@article{wijaya_paramita_uluwiyah_rheza_zahara_puspita_2022, title={Estimating city-level poverty rate based on e-commerce data with machine learning}, volume={22}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85086656634&partnerID=MN8TOARS}, DOI={10.1007/s10660-020-09424-1}, number={1}, journal={Electronic Commerce Research}, author={Wijaya, D.R. and Paramita, N.L.P.S.P. and Uluwiyah, A. and Rheza, M. and Zahara, A. and Puspita, D.R.}, year={2022}, pages={195–221} }
 @article{agor_paramita_ozaltn_2021, title={Prediction of Sepsis Related Mortality: An Optimization Approach}, volume={25}, ISSN={["2168-2208"]}, url={https://doi.org/10.1109/JBHI.2021.3096470}, DOI={10.1109/JBHI.2021.3096470}, abstractNote={Sepsis is a condition that progresses quickly and is a major cause of mortality in hospitalized patients. Data-driven diagnostic and therapeutic interventions are essential to ensure early diagnosis and appropriate care. The Sequential Organ Failure Assessment (SOFA) score is widely utilized in clinical practice to assess septic patients for organ dysfunction. The SOFA score uses points between 0 and 4 to quantify the level of dysfunction in six organ systems. These points are determined based on expert opinion and not informed by data, thus their usefulness can vary among different medical institutions depending on the targeted use. In this study, we propose multiple strategies to adjust the SOFA score using mixed-integer programming to improve the in-hospital mortality prediction of septic patients based on Electronic Health Records (EHRs). We use the same variables and threshold values of the original SOFA score in each strategy. Thus, the proposed approach takes advantage of optimization and data analysis while taking into account the medical expertise. Our results demonstrate a statistically significant improvement ( ) in the prediction of in-hospital mortality among patients susceptible to sepsis when implementing our proposed strategies. Area under the receiver operator curve (AUC) and accuracy values of 0.8928 and 0.8904 are achieved by optimizing the point values of the SOFA score.}, number={11}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Agor, Joseph K. and Paramita, Ni Luh Putu S. P. and Ozaltn, Osman Y.}, year={2021}, month={Nov}, pages={4207–4216} }
 @inproceedings{suciptawati_paramita_aristayasa_2019, title={Customer satisfaction analysis based on service quality: Case of local credit provider in Bali}, volume={1321}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85075333457&partnerID=MN8TOARS}, DOI={10.1088/1742-6596/1321/2/022055}, abstractNote={Abstract Lembaga Perkreditan Desa (LPD) or village credit institution is a financial institution that is only exists in Bali. LPDs had been developed to promote the local people economies by providing micro credits as well as to deposit their money. This paper is aimed to study the satisfaction of customers toward LPD’s services. A hundred and fifty customers of LPD Sidakarya that is located at Denpasar were purposive sampling selected as the respondents of the study on July 2017. The data were collected by applying self-organized questionnairé which its items were developed using 7 scale on Likerts’ measurement. The respondents were asked to value their expectation as well as their perception on five dimension of service quality i.e. tangible factor, reliability, responsiveness, assurance, and empathy. The study reveals tangible factor was perceived positively whilest the other determinants need to be improved.}, number={2}, booktitle={Journal of Physics: Conference Series}, author={Suciptawati, N.L.P. and Paramita, N.L.P.S.P. and Aristayasa, I.P.}, year={2019} }