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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
 1 
Search results for , issue "Vol 3 No 01 (2022)" : 6 Documents clear
SOLUSI KLASIFIKASI DATA TIDAK SEIMBANG DENGAN PENDEKATAN BERBASIS COMBINATION OF OVERSAMPLING AND UNDERSAMPLING Riza Susanto Banner; Irwan Budiman; Dodon Turianto Nugrahadi; M. Reza Faisal; Friska Abadi
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

This study applies the Combination of Oversampling and Undersampling method to deal with class imbalances. Researchers do Preprocessing to normalize the attributes used for prediction, then divide the training data and testing data. Researchers resampled unbalanced data using Oversampling, Undersampling and a combination of Oversampling and Undersampling. The results of the classification with the experimental data class balancing approach, the best classification performance is the combination of Oversampling and Undersampling classified by the k-Nearest Neighbor (KNN) method with an accuracy of 0.8672; sensitivity of 0.9000; specificity of 0.3750; and AUC of 0.6651042. Classification with Oversampling has performance results, namely accuracy of 0.875; sensitivity of 0.9250; specificity of 0.1250; and AUC of 0.6078125, while Undersampling classification has classification performance, namely accuracy of 0.3438; sensitivity of 0.33333; specificity of 0.50000; and AUC of 0.3645833.
ANALISIS SOFTWARE DEFINED NETWORK (SDN) MENGGUNAKAN OPENDAYLIGHT CONTROLLER DENGAN ANOVA REPEATED MEASURES Rifki Izdihar Oktvian Abas Pullah Rifki; Dodon T. Nugrahadi; M. Itqan Mazdadi; Andi Farmadi; Ahmad Rusadi
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Abstract The rapid development of technology today makes the technology around us also become more advanced and continues to grow, this has an impact on the development of the internet network. Technology such as Software Defined Network (SDN) is needed because it results in improved performance in network management, control and data handling that allows it to be managed centrally and more easily by network administrators by separating the control plane and data plane. In this study, an analysis of the SDN architecture was carried out using the Opendaylight controller based on the parameters of throughput, delay and jitter which then can be seen how the performance of the SDN architecture in a topology by increasing the number of nodes. The throughput test shows that the custom topology has a significant increase in value and has a better average throughput value among other topologies. While in the delay and jitter test, the custom topology has a better average value even though it has an insignificant increase in the delay and jitter value when there is an increase in the number of nodes.
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Gambar X-Ray Penyakit Covid-19 dan Pneumonia Fitria Agustina fitria; Andi Farmadi; Dwi Kartini; Dodon Turianto Nugrahadi; Ando Hamonangan Saragih
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Abstrak Pneumonia caused by the corona virus is different from ordinary pneumonia. One way to find out which pneumonia is caused by the corona virus is to do an X-ray. The disadvantage of this examination is that it requires a radiologist and the analysis time is relatively long. Therefore, to overcome this problem, deep learning methods can be used by implementing the Convolutional Neural Network (CNN) Algorithm method for X-ray image classification. The implementation of the Convolutional Neural Network (CNN) Algorithm is done by using training data of 4800 images which are trained using batch size values ​​of 16, 32, and 64. The train process with batch size values ​​of 16, 32 and 64 produces an average accuracy of 90%, 91% and 92%, while the loss values ​​are 0.22, 0.16 and 0.25. From this process it was found that batch 64 was the best loss and accuracy result for training data. The test data with batch values ​​of 16, 32, and 64 resulted in an accuracy of 76%, 82% and 76%, while the loss values ​​were 0.79, 0.53 and 0.63. The results of this manual testing of 30 photos contained 7 images that are not recognized by the model because of the images look similar to each other with an accuracy of 76%. From this process it was found that batch 32 was the best loss and accuracy result for testing data.
Text Mining Untuk Mengklasifikasi Judul Berita Online Studi Kasus Radar Banjarmasin Menggunakan Metode TF-IDF dan K-NN Salsabila Anjani; Andi Farmadi; Dwi Kartini; Irwan Budiman; Mohammad Reza Faisal
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

