIS INTEREST POINT DAN RANSAC ]]>
<![CDATA[Filbert Oktariko]]>;
<![CDATA[Lina Lina]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 2 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v6i2.2632
<![CDATA[Rekonstruksi 3D merupakan proses untuk memperoleh kembali informasi objek 3D yang ada di dunia nyata dan menyusunnya kembali ke dalam titik-titik pada komputer sehingga komputer dapat mengolah serta menampilkan informasi yang mirip bahkan sama terhadap objek 3D di dunia nyata.Proses ini diawali dengan mengambil 2 input dari citra kamera yang berbeda tetapi memiliki spesifikasi sama. Proses selanjutnya adalah proses segmentasi otomatis menggunakan Harris Interest Point, Proses Normalized Cross Correlation, dan Random Sample Consensus. Adapun perbandingannya dengan metode segmentasi semi otomatis dengan model warna RGB dan HSV dengan nilai parameter yang telah ditentukan. Dari hasil pasangan titik yang terpilih nantinya akan diambil 8 pasangan titik untuk direkonstruksi 3D.Keberhasilan rata-rata hasil rekonstruksi objek 3D yang didapatkan pengujian dengan segmentasi otomatis sebesar 0% dari 40 kali pengujian sedangkan untuk pengujian dengan segmentasi semi-otomatis sebessar 37,5% dari 8 kali pengujian.]]>
<![CDATA[PEMBUATAN WEB PARTY GAME "WIZARD CLASH" MENGGUNAKAN AIRCONSOLE ]]>
<![CDATA[Kennedy Kennedy]]>;
<![CDATA[Lina Lina]]>;
<![CDATA[Darius Andana Haris]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 2 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v6i2.2642
<![CDATA[Game “Wizard Clash” adalah game dengan genre turn-based dengan tema AirConsole yang menggunakan fitur Web party game dengan animasi 2D. Game ini dirancang dengan menggunakan Game Engine Unity2D dengan C#, javascript dan html 5 sebagai bahasa pemograman dan Graphic Gale untuk animasinya. Permainan akan dimainkan hingga 4 pemain dengan masing-masing mengendalikan 4 penyihir yang menguasai 4 elemen masing-masing akan menahan serangan musuh dengan melindungi crystal yang menjadi pusat pertahanan terakhir penyihir harus memikirkan bagaimana caranya agar dapat bertahan menahan serangan dari monster-monster yang datang dari 8 arah. Pengujian dilakukan dengan kuesioner yang telah diisi oleh 30 responden.]]>
<![CDATA[PENGENALAN PRODUK PADA RAK TOKO MENGGUNAKAN METODE YOU ONLY LOOK ONCE (YOLO) DAN COLOR HISTOGRAM ]]>
<![CDATA[Kevin Kurniawan]]>;
<![CDATA[Lina Lina]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 2 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v9i2.13106
<![CDATA[Basically, it is very easy for humans to detect and recognize the types of products that are in the supermarket on pictures or videos. For computers, the difficulty of recognizing and classifying a product is highly dependent on data. The lighting conditions, complexity, and background contrast of an image or video are one of the main challenges. In addition, the overall image or video quality is also very influential on the recognition results. Dairy products come in a variety of colors. Some models have several other products. The test results have a success rate of 72% for detection and a recognition success rate of 98% for dairy products.]]>
<![CDATA[SISTEM PENGENALAN WAJAH DENGAN METODE 2D-PCA ]]>
<![CDATA[Lina Lina]]>;
<![CDATA[Abdurrahman Johnsen Feriyansah]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 1 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v6i1.4661
<![CDATA[Face recognition systems are very popular for authentication and security purposes. However, the development of recognition algorithms has been a true challenge, especially for handling the biometric characteristics of an object. One popular method for face representation is PCA (Principal Component Analysis). PCA is able to solve many recognition problems effectively and efficient by reducing the object (image) dimensions. However, PCA has its own drawbacks in the implementation. When the developed system uses a very large dimension of the input images, the system with the PCA method will have difficulties in constructing the covariance matrix and calculating the eigenvalues and eigenvectors. To overcome these problems, a face recognition system using the 2DPCA method is developed. The experiments show that the 2DPCA method could give higher recognition accuracies compared with that of the PCA method]]>
<![CDATA[KAJIAN TENTANG PENGENALAN KARAKTER DENGAN METODE ANALYTIC SIGNAL VECTOR ]]>
<![CDATA[Arif Januardi Liuman]]>;
<![CDATA[Lina Lina]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 1 (2013): Jurnal Ilmu Komputer dan Sistem Informasi ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v1i1.3064
<![CDATA[Book searching in the library is an activity that needs a lot of waste of time. Therefore, it is necessary to design a tool that can search for books based on charcter recognition. The characters willl eventually be recognized by the code contained books. The design is expected to help users for easy and practical searching of books. The method used in the paper is the Analytic Signal Vector (ASV). The ASV method works on the vectors of signals basis. The process of ASV method consists of several stages, starting calculating from Inverse Discrete Fourier Transform (IDFT) to obtain the analytical signal, finding the degree of similarity by using the Hermitian inner product, and calculating the Magnitude of Complex Number which is used to find the similarity value from two complex numbers by using this method, it is expected that the characters can be recognized and the system can search the gueries properly]]>
<![CDATA[KAJIAN TENTANG PENDETEKSIAN SEL DARAH PUTIH DENGAN TEKNIK SEGMENTASI WATERSHED ]]>
<![CDATA[David Reynaldo]]>;
<![CDATA[Lina Lina]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 7, No 1 (2019): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v7i1.5800
<![CDATA[White Blood Cell (WBC) Detection is one of many proposed studies in medical field. There are many accomplish researches of this study using several methods such as detection using contour technique or color technique. Unfortunately, most of proposed research to detect WBC still using stained blood cells image to find out the existence of WBC. This staining process take so much time and various substances to do the process. In this paper, a method proposed to detect white blood cell in an unstained image of blood cell preparations using watershed segmentation technique. Firstly, the textures of WBC area are extracted using HSV colorspace. Then watershed segmentation is performed to segment red blood cells area, which is the image result leaving only texture areas. Then each texture area is determined whether the area is a WBC area or not. Experimental result shows the proposed method achieved an average accuracy around 45% success rate.]]>
