Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal
Nisa Utami, Nisa
Jurusan Teknik Sipil FT. UNDIP Jl. Prof. H. Soedarto SH., Tembalang, Semarang 50275
Computer Science & IT Education Nursing Public Health Veterinary

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Kinerja Algoritma Decision Tree Dan Random Forest Dalam Klasifikasi Penyakit Kardiovaskular Utami, Nisa; Baihaqi, Kiki Ahmad; Awal, Elsa Elvira; Waiddin, Deden
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5722

Abstract

Cardiovascular disease is a disease with a fairly high number of deaths. In Indonesia, the term cardiovascular is more popular with heart disease, which is a condition that can cause narrowing and blockage of blood vessels. Cardiovascular disease has two risks, the first is a risk that can be changed, such as stress, increased blood pressure, unhealthy diet, increased glucose levels, abnormal cholesterol and lack of physical activity. Meanwhile, risks that cannot be changed include family disease, gender, age and obesity. In this research, we can examine and analyze the performance of the two best classification algorithms, namely the decision tree algorithm and the random forest algorithm, in classifying cardiovascular disease based on the cause of the disease. The aspects studied are the performance results of each algorithm and evaluated using Area Under the Curve (AUC), classification report, k-Fold Cross Validation and Confusion matrix. The dataset used was taken from the Kaggle website with the data used being Cardiovascular Disease data which consists of 68.205 rows (patient data) and 17 attributes. . Based on the evaluation results using the Area Under The Curve (AUC) value, the highest result was obtained at 0.761 by the Random Forest algorithm with balanced data conditions with Random oversampling. Meanwhile, the lowest AUC value was obtained by the Decision Tree algorithm with unbalanced data of 0.592. Based on these results, it is known that the Random Forest algorithm with a balanced data scheme is a better algorithm, with a balanced data scenario using SMOTE and Random Oversampling techniques.
Comparison of Machine Learning Models for Heart Disease Classification with Web-Based Implementation Ramadhan, Angga Ramda; Saefulloh, Nandang; Utami, Nisa; Diana, Muji; Utomo, Abiyyu Aji Prasetyo; Wicaksana, Yusuf Eka
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8744

Abstract

Heart disease has become one of the most concerning diseases in Indonesia according to research published in 2018 by the Health Ministry of Indonesia. Based on said research, 15 out of 1000 Indonesians suffer from heart disease. Furthermore, according to data published by the Health Ministry of Indonesia, 3 million premature deaths (under 60 years old) occurred in 2013 due to heart disease. Therefore, this research aims to develop a web-based system designed to aid health workers in screening for heart diseases and producing early diagnosis. In developing this system, 5 models were evaluated based on performance and the model with the best metrics was selected to be used in the final system. These models were: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbours. SMOTE and ADASYN was also used to deal with imbalanced data, and the resulting balanced data was used as additional training scenarios in order to compare the result with algorithms trained using imbalanced data. Cross validation, accuracy, precision, recall, f1-score, and ROC with AUC were set as evaluation metrics. Results show that Random Forest trained with data balanced using ADASYN achieved the highest AUC score of 0.920. Meanwhile, Logistic Regression scored lowest with an AUC score of 0.500. These results indicate that Random Forest is the most suitable for this system Therefore, Random Forest was selected as the algorithm to be used in the final system. Furthermore, this system has been tested successfully using the black-box method and is ready to be implemented.
Impact of Nursing Interventions on Physical Recovery of Disaster Victims: A Scoping Review Wiguna, Yutika; Purwandi, Purwandi; Hartati, Tita; Satrio, Rizki Juniar Eko; Utami, Nisa; Sijabat, Pasuria Br; Indriati, Ririn; Trisyani, Yanny
Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal Vol 15 No 3 (2025): Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal: Juli 2025
Publisher : Sekolah Tinggi Ilmu Kesehatan Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32583/pskm.v15i3.3768

Abstract

Disasters, whether natural or human-induced, frequently lead to significant physical injuries and complex health conditions among affected populations. In such contexts, nurses serve as frontline responders who deliver critical care aimed at promoting physical recovery and preventing further complications. This scoping review explores and synthesizes existing literature on the impact of nursing interventions on the physical recovery of disaster victims. The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A systematic search was carried out in three major electronic databases PubMed, Scopus, and Web of Science targeting articles published between 2015 and 2025. The following Boolean search string was used: ((disasters) OR ("disaster victims") OR ("disaster survivors")) AND ((nurses) OR ("nursing intervention") OR ("nursing care")) AND (("physical recovery") OR ("wound healing") OR ("pain management") OR ("mobility improvement") OR ("quality of life")). Following the application of predefined inclusion and exclusion criteria, three studies were selected for the final synthesis. The findings reveal that nursing interventions such as wound care, pain management, infection control, physical rehabilitation, and health education play a vital role in enhancing the physical recovery of individuals affected by disasters. The success of these interventions is often shaped by factors including timeliness, cultural appropriateness, and interdisciplinary collaboration. In conclusion, this review underscores the essential contribution of nurses in disaster recovery efforts and highlights the need for continued development of evidence-based, culturally sensitive, and standardized nursing practices to optimize health outcomes in post-disaster settings.
Co-Authors Awal, Elsa Elvira Baihaqi, Kiki Ahmad Diana, Muji Hartati, Tita Indriati, Ririn Purwandi, Purwandi RAMADHAN, ANGGA RAMDA Saefulloh, Nandang Satrio, Rizki Juniar Eko Sijabat, Pasuria Br Utomo, Abiyyu Aji Prasetyo Waiddin, Deden Wicaksana, Yusuf Eka Wiguna, Yutika Yanny Trisyani