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All Journal Jurnal Computech & Bisnis (e-Journal) ELINVO (Electronics, Informatics, and Vocational Education) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Information Systems and Informatics Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer
Herman
Universitas Ahmad Dahlan
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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PENGEMBANGAN MODEL DEEP LEARNING UNTUK DETEKSI SUARA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK NORITA SINAGA; Imam Riadi; Herman
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8061

Abstract

Sound detection and keyword recognition in audio signals have become rapidly growing research areas due to their wide range of applications, from intelligent audio surveillance to human-computer interaction systems. This study aims to develop a deep learning model based on Convolutional Neural Networks (CNN) to automatically detect and classify specific words in speech recordings. The focus of this research is the detection of the keywords "dog" and "children" contained in speech data. The research methodology includes data preprocessing through noise reduction and normalization, as well as data augmentation techniques such as pitch shifting to improve the robustness of the model. Audio features are extracted using the Short-Time Fourier Transform (STFT) to generate visual representations in the form of spectrograms, which serve as the primary input to the CNN architecture. Experimental results show that the developed model successfully classified the target keywords with an accuracy of 90,00%. The model proved effective in recognizing both spectral and temporal patterns of spoken keywords and has the potential to be implemented in real-time sound detection systems.
PROGRAM PENINGKATAN PENGETAHUAN DAN KEAHLIAN GURU DAN SISWA SEKOLAH MENENGAH PERTAMA TERHADAP SEARCH ENGINE OPTIMIZATION Herman; Imam Riadi; Gusti Chandra Kurniawan; Basit Adhi Prabowo
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 7 No. 2 (2024): Agustus : Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

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Abstract

Mahasiswa Magister Informatika Universitas Ahmad Dahlan melakukan Program Pemberdayaan Umat (PRODAMAT) dengan tema Search Engine Optimization (SEO) di Sekolah Menengah Pertama (SMP) Muhammadiyah 1 Berbah Sleman. Salah satu problem di dalam melakukan dakwah melalui dunia maya adalah konten tidak muncul di halaman pertama mesin pencari meskipun konten dibuat lebih dulu. PRODAMAT ini bertujuan untuk meningkatkan keahlian menulis judul dan konten menerapkan SEO sehingga memperbesar peluang konten muncul di halaman pertama mesin pencari. Pelatihan ini diikuti 17 guru, 10 siswa dan satu karyawan SMP Muhammadiyah 1 Berbah Sleman secara luring di laboratorium komputer sekolah. Pretest dilakukan sebelum melakukan pelatihan. Sesi pertama memberikan materi tentang faktor-faktor yang mempengaruhi indexing mesin pencari agar konten muncul di halaman pertama yang ditampilkan oleh mesin pencari berdasarkan kata kunci. Sesi pertama juga memberikan materi tentang cara menulis konten dengan template yang menerapkan SEO. Sesi kedua mempraktikkan pembuatan template yang menerapkan SEO dan menulis konten menggunakan template yang sudah dibuat. Posttest dilakukan setelah pelatihan usai. Hasil tes pretest dan post test menunjukkan bahwa nilai persentase guru meningkat 26%, persentase siswa meningkat 31% dan persentase karyawan meningkat 45%. Secara keseluruhan, nilai persentase meningkat 28%. Hasil ini mengindikasikan adanya peningkatan yang signifikan dalam pengetahuan wawasan dan soft skill tentang penerapan SEO tanpa menggunakan tools SEO.Kata Kunci: SEO, Template, Konten
Optimizing KNN Classification for Heart Disease Prediction Using Sequential Forward Selection Herman; Rusydi Umar; Deni Kuswandani
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1744

Abstract

Irrelevant attributes often degrade the effectiveness of distance-based algorithms like K-Nearest Neighbors (KNN) in heart disease prediction. This study enhances a KNN model using Sequential Forward Selection (SFS) on the Cleveland dataset to optimize computational efficiency and precision while maintaining stable recall. To rigorously prevent data leakage, data partitioning was executed prior to mode imputation and normalization, followed by feature selection within a 5-fold stratified cross-validation framework. To further guarantee model robustness and rule out arbitrary selection, a 5-repeated 10-fold cross-validation and a 50-iteration feature stability analysis were executed. Compared to a baseline model (k=7, Euclidean; 80.33% accuracy) utilizing all 13 original attributes, the optimal 7-feature subset (cp, trestbps, chol, thalach, oldpeak, ca, thal) reduced the dimensional space by 46% and achieved 81.97% Accuracy, 77.42% Precision, 85.71% Recall, 78.79% Specificity, 0.9183 ROC-AUC, and an 81.36% F1-Score on an independent hold-out test set comprising 61 samples. Although McNemar's test (p = 1.0000) indicated the absolute accuracy improvement was not statistically significant, the 7-feature model successfully eliminated unstable features and reduced statistical noise. Ultimately, applying SFS provides a highly efficient, computationally lightweight framework for heart disease prediction without compromising predictive reliability.
Co-Authors Abdul Fadlil Aldi Rifki Andriawan Aldibangun Pidekso Putro Anton Yudhana Anton Yudhana Aqid Fahri Hafin Aulyah Zakilah Ifani Basit Adhi Prabowo Cucut Hariz Pratomo Deni Kuswandani Dikky Praseptian M Gusti Chandra Kurniawan Imam Riadi Indah Rahma Ilmiana Irhas Ainur Rafiq Irhash Ainur Rafiq Izzan Julda D.E Purwadi Putra Muhammad Kunta Biddinika NORITA SINAGA Rusydi Umar Suwanti Tugiman Tugiman Yudi Kurniawan