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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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IMPLEMENTATION OF LOAD BALANCE EQUAL COST MULTI PATH (ECMP) BETWEEN ROUTING PROTOCOL BORDER GATEWAY PROTOCOL (BGP) AND OPEN SHORTEST PATH FIRST (OSPF) USING DUAL CONNECTION Aji Triwerdaya; Dodon Trianto Nugrahadi; Muhammad Itqan Masdadi; Irwan Budiman; Ahmad Rusadi Arrahimi
Journal of Data Science and Software Engineering Vol 1 No 02 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Currently, Internet is needed by everyone to lighten their work, then a method has been developed to be able to access the internet using 2 ISPs (Internet Service Providers), namely using load balance. This method can perform bandwidth management so that it can balance the bandwidth of 2 ISPs. To support this method, Load Balance Equal Cost Multi Path (ECMP) is used. Another innovation that continues to be developed routing, the process of exchange data packets between different IP networks and to identify the best route to each connected network, that can make routing better by using dynamic routing types, to unify the network if a change occurs of topology by exchanging new topology information with each other on a network using the Open Shortest Path First (OSPF) routing or using the Border Gateway Protocol (BGP). OSPF is an open source routing protocol that is often used[4] and OSPF is a link-state in the routing algorithm. This routing use the Dijkstra or SPF (Short Path First) algorithm to calculate the shortest path from each route. Coinciding with the increase in routers in an area, the information that routers in the same area must have at the same time will increase, then the Border Gateway Protocol (BGP) is the new routing protocol[7]. BGP is a vector-path protocol where each router decides locally the "best AS" line per destination. The local preference attribute is used to set the policy for outgoing traffic. Testing is done by comparing the performance of an ECMP network using OSPF routing and an ECMP network using BGP routing[3]. Testing is done by measuring based on the throughput and data delay parameters using 16, 32, 48 routers. the topology is divided into 3 areas, namely area 1 for user load balance, area 2 for ISP 1 and area 3 for ISP 2. Throughput is used to measure routing performance on the TCP transport protocol and UDP transport protocol. Then, data delay is for measuring the performance of routing on the TCP and UDP transport protocol with the addition of variations. The testing that have been carried out show that the network throughput with OSPF routing (764.13 bps) has a lower performance than the network with BGP routing (818.81 bps) when sending TCP and UDP data, and network delay with OSPF routing (85.61 ms) has a significant increase than the network with BGP routing (89.23 ms) when sending TCP and UDP data.
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%.
IMPLEMENTATION OF AAC AND DC FEATURE EXTRACTION FOR CLASSIFICATION OF LYSINE PROTEIN ACETYLATION USING THE SUPPORT VECTOR MACHINE METHOD Annisa Rizqiana; Mohammad Reza Faisal; Favorisen Rosyking Lumbanraja; Muliadi; Rudy Herteno
Journal of Data Science and Software Engineering Vol 2 No 01 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Post-Translational Modification (PTM) is a change that occurs in the chemical structure of a protein. One type of PTM is acetylation which commonly occurs in lysine proteins where this type of PTM plays an important role in biological processes. Existing research has identified lysine acetylation using computational methods, which is classification. Methods for protein classification have been developed, but much remains to be explored to identify lysine acetylation. Protein classification begins with extracting protein sequences into numerical features with protein descriptors which in this study used Amino Acid Composition (AAC) and Dipeptide Composition (DC). Furthermore, protein classification is carried out using the Support Vector Machine method. Support Vector Machine is a classification method that can be used for protein identification. This study provides the best performance results on the use of the combination of AAC and DC descriptors, which is 76.20%.
IMPLEMENTASI ALGORITMA GENETIKA DENGAN TEKNIK SELEKSI TOURNAMENT UNTUK PENYUSUNAN JADWAL KULIAH Faisal Murtadho; Andi Farmadi; Dodon Turianto Nugrahadi; Irwan Budiman; Dwi Kartini
Journal of Data Science and Software Engineering Vol 2 No 01 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Genetic Algorithms can help human work, one of which is compiling course schedules. Preparation of course schedules, if done manually, will take a long time because you have to make a schedule where there are no schedule conflicts between one course and another. Therefore, this study will implement a Genetic Algorithm for the preparation of course schedules, so that it will speed up the preparation of course schedules compared to manual scheduling. In this study, the Genetic Algorithm with Tournament Selection was carried out with the input of control parameters, namely Population Size = 10, Crossover Rate (CR) = 0.75, and Mutation Rate (MR) = 0.01. In this study, the Genetic Algorithm has succeeded in obtaining the desired solution, namely scheduling courses where there are no schedule conflicts between one course and another. This search process took 88 generations to find the best solution.
GRU, AdaGrad, RMSprop, Adam Implementasi Metode Gate Recurrent Unit (GRU) dan Metode Optimasi Adam Untuk Prediksi Harga Saham Muhammad Mada; Andi Farmadi; Irwan Budiman; Mohammad Reza Faisal; Muhammad Itqan Mazdadi
Journal of Data Science and Software Engineering Vol 2 No 01 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

