Extended Kalman Filter In Recurrent Neural Network: USDIDR Forecasting Case Study

Artificial Neural Networks (ANN) especially Recurrent Neural Network (RNN) have been widely used to predict currency exchange rates. The learning algorithm that is commonly used in ANN is Stochastic Gradient Descent (SGD). One of the advantages of SGD is that the computational time needed is relatively short. But SGD also has weaknesses, including SGD requiring several hyperparameters such as the regularization parameter. Besides that SGD relatively requires a lot of epoch to reach convergence. Extended Kalman Filter (EKF) as a learning algorithm on RNN is used to replace SGD with the hope of a better level of accuracy and convergence rate. This study uses IDR / USD exchange rate data from 31 August 2015 to 29 August 2018 with 70% data as training data and 30% data as test data. This research shows that RNN-EKF produces better convergent speeds and better accuracy compared to RNN-SGD

Face Image Generation and Enhancement Using Conditional Generative Adversarial Network

The accuracy and speed of a single image super-resolution using a convolutional neural network is often a problem in improving finer texture details when using large enhancement factors. Some recent studies have focused on minimal mean square error, resulting in a high peak signal to noise ratio. Generally, although the peak signal to noise ratio has a high value, the output image is less detailed. This shows that the determination of super-resolution is not optimal. Conditional Generative Adversarial Network based on Boundary Equilibrium Generative Adversarial Network, by combining Mean Square Error Loss and GAN Loss as a loss function to optimize the super-resolution model and produce super-resolution images. Also, the generator network is designed with skip connection architecture to increase convergence speed and strengthen feature distribution. Image quality value parameters used in this study are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The results showed the highest image quality values using dataset validation were 26.55 for PSNR values and 0.93 for SSIM values. The highest image quality values using the testing dataset are 24.56 for the PSNR value and 0.91 for the SSIM value

Reccomendations on Selecting The Topic of Student Thesis Concentration using Case Based Reasoning

Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration.               This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods.               The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance.  The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%

Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%

Ontology-based Complementary Breastfeeding Search Model

Children's nutritional requirements differ from those of adults. The health ministry's Indonesian data shows that in 2017, there were 17.8% of malnourished children under five years old (toddlers), one of which was related to complementary breastfeeding problems. Complementary breastfeeding is given to babies starting at 6–24 months of age. This research aims to build a complementary breastfeeding search model and be able to present it as a treatment for malnourished babies. A search model is built to understand natural language input given by a user. Also, it can do reasoning by applying a set of rules to obtain implicit knowledge about the complementary breastfeeding menu recommended for babies. The methods used in this research are data collection, designing a search model, building an ontology model, building SWRL, natural language processing, and usability testing by users and nutritionists. This research succeeded in building an ontology-based complementary breastfeeding search model in the form of a semantic web. The testing result shows that the web can provide an alternative complementary breastfeeding menu according to the baby’s nutritional needs and has a high usability capability of 4.01 on a scale of 1 to 5

Adaptive Moment Estimation On Deep Belief Network For Rupiah Currency Forecasting

One approach that is often used in forecasting is artificial neural networks (ANN), but ANNs have problems in determining the initial weight value between connections, a long time to reach convergent, and minimum local problems.Deep Belief Network (DBN) model is proposed to improve ANN's ability to forecast exchange rates. DBN is composed of a Restricted Boltzmann Machine (RBM) stack. The DBN structure is optimally determined through experiments. The Adam method is applied to accelerate learning in DBN because it is able to achieve good results quickly compared to other stochastic optimization methods such as Stochastic Gradient Descent (SGD) by maintaining the level of learning for each parameter.Tests are carried out on USD / IDR daily exchange rate data and four evaluation criteria are adopted to evaluate the performance of the proposed method. The DBN-Adam model produces RMSE 59.0635004, MAE 46.406739, MAPE 0.34652. DBN-Adam is also able to reach the point of convergence quickly, where this result is able to outperform the DBN-SGD model

TOPSIS and SLR methods on the Decision Support System for Selection the Management Strategies of Funeral Land

