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Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Program Aplikasi Steganografi Menggunakan Metode Spread Spectrum pada Perangkat Mobile Berbasis Android Rojali Rojali; Afan Galih Salman; Teddy Nugraha
ComTech: Computer, Mathematics and Engineering Applications Vol. 3 No. 2 (2012): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v3i2.2305

The exchange of traffic information in cyberspace grows fast. In all areas of life utilize technology to exchange information. One of the media owned by many people is mobile device such as mobile phone and tablet computer. In fact many people have been using mobile devices for information exchange function, and expect information to be transmitted quickly, accurately, and safely. The information security sent will be very important when the information is confidential. One way to secure information sent is the concealment of information into a media so that information hidden is beyond recognition by the human senses, which is commonly referred to steganography. This research studied and implemented steganography using spread spectrum Method on Android-based mobile devices. The results showed that the inserted image before and after the message was inserted is not different with PSNR value of about 75.

Aplikasi Rekomendasi Pola Makan Berbasis iOS Afan Galih Salman; Yen Lina Prasetio; Bayu Kanigoro; Anggi Anggi
ComTech: Computer, Mathematics and Engineering Applications Vol. 3 No. 2 (2012): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v3i2.2308

The goal for implementing this system is to help users manage and track history about their eat pattern, choose proper food for body’s need, and pick restaurants. Methodology used for this research contains three parts, which is analysis, design, and literature study. In requirement analysis, we do some interview with nutritionist and food provider, analysis iOS user, compare with same kind of application, and identify components that we need. In design method, we use Unified Modelling Language approach, ERD design, and user interface design. The result is a food planning mobile application with iOS platform. This application can help user manage and track their eat pattern, help user choose balanced food that suitable for their body, and inform user where they can get food they plan to eat.

Pemodelan Sistem Fuzzy Dengan Menggunakan Matlab Afan Galih Salman
ComTech: Computer, Mathematics and Engineering Applications Vol. 1 No. 2 (2010): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v1i2.2349

Fuzzy logic is a method in soft computing category, a method that could process uncertain, inaccurate, and less cost implemented data. Some methods in soft computing category besides fuzzy logic are artificial network nerve, probabilistic reasoning, and evolutionary computing. Fuzzy logic has the ability to develop fuzzy system that is intelligent system in uncertain environment. Some stages in fuzzy system formation process is input and output analysis, determining input and output variable, defining each fuzzy set member function, determining rules based on experience or knowledge of an expert in his field, and implementing fuzzy system. Overall, fuzzy logic uses simple mathematical concept, understandable, detectable uncertain and accurate data. Fuzzy system could create and apply expert experiences directly without exercise process and effort to decode the knowledge into a computer until becoming a modeling system that could be relied on decision making.

Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso Afan Galih Salman; Yen Lina Prasetio
ComTech: Computer, Mathematics and Engineering Applications Vol. 1 No. 2 (2010): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v1i2.2384

The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3.

Game Edukasi Pengenalan Kebudayaan Indonesia Berbasis Android Afan Galih Salman; Natalia Chandra; Norman Norman
ComTech: Computer, Mathematics and Engineering Applications Vol. 4 No. 2 (2013): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v4i2.2581

Game now is not just an entertainment but merely serves as a educational tool or the so-called educational games. We develop an educational game application on Android platform which provides information about Indonesian culture. The methodology used is the analysis method of information gathering, system design, implementation, and testing. Information about Indonesian culture is displayed in form of Android-based educational game application. Users will be expected to get a lot of information regarding the Indonesian culture.

Implementasi Jaringan Syaraf Tiruan Recurrent Menggunakan Gradient Descent Adaptive Learning Rate and Momentum Untuk Pendugaan Curah Hujan Afan Galih Salman; Yen Lina Prasetio
ComTech: Computer, Mathematics and Engineering Applications Vol. 2 No. 1 (2011): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v2i1.2707

The artificial neural network (ANN) technology in rainfall prediction can be done using the learning approach. The ANN prediction accuracy is measured by the determination coefficient (R2) and root mean square error (RMSE). This research implements Elman’s Recurrent ANN which is heuristically optimized based on el-nino southern oscilation (ENSO) variables: wind, southern oscillation index (SOI), sea surface temperatur (SST) dan outgoing long wave radiation (OLR) to forecast regional monthly rainfall in Bongan Bali. The heuristic learning optimization done is basically a performance development of standard gradient descent learning algorithm into training algorithms: gradient descent momentum and adaptive learning rate. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 leap 74,6% while the second data group that is 50% training data and 50% testing data produce the maximum R2 leap 49,8%.

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