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Rudy Herteno
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Journal of Data Science and Software Engineering
Published by Universitas Lambung Mangkurat
ISSN : 27755320     EISSN : 27755487     DOI : https://doi.org/10.20527/jdsse.v1i01.13
Core Subject : Science,
Computer Science & IT
Journal of Data Science and Software Engineering adalah jurnal yang dikelola oleh program studi Ilmu Komputer Universitas Lambung Mangkurat untuk mempublikasikan artikel ilmiah mahasiswa tugas akhir. Terbit tiga kali dalam setahun.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol 2 No 02 (2021)" : 6 Documents clear
APPLICATION OF THE SHANNON ENTROPY AND MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS PLUS FULL MULTIPLICATIVE FORM (MULTIMOORA) ON TAEKWONDO BELT INCREASE SELECTION Norlatifah
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

This research uses the Shannon Entropy method for weighting and the MULTIMOORA method is used for the ranking process. In this study, the selection of belt increases will be carried out by considering several criteria, namely, KIbun Dongjak (Basic Movement), Poomsae, Poomsae Options, Chagi (Kicks), Kyorugi (Fighting), and Theory. Which aims to determine the level of accuracy generated by the two methods. The data used are the selection data for the increase in the taekwondo belt. The result of this study on the application of the Shannon Entropy method and the Multi-Objective Optimization Method On The Basis Of Ratio Analysis Plus Full Multiplicative Form (MULTIMOORA) for Geup 6 with 11 alternative data has an accuracy rate of 76%, for Geup 7 with 12 alternatives the data has an accuracy rate of 65%, for Geup 8 with an alternative number of 13 data has an accuracy rate of 77%, for Geup 9 with an alternative number of 14 data has an accuracy rate of 67%, while for Geup 10 with an alternative number of 6 data has an accuracy level of 86%.
DEEP NEURAL NETWORK ON SOFTWARE DEFECT PREDICTION Arie Sapta Nugraha; Mohammad Reza Faisal; Friska Abadi; Radityo Adi Nugroho; Rudy Herteno
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Software defect prediction is often performed in research to determine the performance, accuracy, precision, and performance of the prediction model or method used in research, using various software metric datasets such as NASA MDP. In this research, we used Deep Neural Network to classify the software metrics dataset modules into Defective and Non-Defective. The data validation technique used to validate the model is Stratified 10-Fold Cross Validation. Performance of the Deep Neural Network model is reported using Area Under the Curve (AUC) for evaluation measurement. AUC of Deep Neural Network is obtained as 0.815 on MC1 dataset and 0.889 on PC1 dataset. Both AUC values obtained in the MC1 and PC1 datasets are included in Good Classification category.
Klasifikasi Tanda Tangan Menggunakan Metode Template Matching Ahmad Faris Asy'arie; Andi Farmadi; Irwan Budiman; Dwi Kartini; Ahmad Rusadi Arrahimi
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (238.279 KB)

Abstract

Template Matching is one of the methods used for digital image processing, usually used to recognize the shape or pattern of an image. The shape or pattern that is often used to be recognized is in the form of character images, letters, numbers, or fingerprints. In the research conducted, signature pattern recognition was made using Template Matching for signature classification. Signature is chosen in research conducted with the aim of knowing whether the signature can be recognized using the Template Matching in addition to character images of letters, numbers, or fingerprints. Template Matching works by matching each pixel in the image matrix that has been digitally processed with the reference image (template) and because Template Matching is an applied method of convolutional technique, Template Matching combines two numbers to produce a third number series, so that the correlation coefficient (r) of the Template Matching will be obtained between -1 and +1. The results of the trials carried out show that the signature pattern recognition with Template Matching can recognize the signature image tested with a recognition accuracy rate of 96% with as many as 100 signature images.
IDENTIFIKASI PESAN SAKSI MATA PADA BENCANA KEBAKARAN HUTAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Rinaldi; Mohammad Reza Faisal; Muhammad Itqan Mazdadi; Radityo Adi Nugroho; Friska Abadi
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Social media, one of which is Twitter, is a medium for disseminating information that is growing rapidly at this time. The advantage of Twitter which has such a huge impact is its speed in spreading news and information that is happening. One of the information that is often reported through social media is information about natural disasters. Therefore, a lot of research on sensor social networks has been carried out by researchers using data from social media with the aim of obtaining valid data for the disaster emergency response process. In this study, the classification of eye witness messages for forest fires was carried out using Convolutional Neural Network and feature extraction Word2Vec with dimensions of 100. Twitter data used amounted to 3000 data and divided into 3 classes, namely eyewitnesses, non-eyewitnesses, and unknowns. The research was conducted to determine the accuracy performance obtained from testing using several types of configurations hyperparameter. Based on the results of the tests carried out, the best accuracy value was 81.97%.
PENGARUH OPTIMASI BOBOT MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI TINGKAT KERAWANAN DBD Bayu Hadi Sudrajat; Muliadi; Muhamad Reza Faisal; Radityo Adi Nugroho; Dwi Kartini
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.92 KB)

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease transmitted by the Aedes Ageypti mosquito. In South Kalimantan, especially in the city of Banjarbaru, the number of cases tends to increase every year. Existing research has identified the level of dengue susceptibility by using computational methods, one of which is classification. The method used in this research is Neural Network Backpropagation with weight optimization using Genetic Algorithms for data classification of dengue disease in Banjarbaru City. The purpose of this study was to determine the performance of the classification of dengue susceptibility levels using Neural Network Backpropagation and weighting using Genetic Algorithms. The results showed that the performance obtained for the classification of the level of dengue susceptibility using the Neural Network Backpropagation Algorithm was 83.33% in the accuracy, 96.51% precision, and 84.69% recall, whereas when using the Neural Network Backpropagation Algorithm based on Genetic Algorithm for weight optimization, obtained an accuracy value of 96.29%, a precision of 98.97%, and a recall of 97%.
PENGARUH RESOLUSI CITRA DALAM MENDETEKSI RAMBU LALU LINTAS SIRKULER MENGGUNAKAN HOUGH CIRCLE TRANSFORM Zaini Abdan; Andi Farmadi; Rudy Herteno; Radityo Adi Nugroho; Muhammad Itqan Mazdadi
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (257.976 KB)

Abstract

The traffic signs have several shapes, one of which is circular. Hough Circle Transform is a function that detects a circular in an image based on the gradient. This function also needs some parameters, one of which is the image resolution. The traffic signs in the frame will have varying sizes. If after cropping, it will produce images with varying resolution sizes. Therefore, resizing image resolution is required so that all image data have the exact image resolution. Image resolutions to be tested are 25 × 25 pixels, 50 × 50 pixels, 75 × 75 pixels, 100 × 100 pixels, 125 × 125 pixels, 150 × 150 pixels, 175 × 175 pixels, and 200 × 200 pixels. This research proves that the image resolution in shape detection using Hough Circle Transform affects the shape detection accuracy. The data used are No Stopping signs and No Parking signs for True detection test, whereas Other Dangers signs and Pedestrian Crossing signs for False detection test. The highest accuracy was generated at a resolution of 75 × 75 pixels.

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