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All Journal JURNAL PROMOSI KESEHATAN INDONESIA Riau Journal of Computer Science Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Komunikasi : Jurnal Komunikasi ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknologi Sistem Informasi dan Aplikasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Informatika : Jurnal Informatika, Manajemen dan Komputer JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Technopreneurship and Information System (JTIS) Infotekmesin Jurnal Teknologi Informasi dan Multimedia Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Community Engagement and Emergence Journal (CEEJ) Jurnal Komunikasi dan Organisasi (J-KO) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) International Journal of Advances in Data and Information Systems Jurnal Ilmu Komputer Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal J-PEMAS Jurnal Ipteks Terapan : research of applied science and education pendidikan, science, teknologi, dan ekonomi Jurnal Rekam Medis (Medical Record Journal) Jurnal Teknik Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Telecommunication Control and Intelligent System Journal of Software Engineering and Information System (SEIS) SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Jurnal 7 Samudra Politeknik Pelayaran Surabaya Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Terapan Komunikasi : Jurnal Komunikasi
Yuda Irawan
Universitas Hang Tuah Pekanbaru
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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An IoT-Driven Hybrid Stacking Ensemble with Deep Meta-Learning for Vending Machine Sales Forecasting Yulisman Yulisman; Zupri Henra Hartomi; Rian Ordila; Uci Rahmalisa; Arie Linarta; Yuda Irawan
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1395

Abstract

Accurate sales prediction is essential for optimizing inventory management and supporting dynamic pricing strategies in the retail industry, particularly for vending machines (VMs) integrated with IoT technologies. The availability of real-time transactional and environmental data from IoT sensors provides opportunities to improve forecasting accuracy by capturing complex temporal patterns and external influences on consumer behavior. However, traditional time series models and single machine learning approaches often struggle to model nonlinear relationships and long-term dependencies in such data. This study proposes a hybrid stacking ensemble model that integrates machine learning and deep learning techniques to enhance the prediction of daily sales volume per Stock Keeping Unit (SKU) in IoT-enabled vending machines. The proposed framework employs Random Forest Regressor (RF), Support Vector Regression (SVR), and XGBoost Regressor (XGB) as Level-0 base learners. Their predictions, along with corresponding residuals, are utilized as meta-features for a Long Short-Term Memory (LSTM)-based meta-learner, enabling effective modeling of both nonlinear and temporal characteristics. The model incorporates diverse features derived from IoT data, including lagged sales, rolling statistics, temporal attributes (day of week and weekend indicators), and environmental variables such as temperature and humidity collected from IoT sensors. Hyperparameter optimization of the LSTM meta-model is performed using Optuna to improve model stability and generalization. The proposed approach is evaluated using 10-Fold Time Series Cross-Validation to preserve temporal data structure. Experimental results show that the proposed model achieves an R² of 0.9967 and an RMSE of 0.0899, outperforming the best individual base model, XGBoost (R² = 0.9946, RMSE = 0.1121). Although the improvement is marginal, it consistently demonstrates the advantage of combining machine learning and deep learning through a stacking ensemble strategy. These findings indicate that integrating meta-features, residual learning, and IoT-based feature engineering can improve predictive performance and support adaptive decision-making in real-time vending machine operations.
INTEGRASI SENSOR IOT DAN OPTIMASI ALGORITMA MACHINE LEARNING UNTUK DETEKSI REAL-TIME TINGKAT STRES MAHASISWA Richi Andrianto; Mustopa Husein Lubis; Rina Irawan; Yuda Irawan; Urfi Utami
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 (SpOâ‚‚), 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 (SpOâ‚‚), 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
An Optimized Heterogeneous Stacking Ensemble with Hyperparameter Optimization for Multi Class Hypertension Risk Classification Novi Yona Sidratul Munti; Yuda Irawan; Muhammad Habib Yuhandri
International Journal of Advances in Data and Information Systems Vol. 7 No. 2 (2026): August 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i2.1637

Abstract

Hypertension is a major global health concern and a leading risk factor for cardiovascular disease, stroke, kidney failure, and premature mortality. Accurate and interpretable prediction of hypertension risk is essential for supporting early intervention and preventive healthcare. This study proposes an optimized and explainable stacking ensemble framework for multi class hypertension classification by integrating LASSO feature selection, SMOTEENN data balancing, heterogeneous ensemble learning, CatBoost meta learning, GridSearchCV optimization, and SHAP based explainability. The proposed architecture combines five base learners, namely XGBoost, LightGBM, Random Forest, Extra Trees, and Support Vector Machine, whose probability outputs are transformed into meta features and processed by an optimized CatBoost meta learner. Experiments conducted on a dataset containing 3,000 hypertension related records demonstrated superior classification performance, achieving 97.50% accuracy, 97.46% precision, 97.50% recall, and 97.48% F1 score. 10 fold cross validation further confirmed the robustness of the framework with a mean accuracy of 97.50% ± 0.0027. ROC analysis produced AUC values above 0.97 for all classes, indicating excellent discriminative capability. The results demonstrate that the proposed framework provides both high predictive accuracy and strong interpretability, making it a promising solution for intelligent hypertension risk assessment and clinical decision support.
EduDiab-Online: Uses Peer support to Improve Health Literacy in Type 2 Diabetes Patients Nopriadi Nopriadi; Yuda Irawan; Emy Leonita
Jurnal Promosi Kesehatan Indonesia Vol 21 No 3: July 2026
Publisher : Master Program of Health Promotion Faculty of Public Health Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpki.21.3.223-230