In terms of their potential, stocks are one of the most profitable investment options today. If done well and right, stocks can be a very profitable investment. However, volatile stock prices make it necessary to predict stock prices to make a profit. Gated Recurrent Unit (GRU) is a method for predicting time series data such as stock prices. The Optimization method is needed to get accurate prediction results. The weight renewal optimization method such as Adam is implemented to obtain the best weight in the Gated Recurrent Unit (GRU) and to find out the best loss function value generated by the Adam optimization method. The GRU-Adam implementation is carried out on two stock data, namely ICBP and YULE. The results of this research are that the ICBP data yields the respective loss function values, namely train loss 0.0016 and validation loss 0.0007. Whereas the YULE data resulted in a train loss value of 0.0051 and a validation loss of 0.0031. The MAPE generated in the ICBP stock data is 0.97%. While the YULE data is 3.00%.
ANALISIS PERBANDINGAN METODE FUZZY TIME SERIES DAN FUZZY TIME SERIES CHENG PADA PREDIKSI TANAMAN JAGUNG Yenni Rahman; M. Reza Faisal; Dwi Kartini; Andi farmadi; Friska Abadi
Journal of Data Science and Software Engineering Vol 2 No 01 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Domestic maize production for several years has not been able to meet the needs on a national scale. Many aspects affect this. This problem can be overcome by increasing production. One of the efforts to increase production is to predict future annual maize production using time series data. The time series data in question is data on corn production taken from the Ministry's Website. In this study, there are two prediction methods used to determine the annual maize yield for the coming year. Fuzzy Time Series and Fuzzy Time Series Cheng methods are the best prediction methods to be used in time series data where there are different stages between the two methods at the time of the formation of FLRG. In addition, researchers also used MAPE to compare the results of the accuracy of predicting corn production against the two methods. The corn production data used during 1970-2019 were 48 data. From the results of the tests carried out, the prediction results using the fuzzy time series method have a higher level of accuracy with the results of the corn accuracy value is 95.12% with a MAPE of 4.88% compared to the Fuzzy Time Series Cheng method with a result of 91,37%. with a MAPE of 8,63%.
EFEK NORMALISASI DATA GENRE MUSIC TERHADAP KINERJA KLASIFIKASI DENGAN RANDOM FOREST Wahyudi Wahyudi; M Reza Faisal; Dwi Kartini; Irwan Budiman; Andi Farmadi
Journal of Data Science and Software Engineering Vol 2 No 01 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

This research is about the classification of the music genre using the Random Forest method. This test uses a dataset from GitHub or GITZAN about the music genre with 10 labels, 26 features and 1000 total data. This research is divided into two stages, namely by classifying all data without being normalized, and by using all normalized data. . In this research, Min-Max is used for data normalization method, and for accuracy calculation using Confusion Matrix method. The resulting accuracy when using all data with data that is not normalized produces an accuracy of 66.3%, while the resulting accuracy performance when using all data with normalized data results in an accuracy of 65.1%.
RANCANG BANGUN PROTOTYPE PINTU AIR IRIGASI OTOMATIS PENCEGAH KEBAKARAN LAHAN GAMBUT MENGGUNAKAN MIKROKONTROLER Ahmad Ryan Nur Rahman
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Forest and land fires always occur every year in Indonesia. Data from the Ministry of Environment and Forestry of the Republic of Indonesia recorded that from January to September 2019, a total of 227,304 ha of fires occurred for peatlands, and peat fires in Borneo with a burned area of 135,991 ha have the biggest impacts in Indonesia, which would increase every year if not handled well. Peatlands that experience drought are one of the reasons why they burn easily. One of the efforts made by the Indonesian government to prevent land fires is by making irrigation gates that function to wet the land (Rewetting), because in normal condition peatlands have to be submerged in water. In this study, a prototype design of an irrigation sluice gate with automatic control using a microcontroller were developed which works by detecting the condition of the peatland ecosystem using sensors of soil moisture and air temperature. The irrigation water gate prototype can move wide open, moderately open, and closed, according to the state of the peatland ecosystem, namely: when it is dry, wet, and the peatland is submerged.
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

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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.

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