OPTICAL CHARACTER RECOGNITION USING MODIFIED DIRECTION FEATURE AND NESTED MULTI LAYER PERCEPTRONS

ABSTRAKSI: The studies of Optical Character Recognition (OCR) are being developed since it still needs a performance improvement. The previous study of alphanumeric character recognition had been conducted by Blumenstein and Liu using Modified Direction Feature (MDF) and Multi Layer Perceptrons (MLP) network. The study reaches the accuracy rate of 70.22% for lowercase characters and 80.83% for uppercase characters.In this study the OCR system is proposed to improve the existing performance and have a capability to recognize all case-sensitive alphanumeric characters simultaneously. One of the problems is that there are several characters having similarities in gesture and shape, so that the classifier of the OCR system encounters many ambiguities when classifying some particular characters, especially when recognizing all case-sensitive alphanumeric characters.To overcome those problems, this study proposes a technique of grouping. All character classes are clustered into some groups using Fuzzy C-Means (FCM) clustering method. The OCR system that uses MDF and nested MLP network solves the problems and reach the research objectives. The nested MLP is the novelty method that is implemented in this study. This is a kind of multi-level MLP network that classifies the problem domain hierarchically. The first level classifies the character into the designated group and the second level continues the classification into the recognized character class.The OCR system using the methods in recognizing all case-sensitive alphanumeric characters yields an accuracy rate of 84.38% for the uppercases, 76.43% for the lowercases, and 78.92% for the digits respectively. Any misclassified characters are mostly happened in distinguishing several uppercase and lowercase characters having similarities in gestures and shapes.Kata Kunci : recognition, OCR, MDF, nested MLP, case-sensitive alphanumeric characters.ABSTRACT: The studies of Optical Character Recognition (OCR) are being developed since it still needs a performance improvement. The previous study of alphanumeric character recognition had been conducted by Blumenstein and Liu using Modified Direction Feature (MDF) and Multi Layer Perceptrons (MLP) network. The study reaches the accuracy rate of 70.22% for lowercase characters and 80.83% for uppercase characters.In this study the OCR system is proposed to improve the existing performance and have a capability to recognize all case-sensitive alphanumeric characters simultaneously. One of the problems is that there are several characters having similarities in gesture and shape, so that the classifier of the OCR system encounters many ambiguities when classifying some particular characters, especially when recognizing all case-sensitive alphanumeric characters.To overcome those problems, this study proposes a technique of grouping. All character classes are clustered into some groups using Fuzzy C-Means (FCM) clustering method. The OCR system that uses MDF and nested MLP network solves the problems and reach the research objectives. The nested MLP is the novelty method that is implemented in this study. This is a kind of multi-level MLP network that classifies the problem domain hierarchically. The first level classifies the character into the designated group and the second level continues the classification into the recognized character class.The OCR system using the methods in recognizing all case-sensitive alphanumeric characters yields an accuracy rate of 84.38% for the uppercases, 76.43% for the lowercases, and 78.92% for the digits respectively. Any misclassified characters are mostly happened in distinguishing several uppercase and lowercase characters having similarities in gestures and shapes.Keyword: recognition, OCR, MDF, nested MLP, case-sensitive alphanumeric characters

ANALISIS DAN IMPLEMENTASI SISTEM FUZZY DAN EVOLUTIONARY PROGRAMMING PADA PENGATURAN LAMPU LALU LINTAS CERDAS

ABSTRAKSI: Kondisi kepadatan di persimpangan jalan senantiasa berubah di setiap saat. Untuk membuat pengaturan lampu lalu lintas yang adil dan mengurangi risiko terjadinya kemacetan, petugas polisi terkadang harus turun di jalan. Dalam menjalankan tugasnya, petugas polisi akan menggunakan intuisinya untuk mengukur kepadatan setiap ruas jalan dan memberikan keputusan berapa lama suatu ruas boleh berjalan. Namun, tak setiap saat dan tak selamanya polisi dapat mengatasinya. Oleh karena itu, dibutuhkan suatu sistem pengatur lampu lalu lintas yang secara adaptif mampu menyesuaikan kondisi kepadatan tiap ruas jalan dengan mengadopsi kecerdasan dan intuisi petugas polisi.Solusi untuk permasalahan tersebut ialah dengan menerapkan sistem berbasis logika fuzzy yang mempunyai karakteristik mampu mengadopsi intuisi manusia dan lebih ‘manusiawi’ dalam memberikan keputusan. Untuk membangun Sistem Fuzzy yang handal, dibutuhkan a priori information sebagai basis pengetahuannya. Namun, apabila kita belum memilikinya, dibutuhkan algoritma optimasi untuk menemukan rancangan Sistem Fuzzy yang optimal. Evolutionary Programming merupakan salah satu algoritma optimasi yang dimaksud. Jadi, Sistem Fuzzy yang didukung oleh Evolutionary Programming akan menghasilkan sistem pengatur lampu lalu lintas yang adaptif dengan mengadopsi kecerdasan dan intuisi petugas polisi.Dari observasi dan pengujian yang dilakukan, hasil terbaik yang diperoleh ialah 94,67% yang menunjukkan tingkat kemiripan sistem dengan kecerdasan dan intuisi petugas polisi.Kata Kunci : persimpangan, traffic light, polisi, intuisi, Sistem Fuzzy, Evolutionary Programming.ABSTRACT: Density conditions at the crossroads always change in every moment. To set up fair traffic lights and reduce the risk of traffic jams, police officers sometimes have to come down on the road. In conducting their duties, police officers will use their intuition to measure the density of each segment of the road and give a decision how long a segment should run. However, the police surely can\u27t handle it everytime. Therefore, it requires a regulatory system of traffic lights that can adaptively adjust the density conditions by adopting police officers’s intelligence and intuition.The solution is by applying the fuzzy logic-based system that can adopt the characteristics of human intuition in giving the decision. Building a reliable Fuzzy System needs a priori information as a knowledge base. But, if we don’t have it, we need an optimization algorithm to find the design of optimal Fuzzy Systems. Evolutionary Programming is one of them we need. So, a Fuzzy System supported by Evolutionary Programming will generate an adaptive regulatory system of traffic lights by adopting police officers’s intelligence and intuition.From observation and testing, the best result is 94.67%, showing the similarity level between system and police officers\u27s intelligence and intuition.Keyword: crossroads, traffic light, police, intuition, Fuzzy System, Evolutionary Programming

