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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pengabdian Masyarakat Bangsa
Septiana, Vina Tri
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Implementasi Perilaku Hidup Bersih dan Sehat di Lingkungan Pesantren Al-Falah Padang Febrianto, Budi Yulhasfi; Septiana, Vina Tri; Jelmila, Sri Nani; Hasni, Dita
Jurnal Pengabdian Masyarakat Bangsa Vol. 1 No. 11 (2024): Januari
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v1i11.638

Abstract

Perilaku Hidup Bersih dan Sehat (PHBS) di antara santri merupakan hal yang penting untuk meningkatkan kesehatan di linkungan Pondok pesantren. Hal ini mencakup perihal kebersihan diri, sanitasi, gizi, aktivitas fisik, dan kesehatan mental. Implementasi PHBS di pondok pesantren juga memiliki dampak positif pada kualitas hidup, produktivitas, dan kesejahteraan sosial santri. Identifikasi masalah pada pondok pesantren Al-Falah Kota Padang mencakup kurangnya kesadaran, sarana dan prasarana yang kurang memadai, serta kekurangan pengetahuan tentang gizi seimbang. Fokus pengabdian masyarakat ini adalah meningkatkan kesadaran PHBS para santri, dan meningkatkan pengetahuan gizi di pondok pesantren dengan metode penyuluhan dan tanya jawab yang interaktif menggunakan power point. Tampak antusiasme para santri dalam proses penyampaian materi dan diskusi. Hasil dari kegiatan ini diharapkan terdapat peningkatan kesadaran pentingnya berprilaku hidup bersih dan sehat yang bisa diaplikasikan sehari-hari oleh para santri. Kesimpulan kegiatan ini adalah penyuluhan membantu meningkatkan pengetahuan tentang PHBS secara subjektif.
Optimizing the gallstone detection process with feature selection statistical analysis algorithm Yanto, Musli; Yuhandri, Yuhandri; Tajuddin, Muhammad; Septiana, Vina Tri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1183-1191

Abstract

Early detection is one form of early anticipation in treating gallstone disease patients using medical images. However, the problem that exists is that there are still many shortcomings in medical images, such as noise in the image that causes the detection process to not run optimally. Based on this, this study aims to carry out the process of detecting gallstone objects in magnetic resonance cholangiopancreatography (MRCP) images by optimizing the performance of extraction techniques for feature selection. Optimization of extraction techniques in feature selection is carried out using the performance of the feature selection statistics analysis (FSSA) algorithm. The performance of the FSSA algorithm can provide improvements in the feature selection process by excelling in the performance of classification methods such as k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), and the Pearson correlation (PC) method. Based on the tests that have been carried out, the performance of the FSSA algorithm in the detection process provides an accuracy level of 95.69%, a sensitivity of 89.65%, and a specificity of 98.43%. Overall, this study can contribute to the development of extraction and provide a significant technical impact on optimizing the gallstone detection process.
Co-Authors Febrianto, Budi Yulhasfi Hasni, Dita Jelmila, Sri Nani Tajuddin, Muhammad Yanto, Musli Yuhandri Yuhandri, Yuhandri