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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 02 (2022)" : 6 Documents clear
PERBANDINGAN ADAPTIVE MOMENT ESTIMATION OPTIMIZATION DAN NESTEROV-ACCELERATED ADAPTIVE MOMENT ESTIMATION OPTIMIZATION PADA METODE CONVOLUTIONAL NEURAL NETWORK UNTUK MELAKUKAN DETEKSI BUAH Ismail Didit Samudro; Andi Farmadi; Dwi Kartini; Dodon Turianto Nugrahadi; Muliadi
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (389.466 KB)

Abstract

Convolutional Neural Networks are often used in research to conduct training, validation, classification, prediction and detection of images using Deep Neural Network. Optimization algorithm is used to change the hyperparameter values ​​in the Neural Network such as learning rate, optimization is needed to reduce losses and increase the accuracy of the model. Optimization algorithm that is widely used because of its good performance is Adam and Nadam optimization, but the learning rate setting still needs to be updated manually. In this research architecture that was based on VGG16 will be used, Learning Rate Scheduler is used in optimization to control the learning rate value by updating the learning rate value in each step during model training. In this study, a comparison of the optimization of Adam and Nadam was carried out when the Learning Rate Scheduler was used to update the learning rate value in model training and obtained prediction accuracy using Adam 98.85% and Nadam 95.02% and then obtained MAP model performance value using Adam 93.58%. and Nadam 75.28%.
IMPLEMENTASI METODE TEMPLATE MATCHING TERHADAP PENGENALAN CITRA PLAT NOMOR KENDARAAN BERMOTOR Ahmad Shofi Khairian; Irwan Budiman; Muhammad Itqan Mazdadi; Andi Farmadi; Dwi Kartini
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (851.121 KB)

Abstract

Abstract The motorized vehicle number (TNKB) sign or commonly referred to as the police license plate is a plate made of aluminum that shows the sign of a motorized vehicle in Indonesia that has been registered with the Samsat Office. The motor vehicle number sign in the form of an aluminum plate consists of 2 (two) lines, the first line showing the area code (letters), police number (numbers), and the final code/series. This study uses 10 license plates of motorized vehicles as test data taken for each character and 3 data sets of letters AZ and numbers 0-9 number plates of motorized vehicles for each character as training data. The purpose of this study was to determine the level of accuracy of the method Template Matching on image recognition of motor vehicle numbers. The results of the implementation of the method Template Matching on the image recognition of motorized vehicle license plates is to produce an accuracy rate of 95.56%.
IMPLEMENTASI ALGORITMA GENETIKA UNTUK OPTIMASI NEURAL NETWORK PADA STUDI KASUS PERMAINAN TRON Muhammad Darmadi; Irwan Budiman; Muliadi; Andi Farmadi; Triando Hamonangan Saragih
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1491.944 KB)

Abstract

Abstract Tron is played in an arena composed of grids and often both players are placed at different starting points, each player basically playing the game by aiming straight, turning left or turning right until one or both of them hit a wall or laser object. This study aims to examine how good genetic algorithms are in optimizing neural networks for artificial intelligence. As well as to find out what the winning percentage is for each researched artificial intelligence. The results obtained are that N5 is faster in obtaining optimal results, which only requires 9 generations but has the lowest percentage. So it can be concluded that the faster finding optimal results does not guarantee that artificial intelligence will be better..
PREDIKSI DATA PENARIKAN UANG TUNAI DI MESIN ATM MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) Fitrinadi; Irwan Budiman; Andi Farmadi; Dodon Turianto Nugrahadi; Muhammad Itqan Mazdadi
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1712.756 KB)

Abstract

Abstract Data mining is a series of processes to explore the added value of knowledge that has been unknown from a data set. Many algorithms can be used in solving a problem related to prediction or forecasting a new data value for the future based on pre-existing data. Sarima model is a model in time series analysis. The performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) method produces a suitable or good model used to predict cash withdrawal data at ATM machines. The data used in the study is a dataset of ATM transactions originating from Finhacks. The result of error using MAPE (Mean Absolute Percenttage Error) on the predicted result of cash withdrawal data at atm machines is K1 16.75%, K2 18.09%, K3 7.85%, K4 5.67%, and K5 11.80%. So it can be concluded that the data matches using the SARIMA model that has been selected because the MAPE value is smaller than 20%.
FORECASTING DENGAN MENGGUNAKAN METODE FUZZY LOGIC RELATIONSHIP GROUP PADA DATA PEMBUATAN PASPOR KANTOR IMIGRASI Aidil Akbar; Andi Farmadi; Muliadi; Dwi Kartini; Muhammad Itqan Mazdadi
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (753.228 KB)

Abstract

Stationarity is a term used to describe the pattern of trend in time series data. In time series data, this term known as stationary and non-stationary. Non-stationary data is a data that has an unstable pattern of increase and decrease. This condition makes forecasting more difficult. Fuzzy Time Series is one of many forecasting methods that can be used. In this algorithm, adding order is an option that can be used to increase the accuracy of the method. Application up to order three are carried out to determine the effect of addition order to the resulted accuracy value. Experiment is done by applying the used method to the data which is divided into several amounts of data. From the experiment, the average accuracy value of the three Order of Fuzzy Logic Relationship Groups (FLRG) Order-1, Order-2, and Order-3 are 84.06719%, 85.77546%, 92.01034%. FLRG Order-3 has the largest accuracy value while the smallest accuracy value is owned by FLRG Order-1. From this, it is proven that the addition of order able to reduce the error in accuracy value while forecasting using non-stationary data but the accuracy produced by different amounts of data are erratically increasing and decreasing. the experiment concluded that the order, the amount of data, and the data pattern are factors that affect the accuracy result.
SISTEM PEMANTAUAN LOKASI PEGAWAI ULM BERBASIS PRESENSI BERGERAK Ahmad Juhdi; Radityo Adi Nugroho; Friska Abadi; Andi Farmadi; Rudy Herteno
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (558.312 KB)

Abstract

ULM attendance is usually done in each faculty using a fingerprint-based attendance machine. However, fingerprint-based presence during the pandemic is very dangerous due to the COVID-19 outbreak which allows the spread of the virus to be transmitted through finger intermediaries who use the presence machine simultaneously. As well as the existence of a letter prohibiting going home issued by the MENPENRB regarding "Restrictions on traveling activities outside the region or homecoming activities or leave for ASN in an effort to prevent the spread of Covid-18". In this study, we use a smartphone-based electronic system to overcome fingerprint-based attendance problems so that we can get an increase in terms of costs, and minimize the spread of the COVID-19 outbreak. By knowing the level of profit achieved through investment in the application development that the researcher has proposed, it is necessary to conduct a feasibility study (Feasibility Analysis) as a tool in drawing conclusions about what will be done electronically, a comparison will be made against the implementation of attendance in the previous year. The operational costs required are Rp. 27,665,070, while the costs incurred for application development are Rp. 1,613,666, it can be seen that there is an implementation cost savings of Rp. 26,051,404, when operational cost savings are included in the economic feasibility study, the Return on Investment (ROI) and Break-Event Point (BEP) values since the first year the application was implemented showed a positive value. Until the fourth year, ROI and BEP entered the feasible criteria so that from an Economic Feasibility perspective it can be seen that the application is economically feasible. And the application that is made is able to provide convenience in using the application as evidenced by validity and reliability tests.

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