MALARIA INCIDENCE FORECASTING FROM INCIDENCE RECORD AND WEATHER PATTERN USING GMDH POLYNOMIAL NEURAL NETWORK

ABSTRAKSI: Malaria affects over 100 million persons worldwide each year. The impact of malaria can cause approximately 2,414 deaths a day in average. Indonesia has a great number of incidents it is on the third highest position of malaria incident in South East Asia, with number of confirmed cases of 229,819 cases reported and 432 deaths only at 2010. The Malaria incidence prediction is badly needed so that the Health Department of Ministry of Indonesia is able to make the necessary preparation to prevent and reduce the impacts. Malaria incidence Prediction is a problem of Time series prediction, and a Time series prediction involves the determination of an appropriate model, which can encapsulate the dynamics of the system, described by the sample data. Previous work has demonstrated the potential of neural networks in predicting the behavior of complex, non-linear systems. Group Method of Data Handling (GMDH) Polynomial Neural Network was applied in a great variety of areas for data mining and knowledge discovery, forecasting and systems modeling, optimization and pattern recognition. This paper proposed a modified GMDH Polynomial Neural Network to reduce the learning time and computation while maintaining the accuracy. Based on the experiments, it was proven that the modified GMDH PNN was able to reduce the learning time by 72% and improve the accuracy by 7%, 5.37%, and 4.97% into the accuracy of 88.02%, 86.12%, and 83.90% for 1st, 2nd, and 3rd month prediction compared to the original GMDH PNN.br>Kata Kunci : Malaria, Prediction, Polynomial Neural Network.ABSTRACT: Malaria affects over 100 million persons worldwide each year. The impact of malaria can cause approximately 2,414 deaths a day in average. Indonesia has a great number of incidents it is on the third highest position of malaria incident in South East Asia, with number of confirmed cases of 229,819 cases reported and 432 deaths only at 2010. The Malaria incidence prediction is badly needed so that the Health Department of Ministry of Indonesia is able to make the necessary preparation to prevent and reduce the impacts. Malaria incidence Prediction is a problem of Time series prediction, and a Time series prediction involves the determination of an appropriate model, which can encapsulate the dynamics of the system, described by the sample data. Previous work has demonstrated the potential of neural networks in predicting the behavior of complex, non-linear systems. Group Method of Data Handling (GMDH) Polynomial Neural Network was applied in a great variety of areas for data mining and knowledge discovery, forecasting and systems modeling, optimization and pattern recognition. This paper proposed a modified GMDH Polynomial Neural Network to reduce the learning time and computation while maintaining the accuracy. Based on the experiments, it was proven that the modified GMDH PNN was able to reduce the learning time by 72% and improve the accuracy by 7%, 5.37%, and 4.97% into the accuracy of 88.02%, 86.12%, and 83.90% for 1st, 2nd, and 3rd month prediction compared to the original GMDH PNN.Keyword: Malaria, Prediction, Polynomial Neural Network

Deteksi Wajah Menggunakan GMDH Polynomial Neural Network dan Nonlinear Principal Component Analysis

ABSTRAKSI: Wajah merupakan salah satu bagian yang memiliki peran sangat penting dalam kehidupan manusia. Melalui teknik pengenalan wajah, beragam informasi dapat diperoleh. Meskipun memiliki peran yang sangat penting dalam sistem pengenalan wajah, deteksi wajah kurang mendapat perhatian dalam komunitas riset. Padahal sebelum bisa mendapatkan berbagai informasi dari sebuah wajah, pertama-tama perlu didapatkan terlebih dahulu wajah tersebut. Sistem deteksi wajah memberikan banyak tantangan dan kesulitan yang harus dihadapi yang disebabkan karena terdapat keberagaman variasi penampakan wajah pada citra, juga kompleksitas dari latar belakang citra. Dalam tugas akhir ini, digunakan metode GMDH Polynomial Neural Network (PNN) untuk mendeteksi kemunculan wajah pada suatu citra. PNN akan membagi region lokal pada multiscale sliding windows menjadi dua kelas, yaitu bagian wajah dan bukan wajah dengan memanfaatkan input data citra yang telah dilakukan proses reduksi dimensi untuk mengurangi kompleksitas komputasi menggunakan Nonlinear PCA. Klasifikasi dilakukan dengan teknik soft-classification. Dari hasil pengujian dapat diketahui jaringan GMDH-PNN dan tingkat reduksi NLPCA yang menghasilkan akurasi sistem sebesar 99,87 % untuk data train dan akurasi 81,96% untuk data testing. Kata Kunci : deteksi wajah, Nonlinear PCA, GMDH, Polynomial Neural Network.ABSTRACT: The face is one part that has a very important role in human life. Through face recognition techniques, a variety of information can be obtained. Despite having a very important role in face recognition system, face detection received less attention in research community. Whereas before we could get any information from a face, first of all the face itself need to be obtained in advance. Face detection system provides many challenges and difficulties that must be faced that there are diverse variations caused by the appearance of the face on the image, also the complexity of the background image. In this thesis, the method used polynomial GMDH Neural Network (PNN) to detect the occurrence of faces in an image. PNN will divide the local region on multiscale sliding windows into two classes, namely the face and not the face by using the input image data that has made the reduction process to reduce the computational complexity dimensions using Nonlinear PCA. Classification was done by using soft-classification. From the test results can be known-PNN GMDH network and the level of reduction NLPCA resulting system accuracy of 99.87% for training data and accuracy of 81,96% for testing data.Keyword: Face detection, Nonlinear PCA, GMDH, Polynomial Neural Networ

