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Talent management (TM) necessitates both quantitative and qualitative skills; it is a determining factor in organisational success, including in university institutions. A team member's position in an organisation was closely related to TM. Artificial intelligence (AI) technology, such as an expert system, has also influenced human resource management. As well as to help human experts in TM, especially for academic staff TM, this study proposed a mobile app expert system called "ASTMES". This research method was used the certainty factor method with a forward-chaining inference machine, then programmed onto Android-based app with waterfall-SDLC technique. The result shown as ASTMES can categorise talent criteria: insufficient talent (99.7450 %), good talent (99.9942 %), and potential talent (99.9908 %), with a percentage of confidence level, respectively. [ABSTRACT FROM AUTHOR]

This research aims to review systematically and analyze the results of research regarding the use of microcontrollers for the Internet of Things (IoT). This literature review is aimed specifically to find out the use of microcontroller technology of IoT for the health sector. The microcontroller system being studied is specific using the Arduino board. The research method uses a systematic review with 4 (four) stages of work: identification, screening, eligibility, and input. Search articles using three databases: IEEE Xplore, IOP and SpringerLink during these past five years (2015-2019). The articles are should based on international proceedings and journals in English that accessed by full text. The result of study found that 859 research articles have discussed the use of the microcontroller to the IoT. Most of the articles published in international proceedings and the researches are from developed countries. The Arduino board uses the Arduino uno model and the temperature type of sensors, humidity sensors, gas sensors, smoke sensors, camera sensors, light sensors, and motion sensors. Microcontroller systems for IoT can be used in various fields such as house construction, industry, transportation, agriculture, sports, health, and the environment. The application of microcontroller technology to IoT in the field of health is to monitor patient health condition. For example: monitoring the patient's heart rate, hearing aid technology, navigation assistance for the visually impaired, smart home for disabled people, and smart box for medicine. [ABSTRACT FROM AUTHOR]

The weak of the security system in the bicycle parking lot causes a high rate of bicycle thievery. This study aims to improve the security system in a bicycle parking lot. Using research and development methods from existing studies, we create a bicycle parking security system by locking the bicycle tires if the bicycle owner has registered his fingerprint. This safety system uses a microcontroller (Arduino Mega 2560) to control all imputed data. When someone tries to steal a bicycle forcefully, the microcontroller will make the alarm sound and send the WhatsApp (WA) number of the bicycle owner to the raspberry pi. Furthermore, the raspberry pi will send the WA number to the Twilio application that programming interface (API) to be able to send a "stolen bicycle" message to the owner. The results showed that the BiSS had worked well with a success rate of 96% and all sensors could work optimally. When a thievery occurs, this system can send WA messages to bicycle owners with an average delivery time of 5.3 seconds. [ABSTRACT FROM AUTHOR]

This study aims to develop an Electronic Dictionary of Indonesian Sign Systems (ED-ISS) based on Raspberry Pi. The ED-ISS consists of input modules (RFID card, keyboard, and microphone); a raspberry pi as process module; and LCD display as well as audio as output modules. ED-ISS visualizes words into sign language (hand motion) that can be understood by deaf students. The results of the study reveal that the content validity of the ED-ISS is 89.7% (according to learning material experts), while the construct validity is 87.95% (according to media experts). Moreover, deaf students value the ED-ISS as 87.90% effective to translate words into sign language (hand motion). [ABSTRACT FROM AUTHOR]