AUTOMATIC FEATURE REDUCTION FRAMEWORK FOR IDENTIFICATION PROCESS IN PALM VEIN RECOGNITION

Feature or dimensionality reduction has become one of fundamental problem in the field of pattern recognition such as biometrics. The selecting the number of feature or dimension has become one challenge. Instead selecting number of feature manually, in this research propose a framework or procedure for feature reduction by finding the correlation between recognition rates and number of features. This study was applied on a palm vein biometrics system which used DCT and k-PCA as features extraction method. The results of the experiment showed that the procedure was able to achieve models that had an average error of less than 6 from optimal features and about 1.1% from the real recognition rates. In addition, the proposed procedures could reduce the processing time by an order of 102. Keywords: Feature Reduction, Pattern Recognition, Biometrics, Palm Ve

PENGENALAN TELAPAK TANGAN MELALUI CITRA DIGITAL DENGAN MENGGUNAKAN METODE EIGENPALM DAN SUPPORT VECTOR MACHINE

ABSTRAKSI: Pengenalan telapak tangan menjadi salah satu bagian penelitian yang banyak diminati dalam bidang biometrik, dibandingkan biometrik yang telapak tangan memiliki kelebihan seperti kestabilan ciri dan kecilnya biaya yang digunakan dalam penggunaannya. Dalam tugas akhir ini ditawarkan salah satu cara pengenalan telapak tangan dalam pengenalan identitas manusia atau individu dengan menggunakan metode pengenalan ciri Eigenspace dan klasifikasi Support Vector Machine (SVM).Eigenspace sebelumnya banyak digunakan dalam kasus pengenalan wajah yang biasanya dikenal dengan Eigenface atau Principal Component Analysis. Dalam kasus pengenalan telapak tangan ini metode eigenspace ini dikenal dengan nama Eigenpalm. Data citra telapak tangan ditranformasikan dengan menggunakan Karhunen-Loeve Transform sehingga dihasilkan vektor ciri “eigenpalm” yang merepresentasikan ciri dari keseluruhan data citra telapak tangan aslinya. Hasil dari pengenalan ciri ini kemudian diklasifikasikan dengan menggunakan metode klasifikasi SVM dimana data hasil ektraksi ciri dipisahkan kedalam sebuah ruang ciri berdimensi tinggi. Selanjutnya diklasifikasikan kedalam kelas-kelas telapak tangan. Data keseluruhan yang digunakanData keseluruhan yang digunakan dalam tugas akhir ini berjumlah 1.500 citra telapak tangan yang diperoleh dengan menggunakan kamera digital. Pengujian sistem dilakukan dengan penentuan pengambilan feature length (ciri citra), nilai parameter C dan parameter fungsi kernel Polynomial pada SVM. dari hasil pengujian diperoleh hasil pengujian terbaik dengan akurasi 96,60% untuk data citra anggota kelas dan 100% untuk data kelas unknown.Kata Kunci : Biometrik, Pengenalan Telapak Tangan, Eigenspace, Eigenpalm, Karhunen-Loeve Transform, Support Vector Machine, Akuisisi Kamera DigitalABSTRACT: Palmprint recognition became one part of study that demand most in the field of biometric. Compare with others biometrics tecknologies, palmprint method give more advantages such as stable structure feature and low cost. In this thesis is offered one of the way for palmprint recognition in human or personal identification with Eigenspace (Eigenpalm) as feature extraction and Support Vector Machine as classifier (SVM).The concept of an eigenspace has been widely used in the face recognition that is usually known as Eigenface or Principal Component Analysis. In this case eigenspace will be known as Eigenpalm. The original palmprint images are transformed using Karhunen-Loeve Tranform to produce feature vector called “eigenpalm” which represents the overall characteristics of the original palmprint images. The result of feature extraction the classified using SVM method, where the data is separated into high dimensional space, to further classified base on palmprint classes.Overall the data of palmprint images that are used in this thesis is 1.500 palmprint images obtained by using a digital camera. System testing is done by determining the value of feature length, value of parameter C and degree of kernel function of SVM. From the test result, obtained the best result with highest that is 96.60% for data from client class and 100% for data from unknown class.Keyword: Biometric, Palmprint Recognition, Eigenface, Eigenpalm, Karhunen-Loeve Tranform, Support Vector Machine, Acquisition Digital Camer

Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram

Currently, most of the smartphones recognize uses based on static biometrics, such as face and fingerprint. However, those traits were vulnerable against spoofing attack. For overcoming this problem, dynamic biometrics like the keystroke and gaze are introduced since it is more resistant against spoofing attack. This research focuses on keystroke dynamics for strengthening the user recognition system against spoofing attacks. For recognizing a user, the user keystrokes feature used in the login process is compared with keystroke features stored in the keystroke features database. For evaluating the accuracy of the proposed system, words generated based on the Indonesian anagram are used. Furthermore, for conducting the experiment, 34 participants were asked to type a set of words using the smartphone keyboard. Then, each user’s keystroke is recorded. The keystroke dynamic feature consists of latency and digraph which are extracted from the record. According to the experiment result, the error of the proposed method is decreased by 23.075% of EER with FAR and FRR are decreased by 16.381% and 10.41% respectively, compared with Kim’s method. It means that the proposed method is successful increase the biometrics performance by reducing the error rate

Crude Oil Price Forecasting Using Long Short-Term Memory

Crude oil has an important role in the financial indicators of global markets and economies. The price of crude oil influences the income of a country, both directly and indirectly. This includes affecting the prices of basic needs, transportation, commodities, and many more. Therefore, understanding the future price of crude oil is essential in helping to budgeting and planning for a better economy. The contribution of this research is in finding the best hyperparameters and using early stopping methods in the LSTM model to predict oil prices. This research implemented Long Short-Term Memory (LSTM), an artificial neural network that can handle long-term dependencies and the problems of time series data. The LSTM method will be used to predict Brent oil prices on daily and weekly time frames. The experiment has been conducted by tuning some parameters to obtain the best result. From the daily time frame experiment, the model obtained RMSE and MAE of 1.27055 and 0.92827, respectively, while the weekly time frame has RMSE and MAE of 3.37817 and 2.60603, respectively. The results show that the LSTM model can improve to the trends that occur in the original data

Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia

This study focuses on predicting the apartment price index in Indonesia using property survey data from Bank Indonesia. In the era of the Covid-19 pandemic, accurately predicting the sale and purchase price of apartments is essential to minimize the impact of losses, thus making apartment prices attractive to predict. The machine learning approach used to predict the apartment price index are the Random Forest method, the Multiple Regression method, and the Backpropagation method. This study aims to determine which method is more effective in predicting small amounts of data accuracy. The data used is apartment price index data from 2012 to 2019 in the JABODEBEK area. The research will produce prediction accuracy that will determine the effectiveness of the application of the method. The Random Forest method with parameters n_estimators=100 and max_features=”log2” produces an R2 accuracy of 0.977. The Multiple Regression method with a correlation between the selling price and rental price variables is 0.746, and the rental inflation variable is 0.042 produces an R2 accuracy of 0.559. The Backpropagation method with a 1000-4000-1 hidden scheme and 20000 iterations produces an R2 accuracy of 0.996. Therefore, the Backpropagation method is more suitable in this study compared to the other two methods. The Backpropagation method is suitable because it gets almost perfect accuracy, so this method will minimize losses in investing in buying and selling apartments in the Covid-19 pandemic era

Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia

This study focuses on predicting the apartment price index in Indonesia using property survey data from Bank Indonesia. In the era of the Covid-19 pandemic, accurately predicting the sale and purchase price of apartments is essential to minimize the impact of losses, thus making apartment prices attractive to predict. The machine learning approach used to predict the apartment price index are the Random Forest method, the Multiple Regression method, and the Backpropagation method. This study aims to determine which method is more effective in predicting small amounts of data accuracy. The data used is apartment price index data from 2012 to 2019 in the JABODEBEK area. The research will produce prediction accuracy that will determine the effectiveness of the application of the method. The Random Forest method with parameters n_estimators=100 and max_features=”log2” produces an R2 accuracy of 0.977. The Multiple Regression method with a correlation between the selling price and rental price variables is 0.746, and the rental inflation variable is 0.042 produces an R2 accuracy of 0.559. The Backpropagation method with a 1000-4000-1 hidden scheme and 20000 iterations produces an R2 accuracy of 0.996. Therefore, the Backpropagation method is more suitable in this study compared to the other two methods. The Backpropagation method is suitable because it gets almost perfect accuracy, so this method will minimize losses in investing in buying and selling apartments in the Covid-19 pandemic era

Gold price prediction using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)

Gold has an important role in worldwide economics. Gold is not only used in jewelry but also can be a good deal for investment however several factors can affect the fluctuation in gold which can make the risk of investing in gold is bigger for many people. Therefore, is very important to predict the gold price for people who invest in gold in order to help reduce the investment risk. This study will implement a hybrid method from Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN can extract useful knowledge and learn the internal representation of time-series data, and LSTM networks will identify short-term and long-term dependencies effectively. This research will use daily time frame data and weekly time frame data. This research also tried some experiments to find the best hyperparameters of batch size and epochs in ratio data 60:40 and 80:20. The best result obtained in the daily time of ratio data 60:40 with RMSE 13.67953 and MAE 9,40998, while in ratio data 80:20 has RMSE 15,53199 and MAE 12,78120. In weekly time has obtained the RMSE 38,01949 and MAE 28,32035 for ratio data 60:40 while in ratio data 80:20 the result was RMSE 32,61283 and MAE 22,74638. Those results shows that CNN-LSTM model can predict the trend of daily time frame gold price

Deteksi Risiko Kredit dalam Peer-to-Peer Lending Menggunakan CatBoost

P2P lending (Peer-to-peer lending) is widely used by private borrowers, small businesses, and MSMEs because P2P lending allows individuals and businesses to be able to lend money directly from lenders without the stringent requirements and criteria of traditional banks and financial institutions. However, P2P lending has a credit risk problem characterized by a high failure rate for borrowers to repay their loans. Therefore, a system was necessary to detect credit risk for minimizing P2P lending credit risk. In this study, a system had been built using the CatBoost method, which the used dataset was taken from loan dataset of the Bondora company. To measure the performance of the CatBoost algorithm, an evaluation matrix used ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve) had been conducted. The experiment consists of three scenarios which the best result regard to scenario 2 with a data splitting of 90:10. It was caused by the result of AUC value’s 0.80329 compared to scenario 1 with a data splitting of 80:20 with the AUC value around 0.789583, and scenario 3 with a data splitting of 70:30 with the AUC value around 0.781066, respectively

Crude Oil Price Forecasting Using Long Short-Term Memory

Crude oil has an important role in the financial indicators of global markets and economies. The price of crude oil influences the income of a country, both directly and indirectly. This includes affecting the prices of basic needs, transportation, commodities, and many more. Therefore, understanding the future price of crude oil is essential in helping to budgeting and planning for a better economy. The contribution of this research is in finding the best hyperparameters and using early stopping methods in the LSTM model to predict oil prices. This research implemented Long Short-Term Memory (LSTM), an artificial neural network that can handle long-term dependencies and the problems of time series data. The LSTM method will be used to predict Brent oil prices on daily and weekly time frames. The experiment has been conducted by tuning some parameters to obtain the best result. From the daily time frame experiment, the model obtained RMSE and MAE of 1.27055 and 0.92827, respectively, while the weekly time frame has RMSE and MAE of 3.37817 and 2.60603, respectively. The results show that the LSTM model can improve to the trends that occur in the original data