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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JAREE (Journal on Advanced Research in Electrical Engineering)
Heri Suryoatmojo
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Disain Konverter Charge Pump Rasio Tinggi Untuk Aplikasi Mobil Listrik Heri Suryoatmojo; Priyo Edy Wibowo; Mochamad Ashari; A. Musthofa.
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Abstrak— Mobil listrik dengan penggerak motor induksimembutuhkan tegangan DC yang tinggi pada sisi masukan dariinverter. Sementara itu, baterai di pasaran saat ini memilikiketerbatasan pada tegangan keluaran. Oleh karena itu, DC-DCkonverter diperlukan untuk mengubah tegangan DC dalam rasiokonversi yang tinggi. Pada penelitian ini dilakukan perancangandan implementasi konverter boost dengan metode charge pump.Konverter ini dirancang untuk daya 5 kilo-watt dengan teganganmasukkan 96 volt dan tegangan keluaran 550 volt. Hasil implementasikonverter boost dengan metode charge pump padategangan masukan 24 volt mampu menaikkan dua kali lipattegangan dari konverter boost konvensional dengan error dibawah 4,6% untuk duty cycle kurang dari 50%. konverter inimemiliki efisiensi 76% pada duty cycle 30% - 65%.Kata Kunci— konverter boost, charge pump, rasio konversitinggi.
An Implementation of the Convolutional Neural Network Algorithm for Detecting Crack in 150 kV Transmission Line Insulator Andhika Rizki Priambodo; Ronny Mardiyanto; Heri Suryoatmojo
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.537

Abstract

High Voltage Overhead Transmission Lines require routine inspections to detect potential fault and ensure reliable operation. These inspections are conducted through manual tower climbing, known as Climb Up Inspection (CUI). With the advancement of technology, drones—Unmanned Aerial Vehicles (UAVs)—have increasingly been adopted as a more efficient alternative for conducting such inspections. While drone-based inspections significantly improve the speed of data acquisition, they often create a bottleneck at the stage of data analysis and image processing due to limited human resources and concurrent operational tasks. To address this issue, this study presents the development of an Artificial Intelligence (AI) based software application utilizing Deep Learning, specifically the Convolutional Neural Network (CNN) algorithm, to automate the classification of insulator conditions captured by drone imagery. The proposed application is designed to categorize insulators into two conditions: broken and normal. By automating the analysis process, this system is expected to enhance inspection efficiency, reduce maintenance response time, and support the creation of a more reliable and resilient power transmission network.
Co-Authors A. Musthofa. Andhika Rizki Priambodo Mochamad Ashari Priyo Edy Wibowo Ronny Mardiyanto