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All Journal Semantik Jurnal Pseudocode Journal of Development Research
Abdul Syukur
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Social Sciences Other

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ROBUST COLOR IMAGE WATERMARKING DENGAN KOMBINASI TRANSFORMASI DCT-DWT UNTUK MENINGKATKAN KETAHANAN DARI KOMPRESI JPEG De Rosal Ignatius Moses Setiadi; Abdul Syukur; Ferda Ernawan
Semantik Vol 2, No 1 (2012): Prosiding Semantik 2012
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (148.93 KB)

Abstract

Saat ini perkembangan teknologi semakin pesat sehingga pendistribusian citra digital semakin praktis, bahkan citra digital juga dapat di akses melalui perangkat mobile. Pengaksesan citra digital melalui perangkat mobile membuat citra digital sering mengalami proses kompresi, oleh karena itu dibutuhkan skema image watermarking yang lebih tahan terhadap kompresi citra dan tidak kasat mata. Pada penelitian ini akan diusulkan skema image watermaking dengan model kombinasi transformasi DCT-DWT yang dapat meningkatkan ketahanan image watermarking terhadap kompresi JPEG. Selain itu diusulkan penggunaan konversi ruang warna pada citra RGB ke ruang warna YCbCr yang dapat mengoptimalkan karakteristik penglihatan manusia untuk 24-bit true color image dalam konten multimedia pada berbagai perangkat serta optimalisasi ketahanan image watermarking pada citra digital terhadap kompresi citra JPEG.Kata kunci :Image Watermarking, Kombinasi DCT-DWT, Kompresi JPEG
PENINGKATAN AKURASI ALGORITMA BACKPROPAGATION DENGAN SELEKSI FITUR PARTICLE SWARM OPTIMIZATION DALAM PREDIKSI PELANGGAN TELEKOMUNIKASI YANG HILANG Irvan Muzakkir; Abdul Syukur; Ika Novita Dewi
Jurnal Pseudocode Vol 1, No 1 (2014)
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.688 KB) | DOI: 10.33369/pseudocode.1.1.1-10

Abstract

Abstrak: Telekomunikasi adalah salah satu industri, di mana pelanggan memerlukan perhatian khusus, oleh  karena  itu,  manajemen  di  sebuah  perusahaan  telekomunikasi  ingin  kehilangan  pelanggan  model prediksi untuk efisien memprediksi berpotensi kehilangan pelanggan. Jaringan syaraf adalah metode yang sering digunakan untuk memprediksi. Teknik yang paling populer dalam metode adalah saraf algoritma jaringan backpropagation. Namun algoritma backpropagationmemiliki kelemahan pada kebutuhan untuk data  pelatihan  besar  dan  optimasi  yang  digunakan  kurang  efisien.  Particle  Swarm  Optimization (PSO) adalah  suatu  algoritma  optimasi  yang  dapat  memecahkan  yang  efektif  masalah  pada  algoritma  neural network umumnya  menggunakan  algoritma  backpropagation.  Pengujian  model  dengan  berbasis menggunakan  Backpropagation Particle Swarm Optimizationmenggunakan data pelanggan hilang pada telekomunikasi. Model yang dihasilkan diuji untuk memperoleh akurasi dan nilai-nilai AUC dari masingmasing  algoritma  untuk  mendapatkan  tes  menggunakan  nilai  yang  diperoleh  akurasi  Backpropagation adalah 85.48% dan nilai AUC adalah 0.531. Sementarapengujian dengan menggunakan Backpropagation berbasis  Particle  Swarm  Optimization dipilih  atribut  dan  penyesuaian  nilai  parameter  yang  diperoleh 86.05% akurasi dan nilai AUC adalah 0,637. Dengan demikian dapat disimpulkan bahwa data pelanggan uji  hilang  dalam  telekomunikasi  menggunakan  aplikasi  Particle  Swarm  Optimization  Backpropagation dan dalam pemilihan atribut  diperoleh bahwa  metode  ini  lebih akurat dalam prediksi pelanggan  hilang telekomunikasi dibandingkan dengan Backpropagation, ditandai dengan peningkatan akurasi 00:57% dan nilai-nilai AUC dari 0.106, dengan nilai yang dimasukkan ke dalam akurasi klasifikasi cukup.Kata  Kunci:  Telekomunikasi,  Neural  Network,  Backpropagation,  Particle  Swarm  Optimization.
Classification of Guarantee Types Using Leaf Feature Extraction with Minutiae and GLCM Using K-NN Method Muhammad Haris Zuhri; Adhe Irham Thoriq; Abdul Syukur; Affandy Affandy; Muslih Muslih; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.201

