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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science JSR : Jaringan Sistem Informasi Robotik Community Engagement and Emergence Journal (CEEJ) Jurnal Rekam Medis (Medical Record Journal) Jurnal Teknik Informatika
Mohd Rinaldi Amartha
Universitas Hang Tuah Pekanbaru
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Medicine & Pharmacology Public Health Social Sciences

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RANCANG BANGUN ALAT MONITORING SISTEM JEMURAN IKAN ASIN OTOMATIS DENGAN SENSOR HUJAN DAN RTC BERBASIS WEMOS DAN SMS Saputra, Haris Tri; Rinaldi Amartha, Mohd; Wulandari, Denok
JSR : Jaringan Sistem Informasi Robotik Vol 8, No 1 (2024): JSR: Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v8i1.366

Abstract

Pemanasan global yang sekarang ini sedang terjadi menyebabkan musim di Indonesia menjadi kurang menentu, sehingga musim kemarau dan musim penghujan tidak dapat di prediksi lagi, seperti sering terjadi hujan secara tiba tiba. Kondisi yang tidak menentu akan sangat merepotkan ketika menjemur ikan asin. Sehingga ikan asin yang dijemur tidak maksimal. Untuk mengatasi hal tersebut maka dibuat perancangan prototype jemuran ikan asin otomatis berbasis Wemos D1 R1. Jemuran ini bekerja apabila sensor hujan mendeteksi lingkungan sekitar dan RTC DS1307 sebagai pengatur waktu untuk membuka dan menutup jemuran. Kemudian hasil sensor hujan dan RTC DS1307 tersebut diolah oleh Wemos D1 R1, yang digunakan untuk membuka dan menutup tutup jemuran menggunakan motor servo. LCD 16 x 2 akan menampilkan data sesuai dari sensor hujan dan RTC DS1307. Alat ini juga dapat membantu pengusaha ikan asin untuk memantau keadaan alat jemuran ikan asin melalui SMS.Kata Kunci: Ikan asin, Wemos D1 R1, RTC DS1307, Sensor hujan, SMS
Optimization of a New Adaptive Stacking Ensemble Model Integrated with IoT for Stress Level Detection Based on Physiological Signals Muhardi; Mohd Rinaldi Amartha; Rika Melyanti; Yuda Irawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6770

Abstract

Mental health issues among college students are receiving increasing attention, particularly because of academic and social pressures and the impact of technology use. This study aims to develop a real-time stress level prediction model using a New Adaptive Stacking Ensemble approach based on physiological data and IoT devices. The data included heart rate, SpO₂, body temperature, and systolic and diastolic blood pressure. Five machine learning algorithms are used as base models: SVM, C4.5, Decision Tree, KNN, and Random Forest. The MLP serves as the meta-model, which is then optimized using Optuna. The model training process begins with pre-processing, feature standardization using StandardScaler, and data balancing using SMOTE. The results showed that the stacking model with the MLP meta-model achieved an accuracy of 90.00% under the individual Random Forest and KNN models, and increased to 97.00% after hyperparameter optimization. This model was then integrated with IoT devices using MAX30102, MLX90614, and digital tensiometer sensors, as well as a Streamlit interface to display real-time stress classification results. The system built not only excels in accuracy but can also be implemented to directly detect stress levels, thereby potentially supporting early intervention and mental health promotion in campus environments.
Co-Authors Agus Alamsyah Defry Adryansyah Putra Elisawati, Elisawati Hadi Asnal, Hadi Hartomi, Zupri Henra Henny Maria Ulfa Isti Qomah Kholili, Ulil Melyanti, Rika Mitrin, Abdullah Muhardi Nurhadi Octaria, Haryani Putri Yulinda Refni Wahyuni renaldi, reno Renaldi, Reno Riswanto Rizer Fahlepi Rizki Aprianto Rometdo Muzawi, Rometdo Saputra, Haris Tri Sy Effi Daniati Ulil Kholili Wulandari, Denok Yogi Yunefri, Yogi Yuda Irawan