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PENERAPAN SISTEM INFERENSI FUZZY DALAM MENENTUKAN PRIORITAS HEURISTIK PADA APLIKASI GAME FIGHTING SEDERHANA Mahasati, Dikki; Kushartantya, Kushartantya; Wibawa, Helmie Arif
Jurnal Masyarakat Informatika Vol 2, No 4 (2011): Jurnal Masyarakat Informatika
Publisher : Department of Informatics, Universitas Diponegoro

Most of the artificial intelligence implementations in fighting games is based on procedural artificial intelligence, which makes the system only act based on the rules that already configured. System would not have the capacity to adapt with the user’s playing patterns. By using monotonic selection, one of the methods of fuzzy inference engine, connection between position and attack type variables with user’s playing pattern are shown. The result is, when the system are tested by playing with user’s character with a different playing pattern, the system playing pattern’s changed according to the rules that were decided through the knowledge acquisition.

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.

Steganografi Pesan Teks ke Dalam Citra Dengan Metode LSB pada Ruang Warna YCoCg Terdekomposisi IWT Herwindyo, Sony; Wibawa, Helmie Arif
Jurnal Masyarakat Informatika Vol 9, No 2 (2018): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Keamanan adalah hal yang sangat penting dalam proses komunikasi. Integritas data dan kerahasiaan menjadi hal mutlak yang harus dipenuhi. Salah satu cara mengamankan komunikasi adalah dengan Steganografi. Penelitian ini membahas tentang implementasi Steganografi pesan teks menggunakan LSB (Least Significant Bit) dengan menyisipkanya ke dalam ruang warna YCoCg yang sudah terdekomposisi dengan IWT (Integer Wavelet Transform). Berdasarkan hasil analisis didapat nilai rata-rata PSNR sebesar 57,51 DB yang berarti memiliki nilai kerusakan yang rendah atau secara kasat mata sangat sulit untuk membedakan citra kover dengan citra stego. Dalam hal ketahanan citra stego terhadap serangan sangat lemah dikarenakan penggunaan pesan teks yang tidak dapat mentoleransi perubahan sedikitpun. Namun kesamaan pesan hasil ekstraksi terhadap pesan yang disisipkan selalu identik bila tidak terjadi perubahan pada citra stego yang dapat merubah nilai dan posisi piksel.

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.

Prediksi Penyakit Diabetes Menggunakan Algoritma ID3 dengan Pemilihan Atribut Terbaik Muhamad Subhan Efendi; Helmie Arif Wibawa
JUITA : Jurnal Informatika JUITA Vol. 6 Nomor 1, Mei 2018
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Penyakit diabetes atau sering disebut dengan penyakit kencing manis adalah suatu penyakit gangguan metabolik menahun yang ditandai oleh kadar glukosa dalam darah yang melebihi nilia normal. Penyakit diabetes sering disebut sebagai silent killer dengan mengacu pada banyaknya yang tidak menyadari bahwa dirinya terkena penyakit diabetes sampai diketahui sudah kronis. Hal ini memicu peningkatan jumlah penderita diabetes dari tahun ke tahun. Maka dari itu penelitian ini mencoba menerapkan suatu metode klasifikasi Data Mining untuk memprediksi apakah seseorang terkena penyakit diabetes atau tidak. Algoritma yang digunakan adalah algoritma Decision Tree ID3 dengan bantuan seleksi atribut dalam pemilihan atribut yang digunakan. Algoritma seleksi atribut yang dimaksud adalah Correlation based Feature Selection (CFS) dan Information Gain. Berdasarkan hasil penelitian ini diperoleh bahwa performa tertinggi dicapai ketika algoritma ID3 menggunakan 5 atribut yaitu gpost, glun, upost, urn, dan actn. Dimana kelima atribut tersebut diperoleh menggunakan algoritma Correlation based Feature Selection (CFS) dengan nilai rata-rata akurasi sebesar 84.77, nilai rata-rata sensitifity sebesar 87.18, dan nilai rata-rata specificity sebesar 82.37.Kata Kunci : Penyakit Diabetes, Data Mining, ID3, Seleksi Atribut

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