Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNOLOGI DAN OPEN SOURCE Journal of Computer System and Informatics (JoSYC)
Rafiska, Rian
IAIN Kerinci, Kerinci
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Analisis Rekam Medis untuk Menentukan Pola Kelompok Penyakit Menggunakan Algoritma C4.5 Rian Rafiska; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 1 (2018): April 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.523 KB) | DOI: 10.29207/resti.v2i1.275

Abstract

The Medical Record contains records and documents of patient identity, examination results, treatment, actions and services provided to the patient. Medical records are very important for patient care because with complete data can provide information in determining diagnostic and clinical decisions. The completeness of the medical record determines the quality of the services provided. Regarding the pattern of the tendency of disease suffered by a group of people still not excavated to be used as a reference when doing panyuluhan or prevention of disease. Finding a common pattern of disease groups in the community based on the International Classification of Diseases (ICD) -X. In this study used the classification method with algorithm C4.5 with the amount of data as much as 709 sourced from the Medical Record of General Hospital General Hospital (RSUD) Major General H.A Thalib Kerinci. Determination of the next analysis is to apply the grouping into several attributes, namely group of regions, age groups, disease groups and groups of sex. Further data is processed and done by using Rapid Miner software. The results of the calculation is a pattern that can be used to analyze patterns of disease tendency experienced by the community.
Prediksi Jumlah Kunjungan Pasien Menggunakan Simulasi Monte Carlo Rian Rafiska
Journal of Computer System and Informatics (JoSYC) Vol 3 No 3 (2022): May 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i3.1690

Abstract

Klinik adalah jenis fasilitas pelayanan kesehatan yang menyediakan pelayanan rawat jalan, rawat inap, dan gawat darurat kepada masyarakat. Klinik Utama Khusus Mata (KUKM)-Kita merupakan fasilitas kesehatan milik swasta yang memberikan pelayanan kesehatan khususnya dengan keluhan mata bagi masyarakat Kota Sungai Penuh dan Kabupaten Kerinci. Karena jumlah pasien yang datang setiap harinya tidak bisa diketahui, hal ini mengakibatkan pihak manajemen KUKM-Kita tidak bisa menyiapkan sumber daya yang optimal dalam memberikan pelayanan kepada masyarakat. Untuk mengatasi masalah ini, maka perlu dilakukan simulasi untuk memprediksi jumlah pasien yang akan berkunjung di masa yang akan datang. Metode yang digunakan dalam penelitian ini ialah metode simulasi Monte Carlo. Penelitian ini bertujuan untuk memberikan informasi yang dibutuhkan pihak KUKM-Kita dalam memprediksi jumlah kunjungan pasien kedepannya. Dalam penelitian ini data yang diolah ialah data jumlah kunjungan pasien pada tahun 2019 sampai 2021 di KUKM-Kita. Hasil dari penelitian ini adalah prediksi jumlah kunjungan pasien yang setiap tahunnya mengalami peningkatan. Dengan hasil ini pihak KUKM-Kita bisa munggunakan informasi yang didapatkan untuk menjadi rujukan dalam membuat keputusan dan kebijakan untuk memperbaiki pelayanan kedepannya.
The Implementation of Data Mining in Measuring Student Satisfaction Levels at IAIN Kerinci Rian Rafiska
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i1.3565

Abstract

Measuring student satisfaction is crucial to consider, given the high level of competition in the education sector alongside the advancement of knowledge and technology. It is essential to ensure that the services expected by students align with what they actually receive. Measuring student satisfaction can significantly assist higher education institutions in improving the quality of services, which in turn can impact the increase in student enrollment. This study employs a quantitative method, utilizing one of the data mining techniques, namely classification with the C4.5 algorithm, to measure student satisfaction levels. The population in this study consists of active students at IAIN Kerinci, with a sample size of 100 respondents. These students act as subjects who provide assessments or opinions on variables characterized by Tangible, Reliability, Responsiveness, Assurance, and Empathy. The data is then processed using data mining classification techniques, and testing is performed with the aid of RapidMiner software. The calculation and testing results demonstrate that data mining successfully classifies the variables in measuring student satisfaction with excellent performance, producing 10 rules from the decision tree with an accuracy rate of 98.22%. These rules are expected to serve as a basis for decision-making to determine actions that need to be taken to enhance student satisfaction.
Implementation of Data Mining in Measuring Student Satisfaction at IAIN Kerinci Rian Rafiska
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.3997

Abstract

Measuring student satisfaction is crucial, especially considering the increasing competition in the field of education along with the advancement of knowledge and technology. It is essential to assess whether the services expected by students align with the services they actually receive. Evaluating student satisfaction can significantly help higher education institutions improve service quality, which in turn may lead to an increase in student enrollment.This study employs a quantitative method using one of the data mining techniques—classification—through the C4.5 algorithm to measure student satisfaction levels. The population of this research includes active students at IAIN Kerinci, with a sample size of 100 respondents. The students serve as the subjects providing evaluations or opinions on variables characterized by Tangibles, Reliability, Responsiveness, Assurance, and Empathy.The data is processed using data mining classification techniques, with testing conducted through RapidMiner software. The results of the analysis and testing indicate that data mining effectively classifies the variables in measuring student satisfaction, generating 10 decision tree rules with an accuracy rate of 98.22%. These resulting rules are expected to serve as a foundation for making informed decisions on actions needed to enhance student satisfaction.
Co-Authors Gunadi Widi Nurcahyo Sarjon Defit