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All Journal Sinergi ICMSA Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Entomologi Indonesia Journal of Research in Mathematics Trends and Technology International Journal of Engineering, Science and Information Technology APPLICATION: Applied science in Learning Research Jurnal Kesehatan Gigi dan Mulut (JKGM) Jurnal Natural Indonesian Journal of Applied Mathematics Transcendent Journal of Mathematics and Applications JURNAL PENGABDIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (JP3L) Jurnal Teknokes
Siti Rusdiana
Department Of Mathematics, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
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Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters Melinda, Melinda; Waladah, Buleun; Yunidar, Yunidar; Mahfuzha, Raudhatul; Gazali, Syahrul; Rusdiana, Siti; Basir, Nurlida
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.023

Abstract

This study investigates the effectiveness of two Finite Impulse Response (FIR) filter designs based on the Hanning and Blackman-Harris windows for preprocessing electroencephalography (EEG) signals collected from both neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). EEG signals were recorded using a 16-channel setup and band-pass filtered between 0.5 and 40 Hz to isolate relevant neural activity. Subsequently, the signals were processed independently using each FIR filter type. Performance evaluation was conducted using four quantitative metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Power Spectral Density (PSD). The Hanning window filter showed MAE values ranging from 0.079 to 0.325, MSE from 0.026 to 0.177, SNR between 7.56 and 15.86 dB, and PSD values from 5.3 to 9.08 × 10⁻³. These results demonstrate good noise attenuation while preserving signal morphology. In contrast, the Blackman-Harris window produced higher MAE (0.061–0.318) and MSE (0.019–0.172) but achieved significantly greater SNR improvements (7.77–17.4 dB) and tighter control over PSD (4.904 – 8.442 × 10⁻³), indicating superior noise suppression and reduced spectral leakage. A paired t-test confirmed that differences in all four performance metrics were statistically significant (p < 0.05) across both neurotypical and ASD subject groups. Despite the Hanning filter's computational simplicity, the Blackman-Harris filter demonstrated more robust performance, making it a more suitable choice for high-fidelity EEG signal analysis in clinical diagnostics and neuroscience research.  
Comparative Analysis of Multispectral Image Classification Based on EfficientNetB0, ResNet152, DenseNet161, DenseNet121, and HSV Segmentation Melinda; Nurdin, Yudha; Mufti, Alfatirta; Anzella, Syifa; Rusdiana, Siti; D Acula, Donata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6873

Abstract

This study established a classification system based on Convolutional Neural Networks (CNNs) to detect High-High Fluctuation (HHF) patterns in multispectral data derived from pure water (H2O) and a water-sodium hydroxide (NaOH) solution. This study combines HSV color-space-based segmentation to identify areas with the highest signal amplitude, thereby enhancing the feature extraction of the CNN model. Data augmentation techniques, including random flipping, rotation, and color jitter, along with training parameters such as a learning rate of 0.0001 and a batch size of 32, have been shown to effectively improve model generalization and reduce overfitting. Four different CNN architectures were evaluated: ResNet-152, DenseNet-161, DenseNet-121, and EfficientNet-B0. As a result, ResNet152 achieved the highest accuracy of 97.6%, attributed to its network depth and residual connections that effectively address the vanishing gradient problem. DenseNet161 and DenseNet121 also demonstrated competitive performance, achieving accuracies of 96.7% and 96.2%, respectively, which is supported by their dense connectivity that optimizes feature reuse. Conversely, EfficientNetB0, despite showing lower accuracy (90%), provides significant computational efficiency, making it suitable for real-time applications. These results underscore the importance of selecting a CNN architecture that balances accuracy and efficiency for multispectral data classification.
Pengembangan Produk Kerajinan Serabut Kelapa Sebagai Komoditas Ekonomi Kreatif Di Gampong Lamnga Aceh Besar Dewi, Rosmala; Rusdiana, Siti; Lubis, Andriani; Haikal, Muhammad; Reza, M.Alfa; Awalyani, Rafiqah Nabila; Rukmana, Sufla; AlQanita, Najah; ramli, ichwana
JURNAL PENGABDIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (JP3L) Vol 3 No 2 (2026): JURNAL PENGABDIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (JP3L): Volume 3 Nomor 2,
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/jp3l.v3i2.107

