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All Journal Jurnal Transformatika Jurnal Ilmiah Teknik Informatika dan Komunikasi Nusantara Mengabdi kepada Negeri Jurnal DIMASTIK
Indra Abdam Muwakhid
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Statistical Feature Extraction Based on Wavelet Transform for Arrhythmia Detection Muwakhid, Indra Abdam; Indra Abdam Muwakhid
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12339

Abstract

Early detection of arrhythmia through electrocardiogram (ECG) signals is crucial for preventing severe cardiac conditions. This study proposes a binary classification approach using statistical features derived from wavelet-transformed ECG signals. The MIT-BIH Arrhythmia Database was used, with signals filtered using a 0.5–50 Hz Butterworth bandpass filter. Signals were segmented into 360-sample windows with 100-sample overlap and labeled based on the majority annotation within each window. Wavelet transformation using Symlet 8 at level 4 was applied, followed by the extraction of eight statistical features: mean, standard deviation, variance, skewness, kurtosis, interquartile range (IQR), root mean square (RMS), and zero crossing rate (ZCR). These features were classified using MLP, KNN, and SVM models. MLP and KNN achieved the highest accuracy of 92.46%, while SVM had lower accuracy (72.99%) but high recall (94.21%). The results demonstrate the effectiveness of wavelet-based statistical features for lightweight and accurate arrhythmia detection.
Peningkatan Literasi Digital Keluarga terhadap Link Scam dan Pemanfaatan AI di Wilayah RW 03 Kalipancur Muwakhid, Indra Abdam; Indra Abdam Muwakhid; Okti Tri Hastuti; Dewi Nurdiyah
Jurnal DIMASTIK Vol. 3 No. 2 (2025): Juli
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/dimastik.v3i2.12375

Abstract

Berbagai manfaat telah dirasakan oleh Masyarakat dengan adanya perkembangan teknologi digital, namun juga menimbulkan resiko keamanan seperti penipuan daring (link scam) dan penyalahgunaan kecerdasan buatan (AI). Kurangnya literasi digital di lingkungan keluarga berpotensi membawa kerugian finansial, kebocoran data, hingga penyalahgunaan teknologi. Kegiatan pengabdian Masyarakat ini bertujuan untuk meningkatkan pemahaman warga RW 03 Kelurahan Kalipancur Kota Semarang terhadap ancaman link scam serta memanfaatkan AI dengan aman. Metode yang digunakan meliputi ceramah interaktif, diskusi, simulasi, pre-test, dan post-test. Hasil pemahaman warga menunjukkan peningkatan dari rata-rata pre-test 43% menjadi 84% pada saat post-test. Kegiatan ini efektif dalam meningkatkan literasi digital warga untuk mengantisipasi awal dugaan adanya scam.
Penguatan Literasi Digital Keluarga dalam Menghadapi Link Scam dan Risiko Penyalahgunaan AI di RW 03 Kalipancur Indra Abdam Muwakhid; Dewi Nurdiyah
Nusantara Mengabdi Kepada Negeri Vol. 2 No. 4 (2025): November : Nusantara Mengabdi Kepada Negeri
Publisher : Asosiasi Peneliti dan Pengajar Ilmu Hukum Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/numeken.v2i4.1594

Abstract

Digital transformation has reshaped community life by increasing internet dependency in daily communication, financial transactions, and public services. However, this rapid development has also intensified cybersecurity risks, particularly link-based scams and the misuse of Artificial Intelligence (AI). Limited digital literacy at the family level makes communities vulnerable to phishing attacks, personal data breaches, and AI-generated fraud. This community service program aimed to strengthen digital literacy among residents of RW 03 Kalipancur, Semarang, through participatory education focusing on link scam detection and responsible AI usage. The program involved interactive lectures, case discussions, simulations, and pre-test and post-test evaluations with 18 participants from family groups, PKK cadres, and elderly residents. The results showed a significant increase in understanding, from an average pre-test score of 43% to a post-test score of 84%. Beyond knowledge improvement, participants demonstrated increased awareness and behavioral change toward safer digital practices. The findings suggest that community-based participatory approaches effectively enhance family-level digital resilience and can serve as a replicable empowerment model in other communities facing similar digital threats.
Pengaruh Variasi Ukuran Jendela terhadap Kinerja Klasifikasi Aritmia Berbasis EKG Indra Abdam Muwakhid; Dewi Nurdiyah
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.2115

Abstract

Arrhythmia detection using electrocardiogram (ECG) signals requires an appropriate segmentation strategy to optimally represent cardiac morphological information. Although time-based windowing is widely applied in automated detection systems, the impact of window size variation on classification performance has not been systematically investigated. This study aims to evaluate the influence of different window sizes on arrhythmia detection performance using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 48 records from the MIT-BIH Arrhythmia Database sampled at 360 Hz. ECG signals were preprocessed using a 0.5–50 Hz bandpass filter and segmented with window sizes of 180, 360, 720, and 1000 samples using 50% overlap. Time-domain statistical features were extracted and used as input to the classification model. Experimental results indicate that the 180-sample window achieved the best performance, with an accuracy of 89.58% and an F1-score of 83.27%. These findings suggest that shorter segmentation windows increase training data density and are more suitable for distance-based classifiers such as KNN. This study highlights that window size selection is a critical parameter in arrhythmia detection system design and should be aligned with the characteristics of the classification algorithm employed.
Co-Authors Dewi Nurdiyah Indra Abdam Muwakhid, Indra Abdam Okti Tri Hastuti