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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 46 Documents
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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

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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

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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.
PERFORMANCE COMPARISON OF ADAPTIVE NEURO FUZZY INFERENCE SYSTEM AND SUPPORT VECTOR MACHINE ALGORITHM IN BALANCED AND UNBALANCED MULTICLASS DATA CLASSIFICATION Muhammad Irfan Saputra; Irwan Budiman; Dwi Kartini; Dodon Turianto Nugrahadi; Mohammad Reza Faisal
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Data is a record collection of facts. At first the data in the real world were largely unbalanced. Although, the existence of data on fewer categories is much more important to know data on more categories. However, there are some balanced data. This balanced data is the possibility of a ratio of 1:1 in which, the data in the dataset is the same. In this study, using the ANFIS algorithm and SVM to see affected performance on balanced and imbalanced data with multiclass. Data is taken from the UCI Machine Learning. From the research conducted, it is known that the SVM method on the Wine dataset has an accuracy of 96.6% and the ANFIS method on the Iris dataset has an accuracy of 94.7%.
HYPERPARAMETER TUNING METHOD OF EXTREME LEARNING MACHINE (ELM) USING GRIDSEARCHCV IN CLASSIFICATION OF PNEUMONIA IN TODDLERS Pirjatullah; Dwi Kartini; Dodon Turianto Nugrahadi; Muliadi; Andi Farmadi
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Pneumonia is a disease that is susceptible to attack toddlers. According to data from the Ministry of Health, the cause of under-five mortality due to pneumonia is number 2 of all under-five deaths. The dataset used is pneumonia disease data at the MTBS Health Center of East Martapura Health Center. The classification method in this study uses the Extreme Learning Machine (ELM) method. The classification process starts from SMOTE upsampling to balance the class, then parameter tunning is performed using GridsearchCV on the hidden layer neurons, then classification is carried out using the ELM method using the Triangular Basis activation function by comparing the test datasets 90:10, 80:20, 70:30, 60:40 and 50:50. This study provides the best performance results with an accuracy of 86.36%, the ratio of training and test data is 90:10 and 3 neurons hidden layer.
Optimasi Bobot Weighted Moving Average Dengan Particle Swarm Optimization Dalam Peramalan Tingkat Produksi Karet Dendy Fadhel Adhipratama Dendy; Irwan Budiman; Fatma Indriani; Radityo Adi Nugroho; Rudy Herteno
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Rubber is a mainstay commodity in the country, in 2014 Indonesia ranked second as the largest natural rubber producing country in the world. However, rubber production in Indonesia experiences uncertain ups and downs so it is necessary to predict it in order to benefit small farmers and the state. Weighted Moving Average ( WMA) is a method for predicting time series data. However, the parameters on the WMA need to be optimized in order to get optimal weight results on the WMA and get accurate results. Algorithm Particle Swarm Optimization implemented to determine the weight value of the method Weighted Moving Average more optimal. PSO-WMA and WMA were carried out on three weights, namely from weighting 3 4 and 5 on rubber production data. So that the results of this study are WMA with 3 weights get 81% accuracy, 4 weight 80.5% and 5 weight 80.3%. And for PSO-WMA, the accuracy at weighting 3 is 81.4%, weighting 4 is 80.9% and for weighting 5 it is 81.6%. The test results of this study have the effect of the weight value on WMA in increasing the accuracy results.
OPTIMASI NILAI N PADA SINGLE MOVING AVERAGE (SMA) DENGAN PARTICLE SWARM OPTIMIZATION (PSO) STUDI KASUS SAHAM BRI Rahman Hadi Rahman; Irwan Budiman; Friska Abadi; Andi Farmandi; Muliadi
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

The stock market is a promising business area. The potential to obtain high returns in a fairly short time is one of the main attractions of this business. Prediction of stock prices has become a very interesting and challenging thing for researchers and academics, recently it was found that stock prices can be predicted with a certain degree of accuracy. Single Moving Average (SMA) is one method for predicting time series data. However, the N value in SMA needs to be optimized in order to get the N value with optimal results at the SMA and get accurate results. The Particle Swarm Optimization Algorithm is implemented to find out the best N value in the Single Moving Average methodwhich is more optimal. SMA+PSO and SMA are calculated using the initial N values ​​of 3,5,7,9,11. So the results of this study are SMA with an accuracy of 97.98464% and for SMA+PSO with an accuracy of 98.15442% . The test results from this study are the influence of PSO on SMA in increasing accuracy in determining the best N value.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN POHON UNTUK RESTORASI LAHAN BEKAS KEBAKARAN DENGAN METODE ANALYTIC HIERARCHY PROCESS (AHP) DAN SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE EXPLOITING RANKS (SMARTER) Muhammad Denny Ersyadi Rahman; Muliadi; Rudy Herteno; Dwi Kartini; Friska Abadi
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Utilization or use of forest and land areas that are not in accordance with conservation principles can cause critical land to occur. Critical land is land inside or outside the forest area that has been damaged, so that it can cause loss or decrease in its function. The lack of knowledge of some people on critical land and the selection of inappropriate plant types sometimes makes the condition of burnt land increasingly become one of the obstacles for the Forest and Land Rehabilitation Program (RHL). Statistical data analysis can be used in the data processing process to become valuable information for the system. Applying statistical analysis methods in making decisions in selecting statistical data that has several criteria. This research is focused on the application of the Analytical Hierarchy Process (AHP) method to see a comparison of criteria. The SMARTER (Simple Multi Attribute Rating Technique Exploiting Rank) method is very suitable to be used to overcome the many alternatives that will be given to different soil samples later. In short, each final weight that affects the alternative is calculated with the results of the alternative assessment, so that the utility value of each alternative is obtained. From the research of the Analytical Hierarchy Process (AHP) and Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER) method, the results of the Balangeran vegetation are obtained as the main recommendation with the greatest utility value, namely 1.321668.
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.

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