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Color Space to Detect Skin Image: The Procedure and Implication Endah, Sukmawati Nur; Kusumaningrum, Retno; Wibawa, Helmie Arif
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12013

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.

Prediksi Angka Kejadian Demam Berdarah Dengue (DBD) Berdasarkan Faktor Cuaca Menggunakan Metode Extreme Learning Machine (Studi Kasus Kecamatan Tembalang) Ichwani, Anneta Shifa; Wibawa, Helmie Arif
Jurnal IPTEK Vol 23, No 1 (2019): May
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Demam Berdarah Dengue (DBD) merupakan penyakit endemis di Indonesia. Meningkatnya angka kejadian demam berdarah ini disebabkan oleh banyak faktor, antara lain keadaan lingkungan, baik lingkungan sosial, biologis, maupun fisik. Kecamatan Tembalang, dari tahun 2007 hingga 2016, menempati peringkat pertama sebagai kecamatan dengan Incident Rate (IR) DBD tertinggi sekota Semarang. Penanganan yang tepat perlu dilakukan sebagai antisipasi kenaikan angka penderita pada tahun-tahun berikutnya. Salah satu penanganan yang dapat dilakukan adalah memprediksi angka kejadian demam berdarah pada waktu-waktu berikutnya sehingga pemerintah dapat menyiapkan tindakan pencegahan. Prediksi angka kejadian demam berdarah ini dapat dilakukan dengan menggunakan prediktor cuaca. Dalam penelitian ini, digunakan tiga prediktor cuaca, yaitu suhu udara, kelembapan, dan curah hujan serta angka kejadian demam berdarah untuk memprediksi angka kejadian demam berdarah pada waktu berikutnya. Penelitian ini menggunakan jaringan saraf tiruan Extreme Learning Machine (ELM) untuk memprediksi angka kejadian demam berdarah berdasarkan faktor cuaca. Hasil penelitian menunjukkan bahwa model ELM dapat menghasilkan MSE pengujian terendah sebesar 0,0116 dan waktu pelatihan kurang dari 1 detik.

Implementasi Kecerdasan Buatan dalam Menentukan Aksi Karakter pada Game RPG dengan Logika Fuzzy Tsukamoto Ratanajaya, Dhemma; Wibawa, Helmie Arif
Khazanah Informatika Vol. 4 No. 2 Desember 2018
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v4i2.6744

Turn-based Role Playing Game (RPG) adalah salah satu genre video game yang menggunakan sistem pertarungan antara dua kubu yang salah satunya dikontrol oleh pemain dan kubu lainnya oleh kecerdasan buatan. Pada RPG yang beredar di pasaran masih banyak game yang memiliki sistem kecerdasan buatan yang masih belum dapat mengambil keputusan yang paling baik untuk memenangkan pertarungan. Pada artikel ini dibahas tentang potongan dari game RPG yang didasarkan pada konvensi genre yang ditemukan pada game serupa. Setelah itu dibuat sistem kecerdasan buatan untuk mengontrol kubu musuh yang mampu melakukan pengambilan keputusan dengan tepat dalam pertarungan pada game tersebut. Logika fuzzy digunakan pada sistem kecerdasan buatan sebagai fungsi untuk melakukan pembobotan atas pilihan keputusan yang dapat dilakukan. Sistem kecerdasan buatan akan menggunakan metode inferensi Tsukamoto dan metode defuzzifikasi centroid. Game dibuat menggunakan engine Unity3D dan bahasa pemrograman C#. Proses pengembangan dilakukan dengan metode extremme programming. Hasil yang didapatkan menunjukkan bahwa implementasi logika fuzzy Tsukamoto dalam game RPG telah mampu mengoptimalkan game tersebut yaitu karakter dalam game mampu memilih aksi yang harus dilakukan terhadap target yang dihadapi.

KETAHANAN WATERMARKING TERHADAP SERANGAN KOMPRESI JPEG Sugiharto, Aris; Wibawa, Helmie Arif
MATEMATIKA Vol 8, No 1 (2005): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.45 KB)

Watermarking is one of the methods that proposed to protected digital data from illegally copy.Â A lot of technic to destroy watermarking that inserted to digital data, one of them are JPEG compression. In this research will be focus as far JPEG compression can influence watermarking integrated especially at similarity test before and after digital data have JPEG compression attact. Â

AUTOMATIC DETECTION OF MOTORCYCLE ON THE ROAD USING DIGITAL IMAGE PROCESSING sutikno, sutikno; Wibawa, Helmie Arif; Saputra, Ragil
Scientific Journal of Informatics Vol 6, No 2 (2019): November 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i2.20143

