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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Record and Library Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Prosiding Seminar Nasional Sains dan Teknologi Terapan IJEEIT : International Journal of Electrical Engineering and Information Technology Jurnal Teknologi Informasi dan Multimedia Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Informatika Teknologi dan Sains (Jinteks) IJCOSIN : Indonesian Journal of Community Service and Innovation Prosiding Seminar Nasional Universitas Ma Chung Jurnal INFOTEL Submit : Jurnal Ilmiah Teknologi Informasi dan Sains
Yesy Diah Rosita
Telkom University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Perbandingan Metode Prediksi untuk Nilai Jual USD: Holt-Winters, Holt's, dan Single Exponential Smoothing Yesy Diah Rosita; Lady Silk Moonlight
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 4 (2024): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.473

Abstract

In the ever-changing landscape of the global economy, the role of the United States Dollar (USD) as the backbone of the international financial system significantly influences market stability and dynamics. The close correlation between fluctuations in the USD exchange rate and internal and external factors demands effective prediction methods to understand and manage associated risks. This study aims to compare the performance of three main prediction methods: Single Exponential Smoothing (SES), Holt's Method, and Holt-Winters Method, in forecasting USD exchange rates. Utilizing historical data from the Central Statistics Agency (BPS) and testing under three training data distribution scenarios (45%, 55%, and 75%), this research provides in-depth findings on the strengths and weaknesses of each prediction method. Performance evaluations include the time required, Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), R-Squared, and correlation for the implementation of each method. If averaged, the results are as follows for SES, Holt’s, and Holt’s Winter, respectively: SES (1.58; 284.20; 68,768.26; 440.07; 0.03; -2.12; Nan), Holt’s (1.39; 890.23; 426,377.44; 1,043.28; 0.06; -24.28; -0.66), and Holt’s Winter (1.20; 997.45; 513,657.58; 1,168.00; 0.07; -30.62; -1.55). Overall, this indicates that the Holt-Winters Method stands out with significant performance, especially in scenarios with larger training data distributions, with a low R-Squared value (-4.618) and satisfactory correlation (0.417). Holt's Method also shows improved accuracy, while Single Exponential Smoothing (SES) offers time efficiency, albeit with limitations in explaining data variations. In conclusion, this research provides valuable guidance for business stakeholders, investors, and policymakers in selecting prediction methods suitable for their data characteristics and analysis goals, with the potential for a positive impact on business strategies, competitiveness, and risk management amid the uncertainty of USD exchange rate fluctuations.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 Abdul Khamid Adi Surya Jinggo Pratama Aditya Dwi Putro Wicaksono Andhika Cahyono Putra Andi Prademon Yunus Andi Saputra, Wahyu Ardiantoro, Luki Aulia Desy Nur Utomo Billy Coster Junior Bintang Rizqi Pasha Daniel Happy Putra Dany Candra Febrianto Erita Devy Prastika Fajar Indra Kurniawan Fikra Titan Syifa Gatra Cahya Ramadhan Gugut Indartak Hanum, Dinarta Hastungkoro, Arya Widya Jayanti, Rani Lady Silk Moonlight Luki Ardiantoro M.Irfan Hanafi Maslikhatul Aini Maulana Ardillan Arendra Mimin Fatchiyatur Rohmah Mohammad Joevan Handyyansyah Nur Ainiyah Nur Azizah Nuuraan Risqi Amaliah Pratama Bagus W Ronny Makhfuddin Akbar Ronny Makkhfuddin Akbar Soffa Zahara Sugianto Sugianto Yanuarini Nur Sukmaningtyas Yeni Susanti Yusril Adil Hidayat