Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Giga Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Konversi Energi dan Manufaktur
Kusuma, Idris
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Education Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Dampak Pandemi COVID-19 terhadap Ketidakseimbangan Beban di Shopping Center serta Solusi Alternatifnya Nugroho, Endang Retno; Niam, Ahmad Lutfi; Kusuma, Idris; Keraf, Adhiarta
Jurnal Ilmiah Giga Vol. 26 No. 2 (2023): Volume 26 Edisi 2 Tahun 2023
Publisher : Universitas Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47313/jig.v26i2.1927

Abstract

Tingkat hunian gedung berkurang akibat Pandemi COVID-19. Gedung-gedung perkantoran, hotel, apartemen dan pusat belanja mengalami penurunan pengunjung.  Sementara sebagian toko menghentikan usahanya. Pihak Pengelola gedung akan tetap mengoperasikan peralatan listrik untuk menunjang kegiatan. Dampaknya beban listrik tidak seimbang. Ini berpengaruh pada kesetimbangan beban dalam gedung. Kondisi ini akan merugikan dan dalam jangka panjang akan merusak transformator. Tujuan penelitian ini melakukan pengukuran tingkat kesetimbangan beban listrik di gedung Tang City Mall. Hasil pengukuran pada 5-unit trafo yang ada menunjukkan ketidakseimbangan antara 0.63% sampai dengan 4.76%. Pengukuran ini dilakukan pada kondisi jam sibuk yaitu pukul 17.30 WIB. Berdasarkan data perhitungan tersebut, selama sebulan akan mengalami kerugian daya arus netral sebesar 2848 kWh dan kerugian daya pada arus ground sebesar 175 kWh. Pegukuran sesaat pada jam-jam rawan buka dan tutup mal tercatat adanya ketidakseimbangan yang signifikan mencapai 7.9% pada trafo2. Untuk menghindari hal tersebut, maka perlu dilakukan pembagian grup ulang khususnya pada beban 1 fasa. Sementara jam buka dan tutup tenant gedung wajib serentak.
Electric Vehicle Review: BEV, PHEV, HEV, or FCEV? Kusuma, Idris; Ruliyanta; Kusumoputro, R. A. S.; Iswadi, Agung
Jurnal Konversi Energi dan Manufaktur Vol. 10 No. 1 (2025)
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKEM.10.1.8

Abstract

Electric vehicles (EVs) are rapidly advancing as a sustainable transportation solution in the global effort to reduce carbon emissions. There are four main types of EVs: battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and fuel cell electric vehicles (FCEVs). This article reviews each EV type's advantages, limitations, and prospects based on energy efficiency, carbon emissions, technological development, and infrastructure readiness. The findings indicate that BEVs hold the most significant potential for personal vehicles and urban transportation, while FCEVs are more suited for heavy-duty and long-distance applications. PHEVs and HEVs are considered transition solutions, but their relevance is expected to decrease as technology and global policies evolve. BEVs provide zero emissions. PHEVs provide high flexibility, while HEVs do not require charging infrastructure, and FCEVs offer zero emissions and long-range and fast charging times. FCEV can travel up to more than 800 KM, which is very promising for the distance travelled problem, which is a challenge for electric vehicles.
IDENTIFIKASI KELAINAN JANTUNG DARI DATA EKG MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK Sumiati, Sumiati; Sigit, Haris Triono; Achmad, Wahyudin Nor; Kusuma, Idris
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/atzc0r27

Abstract

This study is one of the initial approaches in implementing Backpropagation Neural Network for ECG signal classification. The condition of the human heart can be known based on the results of electrocardiogram medical records, so that with the results of electrocardiogram medical records it can be known whether the heart is normal or abnormal. Symptoms of abnormal heart disease in the heart often come suddenly. Early recognition of heart disease with further procedures and treatment can prevent an increase in the risk of fatal heart attacks. This study has a very important goal in an effort to detect and classify heart abnormalities more efficiently. By utilizing artificial neural networks (ANN) and backpropagation methods, it can utilize computing capabilities to analyze patterns in electrocardiogram (ECG) data. The results show that the classification of heart abnormalities with an epoch value of 2000, a learning rate of 0.01 with normal and abnormal targets, obtained the number of Hidden Neurons as many as 25, the number of weight patterns 44 and a mean squared error (MSE) value of with an accuracy of 0.61364 from 25 inputs.
Co-Authors Achmad, Wahyudin Nor Endang Retno Nugroho, Endang Retno Iswadi, Agung Keraf, Adhiarta Kusumoputro, R. A. S. Niam, Ahmad Lutfi Ruliyanta Ruliyanta Sigit, Haris Triono Sumiati Sumiati