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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) BERNAS: Jurnal Pengabdian Kepada Masyarakat Instal : Jurnal Komputer eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Sama Jiva Jnanam (International Journal of Social Studies)
Gede Agung Ary Wisudiawan
Universitas Telkom
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Journal : JURIKOM (Jurnal Riset Komputer)

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Process Mining for Disease Trajectory Analysis on the Indonesia Health Insurance Data Angelina Prima Kurniati; Guntur Prabawa Kusuma; Gede Agung Ary Wisudiawan
JURIKOM (Jurnal Riset Komputer) Vol 9, No 5 (2022): Oktober 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i5.4924

Abstract

Process mining has been implemented in many domains, including healthcare. In healthcare, process mining projects aimed to inform sequential patterns of processes based on the actual process executions as they are recorded in the event log. Event log as the main input of process mining tasks can be extracted from the automatically recorded data of patient treatments or diagnoses. By understanding common patterns of patient diagnoses, we can analyse disease trajectories of a cohort of patients. Disease trajectory analysis has been used to describe the course or progression of diseases, especially chronic diseases, as experienced over time. We applied process mining as the main methodology for disease trajectory analysis, following the process mining project methodology, to analyse patient records on the Indonesia Health Insurance  (BPJS Kesehatan) Data Samples. We extracted the data samples, transform them into an event log, discover the disease trajectories based on process discovery algorithm, analyse it to inform their conformance to the event log. Contributions of our research are to promote process mining for disease trajectory analysis and to open wider opportunities to analyse Indonesia Health Insurance data representing Indonesia health conditions. As a case study, we explored disease trajectory of cancer patients
Process Mining using Inductive Miner Algorithm to Determine the actual Business Process Model Muhammad Wanda Wibisono; Angelina Prima Kurniati; Gede Agung Ary Wisudiawan
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4769

Abstract

At the beginning of 2019, the COVID-19 pandemic entered the country of Indonesia resulting in all learning activities being carried out online in all cities of Indonesia. Likewise, Telkom University concentrates all teaching and learning activities online using the CeLOE Learning Management System. Learning Management System is a system that helps lecturers in managing teaching and learning activities independently in educational institutions. CeLOE is a learning management system of Telkom University developed based on Moodle. In this study, we analyse the CeLOE event log using the process mining method. The goal is to find out the learning patterns of students using CeLOE during the COVID-19 pandemic. This research case study focuses on the activities of students of the Telkom University S1 Informatics study program for the first semester of 2020/2021 in using CeLOE LMS. The analysis of this study conducted a comparison of the performance of three variants of the inductive miner (IM) algorithm through conformance checking values. The results of the analysis obtained are value of conformance checking from the three variants of the inductive miner (IM) algorithm have an average fitness value of up to 1 prove that the inductive miner (IM) algorithm can make a model based on the event log well. Besides that, it has a fairly high precision value with a value range of 0.750-0.850 shows that the inductive miner (IM) makes a process model with relatively many variations of activities outside the event log and the IM process model is "overfit-ting" for all variants of the IM algorithm. Inductive miner (IM) is the best inductive miner (IM) algorithm variant with a fitness value of 1.0, precision value of 0.750, and the generalization value of this algorithm is relatively high (0.984). It is hoped that this research can contribute to the addition of new perspectives related to the implementation of process mining using inductive miner (IM) algorithm in the field of education
Co-Authors Abdurrahman Al Afifi Abid Fauzanu Adi Nuratmojo Ahmad Muflih Nawir Al Ghozali, Yazid Angelia Brigitta Maharani M.P Angelina Prima Kurniati Anisa Herdiani Aristyo Hadikusuma Christian Wahyu Pambayun Dewa Ayu Putu Rahyuni Dinda Gazella Dyvanno Pramana Caesario Eko Darwiyanto Erdiansyah, Muhammad Farras Fitri Handayani Gana Permana Ariyanzah Giffarinda, Raihan Ekaputra Guntur Prabawa Kusuma Hendra Lesmana Putra Hendrianto Suryo Ardi Nugroho Herdiani , Anisa I Kadek Andrean Pramana Putra I Nyoman Hendra Laksmana Ilham Kamil Noviandi Indra Lukmana Sardi Iskandar, Fahry Fauzan Joseph, Aaron Josua Romario Simanjuntak Kemas Rahmat Saleh Wiharja M. Mujahid Biagi Usama Mahendra Dwifebri Purbolaksono Mahesa, Indra Mira Kania Sabariah Mira Sabariah Moch. Irfandi Susanto Mohammad Fariz Ikhsan Monterico Adrian Muhammad Afriyano Muhammad Bambang Hidayanto Muhammad Wanda Wibisono Muthia Bianda Muthia Khairunissa Nasywa , Aini Natassya Amalia Anza Ni Putu Nurwita Pratami Wijaya Niam, Wildan Syukri Nungki Selviandro Nyoman Bogi Aditya Karna Pramoedya Syachrizalhaq Lyanda Pramoedya Syachrizalhaq Lyanda Raihan Sulthon Yaafi Ricky Darius Putra Setiabakti, Muhammad Tegar Zharfan Humam Seto Adi Prakasa Shinta Yulia Puspitasari Situmeang, Reinhard Efraim Siva Syarafina Tasyrika Nurul Hajar Tetuko Muhammad Hanurogo Tiara Lasya Azzara Yudi Priyadi Zhafran Hafizh Izdihar Riyadi Zulfikar Al Azhari