Indonesian Stock Prediction using Support Vector Machine (SVM)

This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence) or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK) stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data) and the remainder (20 %) was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22

IoT for Real Time Data Logger and pH Controller

Acidity in wastewater is the critical problem in developing country. The absence of efficient wastewater management has caused serious environmental problems and cost issues. Therefore, in this paper IoT-based data logger and pH controller is proposed to reduce the inefficiency. IoT is a concept whereby objects around us can interact and exchange information with each other without human intermediaries through the Internet. One of the implementation of IoT is to monitor the level of liquid acidity through smartphones. It needs additional tools such as sensors, microcontrollers, and other devices that are then connected to the internet. Android-based mobile phone is used to interact with sensors, microcontroller, and other tools through the internet wherever the user is. From the testing, there is a successful communication between the components of the device, sensors, and Android devices. It is possible to adjust the acidity of the liquid automatically by activating the pump in accordance with the results of the pH reading

Smart Home System Using Internet of Things

The Internet of Things (IoT) is happening now. By implementing IoT, we can build smart home system. Smart home is an application that is a combination of technology and services that specialize in the home environment with specific functions aimed at improving the efficiency, comfort and security of the occupants. Smart homes filled with connected products are loaded with possibilities to make our lives easier, more convenient, and more comfortable. This intelligent home system uses a microcontroller to process functions that provided by smart home system, such functions as RFID for door access and PIR sensors for motion detection. By using Android users could control the sensors anytime and anywhere. Microcontroller used is Arduino IDE with WeMos D1R2 board. Based on the testing process, there was a successful communication between the components of the device, sensors, and Android devices. Users could open or close the solenoid, users can also turn off or turn on electronic devices using Android

Stance Classification Post Kesehatan di Media Sosial Dengan FastText Embedding dan Deep Learning

Misinformasi merupakan fenomena yang semakin sering terjadi di media sosial, tidak terkecuali Facebook, salah satu media sosial terbesar di Indonesia. Beberapa penelitian telah dilakukan mengenai teknik identifikasi dan klasifikasi stance di media sosial Indonesia. Akan tetapi, penggunaan Word2Vec sebagai word embedding dalam penelitian tersebut memiliki keterbatasan pada pengenalan kata baru. Hal ini menjadi dasar penggunaan fastText embedding dalam penelitian ini. Dengan menggunakan pendekatan deep learning, penelitian berfokus pada performa model dalam klasifikasi stance suatu judul post kesehatan di Facebook terhadap judul post lainnya. Stance berupa for (setuju), observing (netral), dan against (berlawanan). Dataset terdiri dari 3500 judul post yang terdiri dari 500 kalimat klaim dengan enam kalimat stance terhadap setiap klaim. Model dengan fastText pada penelitian ini mampu menghasilkan F1 macro score sebesar 64%

Multivariate Inputs on a MIMO Neuro-Fuzzy structure with LMA training. A study case: Indonesian Banking Stock Market

The paper describes the design and implementation of the multivariate inputs of multi-input-multi-output neuro-fuzzy with Levenberg-Marquardt algorithm training (MIMO neuro-fuzzy with accelerated LMA) to forecast stock market of Indonesian Banking. The accelerated LMA is efficient in the sense that it can bring the performance index of the network, such as the root mean squared error (RMSE), down to the desired error goal, more efficiently than the standard Levenberg-Marquardt algorithm. The MIMO neuro-fuzzy method is a hybrid intelligent system which combines the human-like reasoning style of fuzzy systems with the learning ability of neural nets. The main advantages of a MIMO neuro-fuzzy system are: it interprets IF-THEN rules from input-output relations and focuses on accuracy of the output network and offers efficient time consumption for on-line computation. The proposed architectures of this paper are a MIMO-neuro-fuzzy structure with multivariate input such as fundamental quantities as inputs network (High, Low, Open and Close) and a MIMO-neuro-fuzzy structure with other multivariate inputs, which is a combination inputs between two fundamental quantities (High and Low) and two inputs from technical indicator Exponential Moving Average (EMA High and EMA Low). Both proposed learning procedures, which are using accelerated LMA with optimal training parameters with at least one million iterations with different 16 membership functions, employ 12% of the input-output correspondences from the known input-output dataset. For experimental database, both structures are trained using the seven-year period (training data from 2 Oct 2006 to 28 Sept 2012) and tested using two-weeks period of the stock price index (prediction data from 1 Oct 2012 to 16 Oct 2012) and the proposed models are evaluated with a performance indicator, root mean squared error (RMSE) for mid-term forecasting application. The simulation results show that the MIMO-neuro-fuzzy structure with combination of fundamental quantities and technical indicators has better performance (RMSE) for two-weeks forecast. Key words: MIMO neuro-fuzzy; accelerated Levenberg-Marquardt algorithm; multivariate inputs, fundamental quantities; technical indicator

