Article Per Year (5 Year)
p-Index From 2021 - 2026
1.578
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Sains Publikauma : Jurnal Administrasi Publik Universitas Medan Area Jurnal Pengabdian Pada Masyarakat Jurnal Keperawatan Silampari TEMALI : Jurnal Pembangunan Sosial Dialektika: Jurnal Ekonomi dan Ilmu Sosial Diseminasi : Jurnal Pengabdian kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Rekayasa Elektro Sriwijaya BAKTI : Jurnal Pengabdian Kepada Masyarakat
Bambang Suprihatin
Jurusan Matematika, Fakultas MIPA, Unsri
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Faktor Determinan dan Motivasi Peserta KB Melalui Program KB Keliling untuk Memilih Metode Kontrasepsi Jangka Panjang Ovi Hendrika; Maryadi Maryadi; Bambang Suprihatin
Jurnal Keperawatan Silampari Vol 6 No 1 (2022): Jurnal Keperawatan Silampari
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (218.6 KB) | DOI: 10.31539/jks.v6i1.4420

Abstract

This study aims to determine what factors influence the selection of MKJP (Long Term Contraception Method) through the mobile family planning program in Prabumulih City. This research method is a quantitative analysis using secondary data from the 2021 Family Data Collection (PK21). The results showed that the Nagelkerke R Square value was 0.117 and Cox & Snell R Square 0.010, indicating that the independent variable's ability to explain the dependent variable is 0.010 or 1 percent. Ninety-one percent of other factors outside the model define the dependent variable. In conclusion, the element that has a significant influence on the selection of long-term contraceptive methods is the factor of access to family planning services. Keywords: Determinant Factors, Mobile Family Planning, Motivation
Algoritma Extreme Gradient Boosting (XGBoost) dan Adaptive Boosting (AdaBoost) Untuk Klasifikasi Penyakit Tiroid Anita Desiani; Siti Nurhaliza; Tri Febriani Putri; Bambang Suprihatin
Jurnal Rekayasa Elektro Sriwijaya Vol. 6 No. 2 (2025): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jres.v6i2.145

Abstract

Thyroid disease is a disease of the thyroid gland that can interfere with daily activities. Early detection of thyroid disease can have an important impact in optimizing the development of early detection systems that are more effective and accurate in detecting the disease. Data mining approaches can be used to solve this problem by utilizing various available algorithms, such as Adaptive Boosting and Extreme Gradient Boosting. This research aims to improve the development of early thyroid disease prediction by comparing the two algorithms by utilizing the percentage split method. This research provides results if the Adaptive Boosting algorithm provides an accuracy value of 97%. In class 0, the precision and recall values are the same at 98%, while in class 1 it is 80% and 90%. Meanwhile, testing using the Extreme Gradient Boosting algorithm gives an accuracy value of 98%. In class 0, the same precision and recall values are 99%, while for class 1 it is 86% and 90%. Based on the comparison by considering the accuracy, precision, and recall values, as well as the performance of the two algorithms, it is concluded that the implementation of the Extreme Gradient Boosting algorithm has the best performance for thyroid disease detection.
Co-Authors Agustina Bidarti Alga Mahida Ali Amran Anita Desiani Carolina Rahman Dian Cahyawati Dina Suzzete Sitorus Eka Susanti Endang Sri Kresnawati Erwin, Erwin Faishal Fitra Ramadhan Herlina Hanum Hermansyah Hermansyah Hermansyah Listina Tri Dharmayanti Maryadi Maryadi Mega Tiara Putri Muhammad Azwar Annas NEYSA ADIRATNA AGMI MASYITHAH Nidatri Mutiah Zatzah Novi Rustiana Dewi Ovi Hendrika Rifki Kurniawan SINTA ELPATRIKA ABELIA Siti Nurhaliza Sugandi Yahdin Tri Febriani Putri Yulia Resti Yusuf Hartono Zunaidah Zunaidah