Ensemble machine learning approach for electronic nose signal processing

Electronic nose (e-nose) systems have been reported to be used in many areas as rapid, low- cost, and non-invasive instruments. Especially in meat production and processing, e-nose system is a powerful tool to process volatile compounds as a unique ‘fingerprint’. The ability of the pattern recognition algorithm to analyze e-nose signals is the key to the success of the e-nose system in many applications. On the other hand, ensemble methods have been reported for favorable performances in various data sets. This research proposes an ensemble learning approach for e-nose signal processing, especially in beef quality assessment. Ensemble methods are not only used for learning algorithms but also sensor array optimization. For sensor array optimization, three filter-based feature selection algorithms (FSAs) are used to build ensemble FSA such as reliefF, chi-square, and gini index. Ensemble FSA is developed to deal with different or unstable outputs of a single FSA on homogeneous e-nose data sets in beef quality monitoring. Moreover, ensemble learning algorithms are employed to deal with multi-class classification and regression tasks. Random forest and Adaboost are used that represent bagging and boosting algorithms, respectively. The results are also compared with support vector machine and decision tree as single learners. According to the experimental results, our ensemble approach has good performance and generalization in e-nose signal processing. Optimized sensor combination based on filter-based FSA shows stable results both in classification and regression tasks. Furthermore, Adaboost as a boosting algorithm produces the best prediction even though using a smaller number of sensor

Pembangunan Aplikasi Mobile Pengenalan Objek Untuk Pendidikan Anak Usia Dini

Penggunaan ponsel sudah sangat erat dengan kehidupaan anak usia dini sehingga menimbulkan beberapa dampak negatif bagi anak usia dini terutama berkurangnya interaksi dengan dunia sekitarnya. Salah satu teknologi yang dapat dikembangkan pada ponsel adalah computer vision. Salah satu penggunaan computer vision adalah object recognition yang memberikan solusi untuk membantu mengenali objek. Pada penelitian ini dibangun sistem pengenalan objek benda di dalam rumah yang diaplikasikan pada ponsel yang diharapkan membantu anak usia dini mengenali benda disekitarnya. MobileNet merupakan salah satu feature extraction yang memiliki kinerja yang baik dan ringan digunakan pada perangkat ponsel. Arsitektur MobileNet terdiri dari layer depthwise convolution dan layer pointwise convolution dalam mengekstraksi fitur. Percobaan ini juga menggunakan arsitektur Single Shot Multibox Detector (SSD) sebagai metode dalam mendeteksi objek. Pre-trained model dari dataset COCO digunakan pada eksperimen, untuk mengenali 20 jenis objek benda di dalam rumah. Dari hasil eksperimen, MobileNetV2 menghasilkan nilai mean Average Precision (mAP) yang lebih baik dibandingkan dengan MobileNetV1 dan InceptionV2, yaitu sebesar 99,34%. Abstract Mobile phone usage has been very close to early childhood life, so giving rise to some negative impact on early childhood, especially reduced interaction with the surrounding world. One of the technologies that can be developed on the cellphone is computer vision. One of the uses of computer vision is object recognition that provides solutions to help to recognize objects. This research builds a system for recognition objects inside in house that is developed on a cellphone that is expected to help early childhood recognize objects in the surrounding. MobileNet is one of feature extraction that has good performance and efficient use on a cellphone. MobileNet architecture consists of a depthwise convolution layer and pointwise convolution layer in extracting features. The experiment also uses the architecture of Single Shot Multibox Detector (SSD) as a method of detecting objects. We used MobileNet architecture as a pre-trained model that had previously been trained on COCO datasets, and implement transfer learning for 20 types of objects commonly found inside the house. The experimental result indicates that the mean Average Precision (mAP) of MobileNetV2 could exceed MobileNetV1 and InceptionV2 of 99.34%

Real-Time Wheat Classification System for Selective Herbicides Using Broad Wheat Estimation in Deep Neural Network

Identifying seed manually in agriculture takes a long time for practical applications. Therefore, an automatic and reliable plant seeds identification is effectively, technically and economically importance in agricultural industry. In addition, the current trend on big data and data analysis had introduced scientist with many opportunities to use data mining techniques for better decision in various application. Recently, there are various number of applications that use computer-aided in improving the quality in controlling system. Classifying different types of wheat hold significant and important role in agriculture field. An improvement on such kind of system that makes distinctions based on shape color and texture of wheat plantation is crucial. The main objective of this paper is to develop a machine vision system which identifies wheat base on its location. For this purpose, a real time robotics system is developed in order to find plant in sorrowing area using pattern recognition and machine vision. For real-time and specific herbicide applications, the images are categorized in either expansive or precise categories via algorithm following the principal of morphological operation. Different experiments were conducted in order to gauge the efficiency of the proposed algorithm in terms of distinguishing between various types of wheats. Furthermore, the experiments also performed admirably amid varying field conditions. The simulation results show that the proposed algorithms exhibited 94% success rate in terms of categorizing wheat population which consists of 80 samples and out of them 40 are narrow and 40 broad

Evolutionary Machine Learning : Pembelajaran Mesin Otonom Berbasis Komputasi Evolusioner

xvi, 495 hlm.; 24 c