Article Per Year (5 Year)
p-Index From 2021 - 2026
2.304
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Awang Long Law Review Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Decode: Jurnal Pendidikan Teknologi Informasi RJOCS (Riau Journal of Computer Science) Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani
Utami, Urfi
Unknown Affiliation
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
INTEGRASI SENSOR IOT DAN OPTIMASI ALGORITMA MACHINE LEARNING UNTUK DETEKSI REAL-TIME TINGKAT STRES MAHASISWA Andrianto, Richi; Lubis, Mustopa Husein; Irawan, Rina; Irawan, Yuda; Utami, Urfi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5178

Abstract

Abstract: High levels of stress among university students are a critical issue that can affect mental health, well-being, and academic performance. This study aims to develop a real-time student stress detection system using physiological data integrated with IoT technology and machine learning algorithms. The data used includes body temperature, blood oxygen saturation (SpO₂), heart rate, and blood pressure, acquired via embedded sensors and automatically transmitted to the cloud. The classification model was built using a combination of Random Forest and XGBoost, with enhanced accuracy through SMOTE-based data balancing and hyperparameter optimization using Optuna. The system was tested on a dataset of 3,420 records, classified into four stress levels: anxious, calm, tense, and relaxed. Evaluation results showed that the Random Forest model achieved the highest accuracy of 91%, followed by RF + XGBoost and RF + XGBoost + Optuna with accuracies of 90% each. The final model was deployed in a user interface using Streamlit, allowing real-time stress classification from IoT sensor input and manual input testing. The system proved to be effective and responsive in detecting stress objectively and can support digital-based mental health monitoring and counseling services for students. Keywords: Stress detection, IoT, Machine Learning, Random Forest, XGBoost Abstrak: Tingkat stres yang tinggi di kalangan mahasiswa merupakan permasalahan serius yang dapat memengaruhi kesehatan mental, kesejahteraan, dan performa akademik. Penelitian ini bertujuan untuk mengembangkan sistem deteksi tingkat stres mahasiswa secara real-time menggunakan data fisiologis berbasis teknologi IoT dan algoritma machine learning. Data yang digunakan meliputi suhu tubuh, kadar oksigen dalam darah (SpO₂), detak jantung, dan tekanan darah yang diperoleh melalui sensor terintegrasi dan dikirim ke cloud secara otomatis. Model klasifikasi yang dikembangkan memanfaatkan kombinasi algoritma Random Forest dan XGBoost, dengan peningkatan akurasi melalui teknik balancing data menggunakan SMOTE dan optimasi hyperparameter otomatis menggunakan Optuna. Sistem diuji menggunakan dataset berjumlah 3.420 data dengan distribusi empat kelas stres: cemas, tenang, tegang, dan rileks. Hasil evaluasi menunjukkan bahwa model Random Forest menghasilkan akurasi tertinggi sebesar 91%, disusul oleh RF + XGBoost dan RF + XGBoost + Optuna dengan akurasi masing-masing sebesar 90%. Model akhir kemudian diintegrasikan ke dalam antarmuka pengguna berbasis Streamlit, yang memungkinkan klasifikasi stres secara real-time dari data sensor IoT dan juga melalui input manual. Sistem ini terbukti efektif dan responsif dalam mendeteksi stres secara objektif dan dapat digunakan untuk mendukung layanan konseling atau pemantauan kesehatan mental mahasiswa secara digital. Kata kunci: Deteksi stres, IoT, Machine Learning, Random Forest, XGBoost
Optimizing Lean University Services Through UI/UX Design Using a User Centered Design Approach in Rokan Hulu Utami, Urfi; Kurniawan, Hendry; Andrianto, Richi; Yona, Sri Nelvi; Wati, Emilia
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1607

Abstract

Digital transformation in higher education demands efficient and user-centered digital service systems, particularly in regional universities where service processes are often fragmented, manual, and insufficiently aligned with user needs, such as in the context of Rokan Hulu. Existing studies predominantly address User Centered Design (UCD) or Lean Service separately, leaving a research gap in their integrated application for optimizing university digital services in developing regions. This study aims to bridge this gap by proposing an integrated UCD–Lean framework to redesign UI/UX and streamline service workflows based on empirical user needs and operational efficiency principles. The research adopts a four-stage UCD methodology consisting of context of use analysis through observations and semi-structured interviews with students, lecturers, and administrative staff; requirement specification using user personas and journey mapping; iterative design solutions developed through wireframes and interactive prototypes in Figma; and usability evaluation. Lean principles were operationalized through workflow simplification, elimination of non–value-added steps, and value-oriented interaction design. A purposive sample of 100 respondents representing primary academic service users in Rokan Hulu participated in usability testing using the System Usability Scale (SUS), selected for its reliability in prototype-level assessment. The results show a mean SUS score of 80 (“Good”), indicating acceptable usability and positive user acceptance, while the study theoretically contributes a contextual UCD–Lean integration model and positions its findings as preliminary usability evidence rather than definitive operational effectiveness due to prototype-based evaluation limitations.
Co-Authors Aida, Welven Aluwis Aulia, Annisa Bunda, Yola Permata Dona Dona, Dona Harapan Bunda, Ridho Hasibuan, Wahyu Hendri Maradona, Hendri Hendry Kurniawan, Hendry Iskandar, Aris Kiki Yasdomi Lubis, Mustopa Husein Putra, Riwaldi Richi Andrianto Rifqi, Mi'rajul Rina Irawan Rivalwan Rizky, Ayu Sabariah Sabariah Sabri, Khairul Satria Riki Mustafa, Satria Riki Siti Aisyah Supriyanto, Asep Susi Rahayu Wati, Emilia Yona, Sri Nelvi Yuda Irawan