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Dwi Kartini
FMIPA ULM
Computer Science & IT

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 1 
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%.
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

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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%.
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

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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.
Co-Authors Ahmad Shofi Khairian Aidil Akbar Andi Farmadi Friska Abadi Irwan Budiman Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Wahyudi Wahyudi Yenni Rahman