Traffic accident is one of the causes of death in the world. One of them is traffic accidents on motorcyclist not wearing helmets. To overcome this problem, several researchers have developed detection system of motorcyclist not wear helmet. This system consists of motorcycle detection and motorcyclist head detection. On motorcycle detection, accuracy still needs to be improved. For this reason, this paper proposed motorcycle detection by adding image improvement processes that are enhancing contrast and adding object positioning features.The techniques proposed in this study are divided into 3 stages of image enhancement, feature extraction, and classification. The image enhancement stage consists of enhance contrast, convert RGB image to gray scale, background subtraction, convert gray scale image to binary, closing operation, and small object removal. The features used in this paper are the features of the object area, the circumference of the object, and the location of the object, while the method for classification process using back-propagation neural network and SVM. The proposed method resulted in an accuracy of 96.97%. Error occurs in all image test data not motorcycle objects detected as motorcycle objects. This error is caused because the pixel value between the objects in the image with the background color has a level of difference is too small, so it is detected as an object not a motorcycle.

Automatic Detection of Motorcycle on the Road using Digital Image Processing sutikno, sutikno; Wibawa, Helmie Arif; Saputra, Ragil
Scientific Journal of Informatics Vol 6, No 2 (2019): November 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i2.20143

Traffic accident is one of the causes of death in the world. One of them is traffic accidents on motorcyclist not wearing helmets. To overcome this problem, several researchers have developed detection system of motorcyclist not wear helmet. This system consists of motorcycle detection and motorcyclist head detection. On motorcycle detection, accuracy still needs to be improved. For this reason, this paper proposed motorcycle detection by adding image improvement processes that are enhancing contrast and adding object positioning features.The techniques proposed in this study are divided into 3 stages of image enhancement, feature extraction, and classification. The image enhancement stage consists of enhance contrast, convert RGB image to gray scale, background subtraction, convert gray scale image to binary, closing operation, and small object removal. The features used in this paper are the features of the object area, the circumference of the object, and the location of the object, while the method for classification process using back-propagation neural network and SVM. The proposed method resulted in an accuracy of 96.97%. Error occurs in all image test data not motorcycle objects detected as motorcycle objects. This error is caused because the pixel value between the objects in the image with the background color has a level of difference is too small, so it is detected as an object not a motorcycle.

Color Space to Detect Skin Image: The Procedure and Implication Endah, Sukmawati Nur; Kusumaningrum, Retno; Wibawa, Helmie Arif
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12013

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.

Classification of Road Damage from Digital Image Using Backpropagation Neural Network Sutikno Sutikno; Helmie Arif Wibawa; Prima Yusuf Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

One of the biggest causes of death in the world is a traffic accident. Road damage is one of the cause factors from the traffic accident. To reduce this problem is required an early detection against road damage. This paper describes how to classify road damage using image processing and backpropagation neural network. Image processing is used to obtain binary image consists of a normalization, grayscaling, edge detection and thresholding, while the backpropagation neural network algorithm is used for classifying. The conclusion of this test that the algorithm is able to provide the accuracy rate of 83%. The results of this research may contribute to the development of road damage detection system based on the digital image so that the traffic accidents caused by road damage can be reduced.

Detection of Ship Using Image Processing and Neural Network Sutikno Sutikno; Helmie Arif Wibawa; Priyo Sidik Sasongko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 1: February 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i1.7357

Indonesia is one of the countries in this world that has the most outstanding fishery potential. There are more than 3000 fish species under Indonesia's sea, yet the people are still not able to relish them completely. Illegal fishing by foreign ships in Indonesia's territorial sea is one of the reasons why this happens. In order to minimize this kind of loss, those ships should be detected automatically by implementing image processing and artificial intelligence techniques. The study proposed techniques for automatic detection of ships at sea on digital images. These techniques are global image thresholding and artificial neural network backpropagation. The result of this research is proposed of technique able to detect ship with 85% accuracy level. This method may be improved by adding more training data varieties.

Face Alignment using Modified Supervised Descent Method Mochammad Hosam; Helmie Arif Wibawa; Aris Sugiharto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.3892

Face alignment has been used on preprocess stage in computer vision’s problems. One of the best methods for face aligment is Supervised Descent Method (SDM). This method seeks the weight of non-linear features which is used for making the product and the feature resulting estimation on the changes of optimal distance of early landmark point towards the actual location of the landmark points (GTS). This article presented modifications of the SDM on the generation of some early forms as a sample on the training stage and an early form on the test stage. In addition, the pyramid image was used as the image for feature extraction process used in the training phase on linear regression. 1€ filter was used to stabilize the movement of estimated landmark points. It was found that the accuracy of the method in BioID dataset with 1000 training images in RMSE is approximately 0.882.

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