Abstract

Background: Type 2 Diabetes Mellitus (T2DM) remains a leading contributor to premature mortality and long-term complications, particularly in Indonesia, where health literacy levels are alarmingly low. This study aims to evaluate the effectiveness of EduDiab-Online, a web-based diabetes education model integrated with a peer-support approach, in improving health literacy and clinical outcomes among patients with type 2 diabetes mellitus (T2DM) in community settings. Method: A quasi-experimental pretest-posttest design with a control group was employed by involving 120 T2DM patients from two primary healthcare centers in Pekanbaru, Indonesia, divided equally into intervention and control groups. The intervention consisted of six weekly online educational modules combined with peer-support forums facilitated by trained peer mentors. Data were collected over four months using validated instruments assessing health literacy, self-efficacy, HbA1c, body mass index (BMI), and blood pressure. Statistical analyses, including paired and independent t-tests, were performed using SPSS version 25. Result: Compared with the control group, the intervention group demonstrated significantly greater improvements in health literacy (N-Gain = 0.61 vs. 0.08, p < 0.001), accompanied by a significantly greater increase in self-efficacy and greater reductions in HbA1c, BMI, and blood pressure (all p < 0.05). Furthermore, 88% of participants actively engaged in peer-support activities, indicating high adherence to the program. These findings suggest that EduDiab-Online is an effective and scalable community-based diabetes education model that integrates digital learning with peer support to improve both behavioral and clinical outcomes. Further studies are recommended to evaluate its long-term effectiveness and applicability to other chronic disease management programs.
The Improvement of Stress Coping Behaviour and Learning Engagement Artificial Intelligence Personalized Mental Health Education Emy Leonita; Yuda Irawan; Nopriadi Nopriadi
Jurnal Promosi Kesehatan Indonesia Vol 21 No 3: July 2026
Publisher : Master Program of Health Promotion Faculty of Public Health Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpki.21.3.195-205

Abstract

Background: Mental health problems and declining learning engagement among university students have become major concerns within digitally intensive higher education environments. Although artificial intelligence (AI) technologies have increasingly been integrated into educational systems, previous studies have rarely combined personalized mental health education, adaptive coping recommendations, and behavioral engagement analytics within a single intervention framework. Therefore, this study aimed to examine the effectiveness of AI. personalized mental health education in improving stress coping behavior and learning engagement among university students.Method: This study employed a sequential explanatory mixed methods intervention design involving 289 undergraduate students from Universitas Hang Tuah Pekanbaru and Institut Kesehatan Payung Negeri Pekanbaru, Indonesia, during 2025. Data were collected using structured questionnaires and behavioral engagement analytics from digital learning activities. Quantitative data were analyzed using paired sample t tests and SEM PLS, while qualitative data were analyzed thematically.Result: The findings demonstrated statistically significant improvements across all psychosocial and academic engagement dimensions (p<0.001). It induced positive changes in students stress coping behavior and learning engagement following the intervention. SEM PLS analysis additionally revealed significant direct and indirect effects of AI personalized mental health education on stress coping behavior, emotional wellbeing, and learning engagement. Qualitative findings further indicated that students perceived the intervention as adaptive, emotionally supportive, and beneficial for improving self regulation and academic motivation. These findings provide preliminary evidence that AI personalized mental health education may contribute to improved stress coping behavior and learning engagement among university students.
Peran Chatbot AI dalam Pembentukan Opini Politik dan Kepercayaan Publik di Era Komunikasi Digital Abdullah Mitrin; Rudi Rahman; Yuda Irawan
Jurnal Komunikasi dan Organisasi (J-KO) Vol. 8 No. 2 (2026): AGUSTUS
Publisher : Program Studi Ilmu Komunikasi, FISIP Unismuh Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/aa9egy62

