Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal ITEj (Information Technology Engineering Journals) BAREKENG: Jurnal Ilmu Matematika dan Terapan
Samsoleh, Eddy
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Designing a Lecture Room Monitor System with an Android Application Based on the Chip's ESP8266 Yuliati, Ari; Abdilah, Fajar Hafid; Samsoleh, Eddy
ITEJ (Information Technology Engineering Journals) Vol 5 No 2 (2020): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v5i2.43

Abstract

The classroom monitoring system with an android application based on the ESP8266 chip was created to solve problems on campus in finding unused rooms and to provide solutions to the problems of finding classroom thus far by using inefficient manual methods. The monitor system was made to determine the status of classroom usage with a passive infrared (PIR) sensor which functions to detect human presence in the room and a light sensor with a Light Dependent Resistor (LDR) to detect the use of the projector in the room. The two sensors are connected to the ESP8266 chip which is connected to a realtime database via the internet. The android application is specifically made to access monitoring data stored in a realtime database throughout it is connected to the internet. The results of this system test the PIR sensor reaches a maximum distance of 4 meters, a time delay of about 3 minutes, a reliable tool, and low cost.
INDOOR ACTIVITY RECOGNITION AND DEMENTIA RISK DETECTION USING Wi-Fi RECEIVED SIGNAL STRENGTH INDICATOR (RSSI) AND NAIVE BAYES CLASSIFICATION Rubiani, Hani; Samsoleh, Eddy; Fitri, Sulidar; Taufiq, Muhammad; Amir Fazamin, Wan Mohd
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0465-0480

Abstract

Increasing life expectancy has resulted in a growing elderly population, making neurodegenerative conditions such as dementia a major global health issue. One of the main behavioral symptoms of dementia is wandering, which is characterized by repetitive and purposeless movement. Activity Recognition (AR) technologies, particularly those based on Wireless Sensor Networks (WSN), have gained attention for monitoring human behavior. Among these, Wi-Fi-based tracking using the Received Signal Strength Indicator (RSSI) offers a promising method for indoor activity monitoring and localization. This study aims to monitor the daily routines of elderly individuals, classify their current activity patterns by comparing them with previously recorded behaviors, and track their locations using Wi-Fi RSSI. A Naïve Bayes algorithm is proposed for activity classification and location tracking, while a time-based behavior graph is used to detect potential wandering behavior, aiding in early dementia risk assessment. The research utilizes primary data, which were collected directly through experiments in a controlled indoor environment. The data source comprises RSSI signals obtained from elderly participants. A purposive sampling method was employed to select participants aged 60 years and above, who were physically capable of performing the required tasks. A total of 4150 RSSI data samples were collected and analyzed. The proposed Naïve Bayes model achieved a classification accuracy of 64.60% using cross-validation, with a minimum average localization error of 0.7 meters, demonstrating the potential of this approach for early detection of dementia-related wandering behavior.
Co-Authors Abdilah, Fajar Hafid Amir Fazamin, Wan Mohd Hani Rubiani, Hani Muhammad Taufiq Sulidar Fitri Yuliati, Ari