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All Journal Jurnal Pendidikan dan Konseling Jurnal Sistem Informasi dan Sains Teknologi SmartComp
Daniel Iskandar
Universitas Budi Luhur
Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Improving Healthcare Services Using Clinical Decision Support Systems: A Systematic Review Daniel Iskandar; Wahyu Adi Setyo Wibowo; Gandung Triyono
Jurnal Pendidikan dan Konseling (JPDK) Vol. 4 No. 6 (2022): Jurnal Pendidikan dan Konseling
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpdk.v4i6.9516

Abstract

Patient safety and recovery is the main objective in healthcare. The knowledge and experiences of medical experts and patient big data can be transformed into a Clinical Decision Support System that will deliver benefits to healthcare, especially for the patients. This research is a literacy review that aims to create a framework for reviewing articles on DSS which highly related to patients, which were published by Elsevier from 2017 to October 2022. There were 29 articles found from search results on Elsevier, of which 13 were research articles. After conducting analysis and filtering, we found 6 articles that met the criteria for research objects that examined several DSS topics, namely Pre-hospital Decision Support Tools, Patient Referral, Antimicrobials, Patient Nutrition, and Blood Transfusion. The results of this study showed that DSS implementations in healthcare give a huge impact on medical experts, namely by providing important information for decision-making when treating patients. This enables the hospitals and medical team to provide appropriate therapy from the beginning of the treatment and it increased the success of patient therapy possibility. Another benefit is the medical cost saving since the therapies are only given when it is needed by the patient.
DATA MINING DALAM PREDIKSI JUMLAH PASIEN DENGAN REGRESI LINEAR DAN EXPONENTIAL SMOOTHING Daniel Iskandar
Jurnal Sistem Informasi dan Sains Teknologi Vol 5, No 1 (2023): Jurnal SIstem Informasi dan Sains Teknologi
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/sistek.v5i1.1421

Abstract

The number of patient visits is one of the main factors that determine the strategies and policies decided by hospital management because the number of patients affects all major aspects of hospital services, such as drugs and medical supplies stocks, the number of doctors and nurses, the rooms capacity, and many other services.  Hospital ‘XYZ’ needs a method to predict the number of patients that will visit the hospital in order to provide the best service efficiently.  Previous studies have shown that the Linear Regression and Exponential Smoothing methods produced good predictive values, but no research has been found that comparing the two methods. To fill this gap, the purpose of this study is to compare the prediction results of the number of patients between Linear Regression method and Exponential Smoothing method.  Number of daily patients data in 2021 was collected using Data Mining.  The prediction comparison was carried out two times, first is the prediction of the daily number of patients and the second is the prediction of the number of weekly patients. The first comparison showed that Linear Regression predicted better by having 23.90% MAPE, while Exponential Smoothing had 27.62%. In the second prediction, Linear Regression again produced a better MAPE value with 4.66%, while Exponential Smoothing was 6.82%.
Systematic Literature Review: Implementasi Dan Manfaat Big Data Daniel Iskandar; Deni Mahdiana
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 3 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i3.4024

Abstract

Teknologi dan sistem informasi telah mengubah hampir semua sektor bisnis dan sosial.  Implementasi sistem informasi dalam skala luas menghasilkan jumlah data yang sangat besar, dalam bentuk yang bervariasi, dan tersebar.  Istilah ‘data adalah minyak’ menggambarkan betapa bernilainya data bila kita dapat mengelola dan memanfaatkannya dengan tepat.  Penelitian ini adalah sebuah tinjauan literasi yang bertujuan untuk mengetahui topik yang paling banyak dibahas tentang implementasi dan manfaat Big Data yang dipublikasikan Elsevier mulai tahun 2021 hingga Maret 2022, serta untuk mengetahui permasalahan, metode dan hasil atau kesimpulan yang dipaparkan oleh para peneliti.  Berdasarkan hasil penggunaan fitur pencarian di situs www.sciencedirect.com, kami menemukan 309 literasi mengenai Big Data, dan setelah dilakukan analisa dan penyaringan kami mendapatkan 10 jurnal internasional yang memenuhi kriteria objek penelitian ini.  Terdapat beragam topik yang diangkat oleh para peneliti sebagai permasalahan dalam jurnal-jurnal tersebut, di antaranya mengenai masalah perkotaan, kesehatan, Covid-19, ilmu pengetahuan, Industri 4.0, Internet, dan keuangan.  Hasil penelitian kami menunjukkan implementasi Big Data dalam dunia kesehatan paling banyak dijadikan objek penelitian, sedangkan implementasi dalam masalah perkotaan berada pada urutan kedua.  Implementasi dan pengelolaan Big Data yang baik akan memberikan kita akses kepada informasi yang sangat bermanfaat dan bisa memberikan dampak yang signifikan di semua sektor, hal ini sejalan dengan terus meningkatnya jumlah penelitian mengenai Big Data dalam sepuluh tahun terakhir.Kata kunci: Big data, Elsevier, sciencedirect, systematic literature review, studi literatur
Co-Authors Deni Mahdiana Triyono, Gandung Wahyu Adi Setyo Wibowo