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All Journal Prosiding Seminar Nasional Universitas Ma Chung Jurnal INFOTEL
Yesy Diah Rosita
Telkom University, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Other

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Prototype Innovation of IOT Based Tissue Box Using Microcontroller ESP8266 and Infrared Sensor Yesy Diah Rosita; Nuuraan Risqi Amaliah; Andhika Cahyono Putra; Fikra Titan Syifa; Nur Azizah
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1321

Abstract

Toilet papers in public restrooms are a form of care from facility managers to help maintain hygiene, particularly in sensitive areas. Typically, toilet paper is exposed outside the tissue box, making it a breeding ground for bacteria. The uncontrolled use of tissue allows users to take as much as they wish, indirectly accelerating deforestation. Additionally, information about tissue stock in toilet paper dispensers is needed by cleaning staff or Office Boy (OB) to know when to refill before it runs out. This study aims to develop an innovative tissue box prototype that incorporates minimization, optimization, and efficiency. The prototype is equipped with an infrared (IR) sensor as an input mechanism to dispense tissue, an ESP8266 module connected to the internet to help cleaning staff monitor the tissue stock and implement the tissue usage control system based on an adjustable time interval of n minutes. The prototype has been designed, tested, and proven functional.
Recurrent Neural Network for Human Fall Motion Prediction Andi Prademon Yunus; Bintang Rizqi Pasha; Yesy Diah Rosita
Prosiding Seminar Nasional Universitas Ma Chung (Informatika & Sistem Informasi Bahasa dan Seni
Publisher : Ma Chung Press

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Abstract

Falls pose a significant health risk, especially for older adults, where one in three people over 65 experiences a fall each year. For those over 85, the consequences of falls can be severe, leading to life-threatening injuries and a marked decline in quality of life. To address the critical need for fall prevention, this study proposes a prediction approach using Recurrent Neural Networks (RNNs) to recognize patterns in human motion that may indicate an impending fall. By utilizing the CAUCA fall dataset—carefully designed to detect abnormal fall movements—we implemented and assessed different RNN architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). Our findings show that the GRU model performed best, achieving an accuracy of 0.716, MPJPAE of 32.921 pixels, MPJVE of 22.457 pixels per frame, and Euclidean distance metric outperforming the other models, with RNN closely following at an accuracy of 0.716, MPJPAE of 41.872 pixels, MPJVE of 21.44 pixels per frame. These promising results suggest that RNN models, particularly GRU, can serve as valuable tools in predicting falls, offering a foundation for future technology that can help prevent falls before they happen.
Co-Authors Andhika Cahyono Putra Andi Prademon Yunus Bintang Rizqi Pasha Fikra Titan Syifa Nur Azizah Nuuraan Risqi Amaliah