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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Systems and Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Indonesia Sosial Teknologi
Moch Arief Soeleman
Dian Nuswantoro University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : journal of development research

 1 
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 Abdul Syukur Abi Senoprabowo Adhe Irham Thoriq Aditya Wahyu Ramadhan Affandy Affandy Ahmad Zainul Fanani Ajib Susanto Ali Muqoddas Alzami, Farrikh Anas Syaifudin Anas Syaifudin Ari Yahya A Arry Maulana Syarif Catur Supriyanto Dwi Puji Prabowo, Dwi Puji Edi Noersasongko Edi Sugiarto Elkaf Rahmawan Pramudya Esa Wahyu Andriansyah Farikh Al Zami Fikri Budiman Fikri Budiman Heribertus Himawan Hestiana Putri Novitasari Imam Bagus Pratama Karis W. Khafiidz Hastuti Kristhina Evandari Lu'luul Maknun Magus Sarasnomo Mamay Maida Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muhammad Haris Zuhri Muhammad Munsarif Mula Agung Barata Muljono, - Mursalim Mursalim Muslih Muslih Muslih Muslih Nanang Lestio Wibowo Noor Ageng Setiyanto, Noor Ageng Nurhindarto, Aris Pujiono Pujiono Pulung Nurtantio Andono Puri Sulistiyawati Purwanto Purwanto Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani Saiful Ulya Sifa Ayu Rosita Sari Supriyono Asfawi Syahrul Idhom Via Indriani Kumalasari Widyatmoko Karis