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All Journal Innovation in Research of Informatics (INNOVATICS) Journal of Information Technology (JIfoTech)
Hilmi Rahmawati
Universitas Cipasung Tasikmalaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Data Augmentation Strategies on Spectrogram Features for Infant Cry Classification Using Convolutional Neural Networks Alam Alam; Nuk Ghurroh Setyoningrum; Robby Maududy; Dea Dewi Damayanti; Hilmi Rahmawati
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16823

Abstract

Infant cry classification is an important task to support parents and healthcare professionals in understanding infants needs, yet the challenge of limited and imbalanced datasets often reduces model accuracy and generalization. This study proposes the application of diverse audio data augmentation strategies including time stretching, time shifting, pitch scaling, and polarity inversion combined with spectrogram representation to enhance Convolutional Neural Network (CNN) performance in classifying infant cries. The dataset from the Donate-a-Cry Corpus was expanded from 457 to 6,855 samples through augmentation, improving class balance and variability. Experimental results show that CNN accuracy increased from 85% before augmentation to 99.85% after augmentation, with precision, recall, and F1-score reaching near-perfect values across all categories. The confusion matrix further confirms robust classification with minimal misclassifications. These findings demonstrate that data augmentation is crucial to overcoming dataset limitations, enriching acoustic feature diversity, and reducing model bias, while offering practical implications for the development of accurate, reliable, and real-world applicable infant cry detection systems.
Analisis Sentimen Layanan Akademik Civitas – Suteki Menggunakan Metode Naïve Bayes Hilmi Rahmawati; Nuk Ghurroh Setyoningrum
Journal of Information Technology Vol. 6 No. 2 (2026): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v6i2.1133

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi layanan akademik Civitas–Suteki guna memahami bagaimana pengguna merespons dan mengalami penggunaan aplikasi tersebut. Tingginya proporsi ulasan negatif yang mencapai lebih dari sepertiga total data mengindikasikan perlunya evaluasi sistematis terhadap kualitas layanan aplikasi. Data dikumpulkan melalui teknik web scraping pada Google Play Store, menghasilkan 534 ulasan pada rentang Maret 2020 hingga April 2026. Proses preprocessing meliputi penghapusan duplikat, cleaning, case folding, tokenizing, stopword removal, dan stemming. Pembobotan kata menggunakan TF-IDF dan klasifikasi dilakukan dengan algoritma Multinomial Naive Bayes. Dari 473 ulasan valid, sebanyak 291 ulasan (61,52%) bersentimen positif dan 182 ulasan (38,48%) bersentimen negatif. Model mencapai akurasi sebesar 78,26%, dengan precision 0,79, recall 0,87, dan F1-score 0,83 untuk kelas positif, serta precision 0,77, recall 0,65, dan F1-score 0,71 untuk kelas negatif. Hasil ini menunjukkan bahwa metode Multinomial Naive Bayes dengan pembobotan TF-IDF efektif digunakan dalam klasifikasi sentimen ulasan berbahasa Indonesia dan dapat menjadi bahan evaluasi bagi pengembang aplikasi untuk meningkatkan kualitas layanan.
Co-Authors Alam Alam Dea Dewi Damayanti Nuk Ghurroh Setyoningrum Robby Maududy