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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) TELKOMNIKA (Telecommunication Computing Electronics and Control) JURNAL ELEKTRO ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika CogITo Smart Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) MITRA: Jurnal Pemberdayaan Masyarakat EPIC : Journal of Electrical Power, Instrumentation and Control Jurnal Mantik SABDAMAS Jurnal Locus Penelitian dan Pengabdian Jurnal Teknologi dan Manajemen Industri Terapan ROUTERS: Jurnal Sistem dan Teknologi Informasi Jurnal Pengabdian Masyarakat Charitas Metris: Jurnal Sains dan Teknologi Cylinder : Jurnal Ilmiah Teknik Mesin
Karel Octavianus Bachri
Program Studi Teknik Elektro, Fakultas Biosains, Teknologi Dan Inovasi, Universitas Katolik Indonesia Atma Jaya Jakarta
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences

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Generative Representation of Aggregate Brain Activity: A Deep Autoencoder Approach for EEG Topoplot Summarization Tobias Mikha Sulistiyo; Karel Octavianus Bachri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 2 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2607

Abstract

This research aims to assess a Deep Convolutional Autoencoder (CAE) framework for representative EEG topoplot summarization using latent space aggregation. In order to produce representative EEG topoplot summaries while maintaining important spatial features, we suggest a Deep Convolutional Autoencoder (CAE) with latent space aggregation. Prior to group-level aggregation and image reconstruction, EEG topoplots are simplified into latent representations that resemble baseline artifacts. An adolescent EEG dataset obtained during a Go/No-Go Association Task involving addiction stimuli was used to test our methodology. The frontal-temporal predominance of normal respondents and the prominent temporal-occipital activation of at-risk respondents, primarily in those with slower responses, are caused by distinct activation patterns that are associatively aroused by attentional and memory bias. These results support the use of secure EEG topoplot summarization in addiction research using CAE-based latent space aggregation.
Tomato leaf disease recognition system using Faster R-CNN Karel Octavianus Bachri; Bryan Santoso; Duma Kristina Yanti Hutapea; Catherine Olivia Sereati; Lanny W. Pandjaitan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.25519

Abstract

The objective of this paper is to detect tomato leaf disease using Faster region-based convolutional neural network (R-CNN). The tomato leaf disease recognition system utilizes a dataset consisting of healthy tomato leaves and eight leaf diseases, including early blight, late blight, leaf mold, mosaic virus, septoria, spider mites, yellow leaf curl virus, and leaf miner. The dataset is obtained from various sources, such as Kaggle, Google Images, Bing Images, and Roboflow Universe. Pre-processing techniques, including collage, tile, static crop, and resize, are applied to prepare the dataset for training. Data augmentation methods, such as flipping, 90° rotation, exposure adjustment, and hue modification, are applied to enhance the model’s robustness and generalize its performance. Specifically, we implemented Faster R-CNN as part of Detectron2 using its base models and configurations. The results demonstrate that the X101-FPN base model for Faster R-CNN with the default configurations of Detectron2 is efficient and general enough to be applied to defect detection. This approach results in an average precision (AP) detection score of 87.01% for validation results.
Experimental Comparison of DALI and 0–10V LED Dimming Systems for Smart Lighting MUHAMMAD RISQI NURYANA; MARSUL SIREGAR; KAREL OCTAVIANUS BACHRI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 14, No 1: Published January 2026
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v14i1.42

Abstract

This study compares the dimming performance of DALI and 0–10 V using three commercial LED drivers at eight dimming levels. The testing included power consumption, illuminance, and flicker stability. The results showed that DALI was able to maintain a linear dimming curve down to low levels such as 10%, 5%, and 1%, with consistent flicker within the No Risk zone. Power consumption at 0% was also low, making it suitable for applications requiring deep dimming and stable visual comfort. Conversely, the 0–10 V system performed quite well at high levels and was slightly more efficient at low power, but its performance dropped drastically below 30%. At 10%, the illuminance was almost zero and the flicker level entered the High Risk zone. Overall, DALI is more ideal for smart lighting with smooth transitions, while 0–10 V is more suitable for basic installations.
Pengembangan Kerangka Kerja Manajemen Energi Berbasis Cloud Real-Time Menggunakan Industrial Energy Performance Index untuk Implementasi ISO 50001 pada Industri Tekstil Irpan Restu Ade Putra; Marsul Siregar; Karel Octavianus Bachri
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 8 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i8.6164

