Article Per Year (5 Year)
p-Index From 2020 - 2025
2.459
P-Index
This Author published in this journals
All Journal Humaniora Marwah: Jurnal Perempuan, Agama dan Jender Preventif: Jurnal Kesehatan Masyarakat GHIDZA: Jurnal Gizi dan Kesehatan Jurnal Sisfokom (Sistem Informasi dan Komputer) Jambura Journal of Health Sciences and Research Indian Journal of Forensic Medicine & Toxicology Journal of the Community Development in Asia Jurnal Pengabdian dan Pengembangan Masyarakat Indonesia Tadulako Science and Technology Journal
Rosmala Nur
Program Studi Kesehatan Masyarakat, Fakultas Kesehatan Masyarakat, Universitas Tadulako
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Public Health Social Sciences

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

 1 
Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia Pratama, Moh. Asry Eka; Hendra, Syaiful; Ngemba, Hajra Rasmita; Nur, Rosmala; Azhar, Ryfial; Laila, Rahmah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2097

Abstract

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.
Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia Pratama, Moh. Asry Eka; Hendra, Syaiful; Ngemba, Hajra Rasmita; Nur, Rosmala; Azhar, Ryfial; Laila, Rahmah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2097

Abstract

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.
Co-Authors Adhar Arifuddin Andari, Ega Ayu Annisa Putri Febrina Bau Ratu Christian Lopo Dhevy Puswiartika Dodu , Albertch Yordanus Erwin Ega Ayu Andari Elly B. Yane Fahmi, Nur Hajra Rasmita Ngemba Hajra Rasmita Ngemba Imelda Imelda Indah P Kiay Demak Jamaluddin Jamaluddin Jamaluddin Jaya I. Madina Lintine, Gabriella Bamba Ratih Madina, Jaya I. Marselina Marselina Mayin Nu Muhammad Rusydi Mujahida, Nurul Nur Fahmi Nur Saida Nurdin Rahman Nurul Mujahida P. Dominggo, Nenita Penidas Fodinggo Tanaem Pitriani Pitriani Pratama, Moh. Asry Eka Putra Ramadhan, Adjie Rahma Dwi Larasati Rahmah Laila Rantung, Vivi Redita Novila, Redita Rudy Reynold Richardo Ryfial Azhar, Ryfial Saida, Nur Syaiful Hendra Syam, Sadly