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All Journal Journal of Intelligent Decision Support System (IDSS) Journal of Artificial Intelligence and Digital Business
Januarto, Sigit
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Klasifikasi Status Stunting Balita Tegal Menggunakan Teknik Smote Pada Metode Naives Bayes Gaussain Januarto, Sigit; Murtopo, Aang Alim; Arif, Zaenul
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2349

Abstract

Stunting merupakan salah satu masalah kesehatan serius yang memengaruhi tumbuh kembang anak, terutama pada 1.000 Hari Pertama Kehidupan. Kota Tegal termasuk wilayah dengan prevalensi stunting yang cukup tinggi, sehingga diperlukan metode prediksi yang akurat untuk mendukung intervensi gizi tepat sasaran. Penelitian ini menggunakan metode Naive Bayes Gaussian untuk mengklasifikasikan status stunting balita berdasarkan data antropometri. Permasalahan ketidakseimbangan kelas pada dataset diatasi dengan teknik oversampling Synthetic Minority Over-sampling Technique (SMOTE) guna meningkatkan kemampuan model dalam mengenali kelas minoritas. Hasil pengujian menunjukkan bahwa model sebelum penerapan SMOTE memiliki akurasi rata-rata 91,58%. Setelah penerapan SMOTE, akurasi validasi silang meningkat menjadi rata-rata 96,28% dengan presisi 94,03%, recall 91,58%, dan F1-score 92,12%. Peningkatan ini membuktikan bahwa kombinasi Naive Bayes Gaussian dan SMOTE efektif untuk prediksi status stunting. Model yang dihasilkan berpotensi diimplementasikan sebagai sistem pendukung keputusan dalam deteksi dini dan pencegahan stunting di wilayah rawan
Application of the nearest neigbour interpolation method and naives bayes classifier for the identification of bespectacled faces Murtopo, Aang Alim; Januarto, Sigit; Anandianskha, Sawaviyya; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.242

Abstract

Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology
Co-Authors Aang Alim Murtopo Anandianskha, Sawaviyya Arif, Zaenul Gunawan Gunawan