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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi Informasi dan Ilmu Komputer IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURIKOM (Jurnal Riset Komputer) IJAIT (International Journal of Applied Information Technology) Journal of Applied Science, Engineering, Technology, and Education Prosiding Konferensi Nasional PKM-CSR Jurnal Indonesia Sosial Teknologi eProceedings of Engineering
Angelina Prima Kurniati
Universitas Telkom Bandung, Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences Other

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Journal : JURIKOM (Jurnal Riset Komputer)

 1 
Analysis of Expertise Group Using The Fuzzy K-NN Classification Algorithm (Case Study: School of Computing Telkom University) Jodi Kusuma; Angelina Prima Kurniati; Ichwanul Muslim Karo Karo
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4215

Abstract

The School of Computing at Telkom University has four Expertise Groups that defines the lectures taken by students. Deciding the Expertise Group, will be influential in deciding elective courses and raising the topic of the Final Project. There are many students who are still having difficulty in deciding the Expertise Group and finally only decide based on the most popular Expertise Group without seeing their potential and abilities. The impact of wrong decision of the Expertise Group are delays in graduation time. It will then affect accreditation of study program and university rank, especially in the timely graduation indicator. Therefore, it is necessary to have a system that can predict the decision of the Expertise Group for the School of Computing students based on their academic scores. In this study, prediction using the Fuzzy K-Nearest Neighbor classification algorithm was chosen because it can determine the class based on the nearest neighbor and consider ambiguous data because of the weighting value in each class. There are five tests carried out to get the best model, namely (1) examine the best split training and validation data, (2) examine the best K value, (3) compare Fuzzy K-Nearest Neighbor with Naïve Bayes and Decision Tree (C4.5) which is a commonly used classification algorithm, (4) examine the values of accuracy, precision, recall, f1-score, and (5) examine the values of accuracy using Cross-Validation method. The result is that the model made using Fuzzy K-Nearest Neighbor has an accuracy value of 72% in the case of imbalance data, 62% in the case of applying the undersampling technique, and 56% in the case of applying oversampling. Based on experiments with the other two algorithms, it was found that compared to the other two algorithms, the Fuzzy K-Nearest Neighbor has a higher accuracy value in the case of imbalance data and the case of applying to undersampling, but it has a lower accuracy in the case of applying oversampling, due to the lack of Fuzzy K-Nearest Neighbor in handling small minority data variations.
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 Aji Gunadi, Gagah Al Giffari, Muhammad Zacky Alfian Akbar Gozali Alneta, Isyraqi Putra Angdini Dea Nurillaily Angga Auliya Akbar Arfive Gandhi Arif Wahyudi, Aulia Rahman Dana Sulistiyo Kusumo Ersy Ervina Fadhilah Dwiyanti Basri Fauzi, Fahlar Reva Firdaus Hafidz Gabriella Ayu Pramesti Gandh, Arfive Gede Agung Ary Wisudiawan Guntur Prabawa Kusuma Harin Veradistya Maharani Harnanti, Assyifa Hayati, Annisa Fajri I Gede Dito Wisnu Murti I Putu Nana Sugianta Mandra Ichwanul Muslim Karo Karo Imelda Atastina Indra Lukmana Sardi Jodi Kusuma Kusumo, Dana Sulistyo Mira Kania Sabariah Moc. Arif Bijaksana Muhammad Agung Agung Muhammad Arkan, Zaidan Muhammad Wanda Wibisono Muhammad Zafif Muttaqy Mujantara, Muhammad Reynaldi Mutalib, Sharifah Sakinah Syed Abd. Muyasaroh, Arummi Nugroho, Raditya Pradhyatama Osman, Nurul Aida Owen Ashby Johnson Pristantya, Mayang Purwanto, Zadosaadi Brahmantio Rani Puspitasar Rendy Setiadi Mangunsong Rihan Arifin, Irfan Septia Dusitella Sembiring Setiabakti, Muhammad Tegar Zharfan Humam Supra Yogi Tirta Gumelar, Wahyu Aji Toto Suharto Warih Maharani Wijaya, Yaffazka Afazillah Wildan Khalidy Wisudawan, Gede Agung Ary Yanuar Firdaus A.W Yanuar Firdaus Arie Wibowo Yudhoyono, Adrian Senoaji Teguh Yusoff, Wan Nur Syahidah Wan Zulhelmy