Abstract

The rapid development of Artificial Intelligence (AI) has transformed digital political communication, particularly through the emergence of AI chatbots capable of conducting personalized, adaptive, and real-time conversational interactions. This study aims to analyze the influence of AI chatbots on political opinion formation and public trust within the context of digital political communication. The study employed a quantitative approach using a between-subjects experimental design involving 210 active digital media users divided into experimental and control groups. The experimental group received political information through interaction with an AI chatbot, while the control group received the same information in a conventional non-interactive text format. Data were collected using a Likert-scale questionnaire and analyzed through validity, reliability, descriptive statistical analysis, and independent sample t-testusing IBM SPSS Statistics version 26. The findings indicate that the experimental group demonstrated higher levels of political opinion and public trust compared to the control group. The differences were statistically significant for political opinion (t = 5.87; p < 0.05) and public trust (t = 5.21; p < 0.05). The study reveals that the dialogic and personalized characteristics of chatbot communication enhance user engagement in digital political communication. Furthermore, AI chatbots have the potential to reinforce confirmation bias through algorithmic information personalization. This study confirms that AI chatbots have evolved into political communication actors capable of shaping political opinion and public trust in contemporary digital democracy.
Co-Authors -, Herianto A.A. Ketut Agung Cahyawan W Abdullah Mitrin Abdurrahman Hamid Achmad Deddy Kurniawan Achmad Nizar Hidayanto Adhitya, Ryan Yudha Aditya Rickyta Adyanata Lubis Afresi Yunita Agnita Utami Agus Alamsyah Ahmad Fauzan Azim Akbar, Amri Akhmad Zulkifli Aldiga Rienarti Abidin Anam, M Khairul Andre Wahyu Novrianto Anisa, Lia Anita Febriani Anita Febriani Aprilia, Ulfa Areta Sonya Rahajeng Areta Sonya Rahajeng Arfianto, Afif Zuhri Arie Linarta Arnawilis Arnawilis Arnawilis Bakhrizal Bambang Kurniawan Bayu Saputra Budy Mustika Debi Setiawan Debi Setiawan, Debi Desi Rahmawati Devis, Yesica Dhea Arina Ramadhini Dhini Septhya Diandra, Roni Edriyansyah Eka Sabna Elisawati, Elisawati Fachry Abda El Rahman Fatmawati, Kiki Fitri, Imelda Fonda, Hendry Gilang Citra Lenardo Habib Yuhandri, Muhammad Hadi Asnal, Hadi Hafizh Sallam Hamdani Hamdani Hamid, Abdurrahman Hasnor Khotimah Hayami, Regiolina Hendro Agus Widodo, Hendro Agus Hendry Fonda Herianto Herianto Herianto Herianto Herianto - Herianto - Herianto Heri Herianto Herianto Herianto Herianto Hidayati Kurnia Fitri Hohashi, Naohiro Irwanda Syahputra Jamaris, Muhamad Jamaris, Muhammad Jenli Susilo Jenni Oinike Br Sitorus Jepisah, Doni Jeri Trio Sentana Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Khairunisa Khairunisa Khairunisa, Khairunisa Kharisma Rahayu Kurniawan, Bambang Leonita, Emy Lia Anisa Lubis, Mustopa Husein Lucky Lhaura Van FC, Lucky Lhaura Mardainis Mardeni Mardeni Mardeni Mardeni Mardeni, Mardeni Matthijs B Punt Maulita Yulia Sari Mbunwe Muncho Josephine Mbunwe Muncho Josephine Melyanti, Rika Mitrin, Abdullah Mohd Rinaldi Amartha Muhaimin, Abdi Muhamadiah, Muhamadiah Muhammad Bambang Firdaus Muhammad Habib Yuhandri Muhardi Muhardi Muhardi - Muhardi - Muhardi Muhardi Muhardi Muhardi Muhardi, Muhardi Mulya Rispani Mustopa Husein Lubis Mutiara Sari, Ria Naima Belarbi Naima Belarbi Naohiro Hohashi Nella Sari Nella Sari Nico Chandra Nopriadi Nopriadi Nopriadi Nopriadi Nopriadi Noratama Putri, Ramalia Novi Yona Sidratul Munti Nurhadi Nurhazimah Rafiah Octaria, Haryani Oktavia Dewi Ordila, Rian Perkasa, Reza Prihandoko, P Purnomo, Nopi Purwanti, Siti Putra Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Rahmalisa, Uci Rahman, Rudi Ramalia Noratama Putri Refni Wahyuni Renaldi, Reno Reza Perkasa Rian Ordila Rian Ordila Riananda, Dimas Pristovani Richi Andrianto Rickyta, Aditya Rika Melyanti Rina Irawan Rina Irawan Rofiqoh, Ummi Rometdo Muzawi Roni Diandra Rudi Rahman Ruwahida, Dewi Rizani Ruwahida Sabna, Eka Sakroni Indra Gunawan Salsabila Rabbani Saputra, Haris Tri Sara Herlina Sarjon Defit Sentana, Jeri Trio Siti Aisyah Siti Aisyah Siti Purwanti Sugiati Suherman Sohor Suherman Suherman Suriandi Suriandi Susanti Susanti Susi Oustria Simamora Susilo, Jenli Syamsul Arifin Ulfa Aprilia Urfi Utami Utami, Urfi Vindi Fitria Winda Herrianti Manullang Winda Sari Wulan Sari Yesica Devis Yuhandri Yuhandri, Yuhandri Yulanda Yulanda Yulanda Yulanda, Yulanda YULISMAN Yulisman, Yulisman Yunior Fernando Zufari, Faisal Zufi Pratama Noviardi Zupri Henra Hartomi