The number of motorized vehicles operating in Indonesia is increasing, especially two-wheeled motorized vehicles. This also triggers problems such as traffic jams and accidents. Warning from brake lights is not always effective to prevent collisions. Intelligent Transport System (ITS) offers a solution that is a future trend that refers to wireless communication as a system to detect and prevent accidents. In this research, a Vehicle to Vehicle (V2V) communication system using VLC consists of Leading Vehicle (LV) and Following Vehicle (FV). In LV there is an accelerometer that is used to detect the type of brake, namely No Brake, Brake and Hard Brake. The result of calculation of fuzzy logic data is binary data sent by VLC. VLC transmitter design uses and without lenses send binary data. Binary data is sent by V2V communication between LV and FV using VLC. FV detects binary data using a photodiode in day and night conditions. The performance LV that are fuzzy logic algorithm values used have an accuracy value of 87.5%. The results of brake detection using Fuzzy Logic algorithm are binary data sent 2 second sampling time through visible light communication. The binary data process transmitted process at daytime and night. Result highest accuracy using a lens is 58.33% at daytime and 72.34% at night.
Achieving results, we often used the word “change”, nevertheless the process behind that word is not easy and sometimes intricate. On the other hand, determining whether an organization has changed or not is another matter that requires standardize assessment. It is clear that the business environment is uncertain and faced with tremendous change due to the influence of globalization, information systems and technology, and the knowledge economy which affects the behavior of our society and will continue to do so. Alignment between business strategy and information system strategy remains a challenge for business organizations, especially in the current digital era. The purpose of this research is to produce a Strategic Alignment Maturity Level Model of business-IS alignment through three drivers of change, namely business, technology, customer, and management. Luftman’s Strategic Alignment Maturity Model is the reference used. Preliminary studies, designing and developing an instrument model, determining the strategic alignment maturity level, determining strategic recommendations, and built an application system are the stages of this research. Expert judgment is involved in linguistic assessment and strategy recommendations. The last step was an experiment to see the suitability of the final level and the strategy recommendations issued. The model for determining the alignment level of IS-Business with business, technology and management and customers as a driver of change and dedicated software is the result of this research, can be used as an alternative technique in the information system strategic planning process to determine needs or to evaluate the implementation of recommendations.
One of the intentions of using information systems for organizations is to improve business performance. The information system strategic planning is endeavored to be in line with the business strategy. Information system strategic planning refers to the active and continuous use of information systems and technology to achieve alignment of business and technology strategies. However, planning in this era of technological advancement cannot last long and requires continuous adjustments. Therefore, as part of organizational resources, information systems require operational supervision to ensure investment effectiveness and monitor the influence of business performance growth. The IT balanced scorecard with critical success factors in this study was combined to produce a monitoring and evaluation instrument. Business strategists and linguists are involved in the design validation of monitoring and evaluation instruments. The level of maturity of information systems on business performance is the resulting output. The resulting system software is intended as a model to make it more straightforward for organizations to find out the performance of their information systems. Following the objectives, this study produces monitoring and evaluating instruments for an existing information system based on a combination of an IT balanced scorecard with critical success factors. The result can be an alternative for continuous monitoring and evaluation for existing information systems to be dynamic against changing demands and stay in line with business strategies collaboratively.
Unique visitors are visitors who use one IP in a certain period of time. The number of unique visitors every day is a benchmark for the success of an electronic journal. The increasing number of unique visitors every day shows that scientific periodicals are increasingly in demand by the wider community, which also affects the breadth of distribution, and speeds up the journal accreditation system. Therefore it is necessary to forecast the number of unique visitors in electronic journals in the future. Here, Long Short-Term Memory (LSTM) captures the pattern of data that has been obtained and then used to describe future data. The data used for testing is unique, ending data as of January 1, 2018, until December 31, 2018. After the data is obtained, the data will be normalized, then processed by the LSTM method to get the output. Then the output will be normalized to get the size of MSE, RMSE, and also the level of accuracy. The selection of the learning rate and the determination of the number of neurons in the LSTM process have an effect on the performance test performed. From the research conducted, the highest accuracy results obtained in the learning rate of 0.1 is 66.81%. While the lowest MSE and RMSE were obtained at a learning rate of 0.2 is 189.53 and 13.76. Thus, the results obtained are expected to be able to predict the number of unique visitors to electronic journals in the future to meet the needs of journal accreditation.
The ability to concentrate is needed by students in the learning process. Also, the level of concentration required by each employee when they want to do various kinds of work. Monitoring and evaluating a person's concentration conditions is not easy, one of which is obtained from the information signal Electroencephalography (EEG). This study aims to detect the condition of whether someone is concentrating when doing activities or not. The method used in this study is for Backpropagation Type Neural Networks. Inputs from Artificial Neural Networks are EEG waves with Theta, Low Beta, and High Beta types. The identification system outputs are two classes, namely the full concentration class and the low concentration class. The ANN model obtained through training is then implemented into the Arduino Microcontroller so that it can be used as portable device. So, the classification results obtained are suitable with the output.
