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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining Building of Informatics, Technology and Science Journal of Applied Data Sciences DECODE: Jurnal Pendidikan Teknologi Informasi
Hikmawati, Nina Kurnia
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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User Satisfaction Analysis of Paylater Services Using K-Means Algorithm in Campus Anwar, Syahrul; Hikmawati, Nina Kurnia; Juliane, Christina
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2533

Abstract

In the 4.0-based digital era, the use of e-commerce is increasing. The convenience provided to e-commerce users is increasingly being considered by companies engaged in e-commerce. Paylater is a fairly new payment method among Indonesian e-commerce, so research is needed to improve the service and satisfaction of e-commerce users, especially those using the paylater payment method. The purpose of this study is to analyze user satisfaction with paylater services using the k-means algorithm on campuses in region 3 Cirebon. This research is also to find out the benefits of paylater used by students. This research is a type of quantitative research using the k-means algorithm to determine the classification of paylater user satisfaction in several e-commerce applications at several universities in region 3 Cirebon which is then clustered. The results of the study show that Cirebon students in the Campus 3 area are satisfied with services from companies or online shops that have paylater payment facilities
Exploring ADR Trends: A Data Mining Approach to Hotel Room Pricing, Cancellations, and EDA Hikmawati, Nina Kurnia; Ramdhani, Yudi; Wartika, Wartika
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.165

Abstract

This study investigates the intricacies of hotel reservation cancellations by analyzing a comprehensive dataset that includes information from both City Hotel and Resort Hotel. Through a thorough examination of various aspects, the research provides detailed insights into cancellation tendencies, daily rates, seasonal trends, and the influence of geographic factors and market segments on cancellation behavior. The overall cancellation and non-cancellation ratios indicate a notable non-cancellation rate of 62.86%, showcasing a high level of guest confidence in their reservations. Conversely, the 37.14% cancellation ratio raises concerns about potential negative repercussions. A comparative analysis between City Hotel and Resort Hotel reveals a significant difference in cancellation rates, emphasizing the need for tailored strategies at City Hotel to enhance booking stability. The study on Average Daily Rate (ADR) for both hotels bring attention to price differences and seasonal trends. Resort Hotel's higher ADR suggests potential advantages in location or amenities. Seasonal trends, particularly the highest ADR during the summer, provide valuable insights for resource planning. The variation in cancellation rates based on countries emphasizes the importance of focused strategies in regions with high cancellation rates, as seen with Portugal having the highest cancellation rate (77.70%). Analysis of hotel customer market segments identifies Online Travel Agencies (OTA) as the segment with the highest cancellation rate (46.97%). These findings present opportunities for tailored marketing and cancellation policies based on the characteristics of each segment. In conclusion, this research offers strategic insights for hotel managers to enhance booking stability, design competitive pricing policies, and understand the impact of geographic factors and market segments on cancellation behavior.
RETRACTED : Evaluasi Penerapan Cobit 5 Pada Layanan Pengadaan Secara Elektronik (LPSE) Hulukati, Stephan Adriansyah; Hikmawati, Nina Kurnia
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 2 No. 1 (2018)
Publisher : P3M Politeknik Negeri Banjarmasin

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Abstract

This article has been Retracted because the author has published in another journal at the same time.
Development of Learning Material using Gamification for Students with Autism Spectrum Disorder Hasugian, Leonardi Paris; Sidik, Rangga; Winanti, Marliana Budhiningtias; Hikmawati, Nina Kurnia
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 2: JULI 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i2.351

Abstract

The basic need for students with an autism spectrum disorder in lectures is repetition in their learning activities. The development of gamification for learning material can accommodate these needs. Information System Department, Universitas Komputer Indonesia, has developed multimedia-based learning material with the concept of gamification for them in two courses as prototypes. Data for the needs for developing learning material was carried out through forum group discussions with experts in the education field for students with special needs. When data is collected from stakeholders, then analyzed with the Strength, Weakness, Opportunity, and Threat model to get a description of the internal and external environment in their learning process. After that, the design, development, and testing (using the black box method and user acceptance testing) of the multimedia application are presented. The result is an application as a prototype which is packaged in a gamification concept to provide interactive audiovisual through the provision of theoretical and practical material video and evaluation by the completion of a mission in a game. The goal is to efficiently deliver material in the learning process for them. The impact of the prototype can be able to improve the quality of the learning experience of students with an autism spectrum disorder.
Sentiment Analysis of BCA Mobile App Reviews Using K-Nearest Neighbour and Support Vector Machine Algorithm Zandroto, Yosefin Yuniati; Vitianingsih, Anik Vega; Maukar, Anastasia Lidya; Hikmawati, Nina Kurnia; Hamidan, Rusdi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37773

Abstract

The rapid evolution of digital technology has significantly transformed the financial services landscape, especially in the realm of mobile banking. BCA Mobile stands among the most popular apps for digital banking in Indonesia. Despite its widespread adoption, user reviews reflect diverse viewpoints and sentiments about the app. The objective of this research is to examine the user sentiments regarding the BCA Mobile app, based on reviews sourced from the Google Play Store and App Store. Two classification models, namely Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN), are used in the analysis process. The collected review data undergoes several pre-processing stages and is labeled automatically using a Lexicon-Based technique. For feature weighting, the TF-IDF (Term Frequency-Inverse Document Frequency) approach is used.. Sentiment classification is then carried out using both K-NN and SVM, with performance evaluated through a matrix of confusion based on measurements like F1-score, recall, accuracy, and precision.  The findings show that the SVM algorithm outperforms K-NN in terms of performance, with an accuracy of 94%, while K-NN achieves an accuracy of 82%. This study offers valuable insights for BCA management in understanding user sentiment and enhancing service quality through the application of artificial intelligence
Co-Authors Anastasia Lidya Maukar Anik Vega Vitianingsih Christina Juliane, Christina Hamidan, Rusdi Hulukati, Stephan Adriansyah Leonardi Paris Hasugian Sidik, Rangga Syahrul Anwar, Syahrul Wartika, Wartika Winanti, Marliana Budhiningtias Yudi Ramdhani Zandroto, Yosefin Yuniati