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All Journal Computer Science (CO-SCIENCE) ZONA: Jurnal Pengabdian Masyarakat
Novita, Hilda
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Computer Science & IT Social Sciences Other

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Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung Masruriyah, Anis; Novita, Hilda; Sukmawati, Cici; Ramadhan, Angga; Arif, Siti; Dermawan, Budi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2389

Abstract

Heart disease remains a leading cause of death in Indonesia and worldwide. In the realm of data mining, class imbalance between heart disease and normal samples within datasets presents a significant challenge. This disparity can lead to model bias toward the majority class, resulting in suboptimal performance in identifying instances of heart disease. This study addresses this issue by implementing oversampling techniques, particularly Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). The findings reveal that models without oversampling achieve accuracy and precision exceeding 80%, but exhibit poor class separation performance. In contrast, models employing oversampling, despite experiencing reductions in accuracy and precision, enhance their ability to distinguish between heart disease and normal classes. The top-performing model utilizing the Random forest algorithm with SMOTE attains an AUC value of 0.868, signifying a significant improvement in class separation. These discoveries provide essential guidance for the development of more effective and accurate heart disease classification models. The utilization of oversampling techniques, such as SMOTE, proves to be an effective strategy for mitigating class imbalances in heart disease data mining. While accuracy and precision may decrease, the model's capability to identify heart disease becomes more reliable, with notable outcomes assessed using AUC. This research contributes significantly to enhancing efforts in heart disease prevention and treatment through sophisticated and sustainable data mining techniques.  
Peningkatan Status Gizi Sekolah Dasar Melalui Edukasi Dan Interfensi Gizi Seimbang Dilingkungan Sekolah Nurhalisa, Nurhalisa; Munawarah, Ika; Rohati, Nella; Nurfadila, Nurfadila; Novita, Hilda; Indra, Nikita Nofelia; Melia, Olasri Ratna; Jarwina, Rova; Marniati, Marniati
ZONA: Jurnal Pengabdian Masyarakat Vol 2 No 2 (2025): ZONA: Jurnal Pengabdian Masyarakat
Publisher : Fanshur Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71153/zona.v2i2.163

Abstract

Gizi kurang merupakan kondisi dimana tubuh tidak mendapatkan asupan nutrisi yang cukup untuk memenuhi kebutuhan dasar guna mendukung pertumbuhan dan perkembangan, dan fungsi tubuh yang optimal. Tingginya angka gizi buruk di kalangan anak sekolah dasar, terutama disebabkan oleh kurangnya asupan gizi dan kurangnya edukasi tentang pentingnya makanan bergizi. Tujuan dari penelitian ini yaitu untuk Meningkatkan pengetahuan siswa, orang tua setra guru tentang gizi seimbang dan memperbaiki status gizi melalui edukasi dan penyuluhan yang dulakukan. Program ini dilaksanakan melalui sosialisasi yang meliputi pemberian materi tentang gizi seimbang, praktik langsung, dan distribusi media edukasi seperti leaflet. Evaluasi dilakukan dengan pre-test dan post- test untuk mengukur perubahan pengetahuan siswa. Dari 26 siswa yang terlibat, 65,4% menunjukkan peningkatan pengetahuan tentang pentingnya gizi seimbang setelah mengikuti program. Perubahan signifikan terlihat pada kelas 3 hingga kelas 6. Pendidikan tentang gizi seimbang efektif dalam meningkatkan pengetahuan siswa, meskipun tidak semua kelompok kelas menunjukkan perubahan yang sama. Tingkat pendidikan tidak berpengaruh signifikan terhadap pemahaman siswa. Diperlukan pendekatan yang lebih interaktif dan berkelanjutan dalam sosialisasi gizi serta melibatkan orang tua dan guru untuk mendukung penerapan pola makan sehat di rumah.
Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung Masruriyah, Anis; Novita, Hilda; Sukmawati, Cici; Ramadhan, Angga; Arif, Siti; Dermawan, Budi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2389

Abstract

Heart disease remains a leading cause of death in Indonesia and worldwide. In the realm of data mining, class imbalance between heart disease and normal samples within datasets presents a significant challenge. This disparity can lead to model bias toward the majority class, resulting in suboptimal performance in identifying instances of heart disease. This study addresses this issue by implementing oversampling techniques, particularly Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). The findings reveal that models without oversampling achieve accuracy and precision exceeding 80%, but exhibit poor class separation performance. In contrast, models employing oversampling, despite experiencing reductions in accuracy and precision, enhance their ability to distinguish between heart disease and normal classes. The top-performing model utilizing the Random forest algorithm with SMOTE attains an AUC value of 0.868, signifying a significant improvement in class separation. These discoveries provide essential guidance for the development of more effective and accurate heart disease classification models. The utilization of oversampling techniques, such as SMOTE, proves to be an effective strategy for mitigating class imbalances in heart disease data mining. While accuracy and precision may decrease, the model's capability to identify heart disease becomes more reliable, with notable outcomes assessed using AUC. This research contributes significantly to enhancing efforts in heart disease prevention and treatment through sophisticated and sustainable data mining techniques.  
Co-Authors Arif, Siti Budi Dermawan, Budi Indra, Nikita Nofelia Jarwina, Rova Marniati Marniati, Marniati Masruriyah, Anis Melia, Olasri Ratna Munawarah, Ika Nurfadila Nurfadila, Nurfadila Nurhalisa, Nurhalisa Ramadhan, Angga Rohati, Nella Sukmawati, Cici