ABSTRACT The news media that used to be commonly used were newspapers. However, with the development of the times, the news media is now entering the digital era. Many online news media spread on the internet. The sophistication of the internet makes it easier for readers to choose which news they want to read. Unlike newspapers, online news media have categories where readers can choose. In general, the categorization of a news in online media is determined by the editor. Given the number of news published in a day, of course, makes the editor's job difficult. A category in the news is usually not appropriate because usually the headline is made as attractive as possible to attract the interest of the reader. So there are times when the news title does not match the category that has been entered by the editor. The use of the K-Nearest Neighbor (K-NN) method can be used in determining the categorization of a news. By using a case study of the online media Radar Banjarmasin, a research was conducted to find out how well the Canberra and Euclidean classification methods were using news headline data for categorization. The results obtained in this study are the better classification method is Euclidean and with an accuracy value of 65.00%. Improvements that should be made for further research is to use other methods for comparison.
COMPARATIVE ANALYSIS OF FUZZY TIME SERIES METHOD WITH FUZZY TIME SERIES MARKOV CHAIN ON RAINFALL FORECAST IN SOUTH KALIMANTAN M Kevin Warendra; Irwan Budiman; Rudy Herteno; Dodon Turianto Nugrahadi; Friska Abadi
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Abstract Time series data (TS) is a type of data that is collected according to the order of time within a certain time span. Time Series data analysis is one of the statistical procedures applied to predict the probability structure of future conditions for decision making. FTS (FTS) is a data forecasting method that uses fuzzy principles as its basis. Forecasting systems with FTS capture patterns from past data and then use them to project future data. FTS Markov Chain is a new concept that was first proposed by Tsaur, in his research to analyze the accuracy of the prediction of the Taiwan currency exchange rate with the US dollar. In his research, Tsaur combines the FTS method with Markov Chain, The merger aims to obtain the greatest probability using a transition probability matrix. The results obtained from this research are tests with the best number of presentation values ​​from FTS Markov Chain with FTS, resulting in different accuracy values ​​depending on the two methods. The best accuracy performance is obtained by the Markov Chain FTS method with an error value of 1.6% and an accuracy value of 98.4% and for FTS with an error value of 7.4% and an accuracy value of 92.6%. produce different accuracy values ​​depending on the two methods. The best accuracy performance is obtained by the Markov Chain FTS method with an error value of 1.6% and an accuracy value of 98.4% and for FTS with an error value of 7.4% and an accuracy value of 92.6%. produce different accuracy values ​​depending on the two methods. The best accuracy performance is obtained by the Markov Chain FTS method with an error value of 1.6% and an accuracy value of 98.4% and for FTS with an error value of 7.4% and an accuracy value of 92.6%.
Optimasi SVR dengan PSO untuk peramalan harga Cryptocurrency Arifin Hidayat; Andi Farmadi; Mohammad Reza Faisal; Dodon Turianto Nugrahadi; Rudy Herteno
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Cryptocurrency is the nickname given to a system that uses Cryptography technology to securely transmit data and process digital currency exchanges in a dispersed manner. A Cryptocurrency is a form of risky investment, Cryptocurrency prices are very volatile (changing) making Cryptocurrency prices need to be predicted to make a profit. Support Vector Regression (SVR) is one method for predicting time series data such as Cryptocurrency prices. However, the SVR parameters need to be optimized to get accurate results. The Particle Swarm Optimization (PSO) algorithm is implemented to determine the effect on the optimization of SVR parameters. The implementation of SVR and SVR-PSO is carried out on Bitcoin and Shiba Inu Coin Cryptocurrency data. The result of this research is that the SVR algorithm has an accuracy of 13.19082% (Bitcoin) and 68.3221% (Shiba Inu Coin). The SVR-PSO algorithm obtained an accuracy of 96.92359% (BTC) and 94.74245% (SHIB).

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