The funeral land is one of the public facilities that must be provided by Local Government to support community activities. The need for funeral land in Lubuklinggau continues to increase while the availability of funeral land is decreasing, this is because the number of deaths of the population continues to increase every year. Forecasting the land availability of funeral for the coming year and applying the management strategies of funeral land can overcome the needs of the cemetery. Forecasting the land availability of funeral using Simple Linear Regression. TOPSIS to choose the management strategies of funeral land. Forecasting uses two variables that are the variable number of the population deaths and the variable amount of funeral land in the last 5 years. Forecasting results will be used as one of the assessment criteria in the decision support system for selection of the management strategies of funeral land. The alternative of the funeral management strategy that will be applied and assessed in accordance with Local Regulation of Town of Lubuklinggau. The highest value of the end result of the system will be used as a recommendation for the selection of management strategies.

Estimasi Biaya Proyek Perangkat Lunak Menggunakan JST dan Algoritma Genetika

Estimasi biaya pengembangan proyek perangkat lunak merupakan salah satu masalah yang kritis dalam rekayasa perangkat lunak. Kegagalan dari proyek perangkat lunak diakibatkan ketidak akuratannya estimasi sumber daya yang dibutuhkan. Beberapa model telah dikembangkan dalam beberapa dekade ini. Untuk memberikan keakuratan dalam estimasi biaya proyek perangkat lunak masih menjadi tantangan tersendiri. Tujuan dilakukannya penelitian ini meningkatkan akurasi estimasi biaya proyek perangkat lunak dengan menerapkan algoritma genetika sebagai proses pelatihan pada ANN yang mengakomodasi formula dari Post Architecture Model (COCOMO II). COCOMO II merupakan model berbasis regresi yang digunakan untuk estimasi biaya proyek perangkat lunak. Dataset COCOMO adalah yang biasa digunakan untuk melakukan pelatihan dan pengujian pada jaringan. Magnitude of Relative Error (MRE) dan Mean Magnitude of Relative-Error (MMRE) digunakan sebagai pengkuran indikasi kinerja. Hasil percobaan menunjukan bahwa model yang diusulkan memberikan hasil estimasi biaya proyek perangkat lunak menjadi lebih akurat. Dalam kasus ini MMRE untuk COCOMO II adalah 73.01%, FFNN-BP adalah 39.90% dan FFNN-GA adalah 31.48%

An Absolute Differences K-Means Clustering Approach on Determining Intervals to Optimize Fuzzy Time Series Markov Chain Model

Fuzzy Time Series models have been developed in various ways, one of which is determining the intervals. Several methods were applied to determine the intervals, but the performances are still not optimal. This paper proposes a new approach that uses a combination of Absolute Differences and K-means Clustering in the Fuzzy Time Series Markov Chain model. K-means Clustering made the interval more flexible and compact based on the data it clustered. In addition, Absolute Differences was used as the based method to define how many intervals to be made. This study used Taiwan Capitalization Weighted Stock Index (TAIEX) as benchmark data to evaluate the proposed method, which produced an average Mean Absolute Percentage Error (MAPE) value of 0.42, and an average Root Mean Squared Error (RMSE) value of 51.09 for the test data. The proposed method outperformed other compared researches at the end of this paper in terms of prediction ac

IMPLEMENTASI RESTFULL WEB SERVICE PADA SISTEM INFORMASI RAWAT JALAN PASIEN BERBASIS SMARTPHONE

Personal health information is very valuable. Today, information still something that is desperately needed, completeness and availability of information determines everything from the basics to things that are important. Similarly, in the medical section, a person's health information to define the steps and subsequent medical considerations. Currently the patient is difficult to determine their own common health information. Growth in science and technology, particularly information technology have an impact on data access and information that is available. One of these developments is mobile technology, especially mobile operating system. The health application system, built on the Android mobile platform to inform medical data to patients. The data used in application is the data that hospital system has been running, so it also design an Application Programming Interface (API) for use as a data exchange format between server and client. In the API Webservice, using private-key authentication on each application. As for user authentication token used for each request. End of this research, API has been tested of its input-output so that the implementation on the Android side doesn't matter. On the Android side, this application can display medical information that retrieved from the API. Android application has been tested on several devices, from devices with the old Android operating system 2.3 Gingerbread to the latest operating system Android 4.4 Kitka