Development of A Particle Swarm Optimization-Backpropagation Artificial Neural Network Model And Effects of Age And Gender On Pharmacokinetics Study of Omeprazole Enteric-Coated Tablets In Chinese Population

Background: The effects of age and gender were explored on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population and a plasma concentration prediction model was developed. All the data (demographic characteristics and results of clinical laboratory tests) were collected from healthy Chinese subjects in pharmacokinetics study using 20 mg omeprazole enteric-coated tablets. A noncompartmental method was used to calculate pharmacokinetic parameters, and 47 subjects were divided into two groups based on the calculation of the median age. Pharmacokinetic data from the low-age and high-age groups or male and female groups were compared by Student t-test. After a total of 12 variables were reconstruct and convert into independent or irrelative variables by principal component analysis, particle swarm optimization (PSO) was used to construct a backpropagation artificial neural network (BPANN) model. Result: The model was fully validated and used to predict the plasma concentration in Chinese population. It was noticed that the Cmax, AUC0-t, AUC0-∞ and t1/2 values have significant differences when omeprazole was administered by low-age groups or high-age groups while there were slight or no significant differences of pharmacokinetic data were found between male and female subjects. The PSO-BPANN model was fully validated and there was 0.000355 for MSE, 0.000133 for the magnitude of the gradient, 50 for the number of validation checks. The correlation coefficient of training, validation, test groups were 0.949, 0.903 and 0.874.Conclusion: It is necessary to pay attention to the age and gender effects on omeprazole and PSO-BPANN model could be used to predict omeprazole concentration in Chinese subjects to minimize the associated morbidity and mortality with peptic ulcer.

Prediksi Kelulusan Seleksi Mahasiswa Baru Jalur SBMPN Pada Politeknik Maritim Negeri Indonesia Menggunakan Jaringan Syaraf Tiruan Backpropagation

One of the selection routes for new student admissions at the Indonesian State Maritime Polytechnic (Polimarin) is the Joint Selection to Enter the State Polytechnic (SBMPN). Furthermore, the SBMPN selection results as the results of stage one tests and other specific tests which include interview, psychological, health, and fitness tests as stage two tests can predict the level of accuracy of passing from the required criteria. These criteria are used as variables to predict the graduation of prospective students on the SBMPN pathway conducted by Polimarin. The method used uses Backpropagation Artificial Neural Network with input variables, namely the average value of report cards from semester 1 to 5, interview scores, psychological test scores, health scores, and equality values. This system was developed with Mathlab software. Based on the results of testing the training data, the level of accuracy reaches 100% which can be classified as the best classification, with an accuracy value of 92.85 percent. This system is intended to help management predict the selection of the SBMPN route in the following year.

Pengenalan Citra Huruf Hijaiah Menggunakan Metode Gray Level Co-Occurrence Matrices (Glcm) Dengan 4 Sudut Orientasi Dan Jaringan Syaraf Tiruan Backpropagation

Arabic is one of the international languages according to the United Nations (UN) which was adopted by General Council resolution 3190 (XXVIII) as the official language and working language of the General Council and Main Committees on 18 December 1973. Arabic can be found in the holy book Al - Qur'an. For a Muslim, it is obligatory to learn and master Arabic in order to read and understand the contents of the Al-Qur'an. job applicant from Indonesia is also have to learn Arabic. The Hijaiiyah letter has the same role as the alphabet, which is to compose every word and sentence. Humans have a natural intelligence to be able to recognize each Hijaiiyah letter based on the special characteristics or patterns contained in each letter. However, natural intelligence has deficiencies such as inconsistencies in assessing the similarity of each handwritten Hijaiiyah letter from different people. Therefore this research will develop a system for identifying or recognizing Hijaiiyah handwritten patterns using the Gray Level Co-occurrence Matrices (GLCM) method with 4 orientation angles and Backpropagation Artificial Neural Network (ANN). Data was collected using the Autodesk Sketchbook application so that can reduce the noise. The purpose of this research is to know the level of accuracy and precision of the classification of the Hijaiiyah letter pattern. In this research, the amount of data used was 1500 images of Hijaiiyah letters. The highest accuracy is 45.1111% with a precision 45.1111%.

Pengenalan Pola Tulisan Tangan Aksara Arab Menggunakan Ekstraksi Fitur Discrete Cosine Transform Dan Klasifikasi Backpropagation Artificial Neural Network

Arabic is a language that is spoken as the first or native language of more than 280 million people, most of whom live in the Middle East and North Africa. Apart from the Middle East and North Africa, Arabic is also familiar and often used in Indonesia because of the majority of Indonesia's population is Muslim and Arabic is the language of worship in Islam. The recognition of Arabic handwritten letters is one of the studies that has been done before, where the accuracy results obtained vary according to the research and methods used. This study aims to determine the accuracy resulting from the recognition of Arabic script handwriting patterns using a combination of the DCT(Discrete Cosine Transform) feature extraction method and the ANN Backpropagation classification method. The data used for this study were data from handwritten sources on A4 HVS paper using markers with categories of age from 7-13 years old and 18-23 years old with 15 respondents in each group and a total dataset image of 8400. Testing the best model model obtained on all images produces an accuracy of 80.79%, using the images of age range 17-23 years produces 87.27% accuracy, and the images of age range 7-13 produces an accuracy of 72.84%. Keywords: pengenalan pola, tulisan tangan, aksara, DCT, backpropagation

Akurasi dalam Mengidentifikasi Citra Anggrek Menggunakan Backpropagation Artificial Neural Network

Anggrek merupakan tanaman bunga hias dalam Family Orchidaceae yang habitatnya terdistribusi pada hampir seluruh benua didunia, kecuali benua Antartika. Di Indonesia sendiri, sangat banyak peminat anggrek sehingga menjadikan bunga ini sebagai komoditas yang cukup menjanjikan bagi penggiat tanaman hias. Dengan ragam jenis anggrek yang mencapai lebih dari 25.000 spesies, identifikasi jenis anggrek menjadi sedikit rumit bagi para pecinta anggrek. Tujuan penelitian ini adalah untuk menentukan tingkat akurasi pengidentifikasian jenis anggrek melalui pengenalan gambar, sehingga dapat menjadi acuan dalam menentukan kelayakan metode tersebut. Penelitian ini menggunakan 120 citra anggrek yang terdiri dari 6 spesies. Citra anggrek tersebut diperoleh dengan melakukan pemotretan pada beberapa lokasi menggunakan kamera. Foto tersebut kemudian diolah menggunakan software pengolah citra dengan melakukan cropping dan resizing untuk mempercepat waktu komputasi saat pelatihan jaringan. Selanjutnya software MatLab digunakan untuk melakukan proses ektraksi ciri berupa data warna dan moment invariants. Data hasil ekstraksi ciri dijadikan input untuk melatih jaringan syaraf tiruan dengan metode Back Propagation. Penghitungan tingkat akurasinya dengan uji coba menggunakan data uji yang sudah disediakan. Hasil uji coba menunjukkan bahwa 26 dari 30 berhasil dikenali sehingga tingkat akurasi dapat dihitung yaitu 86,7%. Tingkat akurasi sebesar 86,7% dapat dianggap layak dan bisa dijadikan landasan pertimbangan untuk menggunakan metode yang diuji coba ini sebagai metode yang tepat dalam melakukan identifikasi anggrek melalui citra.