Rancang Bangun Aplikasi Pendeteksi Suara Tangisan Bayi

Suara tangisan bayi merupakan sebuah tanda dari bayi yang mengalami suatu masalah. Namun, tidak semua orang dapat mengenali arti tangis bayi. Beberapa penelitian tentang deteksi suara tangis bayi sudah dilakukan oleh beberapa peneliti, namun saat ini masih belum ada penelitian yang membuat sebuah aplikasi pendeteksi suara tangis bayi berbasis web. Pada penelitian ini, sebuah aplikasi dibuat untuk membantu pengguna mengenali suara tangis bayi berbasis Dunstan Baby Language. Metode yang diterapkan adalah ekstraksi fitur suara tangis bayi dengan algoritma Mel-Frequency Cepstrum Coefficient (MFCC), normalisasi hasil ekstraksi fitur, dan klasifikasi K-nearest Neighbor. Dari berbagai pengujian yang dilakukan, dapat disimpulkan bahwa akurasi rata-rata terbaik sebesar 75,95% dapat dicapai ketika menggunakan parameter wintime pada ekstraksi fitur MFCC sebesar 0,08 detik, proporsi data latih 85% dan data uji 15% dari setiap kelas, normalisasi ekstraksi fitur dengan Standard Deviation Normalization, dan klasifikasi K-nearest Neighbor dengan k=1. Pada pengujian aplikasi dengan seluruh data, akurasi rata-rata yang sebesar 96,57% dapat dicapai ketika menggunakan parameter wintime pada ekstraksi fitur MFCC sebesar 0,08 detik, proporsi data latih 85% setiap kelas, normalisasi ekstraksi fitur dengan Standard Deviation Normalization, dan klasifikasi K-nearest Neighbor dengan k=1

MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV

The difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods

An in-depth performance analysis of the oversampling techniques for high-class imbalanced dataset

Class imbalance occurs when the distribution of classes between the majority and the minority classes is not the same. The data on imbalanced classes may vary from mild to severe. The effect of high-class imbalance may affect the overall classification accuracy since the model is most likely to predict most of the data that fall within the majority class.  Such a model will give biased results, and the performance predictions for the minority class often have no impact on the model. The use of the oversampling technique is one way to deal with high-class imbalance, but only a few are used to solve data imbalance. This study aims for an in-depth performance analysis of the oversampling techniques to address the high-class imbalance problem. The addition of the oversampling technique will balance each class’s data to provide unbiased evaluation results in modeling. We compared the performance of Random Oversampling (ROS), ADASYN, SMOTE, and Borderline-SMOTE techniques. All oversampling techniques will be combined with machine learning methods such as Random Forest, Logistic Regression, and k-Nearest Neighbor (KNN). The test results show that Random Forest with Borderline-SMOTE gives the best value with an accuracy value of 0.9997, 0.9474 precision, 0.8571 recall, 0.9000 F1-score, 0.9388 ROC-AUC, and 0.8581 PRAUC of the overall oversampling technique

Deteksi Objek Menggunakan Metode YOLO dan Implementasinya pada Robot Bawah Air

Penelitian ini membahas penggunaan berbagai arsitektur model deep learning dalam mendeteksi objek bawah air seperti gerbang, tiang, bola, dan baskom untuk meningkatkan performa robot dalam eksplorasi bawah air dalam konteks kompetisi SAUVC (Singapore AUV Challenge). Metode yang digunakan adalah YOLO (You Only Look Once) dan menggunakan berbagai jenis YOLOv5, seperti YOLOv5s, YOLOv5m, YOLOv5l, dan YOLOv5x. Hasil pengujian menunjukkan bahwa YOLOv5x memiliki rata-rata jarak deteksi terjauh sebesar 6,12 meter dan mAP@[0.5:0.95] paling tinggi yaitu 0,881, namun ukurannya yang besar memerlukan daya komputasi yang tinggi. Di sisi lain, YOLOv5s memiliki ukuran model yang lebih kecil yaitu 14,5 MB, namun tetap memberikan performa yang baik dengan mAP@[0.5:0.95] sebesar 0,872. Berdasarkan temuan ini, YOLOv5s lebih sesuai untuk digunakan dalam mendeteksi objek bawah air pada kompetisi SAUVC karena selain ukurannya yang lebih kecil, YOLOv5s juga memberikan performa yang memadai. Penggunaan model ini diharapkan dapat meningkatkan kinerja robot dalam eksplorasi bawah air dan membantu dalam menyelesaikan misi yang ditugaskan dalam waktu yang ditentukan

