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All Journal Jurnal Pendidikan : Riset dan Konseptual Jurnal Al-Muta`aliyah : Jurnal Pendidikan Guru Madrasah Ibtidaiyah Al-MARSHAD: Jurnal Astronomi Islam dan Ilmu-Ilmu Berkaitan Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik Jurnal Penelitian IPTEKS BUSTANUL FUQAHA: Jurnal Bidang Hukum Islam Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal JEETech Civil Engineering Collaboration Al-Afaq: Jurnal Ilmu Falak dan Astronomi Jurnal Akuntansi, Manajemen Dan Ekonomi Islam (JAM-EKIS) At-Thullab : Jurnal Mahasiswa Studi Islam MIYAH: Jurnal Studi Islam JUSTICES: Journal of Law Al-Gafari: Manajemen dan Pendidikan Aslamiah: Jurnal Pendidikan, Ekonomi, Sosial dan Budaya Jurnal Hukum Islam Jurnal Intelek Insan Cendikia International Journal of Law, Crime and Justice Proceeding of International Conference on Social Science and Humanity Makkah: Journal Of Islamic Studies
Ahmad Izzuddin
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal JEETech

 1 
Rekonfigurasi Jaringan Menggunakan Binary Particle Swarm Optimization (BPSO) Pada Penyulang Suryagraha Diana Mulya Dewi; Nuzul Hikmah; Imam Marzuki; Ahmad Izzuddin
Jurnal JEETech Vol. 1 No. 1 (2020): Nomor 1 May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jeetech.v1i1.4

Abstract

A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the endpoint. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses and improve distribution system reliability. Many feeders and buses on the network if calculated manually will be difficult and require a very long time. So it is necessary to solve problems using program assistance. In this case, use Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) algorithm based on the behavior of a herd of insects, such as ants, termites, bees, or birds. BPSO is a development of the PSO algorithm designed to solve the optimization problem in a discrete combination, where the particle takes the value of binary vectors with length n and speed which is defined as the probability of bits to reach value 1. The results show a significant reduction in losses.
Prediksi Jumlah Permintaan Darah Jenis Packed Red Cells Menggunakan Support Vector Regression Ahmad Izzuddin; Nikmah, Farhatin; Hikmah, Nuzul
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 Abdul Haris Rasyidi Agus Yulianto Ahmad Adib Rofiudin Akhmad Nadirin Alfi Karomah Alfi Ramadhan Dakka Fadilah M Harahap Desti Septiana Putri Diana Mulya Dewi Dina Nur Amilah Balbisi Erfaniah Zuhriah Fatmawati Hilal Fika Afhamul Fuscha Hafiz, Zahra Hayati Hafiza Samath Haris Hermawan Hesty Kesuma Putri Hikmah, Nuzul Imam Marzuki Iqbal Muhammad Jekti Rahayu Kharisma Agustina Khoirul Hidayah M. Basthoni M. Syam'un Rosyadi Mahsun Moch Rio Pambudi Muhammad Abdul Aziz Muhammad Abror Suriyanto Muhammad, Naila Arifa Muhyar Fanani nanaridwan, Mau'idhatul Hasanah Ridwan Nikmah, Farhatin Nuzul Hikmah Prisna Dea Leonita Puji Yani Radifa Isnain Nafila Rahma, Rahmasyarita. S Septy Holisa Umami Sinta, Dewi Suhaini Tutik Hamidah Varuq, Nauval Ilham Wahyuni Danial Khotimah Wahyuningsih, Yuniar Yulia Feriska Yulia Feriska Yusron Pahlevi