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All Journal Bulletin of Computer Science Research International Journal of Scientific and Professional Router : Jurnal Teknik Informatika dan Terapan Router : Jurnal Teknik Informatika dan Terapan
Ainna Khansa
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Computer Science & IT Health Professions Medicine & Pharmacology Nursing Public Health Other

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Pengembangan Customer Experience Berbasis Artificial Intelligence pada Startup Marketplace Shopee Ainna Khansa; Tata Sutabri
Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 4 (2024): Desember: Router: Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v2i4.270

Abstract

The function of artificial intelligence (AI) in creating a positive consumer experience on the Shopee e-commerce platform is covered in this study. Nowadays, artificial intelligence (AI) plays a significant role in the e-commerce sector, allowing businesses to offer more individualized, quick, and safe services. This study examines how Shopee customer satisfaction and loyalty are impacted by the use of AI technologies, including chatbots, tailored product suggestions, fraud detection, and inventory management. This study found that AI plays a significant role in making shopping more responsive and convenient by analyzing data on the use of chatbot services, the efficacy of personalized recommendations, the accuracy of predicting customer needs, transaction security, and the efficiency of logistics management. According to the study's findings, implementing AI technology reduced service response times by up to 20% and increased customer satisfaction by 15%. In addition to enhancing service quality, the use of AI increases Shopee customer loyalty. These results demonstrate how AI in e-commerce holds enormous promise for fostering startup expansion and enhancing client connections.
Perancangan Sistem Pelaporan Harian Menggunakan Metode SDLC Pada Stasiun Pengumpul Minyak dan Stasiun Kompresor Gas Ainna Khansa; Billan, Angel Caroline; Tata Sutabri
Bulletin of Computer Science Research Vol. 5 No. 1 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i1.442

Abstract

Current information delivery can be delivered through a system adopted by the company to assist in the company's performance process. One of the systems needed by the company is a reporting system that is used as an effective channel for delivering company data information results. The problem that occurs is that many company employee performance processes are considered inefficient in carrying out the reporting process. Company reporting using Hand Talky (HT) and Radio Link, is considered vulnerable to inaccuracy between data and communication barriers. To adopt a system technology in a large-scale company, of course the system must be easy for users to use and the process is fast. The solution produced for this problem is to design a daily reporting system using the SDLC (System Development Life Cycle) method. This design aims to apply the SDLC (System Development Life Cycle) method to the design of a system that can provide information remotely, provide real-time data updates, and present data in a more structured format for data management. This system is designed to automate the data backup process to reduce manual intervention in company data security. The proposed system is expected to increase the efficiency of operational activities in achieving company targets and improve overall performance in the company's data reporting process. The use of the SDLC method approach by researchers in designing a reporting system has been proven to accelerate system design. The results of the study showed that the design of this system was able to reduce reporting time by 50%, increase data accuracy by 40%, and provide significant user satisfaction.
Implementation of the Backtracking Algorithm for Optimizing Work Shift Scheduling Ainna Khansa; Sutabri, Tata
International Journal Scientific and Professional Vol. 4 No. 2 (2025): March-May 2025
Publisher : Yayasan Rumah Ilmu Professor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56988/chiprof.v4i2.82

Abstract

This research aims to implement the backtracking algorithm for optimizing shift scheduling at PLTU SSP. The study is motivated by the complexity of manual shift scheduling, which is prone to human error and struggles to accommodate various constraints such as employee availability, preferences, and operational needs. The backtracking algorithm was selected due to its ability to search systematically for optimal solutions that satisfy all constraints, based on Depth First Search (DFS). The research methodology includes requirements analysis, system design, algorithm implementation, testing, and results evaluation. The application of the backtracking algorithm produced schedules that accurately meet constraints and consider employee preferences. The results indicate that the backtracking algorithm can generate effective and efficient schedules. The implementation of the backtracking algorithm is expected to improve the quality of shift work management, positively impacting productivity, employee welfare, and the smooth operation of PLTU SSP.
Pengembangan Customer Experience Berbasis Artificial Intelligence pada Startup Marketplace Shopee Ainna Khansa; Tata Sutabri
Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 4 (2024): Desember: Router: Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v2i4.270

Abstract

The function of artificial intelligence (AI) in creating a positive consumer experience on the Shopee e-commerce platform is covered in this study. Nowadays, artificial intelligence (AI) plays a significant role in the e-commerce sector, allowing businesses to offer more individualized, quick, and safe services. This study examines how Shopee customer satisfaction and loyalty are impacted by the use of AI technologies, including chatbots, tailored product suggestions, fraud detection, and inventory management. This study found that AI plays a significant role in making shopping more responsive and convenient by analyzing data on the use of chatbot services, the efficacy of personalized recommendations, the accuracy of predicting customer needs, transaction security, and the efficiency of logistics management. According to the study's findings, implementing AI technology reduced service response times by up to 20% and increased customer satisfaction by 15%. In addition to enhancing service quality, the use of AI increases Shopee customer loyalty. These results demonstrate how AI in e-commerce holds enormous promise for fostering startup expansion and enhancing client connections.
Co-Authors Billan, Angel Caroline Tata Sutabri