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All Journal TEKNIK INFORMATIKA Jurnal Sains Dan Teknologi (SAINTEKBU) Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Jurnal Teknologi Informasi dan Multimedia Jurnal Tunas JASEE Journal of Application and Science on Electrical Engineering Jurnal Fortech SinarFe7 EXPLORE Journal of Informatics Development KREATIF: Jurnal Pengabdian Masyarakat Nusantara Journal Of Electrical Engineering And Technology
Nuzul Hikmah
Universitas Panca Marga
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Other

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Journal : Journal Of Electrical Engineering And Technology

 1 
Rancang Bangun Alat Pendeteksi Kendaraan Dari Arah Berlawanan Pada Tikungan Tajam Berbasis Arduino UNO Ayuni Finda Rika; Nuzul Hikmah
Jurnal JEETech Vol. 2 No. 1 (2021): Nomor 1 May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jeetech.v2i1.158

Abstract

A sharp bend is a bend that has an acute angle with the level of difficulty when the vehicle crosses the road. In sharp turns frequent vehicle accidents occur. Accidents are caused by vehicles traveling at high speeds when they will cross the bend in the road. In addition, the driver also wants to overtake the vehicle when going through the bend. By making this warning system, it is expected to reduce accidents on the road bends. This tool can detect the presence of a vehicle and will give a warning in the form of an indicator light that is controlled by the Arduino UNO microcontroller using an Ultrasonic sensor and will light up if within 10 m before crossing the road bend there is a vehicle that will pass
Prediksi Jumlah Permintaan Darah Jenis Packed Red Cells Menggunakan Support Vector Regression Ahmad Izzuddin; Farhatin Nikmah; Nuzul Hikmah
Jurnal JEETech Vol. 6 No. 2 (2025): Nomor 2 November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i2.6209

Abstract

The Blood Transfusion Unit (UTD) of the Indonesian Red Cross (PMI) in Probolinggo Regency faces challenges in managing blood supplies, particularly for Packed Red Cells (PRC), due to unpredictable fluctuations in demand. The imbalance between supply and demand often leads to shortages or surpluses, affecting the quality of healthcare services. This study aims to predict the demand for PRC to support more efficient inventory management. The method employed is Support Vector Regression (SVR), an approach within the Support Vector Machine (SVM) algorithm that is effective for regression and prediction tasks. Historical blood demand data was used to train the model. The results indicate that SVR provides sufficiently accurate predictions for all blood types, with the best performance achieved for blood type B, yielding a Root Mean Square Error (RMSE) of 0.0589 and a Mean Absolute Percentage Error (MAPE) of 7.11%. In conclusion, the SVR method can be effectively applied to forecast PRC demand and has the potential to support decision-making in blood stock management at the UTD PMI of Probolinggo Regency.
Co-Authors Agustina Widayati Ahmad Efendi Ahmad Fadillah Ahmad Izzuddin Ahmad Izzuddin Ahmad Rifan Syah Ali As'ad Ali Masykur Ibrahim Andrik Sunyoto Arofatul Isnaini Ary Analisa Rahma Ayuni Finda Rika Berli Wahyu Ramadhan Diana Mulya Dewi Diana Mulya Dewi Dyah Ariyanti Dyah Ariyanti Farhatin Nikmah Faridahtul Jannah Fathur Rahman Ferry Agus Pratama Fitria Esa Hendra Arief Pratama Imam Marzuki Ira Aprilia Januar Bagus Prasetyo Linda Kurnia Linda Kurnia S. Linda Kurnia Supraptiningsih Marsetio Wahyu N Maryamah Maryamah Melista Sugesti Miranti Dian P Misdiyanto Misdiyanto Mohammad Yaqin Amirullah Muhammad Anas Muhammad Hasan Fathul Arifin Nisar Qurratul A'yun Noer Fika Dita Noor, Muhammad Fathuddin Rendhy Putra Ririn Indriati Ririn Indriati Rizqy Amalia Sriwidyawati W Syahrian Barret Y.P Tri Hardiyanto Jiwandono Yustina Suhandini Tj