The main principle of license plate recognition is to recognize the characters in the license plate which indicates the identity of the vehicle. This research will provide a system which can be implemented to the automatic payment in highway. Indonesian license plate consists of two parts, every of which has certain characters. These characters may become problem in the recognition process. Another problem is on the type of the license plate since Indonesia applies different color for every type of vehicle. In this research, different approaches are employed in the recognition of license plate; that is using character area as the feature value, also known as feature area, and K-Nearest Neighbor (KNN) as classification method. In addition, another method that has been used in our previous research is also employed to detect the character of license plate. The result shows very significant accuracy of 99.44%. In the process of recognition, scenario 1 gives the best accuracy at the K-1 value; that is 68.57% on the license plate and 92.72% on the characters of license plate. In the scenario 2 was obtained the license plate accuracy of 52% and license plate character accuracy of 89.36% with K-5. The system ran in a relatively short computational time.

Pneumonia is a bacterial, virus and fungi infection that attacks respiratory function. The disease causes air sacs in the lungs inflamed and swollen. It conditions produce lungs filled with fluid and mucus. Generally, the detection of pneumonia was done by chest x-ray images. This study discusses the detection of pneumonia through x-ray images using Convolutional Neural Network. The CNN model was Visual Group Geometry VGG16 and VGG19. As a comparison, we used the modified CNN 35 layer. The experiment using public data from Chest X-Ray Images - Kaggle. Data consist of 2 classes: normal and pneumonia with a total of 624 images. The results using VGG16 show a performance measure of sensitivity 92.75%, specificity 96.8%, and accuracy 94.1%. The result of VGG19 has sensitivity 96.6%, specificity 94.3%, and accuracy 95.7%. For CNN 35 layer has sensitivity 95.1%, specificity 98.5%, and accuracy 96.3%.

This research aims to analyze and get empirical evidence about the the influence of facilities drop box, e-SPT and e-filing submission of a letter innotice (SPT Keyword: Drop Box,E-SPT, E-Filing, Satisfaction Of TaxpayerABSTRAK: Penelitian ini bertujuan untuk menganalisis dan mendapatkan bukti empiris tentang pengaruh fasilitas drop box, e-SPT dan e-filing dalam penyampaian Surat Pemeberitahuan (SPT) terhadap kepuasan wajib pajak. Responden dalam penelitian ini adalah para wajib pajak yang terdaftar di KPP Pratama wilayah Jakarta Pusat. Jumlah wajib pajak yang menjadi sampel penelitian ini adalah 75 responden dari 3 KPP Pratama. Metode penentuan sampel yang digunakan dalam penelitian adalah convenience sampling, sedangkan metode pengolahan data yang digunakan peneliti adalah analisi regresi berganda. Hasil penelitian ini menunjukkan bahwa penerapan fasilitas drop box, e-SPT dan e-filing dalam penyampaian Surat Pemeberitahuan (SPT) memiliki pengaruh secara parsial terhadap kepuasan wajib pajak. Kemudian, fasilitas drop box, e-SPT dan e-filing dalam penyampaian Surat Pemeberitahuan (SPT) memiliki pengaruh secara simultan dan signifikan terhadap kepuasan wajib pajak.

Indonesia is one of the world’s biggest tobacco crop producers. By tobacco farmer, this plant is often even dubbed “green gold”. Madura Island is one of the best tobacco-producing areas in Indonesia. Tobacco is a significant trading crop in the eastern part of Madura Island, specifically in Pamekasan and Sumenep. The decline in tobacco yields is usually caused by pests and diseases that attack tobacco plants. Experts can easily detect conditions in plants (including tobacco) with their eyes, but this is very suitable and requires expensive operational costs when the size of the planting area is vast, and the distance of the planting area is far from the location of the expert. So that digital image processing techniques need to be applied to detect tobacco plant diseases earlier. By using data in the form of photographs of tobacco plant leaves, the condition will be identified. The method used in this research is GLCM (Gray Level Co-Occurrence Matrix) texture feature extraction, while CM (Color Moment) colour feature extraction and Naïve Bayes method are used for classification. The results of testing tobacco identification obtained the best accuracy of 82.2% for Pamekasan tobacco and 84.4% for Sumenep tobacco. The best results are obtained by using the colour feature extraction.

No abstract