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Journal : IJITEE (International Journal of Information Technology and Electrical Engineering)

Adaptive Traffic Light Control Based on Actual Condition Using Google Map API Adi Sabwa Isti Besari Arkanuddin; Selo Sulistyo; Anugerah Galang Persada
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 3, No 2 (2019): June 2019
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1133.992 KB) | DOI: 10.22146/ijitee.49964

Abstract

Traffic congestion is one of the main problems in transportation sector and it causes a lot of drawbacks to public. The traffic light system is used to reduce the level of occurring traffic congestion. Generally, the available traffic light systems use a fixed time setting. This old traffic control system is no longer able to manage the ever-changing traffic conditions effectively and efficiently, causing a long queue of vehicles. To overcome this problem, a traffic light control system that can adapt to actual conditions of road density and can run automatically is offered. This system utilizes Google Map API as a road density data source. The result of this study is a traffic control system that can adjust the green light time duration based on the obtained density values and density trends, simulation of this adaptive system as well as simulation results analysis. A prototype of this adaptive control system was also produced in this study.
Applying Integrating Testing of Microservices in Airline Ticketing System Dearisma Arfinda Ma'ruf; Selo Sulistyo; Lukito Edi Nugroho
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 4, No 2 (2020): June 2020
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.55491

Abstract

Microservices have been applied on several major systems including airlines. The characteristics of microservices which are independent and also interconnected need to be tested. The testing is done to preserve the system’s sequential stage processes, especially the online ticket reservation. Four features which are the search, booking, payment, and booking info feature are tested. This research performed three stages of testing on the microservices, those are unit testing, integrity testing, and end-to-end testing. Unit testing was conducted to test every function on every nodule, integrity testing was done to test interconnection between microservices, and end-to-end testing was to test the final results obtained after the unit test and integrity test were carried out. The three stages of testing must be done sequentially. The system on the airline provides the valid or correct response. Three stages of testing can be applied on other airlines by obtaining a legal API and can be accessed publicly.
Applying Machine Learning for Improving Performance Classification on Driving Behavior Ahmad Iwan Fadli; Selo Sulistyo; Sigit Wibowo
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 4, No 1 (2020): March 2020
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.56919

Abstract

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.
Modified Usability Test Scenario: User Story Approach to Evaluate Data Visualization Dashboard Nurul Tiara Kadir; Rudy Hartanto; Selo Sulistyo
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.61201

Abstract

The data processing results are commonly displayed in a dashboard with various graphic visualization forms to deliver new knowledge easier to understand by users. However, many data analysis dashboards cannot communicate the knowledge effectively and efficiently given the unsuitable design implementation. Therefore, research to measure the interface display's effectiveness in the data analysis system is deemed necessary. This research proposed a scenario modification in the usability test with a user story approach to measuring the system interface display in delivering the information to users. The approach of a usability test with the user story is expected to be capable of helping the researcher in understanding the user habits indirectly. There were 20 participants to validate the proposed method. Participants were asked to use the system and answer several questions to develop their user experience. After developing user experience for each user, the System Usability Scale (SUS) was conducted. SUS score results obtained from this research was 75.25. Besides, the researcher also measured the understanding level among the users using questionnaires. The questionnaire results were converted into numbers and resulted in a mean value of 91.8. Those two values indicate the users' ability to use the system well and obtain the new knowledge displayed in the data analysis dashboard.