Human face recognition is one of the most challenging topics in the areas of image processing, computer vision, and pattern recognition. Before recognizing the human face, it is necessary to detect a face then extract the face features. Many methods have been created and developed in order to perform face detection and two of the most popular methods are Viola-Jones Haar Cascade Classifier (V-J) and Histogram of Oriented Gradients (HOG). This paper proposed a comparison between VJ and HOG for detecting the face. V-J method calculate Integral Image through Haar-like feature with AdaBoost process to make a robust cascade classifier, HOG compute the classifier for each image in and scale of the image, applied the sliding windows, extracted HOG descriptor at each window and applied the classifier, if the classifier detected an object with enough probability that resembles a face, the classifier recording the bounding box of the window and applied non-maximum suppression to make the accuracy increased. The experimental results show that the system successfully detected face based on the determined algorithm. That is mean the application using computer vision can detect face and compare the results.

A cart inverted pendulum is an under actuated system that highly unstable and nonlinear. Therefore, it makes a good problem example which attracts control engineers to validate the developed control algorithms. In this paper, an augmented PID control algorithm is proposed to stabilise a cart inverted pendulum at the desired state. The derivation of a mathematical model of the cart inverted pendulum using Lagrange's equation is discussed in detail. The system dynamics is illustrated to understand better the behaviour of the system. A simulation program has been developed to verify the performance of the proposed control algorithm. The system dynamic behaviours with respect to the variation of the controller parameters are analysed and discussed. Controllers parameters are expressed into two PID gain sets which associated with 2 dynamic states: the cart position (ϰ) and the pendulum angle (θ). It can be concluded from the simulation result that the proposed control algorithm can perform well where acceptable steady errors can be achieved. The best response from the cart inverted pendulum system has been obtained with the value of kPX 190, kDX 50, kIX 5, kPθ 140, kDθ 5, and kIθ 25.

No abstract

No abstract

Abstract-A novel method for retrieving image based on color and texture extraction is proposed for improving the accuracy. In this research, we develop a novel image retrieval method based on wavelet transformation to extract the local feature of an image, the local feature consist color feature and texture feature. Once an image taking into account, we transform it using wavelet transformation to four sub band frequency images. It consists of image with low frequency which most same with the source called approximation (LL), image containing high frequency called horizontal detail (LH), image containing high frequency called vertical detail (HL), and image containing horizontal and vertical detail (HH). In order to enhance the texture and strong edge, we combine the vertical and horizontal detail to be other matrix. The next step is we estimate the important point called significant point by threshold the high value. After the significant points have been extracted from image, the coordinate of significant points will be used for knowing the most important information from the image and convert into small regions. Based on these significant point coordinates, we extract the image texture and color locally. The experimental results demonstrate that our method on standard dataset are encouraging and outperform the other existing methods, improved around 11 %.

Toko dan supplier daging sapi Hari®Zim adalah toko yang menjual berbagai jenis daging sapi seperti jenis daging serloin, tenderloin, lidah, gandik, paru, buntut potong, iga potong, otak, kaki A (kaki matang), kaki b (kaki lepas tulang), kaki c (kaki kerok bulu), kulit, jantung, frozzen AMH, frozzen LVRN, tahu campur, dan rawonan. Toko ini berdiri pada tahun 2012. Namun, selama ini target penjualan untuk masa depan tidak obyektif karena masih berbasis pada manajemen intuitif dan masih dipengaruhi oleh pendapat atau perasaan pembuatnya. Oleh karena itu dibutuhkan sebuah sistem yang dapat membantu manajemen untuk menentukan target penjualan di masa depan secara lebih obyektif, sehingga penentuan target penjualan bisa lebih akuntabel. Selama ini toko daging sapi Hari®Zim dalam pengelolahan stok daging tidak dinamis atau tidak disesuaikan dengan perkiraan penjualan berikutnya karena belum menggunakan sistem prediksi. Oleh karena itu perlu adanya suatu sistem yang dapat memprediksi penjualan daging tersebut. Dalam penelitian ini, permasalahan yang akan dibahas adalah menguji apakah model peramalan time series dengan menggunakan metode Trend Least Square yang akan digunakan sudah sesuai dengan kondisi perusahaan, sehingga Toko daging sapi Hari®Zim tetap eksis dalam menghadapi persaingan usaha yang sejenis. Penelitian ini dilakukan dengan menggunakan data primer berupa data  penjualan daging sapi periode Januari 2015 – Mei 2017 pada toko daging sapi Hari®Zim Malang. Pengujian sistem dilakukan tiga kali, masing-masing uji komposisi menggunakan jumlah data pelatihan yang berbeda. Kemudian akan dibandingkan dengan data penjualan aktual untuk mengetahui hasil peramalan terbaik dan mendapatkan nilai kesalahan peramalan terkecil dengan Mean Absolute Presentase Error (MAPE). Kesalahan perkiraan terbaik ditemukan pada tes ketiga dengan menggunakan data pelatihan 9 bulan dan di dapatkan hasil perhitungan MAPE sebesar 0,022251 (2%). Berdasarkan hasil pengujian sistem menunjukkan bahwa jumlah data yang digunakan mempengaruhi Mean Absolute Presentase Error (MAPE). Aplikasi yang dikembangkan dapat di rekomendasikan untuk menentukan jumlah stok bulan berikutnya pada Toko daging sapi Hari®Zim.

No abstract

Brain signals or EEG are non-stationary signals and are difficult to analyze visually. The brain signal has five waves alpha, beta, delta, gamma, and theta. The five waves have their frequency to describe the level of attention, alertness, character and external stimuli. The five waves can be used to analyze stimulation patterns when turning left and right. Giving weight to the five brain waves utilizes genetic algorithms to get one signal. Genetic algorithms can be used to find the best signal for classification. In this paper, the EEG signal will be classified to determine the right or left movement pattern. After combining the five brain waves with genetic algorithms is then classified using the Logistic Regression, Linear Discriminant Analysis, K-Neighbors Classifier, Decision Tree, Naïve Bayes Gaussian, and Support Vector Machine. From the six methods above that have the highest accuracy is 56% and SVM is a method that has better accuracy than others on this problem.