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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Pengabdian Rekayasa dan Wirausaha
Rachmawati Rachmawati
Politeknik Negeri Lhokseumawe
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering

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Wireless Data Communication Techniques to Coordinate Distributed Rooftop PVs in Unbalanced Three-phase Feeder Rachmawati Rachmawati; Anita Fauziah; Nelly Safitri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7780

Abstract

A necessity of the availability of communication network to provide data transfer amongst the coordinated single-phase rooftop photovoltaic (PV) in unbalanced three-phase low voltage (LV) feeder is essential since fetching data within the sensor of each PV unit requires real-time measurement and reliable data exchange within smart grid (SG), loads and other PV units. The main objective of this paper is to model the popular Wi-Fi, WiMax and ZigBee wireless data communication techniques into algorithms using numerical analysis. Those communication technologies have low cost and low power consumption. The benefits and drawbacks of those considered wireless data communications are shown as the required data that transferred and appropriate coding is also proposed. The number of transmitted symbols and the processing time delay of the proposed data coding are numerically analyzed, the results indicated that the 100% penetration level of PV that resulted higher injected reactive power back into the networks is able to be overcome since the coordinated PVs along the feeder is communicating to lower the unbalanced voltage profile.
Penerapan Komposter Pintar Berbasis IoT dan Energi Surya untuk Edukasi dan Reduksi Limbah Makanan di SMA Al-Mishbah Banda Aceh Rika Sri Utami; Yusuf Diva F Damanik; Muhammad Habib; Rachmawati Rachmawati; Yunida Yunida
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 1 (2025)
Publisher : Universitas Syiah Kuala

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Abstract

Pemborosan makanan merupakan tantangan lingkungan yang signifikan dan kerap terjadi di lingkungan sekolah akibat minimnya edukasi mengenai pengelolaan limbah organik. Berdasarkan data dari Bappenas, satu rumah tangga di Indonesia dapat menghasilkan hingga 1 kg limbah makanan per hari. SMA Al-Mishbah Banda Aceh menghadapi permasalahan serupa, yang ditangani melalui program pengabdian masyarakat bertajuk Nutri Revive: IoT Food Waste Composter. Program ini menerapkan komposter pintar berbasis IoT dan energi surya yang terhubung ke aplikasi mobile untuk memantau suhu, kelembapan, volume, dan status operasional secara real-time. Selama satu tahun, kegiatan meliputi perancangan alat, pelatihan kepada 40 siswa, pengumpulan sampah makanan, hingga integrasi sistem pemasaran berbasis dropshipping. Hasil pelaksanaan menunjukkan penurunan signifikan volume limbah, peningkatan keterampilan siswa dalam produksi dan distribusi kompos, serta terbukanya peluang kewirausahaan dengan skema insentif. Kolaborasi dengan pengusaha lokal seperti panglong kayu dan petani turut memperluas dampak lingkungan program. Monitoring dan evaluasi sistem menunjukkan efektivitas teknologi dalam meningkatkan efisiensi proses komposting. Program ini membuktikan bahwa integrasi teknologi hijau dan pemberdayaan pelajar dapat menjadi strategi berkelanjutan dalam membangun budaya sekolah yang ramah lingkungan.
Deep Learning–Based ASD Detection from EEG Signals: A Comparison of InceptionTime and XceptionTime Architectures Rachmawati Rachmawati; Arsy Febrina Dewi; Melinda Melinda; Razita Nadhira; Aufa Rafiki; Nurlida Basir
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 4, November 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

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Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social-communication impairments and restricted or repetitive behaviors. While clinical assessment remains the diagnostic gold standard, the demand for objective and scalable screening tools has motivated EEG-based automated classification. However, under controlled experimental settings, the relative contributions of preprocessing choices and deep learning architecture selection to ASD detection performance remain insufficiently quantified. This study systematically benchmarks artifact-aware preprocessing and model architecture by comparing InceptionTime and XceptionTime across four end-to-end processing schemes that isolate the effects of independent component analysis (ICA) and classifier design under identical segmentation and evaluation protocols. A public King Abdulaziz University EEG dataset comprising 16 subjects (8 ASD, 8 controls) was used. Signals were bandpass-filtered using a fourth-order Butterworth filter (0.5–45 Hz), optionally denoised via ICA, and segmented into 4-s windows with 50% overlap. Models were evaluated using 8-fold subject-wise cross-validation. Performance was assessed using accuracy, precision, sensitivity, specificity, and F1-score, and statistical significance was tested with the Wilcoxon signed-rank test. The Butterworth+ICA+InceptionTime pipeline achieved the best results, with a mean accuracy of 0.9886 ± 0.0046 and an F1-score of 0.9879 ± 0.0049. ICA inclusion and architecture choice yielded significant improvements in accuracy (  for both, ). These findings indicate that structured artifact suppression and multi-scale temporal modeling jointly enhance EEG-based ASD classification, supporting their use in robust clinically oriented screening systems.
Co-Authors . Melinda Anita Fauziah Arsy Febrina Dewi Aufa Rafiki Muhammad Habib Nelly Safitri Nurlida Basir Razita Nadhira Rika Sri Utami Yunida Yunida Yusuf Diva F Damanik