Pembuatan Market Expert Advisor pada Currency Market Menggunakan Fibonacci, Stochastic dan MACD Indicator

Seiring dengan berkembangnya teknologi, cara melakukan Forex trading sudah berubah dari off-line menjadi on-line dan saat ini dapat dilakukan secara otomatis. Salah satu Forex trading platform yang paling populer adalah Metatrader. Di dalam Metatrader terdapat teknologi bernama Expert Advisor yang dapat digunakan untuk melakukan Forex trading secara otomatis.Proyek ini bertujuan untuk membuat Expert Advisor yang berbasis pada Fibonacci, Stochastic dan MACD. Level-level Fibonacci digunakan untuk menentukan titik beli atau jual dan target keuntungan. Stochastic dan MACD digunakan untuk membantu penentuan titik beli atau jual agar mendapat keputusan yang lebih tepat. Selanjutnya, keputusan-keputusan trading yang dibuat oleh expert advisor dikirimkan pada akun Twitter pengguna sebagai notifikasi.Expert Advisor ini dapat menghasilkan keuntungan rata-rata 74,1% per tahun dimana pengujian dilakukan dari tahun 2007 sampai 2011. Hasil ini dicapai pada pasangan mata uang EURUSD dengan Time Frame D1 pada 2010. Keputusan-keputusan trading dikirimkan ke akun Twitter pengguna, terlebih dahulu dikirimkan ke e-mail server. Setelah itu, server akan memproses untuk mem-posting ke akun Twitter penggun

On analysing supply and demand in labor markets: Framework, model and system

National Research Foundation (NRF) Singapor

PERANCANGAN KOMUNIKASI VISUAL KULINER IKAN BANDENG PRESTO YANG TERDAMPAK PANDEMI COVID-19 DI SEMARANG

Kuliner adalah hasil dari penyajian masakan yang berupa lauk pauk. Di dalam perkembangannya, istilah kuliner digunakan untuk berbagai macam kegiatan, seperti seni kuliner yaitu seni persiapan, memasak, hingga penyajian biasanya dalam bentuk makanan. Ikan bandeng merupakan jenis ikan yang hidup di air tawar. Berbagai vitamin yang didapat dari ikan bandeng yang melimpah sangat baik untuk dikonsumsi. Di kota Semarang, ikan bandeng presto telah dikenal sebagai kuliner khas dan bisa dijadikan oleh-oleh. Dalam proses pengolahannya, ikan bandeng diolah melalui uap yang memiliki tekanan tinggi pada mesin presto sehingga dapat membuat duri bandeng yang tajam menjadi lunak. Akan tetapi di masa pandemi covid-19, ikan bandeng presto merupakan salah satu golongan kuliner yang terkena imbas penurunan yang signifikan. Selain faktor curah hujan yang tinggi dan bencana banjir yang sempat melanda kota Semarang, hal tersebut dapat mempengaruhi pengurangan jumlah produksi bahan baku pada ikan bandeng. Oleh karena itu dibutuhkan perancangan komunikasi visual dalam bentuk strategi promosi untuk menaikkan penjualan ikan bandeng presto di kota Semarang dan merancang media visual utama sebagai edukasi bahwa sangat baik jika rutin mengkonsumsi ikan bandeng prest

Lecture Notes in Electrical Engineering vol. 365

This book includes the original, peer-reviewed research papers from the 2nd International Conference on Electrical Systems, Technology and Information (ICESTI 2015), held during 9–12 September 2015, at Patra Jasa Resort & Villas Bali, Indonesia. The primary objective of this book is to provide references for dissemination and discussion of the topics that have been presented in the conference. This volume is unique in that it includes work related to Electrical Engineering, Technology and Information towards their sustainable development. Engineers, researchers as well as lecturers from universities and professionals in industry and government will gain valuable insights into interdisciplinary solutions in the field of Electrical Systems, Technology and Information, and its applications. The topics of ICESTI 2015 provide a forum for accessing the most up-to-date and authoritative knowledge and the best practices in the field of Electrical Engineering, Technology and Information towards their sustainable development. The editors selected high quality papers from the conference that passed through a minimum of three reviewers, with an acceptance rate of 50.6 %. In the conference there were three invited papers from keynote speakers, whose papers are also included in this book, entitled: “Computational Intelligence based Regulation of the DC bus in the On-Grid Photovoltaic System”, “Virtual Prototyping of a Compliant Spindle for Robotic Deburring” and “A Concept of Multi Rough Sets Defined on Multi-Contextual Information Systems”. The conference also classified the technology innovation topics into five parts: “Technology Innovation in Robotics, Image Recognition and Computational Intelligence Applications”, “Technology Innovation in Electrical Engineering, Electric Vehicle and Energy Management”, “Technology Innovation in Electronic, Manufacturing, Instrumentation and Material Engineering”, “Technology Innovation in Internet of Things and Its Applications” and “Technology Innovation in Information, Modeling and Mobile Applications”