Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.
In this modern age, the impact of globalization is increasingly entering and expanding into most societies. One impact of globalization makes people prefer to learn the language and use a foreign language rather than the local language, especially the Java language. It is very influential on the knowledge of the community about the existence or the existence of Javanese Letter, especially in the field of education. In this study, In this research will be made an application to recognize the writing of Javanese Letter based on Optical Character Recognition (OCR). Matching templates correlation can be used as pattern recognition methods. How the Template Matching Algorithm works by matching the template image with the test image after going through the Pre-processing and segmentation process. From the research that has been done by using 10 character template and 20 data testing get accuracy equal to 93.44% and error rate 6.56%. So the Matching Template Algorithm can well recognize the Javanese Letter pattern.
Kondisi cuaca memiliki kecenderungan berubah, untuk itu badan meteorologi bekerja memprediksi perkiraan cuaca agar dapat memberikan peringatan dini apabila terjadi perubahan cuaca yang mendadak atau bahkan ekstrem. Dengan memprakirakan cuaca yang datang mendadak secara akurat, maka dapat mengambil langkah pencegahan agar dapat meminimalkan kerugian yang akan terjadi. Diperlukan beberapa variable atau parameter yang relevan untuk dapat memodelkan data dengan baik sehingga hasil prediksinya menjadi lebih akurat. Salah satu pendekatan pemodelan data untuk prediksi cuaca adalah supervised learning dengan teknik estimasi. Estimasi memberikan prediksi nilai pada atribut target atau class attribute yang bertipe numerical. Regresi linear berganda merupakan salah satu algoritma estimasi yang handal untuk memprediksi cuaca. Empat variable independent yakni, suhu, kelembaban, tekanan, dan kecepatan angin digunakan untuk memprakirakan curah hujan sebagai variable dependent. Data yang digunakan adalah data BMKG dari Stasiun Meteorologi Ahmad Yani Semarang tahun 2015-2017. Nilai koefisien determinasi R2 sebesar 25.5 persen menunjukkan bahwa keempat variabel yang digunakan secara bersamaan dapat menjelaskan nilai curah hujan sebagai variable dependent.
Banking system collect enormous amounts of data every day. This data can be in the form of customer information, transaction details, risk profiles, credit card details, limits and collateral details, compliance Anti Money Laundering (AML) related information, trade finance data, SWIFT and telex messages. In addition, Thousands of decision are made in Banking system. For example, banks everyday creates credit decisions, relationship start up, investment decisions, AML and Illegal financing related decision. To create this decision, comprehensive review on various reports and drills down tools provided by the banking systems is needed. However, this is a manual process which is error prone and time consuming due to large volume of transactional and historical data available. Hence, automatic knowledge mining is needed to ease the decision making process. This research focuses on data mining techniques to handle the mentioned problem. The technique will focus on classification method using Decision Tree algorithms. This research provides an overview of the data mining techniques and procedures will be performed. It also provides an insight into how these techniques can be used in deposit subscription in banking system to make a decision making process easier and more productive. Keywords - Telemarketing, bank deposit, decision tree, classification, data mining, entropy.
E-Government applications in developing countries are still lagging behind e-Governments in advanced countries. For example, the use of information integration for Web portal content is still very limited. This paper proposes an automated approach for information aggregation over e-Government portals using ontological approach. The study uses data obtained from 10 local government Websites in the Central Java province-Indonesia. The data in the form of HTML Web document text, meta-data, hyperlinks and other rich-contents are effectively crawled. This paper focuses on the development of a crawler, which consists of two main modules, i.e., multi-tread downloader and scheduler. The use of ontology in the focused crawler producesamore effective result as compared to the Breadth First Search (BFS) approach as it reaches 37% of effectiveness in terms of the number of relevant documents downloaded.
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