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All Journal ELINVO (Electronics, Informatics, and Vocational Education) EMITTER International Journal of Engineering Technology JURNAL NASIONAL TEKNIK ELEKTRO E-Dimas: Jurnal Pengabdian kepada Masyarakat Panrita Abdi - Jurnal Pengabdian pada Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JMM (Jurnal Masyarakat Mandiri) Elsains : Jurnal Elektro Journal Technology and Implementation Bussines RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Jurnal Teknologi dan Terapan Bisnis Journal of Computer, Electronic, and Telecommunication (COMPLETE) Journal of Energy, Material, and Instrumentation Technology Jurnal Teknologi dan Terapan Bisnis
Isa Hafidz
Program Studi Teknik Elektro, Fakultas Teknik Elektro, Institut Teknologi Telkom Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics Social Sciences Other

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Journal : EMITTER International Journal of Engineering Technology

 1 
Classification Method in Fault Diagnosis of Oil-Immersed Power Transformers by Considering Dissolved Gas Analysis Rosmaliati; Bernandus Anggo Seno Aji; Isa Hafidz; Ardyono Priyadi; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.702

Abstract

Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers.
Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data Mulyanto, Edy; Yuniarno, Eko Mulyanto; Hafidz, Isa; Budiyanta, Nova Eka; Priyadi, Ardyono; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.832

Abstract

Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation.
Co-Authors Abdulloh Hamid Nushfi Adiputra, Dimas Aditya Yudhistira Afandi, Mas Aly Ahmad Fauzan Adziimaa Ahmad Habibi Akbar, Muhammad Kaymal Aldhitiansyah Putra Alfattah, Hernadimas Anifatul Faricha Ardiansyah Al Farouq Ardyono Priyadi Bernandus Anggo Seno Aji Chaironi Latif Daeng, Lusia Febrianty Dimas Bagas Wicaksono Dinda Karisma Ulfa Dwi Edi Setyawan DWI SURYANTO Eko Mulyanto Yuniarno Fathul Yaqin, Moch. Fikry Fauzan Rasyid Hardynarta, Yayoung Zheka Helmy Widyantara Humaidi, Reza Jose Anto Kurniawan, Christian Khodijah Amiroh Kholima, Henko Ksatria, Arief Budi Lora Khaula Amifia Mauridhi Hery Purnomo Mauridhi Hery Purnomo Moch. Bagus Indrastata Moch. Fauzan Rasyid Moch. Iskandar Riansyah Montolalu, Billy Mulyanto, Edy Nova Eka Budiyanta Permana, Muhammad Evan Putranto, Rifki Dwi Putri, Nur Wahyuni Rafly Daffaldi Rifky Dwi Putranto Rizwansyach, Rizky Robin Addwiyansyah Alfaro Samrat Rosmaliati, Rosmaliati U. Mustakim, Hamzah Ubaidillah Umar, Ubaidillah Wahyu Andy Prastyabudi Wednestwo Samuel, Gilbert Yogianto, Mohammad Fahmi Ilmi