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All Journal TEKNIK Transmisi: Jurnal Ilmiah Teknik Elektro TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Sains dan Teknologi Sainteknol : Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Robotics and Control (JRC) Jurnal Ilmu Teknik dan Komputer Jurnal Abdi Insani Journal of Applied Data Sciences Jurnal Algoritma Jurnal Profesi Insinyur Indonesia Scientific Journal of Informatics Greenation International Journal of Tourism and Management Greenation International Journal of Economics and Accounting Sainteknol : Jurnal Sains dan Teknologi
R. Rizal Isnanto
Computer Science Department, Semarang State University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Transportation Other

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Journal : Journal of Applied Data Sciences

 1 
Enhanced Fall Detection using Optimized Random Forest Classifier on Wearable Sensor Data Afuan, Lasmedi; Isnanto, R. Rizal
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.498

Abstract

This study aims to enhance the performance of fall detection systems for elderly care using wearable sensors by optimizing the Random Forest (RF) algorithm. Falls among the elderly are a major health risk, and timely detection can mitigate serious injuries or fatalities. The primary contributions of this research include developing an optimized RF model specifically tailored for real-time fall detection on resource-constrained devices such as smartwatches. Our approach involves feature engineering, hyperparameter tuning using Grid Search and Randomized Search, and model evaluation to achieve optimal performance. Key findings indicate that the optimized RF model achieved an accuracy of 92%, precision of 91%, recall of 89%, and an F1-score of 90%, with an average processing time of 0.045 seconds per prediction. These metrics underscore the model's capability for real-time deployment, demonstrating improved computational efficiency and predictive accuracy compared to traditional machine learning algorithms and deep learning models. The novelty of this study lies in its targeted optimization of the RF model to balance accuracy with low computational demand, addressing the limitations of existing methods that are either computationally intensive or prone to misclassification. This research provides a scalable solution for continuous fall monitoring, with significant implications for wearable healthcare technology, improving both accessibility and response times in elderly care. 
Applied Data Science and Artificial Intelligence for Tourism and Hospitality Industry in Society 5.0: A Review Hartatik, Hartatik; Isnanto, R. Rizal; Warsito, Budi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.300

Abstract

The primary purpose of this research is to delve into the emerging trends of artificial intelligence and data science with a specific focus on the tourism and hospitality sectors. A comprehensive methodology used to conduct this research includes collecting article data, conducting analysis and then conducting a review study on data science and artificial intelligence trends. These articles were selected based on metadata sourced from web of science and Scopus metadata. In particular, the research scrutinized and assessed the evolving trends in data science and artificial intelligence   within the hotel and tourism category. This analysis drew data from two prominent databases, Web of Science and Scopus, obtained a total of 4155 articles identified using the software and generated 124 terms in the articles with at least ten co-occurrence relationships. The findings of this study explain the huge potential, namely the trend of data application of science and artificial intelligence   in the tourism sector which is categorized in five distinct areas: forecasting tourist demand, implementing customized service recommender systems for the tourism industry, classifying tourist behavior patterns in automation, analyzing and understanding tourist behavior, developing tourist destinations, and planning itineraries. Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, contributions of this research it offers valuable insights into the future directions of apllied data science and artificial intelligent research are represents the pioneering effort to analyze of applying machine learning to advance artificial intelligence and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, it offers valuable insights into the future directions of sentiment analysis research. Notably, this paper represents the pioneering effort to comprehensively analyze the methodology of applying machine learning to advance AI and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.
Co-Authors Achmad Hidayatno Adi Dhama Kameswara Adi Mora Tunggul Adian Fatchur R Adian Fatchur Rochim Adrian Khoirul Haq Adrianus Stephen, Adrianus Afrizal Mohamad Riand Aghus Sofwan agung setiawan Agung Wicaksono Agustiyar Agustiyar Ahmad Bahauddin Ahmad Fashiha Hastawan Ajub Ajulian Zahra Macrina Ali, Sarifa Isna Ali, Sarifa Isna Alwin Indra Fatra Aminullah Ruhul Aflah Anang Paramita Wahyadyatmika Andino Maseleno Andre Lukito Kurniawan, Andre Lukito Angga Setiawan Anggie Salsa Saputra Antonius Dwi Hartanto Antonius Hendry Setyawan Ardian Wijaya Arfriandi, Arief Arie Firmansyah Permana Aris Triwiyanto Aris Triwiyatno Bagus Hario Setiadji Basuki Rahmat Masdi Siduppa Bondhan Tunjung Bowo Leksono Budi Setiyono Budi Warsito Candra Laksono Catur Edi Widodo Causa Prima Wijaya Chairunnisa Adhisti Prasetiorini Chandra Yogatama Chauhan, Rahul Darmawan Surya Kusuma Dela Nurlaila Dewi Lestari Dian Wijayanto Dictosendo Noor Pambudi Rahayu Didik Supriyadi, Didik Djoko Windarto Donny Zaviar Rizky Dony Bagus Rudiyanto Dyah Kusuma Mauliyani, Dyah Kusuma Eko Didik Widianto Eliezer, Petrick Jubel Enda Wista Sinuraya Endang Purbowati Endriawan Endriawan Eskanesiari Eskanesiari Fachrul Rozy Fachry Abda El Rahman Fajar Adi Nugroho Fara Mantika Dian Febriana, Fara Mantika Fardana, Nouvel Izza Febry Santo Ferry Hadi Fifiana Wisnaeni Fikri Ahmad Affandi Habiba, A. Herdhian Cahya Novanto Herjuna Dony Anggara Putra, Herjuna Dony Anggara Heru Prastawa Ilina Khoirotun Khisan Iskandar Imam Santoso Irwan Andaltria Iswanti, Arie Kholid, Kholid Kodrat Imam Satoto Kurnia, Dita Juni Lasmedi Afuan Lathifah Alfat, Lathifah Lukas Aditratika M. Azwar A. G. N. M. Ikhsan Mulyadi M. Wirdan Syahrial Maman Somantri Maria Fitriana Mario Christy Sinuraya Martha Irene Kartasurya Meet Shah, Meet Meidiana Dwidiyanti Melly Arisandi Muhammad Satriya Utama Mukharrom Edisuryana Munawar Agus Riyadi Mutiara Shabrina Nanang Trisnadik Nani Purwati Natanael Benino Tampubolon, Natanael Benino Novettralita, Ucky Pradestha Nugroho Arif Widodo Nur Arifin Akbar Nur Rizky Rosna Putra Nurul Ifan Purba Oky Dwi Nurhayati Patel, Raj Praseti, Agung Budi Praseti, Agung Budi Prasetijo, Aging Budi Pringgo Budi Utomo R. Edith Indera Bagaskara R. G Alam Nusantara P.H, R. G Alam R. Mh. Rheza Kharis Rachmad Arief Setiawan Ragil Aji Prastomo Rahmat Gernowo Raidah Hanifah Raithatha, Bhavya Ramchandani, Paras Relung Satria D Rico Eko Wibowo Rizky Parlika, Rizky Rody Verdika Cahyadi RR. Ella Evrita Hestiandari Saputra, I Gede Dharma Setyowati, Ro'fah Shabrina Mihanora Sharma, Ansh Shriyal, Harsh Siboro, Septihadi Klinsman Sompura, Jayesh Sudjadi Sudjadi Sumardi . Suseno, J.Endro Teguh Dwi Prihartono Theodora Anita Fidelia Tito Tri Pamungkas Tri Murwanto Tri Prasetyo Wahyul Amien Syafei Widyati, Dian Ami Yuli Christiyono Yuli Christyono Yuli Syarif Zaka Bil Fiqhi