PENERAPAN TEKNIK KLASIFIKASI PADA SISTEM REKOMENDASI MENGGUNAKAN ALGORITMA GENETIKA

[Id]Sistem rekomendasi yang dibangun dalam penelitian ini adalah sistem rekomendasi yang dapat memberikan rekomendasi sebuah item terbaik kepada user. Dari sisi data mining, pembangunan sistem rekomendasi satu item ini dapat dipandang sebagai upaya untuk membangun sebuah model classifier yang dapat digunakan untuk mengelompokkan data ke dalam satu kelas tertentu. Model classifier yang digunakan bersifat linier. Untuk menghasilkan konfigurasi model classifier yang optimal digunakan Algoritma Genetika (AG). Performansi AG dalam melakukan optimasi pada model klasifikasi linier yang digunakan cukup dapat diterima. Untuk dataset yang digunakan dengan kombinasi nilai parameter terbaik yaitu yaitu ukuran populasi 50, probabilitas crossover 0.7, dan probabilitas mutasi 0.1, diperoleh rata-rata akurasi sebesar 72.80% dengan rata-rata waktu proses 6.04 detik, sehingga penerapan teknik klasifikasi menggunakan AG dapat menjadi solusi alternatif dalam membangun sebuah sistem rekomendasi, namun dengan tetap memperhatikan pengaturan nilai parameter yang sesuai dengan permasalahan yang dihadapi.Kata kunci:sistem rekomendasi, klasifikasi, Algoritma Genetika[En]In this study was developed a recommendation system that can recommend top-one item to a user. In terms of data mining, it can be seen as a problem to develop a classifier model that can be used to classify data into one particular class. The model used was a linear classifier. To produce the optimal configuration of classifier model was used Genetic Algorithm (GA). GA performance in optimizing the linear classification model was acceptable. Using the case study dataset and combination of the best parameter value, namely population size 50, crossover probability 0.7 and mutation probability 0.1, obtained average accuracy 72.80% and average processing time of 6.04 seconds, so that the implementation of classification techniques using GA can be an alternative solution in developing a recommender system, due regard to setting the parameter value depend on the encountered problem.Keywords:Recommendation system, classification, Genetic Algorith

PENGENALAN AKSARA JAWA TULISAN TANGAN MENGGUNAKAN DIRECTIONAL ELEMENT FEATURE DAN MULTI CLASS SUPPORT VECTOR MACHINE

Javanese character is a set of old traditional letter from Java, Indonesia. It has a complicated structure and it has similiar shape to each other. Optical Character Recognition (OCR) is a field in computer vision that attempted to recognize a certain character within an image. Various kinds of research have been done by using various methods in order to make an OCR system which able to recognize characters properly. Because of Javanese character’s charasteristic, a strong method is needed in order to build a high accurate OCR system in recognizing Javanese character. Directional Element Feature (DEF) is a feature exctraction method that has been used in many researches and has been proven to be strong enough to recognize Chinese characters which has complicated shape structure. DEF builds feature vector by count up image edge neighborhood element in each character. Support Vector Machine (SVM) is a classification method that works by finding a hyperplane with smallest margin to separate two data classes. In some previous research, SVM has been proven to be strong enough to classify data, especially data that has not been seen by the system before. In some other research, SVM has been proven better than common Artificial Neural Network in classifying data. In this research, a Javanese character recognition system is built using DEF and SVM. Test result shows the best recognition accuracy is 93.6% by recognizing 250 handwritten Javanese Character which is 10 letters for each character. Keywords: OCR, handwritten, Javanese character, DEF, SV

Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations

Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object's class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task

Analysis on vowel /E/ in Malay language recognition via Convolution Neural Network (CNN)

In recent years, the silent killer disease, defined as a non-communicable disease, has become a frequent topic discussed in many academic discussions. Although this disease is not transferable from one to another, starting from 1990, the increment trend was annually published by the world statistic data for this disease, e.g., heart attack and stroke. The more significant consequence of these two diseases is to disable one or more human capabilities. One of the stroke disease effects is becoming disabled from hearing. Speech disabilities are the focus of this proposed study in this paper. Since the person diagnosed as a stroke patient requires attending the recovery session or rehabilitation session, the rehabilitation center must prepare and provide a sound module and system to help the patient regain their capability. Rehabilitation is an alternative path to gradually giving routine practice to the patient to improve their capability back. For this purpose, the rehab center requires a quantity of time to provide the patient to attend the training session. The training, however, is conducted in two ways, physically and virtually. For the Malaysia stroke patient, the training for pronouncing the vowel in the Malay language is crucial in getting back the speaking capability. Since the Malay language has 6 types of vowels, which are/a/,/e/,/ê/,/i/,/u/, and/o/. Here, there is a limitation to smartly recognizing the difference between the two/e/vowels. Malay's/e/vowel is crucial as the similar spelling vocabulary conveys two different meanings. This study analyzed the differences in recognizing the two/e/vowels using Convolution Neural Network (CNN) with the help of the existing sound-image dataset

Explainable Artificial Inteligence : Menggunakan Metode-Metode Berbasis Nearest Neighbors

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