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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 Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Entomologi Indonesia Journal of Electronics, Electromedical Engineering, and Medical Informatics 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
Siti Rusdiana
Department Of Mathematics, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Deskripsi ulang rayap tanah (Subulitermes-branch): Oriensubulitermes inanis (Haviland) (Termitidae: Nasutitermitinae) di Indonesia Syaukani, Syaukani; Husni, Husni; Alfizar, Alfizar; Kesumawati, Elly; Novita, Novita; Rusdiana, Siti; Muarrif, Samsul; Pribadi, Teguh
Jurnal Entomologi Indonesia Vol 16 No 2 (2019): July
Publisher : Perhimpunan Entomologi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (699.678 KB) | DOI: 10.5994/jei.16.2.75

Abstract

Oriensubulitermes inanis (Haviland) is one of the endemic termites in the Oriental Region and plays a very important role in the decomposition process in tropical forests. This study aims to redescribe O. inanis from Indonesia. Termite were collected by adopting a Standized Sampling Protocol (Jones & Eggketon 2000) and final taxonomic confirmation were conducted at the Natural History Museum UK) and Florida University (USA). We found 21 colonies of O. inanis from various habitats and altitudes in Indonesia. Distribution of O. inanis is often correlated with biodiversity status in tropical forests. Worker caste mandible provides the most useful character for the description of O. inanis. In Southeast Asia, this rare species is restricted and can be found only in the Malay Peninsula, Borneo and Sumatra, and absence from Java. Decayed wood, base of tree trunks, and other termite nests (epigeal mounds) are selected media used to construct their nests. Limited population number in a colony, restricted alates flying ability, and secretive nest habitats are thought to influence the distribution of O. inanis in Indonesia.
Heavy–Light Soft-Vote Fusion of EEG Heatmaps for Autism Spectrum Disorder Detection Melinda, Melinda; Gazali, Syahrul; Away, Yuwaldi; Rafiki, Aufa; Wong, W.K; Muliyadi, Muliyadi; Rusdiana, Siti
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1377

Abstract

Autism spectrum disorder is a neurodevelopmental condition that affects social communication and behaviour, and diagnosis still relies on subjective behavioural assessment. Electroencephalography provides a noninvasive view of brain activity but is noisy and often analysed with handcrafted features or evaluation schemes that risk data leakage. This study proposes a deep learning pipeline that combines wavelet denoising, EEG-to-image encoding, and heavy-light decision fusion for autism detection from EEG. Sixteen-channel EEG from children and adolescents with autism and typically developing peers in the KAU dataset is denoised using discrete wavelet transform shrinkage, segmented into fixed 4 second windows, and rendered as pseudo colour heatmaps. These images are used to fine-tune five ImageNet pretrained architectures under a unified training protocol with 5-fold cross-validation. Heavy-light fusion combines one heavyweight backbone and one lightweight backbone through weighted soft voting on class posterior probabilities. The strongest single model, ConvNeXt Tiny, attains about 97.25 percent accuracy and 97.10 percent F1 score at the window level. The best heavy light pair, ConvNeXt plus ShuffleNet, reaches about 99.56 percent accuracy and 99.53 percent F1, with sensitivity and specificity in the 99 percent range. Fusion mainly reduces missed ASD windows without increasing false alarms, indicating complementary error patterns between heavy and light models. These findings show that the proposed denoise encode classify pipeline with heavy light fusion yields more robust autism EEG classification than individual backbones and can support EEG-based decision support in autism screening.
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.
Co-Authors Alfatirta Mufti Alfizar Alfizar Amalia Amalia Ami, Mucharommah Sartika Anzella, Syifa Arie Budiansyah Asep Rusyana Asmanidar, Asmanidar Aznita, Meri Basir, Nurlida Candra, Rudi Arif Charlie, Empya D Acula, Donata Delia Khairunnisa Dewi Shinta Durisman Durisman Elly Kesumawati Febriana, Diana Ferijal, T Gazali, Syahrul Hafnani Hafnani Hamdi, Syaiful Husni Husni Ilham, Dirja Nur Intan Syahrini Izzaty, Atika Latifah Rahayu Mahfuzha, Raudhatul MAHMUDI Mahmudi Mahmudi Marwan Ramli Marzuki Marzuki Maulidi, Ikhsan Melinda Melinda Melinda Miftahujjannah, Rizka Muarrif, Samsul Muhammad Ikhwan Mukhra, Uly Handayani Muliyadi Muliyadi Novita Novita Nurillah, Usmau Lidya Nurmaulidar Nurmaulidar Oktavia, Rini Pasaribu, Sri Rizki Aprilianti Qadri Zakaria, Lailatul Radhiah, Radiah Rafiki, Aufa RAHAYU, LATIFAH Rahmaniar, Meutia Hanum Ramli, Ichwana Romi Rosadi 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 Wong, W.K Yudha Nurdin Yunidar Yuwaldi Away Zahnur, Zahnur Zurnila Marli Kesuma