Accepted: 10-06-2022 | Received in revised: 09-07-2022 | Published: 15-07-2022 491 Accredited Ranking SINTA 2 Decree of the Director General of Higher Education, Research, and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026 Published online on: http://jurnal.iaii.or.id JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol. 6 No. 3 (2022) 491 - 498 ISSN Media Electronic: 2580-0760 Factors Affecting the PeduliLindungi User Experience Based on UX Honeycomb Adhim Jati Kusuma 1 , Pantjawati Sudarmaningtyas 2 , Antok Supriyanto 3 1,2 Information System Department, Faculty of Technology and Informatics, Universitas Dinamika 3 Management Department, Faculty of Economic and Business, Universitas Dinamika 1 18410100162@dinamika.ac.id, 2 pantja@dinamika.ac.id * , 3 antok@dinamika.ac.id Abstract With background of Covid-19 pandemic, Indonesian state trying to make various efforts, so people comply health protocols. One of them is through PeduliLindungi application. PeduliLindungi has 3 main functions, namely tracing, tracking, warning and fencing. However, PeduliLindungi is deemed unable to meet user needs in terms of appearance and experience provided. This study aims to find factors that affect user experience in PeduliLindungi app based on UX Honeycomb. UX Honeycomb is a tool that can explain various aspects of user experience design in 7 indicators and grouped into 3 variables. The 3 variables are Think (useful, valuable, credible), Feel (desirable, credible), and Use (findable, accessible, usable). This study uses primary data by distributing online questionnaires to 404 respondents contains 15 statements that represent all UX Honeycomb variables, with 5 scales of answer choices namely strongly disagree/disagree/neutral/agree/strongly agree. From the calculation results, it is found that all variables and indicators significantly affect user experience with greatest level of influence being on the Think variable at 0,418, the second Use at 0,219, and the last is Feel at 0,151. Further research is expected can measure the level of influence on user experience by making direct comparisons with similar health apps. Keywords: PedulliLindungi, User Experience, UX Honeycomb 1. Introduction The condition of the Covid-19 pandemic is felt by all countries and all are competing to eradicate cases, including Indonesia. All activity in the effort to eradicate Covid-19 cases, especially related to vaccination and vaccine certificates, the Minister of Health of the Republic of Indonesia in collaboration with the Minister of Communication and Information of the Republic of Indonesia issued a Joint Decree Number HK.03.01/MENKES/53/2021 Number 5 of 2021 concerning the Implementation of the Information System for One Data for Corona Virus Disease Vaccination 2019 (Covid-19) which includes the information system for One Data Vaccination for Corona Virus Disease 2019, data integration, operation, application integration, up to the implementation of information systems [1]. One application that is integrated with the One Data Covid-19 Vaccination is the PeduliLindungi application which is used in the implementation of health monitoring and control by the Government in dealing with the spread of Covid-19 [1]. The PeduliLindungi application is use for tracing, tracking, warning and fencing [2]. PeduliLindungi has been downloaded by 10,000,000+ users. In addition, until September 29, 2021, 470,521 people reviewed it on Google Play Store, with the accumulated results of the reviews an average of 3.8 [3]. The score of the PeduliLindungi review indicates that the application is necessary to be refined. Several previous studies have been conducted on the PeduliLindungi application. The first research conduct by Nurhidayanti, Sugiyah, and Kartika Yuliantari [4]. Their research focuses on protecting users' personal data to obtain legal certainty over the PeduliLindungi application and the research is based on a literature study to find secondary data using the primary, secondary, and tertiary legal basis for the PeduliLindungi application. The second is a study by Ali Mustopa, Hermanto, Anna, Eri Bayu Pratama, Ade Hendini, and Deni Risdiansyah entitled Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization [5]. Their study discusses the analysis of user reviews (comments) on the Google Play Store on the PeduliLindungi application based on the accuracy