Determination of Location and Severity of Nodules on Lung Cancer CT Image Using YOLO Methods

The severity of lung cancer can be used to determine appropriate treatment measures and reduce the risk of death. The severity identification is monitored based on the size and location of the nodule. However, previous studies still focused on determining the location of nodules without identifying their severity. In this study, the severity of lung cancer is detected based on the size of its nodules. This research contributes to the annotation of severity to the Lung Image Database Consortium image collection (LIDC-IDRI) dataset and the development of automatic severity detection using You Only Look Once (YOLO) methods. The data is given a severity level based on the nodule size calculated based on the number of pixels in the nodule length. Automatic detection is done using YOLO methods, which consist of several versions, namely YOLOv5, YOLOv7, and YOLOv8. YOLO methods can properly detect the location and severity of cancer nodules with the IoU evaluation results obtained using YOLOv5, YOLOv7, and YOLOv8, which are 0.86, 0.6, and 0.87, respectively. From the experiment, it can be concluded that determining the location and severity of cancer based on nodule size using YOLO methods is proven effective and can be done in real-time

COAL DEMAND PREDICTION MODEL USING MACHINE LEARNING METHODS

Forecasting coal demand needs is important to minimize operational costs. Forecasting will help companies determine the right amount and time to order coal from suppliers. Research on coal forecasting in Indonesia generally uses a statistical approach and has not analyzed the performance of other forecasting models. This research aims to forecast coal demand using statistical and machine learning methods, namely ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The evaluation methods used to analyze forecasting performance are Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The new coal demand data used is 1097 daily data taken from January 2021 to December 2022 in the form of a timeseries and is stationary which has been tested using Augmented Dickey-Fuller (ADF). The test results show that the ARIMA model has MAPE value of 5.11%, MAE 2.91 and R-Square 0.925, Exponential Smoothing MAPE 1.07%, MAE 0.55 and R-Square 0.997, SVR with MAPE value of 5.48%, MAE 3.16 and R-Square 0.88, RNN with MAPE value of 5.19%, MAE 2.91 and R-Square 0.896, LSTM with MAPE value of 4.83%, MAE 2.84 and R-Square 0.897. From the test results it was found that exponential smoothing had the smallest error values among the other models. With forecasting results that have a small error rate, it can help management in making decisions to minimize costs in coal ordering and warehouse management

A comparative study of finger vein recognition by using Learning Vector Quantization

Abstract¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129. 

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling

FACIAL INPAINTING IN UNALIGNED FACE IMAGES USING GENERATIVE ADVERSARIAL NETWORK WITH FEATURE RECONSTRUCTION LOSS

Facial inpainting or face restoration is a process to reconstruct some missing region on face images such that the inpainting results still can be seen as a realistic and original image without any missing region, in such a way that the observer could not realize whether the inpainting result is a generated or original image. Some of previous researches have done inpainting using generative network, such as Generative Adversarial Network. However, some problems may arise when inpainting algorithm have been done on unaligned face. The inpainting result show spatial inconsistency between the reconstructed region and its adjacent pixel, and the algorithm fail to reconstruct some area of face. Therefore, an improvement method in facial inpainting based on deep-learning is proposed to reduce the effect of the stated problem before, using GAN with additional loss from feature reconstruction and two discriminators. Feature reconstruction loss is a loss obtained by using pretrained network VGG-Net, Evaluation of the result shows that additional loss from feature reconstruction loss and two type of discriminators may help to increase visual quality of inpainting result, with higher PSNR and SSIM than previous result