Abstract

Efisiensi energi menjadi tantangan penting pada sektor manufaktur akibat meningkatnya biaya energi, tuntutan efisiensi operasional, serta kebutuhan terhadap praktik industri berkelanjutan. Industri tekstil merupakan salah satu sektor dengan konsumsi energi listrik yang tinggi karena memiliki proses produksi berkelanjutan dan karakteristik beban listrik yang kompleks. Namun, sistem monitoring energi konvensional masih banyak bergantung pada pencatatan manual serta belum mampu menyediakan evaluasi performa energi secara terintegrasi dan real-time. Penelitian ini bertujuan untuk mengembangkan dan memvalidasi Real-Time Cloud-Based Energy Management Framework (RCEMF) yang terintegrasi dengan Industrial Energy Performance Index (IEPI) untuk mendukung pemantauan energi, evaluasi performa energi, serta pengambilan keputusan dalam implementasi ISO 50001 pada industri tekstil. Penelitian menggunakan metode Research and Development (R&D) dengan pendekatan eksperimental yang mencakup perancangan arsitektur sistem, pengembangan akuisisi data otomatis, implementasi komunikasi Modbus RTU, integrasi basis data berbasis cloud, pengembangan IEPI, dan validasi framework. Hasil penelitian menunjukkan bahwa RCEMF mampu mengintegrasikan digital power meter, IoT Gateway, komputasi awan, analitik energi, dan sistem pendukung keputusan dalam satu platform terpadu. Framework ini mampu mengurangi waktu akuisisi data lebih dari 96%, mencapai tingkat keandalan komunikasi sebesar 99,9%, serta melakukan evaluasi performa energi secara real-time. IEPI juga mampu merepresentasikan perubahan kondisi operasi melalui integrasi indikator SEC, PF, LF, dan MDU. Kesimpulannya, RCEMF menjadi solusi digital yang efektif untuk meningkatkan sistem manajemen energi, mendukung implementasi ISO 50001, dan meningkatkan efisiensi energi berkelanjutan pada industri tekstil.
Co-Authors Adang Suwandi Ahmad Agi Tama Agi Tama Alexander, Justin Alvinson, Gregorius Andre Sugioko Anthon de Fretes Anthony , Jean Arka Dwinanda Soewono Arwin Datumaya Wahyudi Sumari Bambang Anggoro Soedjarno Bambang Iwan Suryana Bryan Santoso Bunardi, Marcelinus Eric Catherine Olivia Sereati Christine Natalia Duma Kristina Yanti Duma Kristina Yanti Hutapea Enny Widawati Erik Agustian Yulanda Fernanto, Nico Ferry Rippun Gideon Manalu, Ferry Rippun Gideon FLORENTIA ANGELINA Fortino, Antonius Edwin Hadikusuma, Ridwan Satrio Herianto, David Hidayat, Renaldi Irpan Restu Ade Putra Joko Tri Susilo Jonathan, Edoardo Karangga, Yusaphat Phosa Kartadinata, Budi Kelvin, Dimas Kevin Kevin kevin Khayam, Umar Kusmanto, Willy Lanny W Lanny W. Pandjaitan Lestari, Hesti Tri Linda Wijayanti Lukas Maria Angela Kartawidjaja Marsellinus Bachtiar Wahju Marsul Siregar Marten Darmawan Melisa Mulyadi Micha Thesania Katarine Michael Deni Perdana Putera Muhamad Jaelani Sidik Muhammad Risqi Nuryana Nabil, Alfi Nova Eka Budiyanta Osmond, Edbert Pandjaitan, Lanny W. Pangestu, Devin PANJAITAN, LANNY Pardosi, Cristoni Hasiholan Pascalis Paschalis, Sebastian Michael Paskalis, Yohanes Maria Jonathan Glenn PRAWIRA, WASKITO Putera, Michael Deni Perdana PUTRA, SADIANA Rippun G. Manalu, Ferry Rizki Surya Permana Rory Anthony Hutagalung Sachi, Haenuki Sandra Octaviani B. Widiarto Satryanatha, Josaphat Febrian Setiawan, Axel Gilbert Soewono, Arka Soewono, Arka D. Surya Permana, Rizky Surya Widjaja, Williem Sutomo, Febryan Tajuddin Nur Theresia Ghozali Timothy Sutjipto, Marcellus Timothy Wijaya, Hans Tobias Mikha Sulistiyo Trifenaus Prabu Hidayat Veronica Windha Mahyastuty Widodo Basuki Widodo Widjaja Basuki Wijaya, Nickolas Dario Wijaya, Toby Wilson Yudha Harnoko Zefanya, Maria