Abstract

Indonesia is a fertile area that has a sub-tropical climate that makes plants grow well in various parts of Indonesia. There are various variants of guava in Indonesia. Of the several types have differences including the structure of the fruit, tree and leaves. The focus of this research is to classify guava species based on leaf bone image using GLCM feature extraction, minutiae and shape extraction using the K-NN method. In this study using a dataset of 4 types of guava as many as 300 images, where each type of as many as 75 images. In the extraction process to get the leaf bone image in this study, there are several processes, namely preprocessing, grayscale image, binary image and morphology then only get the leaf bone image. After getting the extracted value, then the data is processed using the K-NN method. The highest accuracy in the K-NN method is at k1 = 92.42% with a standard deviation of 6.05% (micro average: 92.45%). Thus GLCM feature extraction, minutiae and shape extraction can potentially increase the level of accuracy in guava classification based on leaf bone images.
Classification of Toxic Plants on Leaf Patterns Using Gray Level Co-Occurrence Matrix (GLCM) with Neural Network Method Mohammad Faishol Zuhri; S. Kholidah Rahayu Maharani; Affandy Affandy; Aris Nurhindarto; Abdul Syukur; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.202

Abstract

Poisonous plants are plants that must be avoided and not consumed by humans, because the presence of poisonous plants is also often found in the surrounding environment without realizing it. Because of the lack of knowledge to classify poisonous plant species, it will be more difficult to find out. With the help of a computer system, it will be easier to identify the types of poisonous plants. There are 3 types of poisonous plants that will be used in this study, namely cassava, jatropha, and amethyst. There are also 3 types of non-toxic plants with almost the same morphology as a comparison, namely cassava, figs, and eggplant. In this study, researchers tried to classify poisonous plant species using leaf pattern features that would be extracted using shape features and Gray Level Co-occurrence Matrix (GLCM). The value taken from the shape feature is the values ​​of area, width, diameter, perimeter, slender, and round. While the value of contrast, entropy, correlation, energy, and homogeneity for Gray Level Co-occurrence Matrix (GLCM) attributes. To classify data using Neural Network with RapidMiner application. From this study, it is known that from 300 total datasets used, the highest accuracy is 96.13% using the Neural Network method. With an AUC value of 0.986 and is included in the very good category.
Seam Cerving and Salient Detection for Thumbnail Photos Much Chafid; Abdul Syukur; Moch Arief Soeleman; Affandy Affandy
Journal of Development Research Vol. 6 No. 1 (2022): Volume 6, Number 1, May 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i1.204

Abstract

Image resizing is a process of processing images or images with the aim of changing the size of the image. The most commonly used methods are cropping or scaling. Scaling is changing the size of the image based on the scale. Contents in the image are not considered in scaling. Seam carving often uses energy functionality that is useful as a determinant of the pixel level contained in an image. Seam is a connecting path of image pixels both vertically and horizontally that is passed by a low energy function. Changing the image size using seam carving is considered better than cropping and scaling. However, the seam carving method still cannot protect the object that is considered the most important. In overcoming this weakness, we can use a combination of seam carving algorithm with salient detection. In this research, we will improve the two methods which function as thumbnail maker. The results of the salient detection of the most important areas of the image will be detected and as a reference in resizing the image (seam carving) The dataset uses 200 images. The accuracy value is calculated by distributing questionnaires to 100 respondents and producing an acceptance rate of 78% so that the results are Very Natural/Natural.
Improving C4.5 Algorithm Accuracy With Adaptive Boosting Method For Predicting Students in Obtaining Education Funding Mohammad Ahmad Maidanul Abrori; Abdul Syukur; Affandy Affandy; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 2 (2022): Volume 6, Number 2, November 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i2.205

Abstract

The level of accuracy in determining the prediction of the provision of educational funding assistance is very important for the education agency. The large number of data on prospective beneficiaries can be processed into information that can be used as decision support in determining eligibility for education funding assistance. The data processing is included in the field of data mining. One method that can be applied in predicting the feasibility of receiving aid funds is classification. There are several classification algorithms, one of which is a decision tree. The famous decision tree algorithm is C4.5. The C4.5 algorithm can be applied in classifying prospective recipients of educational aid funds. This study uses datasets from student data of SMK Al Fattah Kertosono. The purpose of this study is to increase the accuracy of the C4.5 algorithm by applying adaboost in classifying students who deserve education funding and not, by comparing the results before and after applying adaboost. Validation in this study uses cross validation. While the measurement of accuracy is measured by the confusion matrix. The experimental results show that there is an increase in accuracy of 7.2%. The accuracy of the application of the C4.5 algorithm reaches 91.32%. While the accuracy of the application of the C4.5 algorithm with adaboost reached 98.55%.
Co-Authors Adhe Irham Thoriq Affandy Affandy De Rosal Ignatius Moses Setiadi Ferda Ernawan Ika Novita Dewi Irvan Muzakkir Lu'luul Maknun Moch Arief Soeleman Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muhammad Haris Zuhri Muslih Muslih Nurhindarto, Aris S. Kholidah Rahayu Maharani