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All Journal Brilliance: Research of Artificial Intelligence
Dewi Maharani
University Royal Asahan Sumatera Utara, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Web-Based Customer Relationship Management (CRM) System At Enc Audio to Improve Customer Satisfaction Khairun Nisa; Dewi Maharani; Santoso Santoso
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8316

Abstract

This study aims to design and develop a web-based Customer Relationship Management (CRM) information system at ENC AUDIO, a business engaged in musical instrument and sound system rental services, in order to improve customer satisfaction and service quality. The main problem faced is that customer data, transaction records, and rental schedules are still managed manually, which often leads to data recording errors, scheduling conflicts, difficulties in tracking customer history, and inefficiencies in service processes. The research method used is the Operational CRM approach, with data collection techniques including observation, interviews, and documentation. The system is developed using the CodeIgniter framework and MySQL database, while system testing is conducted using the black box testing method to ensure that all system functions operate properly according to user requirements. The results of this study indicate that the developed CRM system is able to automate customer service processes, manage booking schedules in real time, and store customer transaction history in a structured and integrated manner. In addition, features such as online booking, automated scheduling, and live chat significantly enhance communication between customers and administrators. Therefore, the implementation of this system improves operational efficiency, reduces errors, and strengthens customer relationships, ultimately contributing to increased customer satisfaction and business sustainability.
Implementation Of A Perfume Recommendation System Using Ahp And Topsis At Ivan Parfume Winda Nurdiana Putri; Dewi Maharani; Abdul Karim Syahputra
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8354

Abstract

The rapid growth of the refill perfume industry requires business owners to enhance service quality, particularly in assisting customers in selecting suitable fragrance products. At Ivan Parfume, the large variety of available scents often causes confusion among customers, while the current recommendation process remains manual and subjective. This study aims to develop a web-based Decision Support System (DSS) using a combination of the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to provide objective and accurate perfume recommendations The AHP method is employed to determine the priority weights of decision criteria, including price, longevity, packaging design, volume, and scent, while the TOPSIS method is used to rank perfume alternatives based on their closeness to the ideal solution. The system processes ten perfume alternatives and generates a ranked list of recommendations based on multi-criteria evaluation. The results indicate that the system is capable of producing structured and consistent recommendations aligned with user preferences. Furthermore, the system demonstrates good performance in handling multiple criteria simultaneously and provides transparent calculation results that can be easily interpreted by users. The implementation of the AHP-TOPSIS model improves decision-making efficiency by reducing subjectivity and processing time compared to conventional methods. This study demonstrates that the proposed system can effectively support retail businesses in delivering data-driven recommendations and enhancing customer satisfaction.
Co-Authors Abdul Karim Syahputra Khairun Nisa Santoso Santoso Winda Nurdiana Putri