Abstract

Serabut kelapa merupakan limbah pertanian yang melimpah di Gampong Lamnga, Aceh Besar, namun belum dimanfaatkan secara optimal sebagai produk bernilai ekonomi. Kegiatan pengabdian masyarakat ini bertujuan mengembangkan produk kerajinan berbahan serabut kelapa sebagai komoditas ekonomi kreatif untuk meningkatkan pendapatan dan keterampilan masyarakat. Topik ini dipilih karena tingginya ketersediaan bahan baku lokal serta peluang pasar produk ramah lingkungan. Metode pengabdian meliputi identifikasi potensi dan permasalahan, pelatihan teknik pengolahan serabut kelapa, pendampingan desain produk, serta pembinaan manajemen usaha dan pemasaran. Hasil kegiatan menunjukkan peningkatan pengetahuan dan keterampilan masyarakat dalam mengolah serabut kelapa menjadi berbagai produk kerajinan yang memiliki nilai jual, seperti keset dan hiasan rumah. Selain itu, terbentuk kesadaran masyarakat terhadap potensi ekonomi kreatif berbasis sumber daya lokal. Kegiatan ini penting sebagai upaya pemberdayaan masyarakat dan penguatan ekonomi lokal yang berkelanjutan. 
Classification of Autism Spectrum Disorder (ASD) in Children Using the VGG19 CNN Model Based on Facial Landmarks of the Eye and Forehead Areas yunidar; Arya Suyanda; Melinda Melinda; Lailatul Qadri Zakaria; Siti Rusdiana
Jurnal Teknokes Vol. 19 No. 2 (2026): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v19i2.158

Abstract

Early detection of Autism Spectrum Disorder (ASD) is a crucial challenge in child development interventions because conventional screening methods are often subjective and prone to assessor bias. This study proposes an objective solution in the form of a deep learning approach for automatic ASD classification using facial landmark representations that focus exclusively on the eye and forehead areas. The selection of these areas is based on the eye avoidance hypothesis, which states that these regions contain very rich diagnostic information and behavioral biomarkers related to the ASD phenotype. The pre-processing stage involves isolating the eye and forehead areas using Dlib 68-landmark detection to eliminate background visual noise, followed by detailed topological visualization using MediaPipe Face Mesh with 478 landmark points as the model input. The Convolutional Neural Network (CNN) architecture used is the VGG19 model modified with transfer learning techniques and the addition of Dropout layers to improve efficiency and prevent overfitting. The model was trained on a primary dataset of 1,238 images collected under controlled conditions from children in Banda Aceh. The test results showed very promising performance with an overall accuracy of 94.35%. Specifically, the model achieved a recall (sensitivity) of 95.24%, a precision of 93.75%, and an AUC score of 0.9831. This high sensitivity is crucial in a medical context to minimize the risk of misdetection of positive cases. These results demonstrate that landmark visualization in the eye and forehead areas with the VGG19 model is a highly effective, accurate, and practical method for serving as an economical early screening tool for ASD.
Co-Authors Alfatirta Mufti Alfizar Alfizar AlQanita, Najah Amalia Amalia Ami, Mucharommah Sartika Andriani Lubis Anzella, Syifa Arie Budiansyah Arya Suyanda Asep Rusyana Asmanidar, Asmanidar Awalyani, Rafiqah Nabila Basir, Nurlida Candra, Rudi Arif Charlie, Empya D Acula, Donata Delia Khairunnisa Dewi Shinta Durisman Durisman Elly Kesumawati Febriana, Diana G. Gunawan Gazali, Syahrul Hafnani Hafnani Hamdi, Syaiful Husni Husni Ilham, Dirja Nur Intan Syahrini Lailatul Qadri Zakaria Latifah Rahayu Mahfuzha, Raudhatul MAHMUDI Mahmudi Mahmudi Marwan Ramli Marzuki Marzuki Maulidi, Ikhsan Melinda Melinda Melinda Melinda Melinda Miftahujjannah, Rizka Muarrif, Samsul Muhammad Haikal Muhammad Ikhwan Mukhra, Uly Handayani Novita Novita Nurillah, Usmau Lidya Nurmaulidar Nurmaulidar Oktavia, Rini Pasaribu, Sri Rizki Aprilianti Pribadi, Teguh Qadri Zakaria, Lailatul Radhiah, Radiah RAHAYU, LATIFAH Rahmaniar, Meutia Hanum Ramli, Ichwana Reza, M.Alfa Romi Rosadi Rosmala Dewi Rukmana, Sufla Saiful Amri Saputra, T Murdani Sukamto, Ika Sumiyarsi Sulistiawati Sulistiawati Syahrini, Intan Syarifah Meurah Yuni Syaukani Syaukani T. Mohd. Raja Maulana Taufiq Abdul Gani Teguh Pribadi Vina Apriliani Waladah, Buleun Yudha Nurdin Yunidar yunidar Zahnur, Zahnur Zurnila Marli Kesuma