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All Journal Teodolita: Media Komunikasi Ilmiah di Bidang teknik IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Electronic and Electrical Power Application Vortex Jurnal Pengabdian Bersama Masyarakat Indonesia Jurnal Sistem Informasi dan Ilmu Komputer Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Green Engineering: Journal of Engineering and Applied Science J-Proteksion : Jurnal Kajian Ilmiah dan Teknologi Teknik Mesin Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Rahardian Luthfi Prasetyo
Universitas Wijayakusuma Purwokerto
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering 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 Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Transportation Other

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Seleksi Fitur Berbasis Mutual Information untuk Optimalisasi Model Prediksi Tingkat Kematian Penderita Gagal Jantung Menggunakan Machine Learning Nurdiniyah, Elsa; Aisya Nur Aulia Yusuf; Rahardian Luthfi Prasetyo; Rasyida Shabihah Zukron Aini
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 5 No. 2 (2025): Juli: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v5i2.5044

Abstract

Heart failure is one of the leading causes of death in the world that requires an accurate and efficient prediction system to support clinical decision making. This study aims to develop a prediction model for the risk of death in patients with heart failure by optimizing feature selection using the Mutual Information (MI) approach. The main problem raised is the high complexity of clinical data with many features that are not always relevant, which can reduce the accuracy and efficiency of predictive models. The method proposed in this study involves MI-based feature selection to identify the most informative features against the target variable (patient mortality), which are then used to train various machine learning algorithms such as Random Forest, Gradient Boosting, XGBoost, and Logistic Regression. The hyperparameter tuning process is performed to optimize the performance of each model. The test results show that the Random Forest model that has been tuned using five selected features managed to achieve an accuracy of 0.99 and F1-score of 0.99, outperforming other models in terms of balance between accuracy and generalization. The results show that Mutual Information is effective in simplifying model complexity without compromising prediction performance.
Design and Construction of a Savonius Helix-Type Vertical Axis Wind Turbine Using Computational Fluid Dynamics Isra' Nuur Darmawan; Kholistianingsih Kholistianingsih; Rahardian Luthfi Prasetyo; Sandhy Dhannova
Vortex Vol 7, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/vortex.v7i2.4104

Abstract

This study presents the design, simulation, fabrication, and experimental validation of a Savonius Helix vertical axis wind turbine optimized for low wind regimes in Cilacap, Indonesia. Wind data from NASA POWER were analyzed at two locations, revealing dominant low wind conditions with notable temporal variability. The turbine geometry was optimized based on aerodynamic and inertial parameters, followed by Computational Fluid Dynamics (CFD) simulations to evaluate torque, pressure distribution, and power coefficient across four configurations (2B90P, 2B180P, 3B90P, 3B180P). The optimal design was fabricated using fiberglass and tested under no-load and loaded conditions with a Permanent Magnet DC generator. Results indicate strong agreement between CFD trends and experimental performance, despite deviations caused by mechanical losses and atmospheric turbulence. Overall, the Savonius Helix configuration demonstrates effective energy capture capability in low wind environments, confirming its feasibility for small-scale renewable energy applications.
Co-Authors Ahmad Nugroho Jati Aisya Nur Aulia Yusuf Arif Nugraha Hernanjaya Daramawan, Isra' Nuur DARMAWAN, ISRA' NUUR Darmawan, Isra’ Nuur Dody Wahjudi Dody Wahjudi Dwi Agus Setiawan Eko Sudaryanto I Made Adhi Wiryawan Irawan Dharma Sukowati Irvansyah, Mokhamad Isra' Nuur Darmawan Isra' Nuur Darmawan Isra' Nuur Darmawan Iswandi Kholistianingsih ., Kholistianingsih Muhammad Ibkar Azki Himmawan Nurdiniyah, Elsa Nurdiniyah, Elsa Sari Hayunah Nurul Kholisatul Ulya Pramudhipa, Dimas Rendra Rahayu, Eny Sukani Rasyida Shabihah Zukron Aini Sandhy Dhannova Sandy Prasetyo Deniawan Putra Siti Shofiyatun Sobirin, Abdul Aziz Sucipto Tri Watiningsih, Tri Yulita Kartika Sari