New Estimation Rules for Unknown Parameters on Holt-Winters Multiplicative Method

The Holt-Winters method is a well-known forecasting method used in time-series analysis to forecast future data when a trend and seasonal pattern is detected. There are two variations, i.e. the additive and the multiplicative method. Prior study by Vercher, et al. in [1] has shown that choosing the initial conditions is very important in exponential smoothing models, including the Holt-Winters method. Accurate estimates of initial conditions can result in better forecasting results. In this research, we propose new estimation rules for initial conditions for the Holt-Winters multiplicative method. The estimation rules were derived from the original initial conditions combined with the weighted moving average method. From the experimental results it was found that the new approach of the Holt-Winters multiplicative method can outperform the original Holt-Winters multiplicative method

Simulating Shopper Behavior using Fuzzy Logic in Shopping Center Simulation

To simulate real-world phenomena, a computer tool can be used to run a simulation and provide a detailed report. By using a computer-aided simulation tool, we can retrieve information relevant to the simulated subject in a relatively short time. This study is an extended and complete version of an initial research done by Christian and Hansun and presents a prototype of a multi-agent shopping center simulation tool along with a fuzzy logic algorithm implemented in the system. Shopping centers and all their components are represented in a simulated 3D environment. The simulation tool was created using the Unity3D engine to build the 3D environment and to run the simulation. To model and simulate the behavior of agents inside the simulation, a fuzzy logic algorithm that uses the agents' basic knowledge as input was built to determine the agents' behavior inside the system and to simulate human behaviors as realistically as possible

H-WEMA: A New Approach of Double Exponential Smoothing Method

A popular smoothing technique commonly used in time series analysis is double exponential smoothing. Basically, it’s an improvement of simple exponential smoothing which does the exponential filter process twice. Many researchers had developed the technique, hence Brown’s double exponential smoothing and Holt’s double exponential smoothing. Here, we introduce a new approach of double exponential smoothing, called H-WEMA, which combines the calculation of weighting factor in weighted moving average with Holt’s double exponential smoothing method. The proposed method will then be tested on Jakarta Stock Exchange (JKSE) composite index data. The accuracy and robustness level of the proposed method will then be examined by using mean square error and mean absolute percentage error criteria, and be compared to other conventional methods

Brown’s Weighted Exponential Moving Average Implementation in Forex Forecasting

In 2016, a time series forecasting technique which combined the weighting factor calculation formula found in weighted moving average with Brown’s double exponential smoothing procedures had been introduced. The technique is known as Brown’s weighted exponential moving average (B-WEMA), as a new variant of double exponential smoothing method which does the exponential filter processes twice. In this research, we will try to implement the new method to forecast some foreign exchange, or known as forex data, including EUR/USD, AUD/USD, GBP/USD, USD/JPY, and EUR/JPY data. The time series data forecasting results using B-WEMA then be compared with other conventional and hybrid moving average methods, such as weighted moving average (WMA), exponential moving average (EMA), and Brown’s double exponential smoothing (B-DES). The comparison results show that B-WEMA has a better accuracy level than other forecasting methods used in this research

Voice Control in Calorie Tracker Application using Levenshtein Distance Algorithm

Each food consumed by people contains a number of calories needed by the body to perform an activity. Calories can be described as fuel of engine to move and carry out tasks. Public’s indifference to the food consumed can cause some negative effects on health like, too skinny, obesity, and emergence of various diseases. Therefore, it is necessary to have an application which can provide some information about the calorie needs, so that user can control the calorie intake. This paper describes the development of an application to assist user in controlling calorie intake according to the calorie needs. This application supports voice control feature to improve user comfort in performing certain commands and inputting food consumed. In addition, this application also uses Levenshtein Distance algorithm to correct food recognition errors which is spoken by user. This application is developed using Java programming language for Android with SQLite database

Peramalan Data IHSG Menggunakan Fuzzy Time Series

AbstrakFuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia. Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG AbstractFuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia. Keywords – fuzzy time series, time series data, soft computing, IHS

Entity Annotation WordPress Plugin using TAGME Technology

The development of internet technology makes more information can be accessed. It makes information need to be organized in order to be easily managed. One solution can be used is by using the entity annotation approach which generates tags to represent that document. In this study, TAGME technology is implemented on a WordPress plugin, which is used to manage a blog. Moreover, information on Wikipedia ‘Bahasa Indonesia’ is processed to generate an anchor dictionary which is required by the technology that is implemented. This plugin performs entity annotation by giving tag suggestion for posts in a blog. Testing is carried out by measuring the precision, recall, and  of tag suggestions given by the plugin. The result shows that the plugin can give tag suggestions with precision 0.7638, recall 0.5508, and  0.59

Voice Control in Calorie Tracker Application Using Levenshtein Distance Algorithm

Each food consumed by people contains a number of calories needed by the body to perform an activity. Calories can be described as fuel of engine to move and carry out tasks. Public\u27s indifference to the food consumed can cause some negative effects on health like, too skinny, obesity, and emergence of various diseases. Therefore, it is necessary to have an application which can provide some information about the calorie needs, so that user can control the calorie intake. This paper describes the development of an application to assist user in controlling calorie intake according to the calorie needs. This application supports voice control feature to improve user comfort in performing certain commands and inputting food consumed. In addition, this application also uses Levenshtein Distance algorithm to correct food recognition errors which is spoken by user. This application is developed using Java programming language for Android with SQLite database

Rancang Bangun Sistem Rekomendasi Peminatan Fakultas Teknologi Informasi Dan Komunikasi Dengan Metode Analytical Hierarchy Process

. The tight competition in these globalization era encourages university students to pick suitable specialization courses while studying in university. University students will not only gain the knowledge from those particular specialization courses but they will also get the practical skills and the certification, as well as improved talents. Students have to consult to one of the lecturers for guidance to pick the most suitable courses for the students. However this method is time consuming. For such reason, the main purpose of this research is to build a recommendation system that utilizes Analytical Hierarchy Process to allow students to decide the course specialization. This system is be built based on Android and PHP programming language to process the recommendation calculation. According to the questionnaire done, more than 50% of the respondents confirm that this application has good accuracy rate, and as much as 40% respondents confirm that the application is able to provide course specialization recommendation that meets their preferences

Sistem Deteksi Dini Penyakit Mulut dan Gigi dengan Metode Fuzzy Multi Criteria Decision Making

Gigi dan mulut adalah gerbang kesehatan tubuh. Gangguan gigi menjadi faktor risiko penyakit kronis seperti gagal ginjal, diabetes, dan gangguan jantung. Penyakit mulut dan gigi adalah penyakit tidak menular paling banyak yang diderita masyarakat. Layanan kesehatan gigi terbilang mahal, hal ini berdampak luas terhadap ekonomi dan membebani dana jaminan sosial. Sistem pakar adalah sebuah program yang mencoba menirukan proses pemikiran dan pengetahuan dari pakar-pakar dalam menyelesaikan masalah. Oleh karena itu, dibangunlah sebuah sistem yang memiliki pengetahuan seperti pakar gigi dan mulut untuk dapat melakukan diagnosa awal penyakit mulut dan gigi. Android tidak hanya platform smartphone paling popular di dunia, tetapi juga memiliki aplikasi terbanyak. Hal ini menjadi salah satu faktor pendukung pembangunan sistem ini dengan basis Android. Sistem ini dibangun menggunakan metode Fuzzy Multi Criteria Decision Making. Tingkat akurasi sistem dengan pakar dalam mendeteksi awal penyakit mulut dan gigi mencapai 71,67%