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All Journal Jurnal Transformatika Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal DIMASTIK
Indra Abdam Muwakhid, Indra Abdam
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Comparasion Support Vector Machine And K-Nearest Neighbor for Classification fertile And Infertile Eggs Based on GLCM Texture Analysis Nurdiyah, Dewi; Muwakhid, Indra Abdam
Jurnal Tr@nsForMat!ka Vol 13, No 2 (2016)
Publisher : Jurusan Teknologi Informasi Universitas Semarang

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Abstract

Fertility eggs test are steps that must be performed in an attempt to hatch eggs. Fertility test usually use egg candling. The purpose of observation is to choose eggs fertile  (eggs contained embryos) and infertile eggs (eggs that are no embryos). And then fertilized egg will be entered into the incubator for hatching eggs and infertile can be egg consumption. However, there are obstacles in the process of sorting the eggs are less time efficient and inaccuracies of human vision to distinguish between fertile and infertile eggs. To overcome this problem, it can be used  Computer Vision technology is having such a principle of human vision. It used to identify an object based on certain characteristics, so that the object can be classified. The aim of this study to comparasion classify image fertile and infertile eggs with SVM (Support Vector Machine) algorithm and K-Nearest Neighbor Algorithm based on input from bloodspot texture analysis and blood vessels with GLCM (Gray Level Co-ocurance Matrix).  Eggs image  studied are 6 day old eggs. It is expected that the proposed method is an appropriate method for classification image fertile and infertile eggs.
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.
Edukasi Cerdas: Enhancement Kapasitas Kader Tentang Gizi, Managemen Dan Pelaporan Data Balita Berbasis Digital Harsono, Harsono; Sugiharto, Sigit; Rinayati, Rinayati; Muwakhid, Indra Abdam; Erawati, Ambar Dwi; Dyah Retnaningrum, Okti Trihastuti; Iswandari, Hargianti Dini
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 3 (2025): Edisi Juli - September
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i3.6517

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan kapasitas kader Posyandu dalam aspek pemahaman gizi balita, keterampilan pencatatan dan pelaporan data, serta kemampuan edukatif menggunakan pendekatan aplikasi digital. Program ini dilaksanakan di Posyandu Delima (Bantul) dan Posyandu Lestari (Semarang) dengan melibatkan 27 kader. Metode kegiatan terdiri atas lima tahap, mulai dari identifikasi kebutuhan, pelatihan kontekstual, pendampingan langsung dan daring, hingga evaluasi pre- dan post-test. Hasil menunjukkan peningkatan signifikan dalam kompetensi kader, baik dalam memahami grafik KMS, mengoperasikan Aplikasi Posyandu Balita, hingga menyampaikan edukasi kesehatan. Temuan ini menunjukkan bahwa kombinasi pelatihan partisipatif dan teknologi sederhana dapat meningkatkan efektivitas layanan posyandu serta mendukung transformasi layanan primer berbasis komunitas
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.
Co-Authors Ambar Dwi Erawati Dewi Nurdiyah Dewi Nurdiyah, Dewi Hargianti Dini Iswandari Harsono Harsono Indra Abdam Muwakhid Okti Tri Hastuti Okti Trihastuti Dyah Retnaningrum Rinayati Rinayati Sugiharto, Sigit