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Jurnal Informatika dan Bisnis
Published by INSTITUT BISNIS DAN INFORMATIKA KWIK KIAN GIE
ISSN : 23019670     EISSN : 24775363     DOI : https://doi.org/10.46806/jib.v12i2
Core Subject : Economy, Science,
Computer Science & IT Economics, Econometrics & Finance
Jurnal Informasi dan Bisnis (JIB) adalah jurnal yang diterbitkan oleh Institut Bisnis dan Informatika Kwik Kian Gie dengan p-ISSN 2301-9670 dan e-ISSN 2477-5363. JIB terbit dua kali dalam setahun, yakni setiap Juni dan Desember, dan telah terbit dalam bentuk cetak (buku) dan elektronik (PDF). Fokus dan bidang di JIB meliputi: Informatika, Ekonomi, Bisnis, Manajemen, Manajemen Informatika, Informatika Ekonomi, Informatika Bisnis, Pembangunan Ekonomi, Informasi Akuntansi, Manajemen Informatika Produksi, Informatika Produksi, Distribusi Informatika, Informatika Konsumen. Kami mendorong pembaca dan penulis untuk mengirimkan artikel berisi ide atau kontribusi orisinal terbaik. Naskah bisa berupa artikel riset kuantitatif, artikel riset kualitatif, artikel review, short communication, metoda riset dan rancangan sistem, dengan syarat memiliki kebaruan dan bermanfaat secara praktis dan akademis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 4 Documents
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Search results for , issue "Vol. 14 No. 2 (2025): Juli - Desember" : 4 Documents clear
Implementasi Sales Force Automation untuk Mengoptimalkan Efisiensi Operasional pada Klinik Kecantikan Ellea Liora di Tasikmalaya Caesar, Calvin; Budi, Akhmad
Jurnal Informatika dan Bisnis Vol. 14 No. 2 (2025): Juli - Desember
Publisher : Institut Bisnis dan Informatika Kwik Kian Gie

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46806/jib.v14i2.1880

Abstract

This study aims to develop a web-based Sales Force Automation (SFA) system as a solution for managing customer information and operations at Ellea Liora, a beauty clinic in Tasikmalaya. With the increasing demand for beauty services, the manual system currently in use leads to inefficiencies in transaction recording, appointment scheduling, and customer data management. This research employs a qualitative method with data collection techniques including direct observation, documentation, and literature review to understand system requirements. The system development follows the Rapid Application Development (RAD) methodology to ensure an iterative solution that meets end-user needs. The resulting system features transaction recording, self-service appointment scheduling, electronic invoice generation, inventory management, and structured customer data management. The implementation of this system is expected to enhance Ellea Liora's operational efficiency, accelerate sales processes, reduce human errors, and improve service quality and competitiveness in the beauty industry.
Aplikasi Frontend untuk Meningkatkan Customer Experience dan Mendapatkan Data Customer Menggunakan Metode Scrum pada Studi Kasus Livera Anthony, Farrel; Birowo, Sigit
Jurnal Informatika dan Bisnis Vol. 14 No. 2 (2025): Juli - Desember
Publisher : Institut Bisnis dan Informatika Kwik Kian Gie

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46806/jib.v14i2.1881

Abstract

The development of digital technology has had a significant impact on human life, including institutions, organizations, and business actors. Rapid technological advancement has become a crucial factor for businesses to remain competitive in an increasingly dynamic market. Livera is a company engaged in the health beverage industry, with a focus on weight loss products. Currently, Livera does not yet have an application to fulfill customer satisfaction after purchase. Therefore, this study aims to design a website-based application intended to improve customer satisfaction, where customers can consume products according to a predetermined schedule, and the website application can also facilitate administrators in inputting and automatically accessing data. Customer experience represents an evaluation of feelings that can be identified through the experiences perceived by customers. Customer experience can be one of the most important determining factors for customers. This research is conducted using the Scrum method. The Scrum activities include product backlog, sprint backlog, daily Scrum, sprint review, and sprint retrospective. The roles in Scrum consist of the product owner, Scrum master, and the development team. This study employs a qualitative research method and the Scrum framework for system development. Data collection techniques used include non-participant observation, unstructured interviews, and literature studies from books and academic journals that serve as references related to customer experience, website design, and the Scrum method.
Implementasi Sistem Pengelolaan dan Perencanaan Keuangan Pribadi dengan Fitur Speech Recognition Berbasis Mobile Yemima, Helen Ruth; Dasawaty, Elis Sondang
Jurnal Informatika dan Bisnis Vol. 14 No. 2 (2025): Juli - Desember
Publisher : Institut Bisnis dan Informatika Kwik Kian Gie

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46806/jib.v14i2.1909

Abstract

Recent technological advancements have significantly impacted various aspects of daily life, including personal financial management and planning. However, many individuals still rely on manual systems, which can be time-consuming. Manual systems require users to enter transaction data one by one, making the process tedious and lengthy. In recent years, Artificial Intelligence (AI) has become increasingly prevalent in everyday life. Speech recognition, a subfield of AI, allows machines to convert human speech into written text, facilitating human-machine interaction. Implementing speech recognition features in devices can help save time in personal financial management and planning. This research focuses on personal finance and utilizes a observation. The Extreme Programming (XP) technique is used as the software development methodology. Data measurement techniques employ formulas related to personal finance. System design begins with creating Use Case and Class Diagrams, followed by database design. The research output is a mobile application with speech recognition features for personal financial management and planning. The developed personal financial management and planning system can save time through the implementation of speech recognition features, eliminating the need for manual data entry. The system is implemented on a mobile device, providing easy access for daily personal financial management and planning.
Optimasi Prediksi Jumlah Kontainer Aktual di Kapal Menggunakan Random Forest dan XGBoost dengan Hyperparameter Tuning Permana, Endi; Susilo, Joko
Jurnal Informatika dan Bisnis Vol. 14 No. 2 (2025): Juli - Desember
Publisher : Institut Bisnis dan Informatika Kwik Kian Gie

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46806/jib.v14i2.1921

Abstract

The maritime logistics industry plays a crucial role in ensuring the smooth flow of trade; however, it often faces discrepancies between the number of containers booked and the number actually loaded onto ships. These discrepancies can lead to operational inefficiencies, shipment delays, and additional costs for companies. PT XYZ, as a maritime logistics service provider, encounters similar challenges. Therefore, this study aims to analyze the factors causing container discrepancies and to develop a predictive system for estimating the actual number of containers as a decision-support tool. This research adopts data mining, machine learning, and ensemble learning approaches, focusing on the Random Forest Regressor and Extreme Gradient Boosting (XGBoost) algorithms combined through a Voting Regressor. Hyperparameter tuning using GridSearchCV is applied to improve the model’s ability to capture complex data patterns. A quantitative approach following the CRISP-DM framework is employed, including data exploration, cleaning, feature selection, modeling, and evaluation. The study utilizes historical container booking data from PT XYZ in 2023, consisting of more than 138,000 records. The results show that the Voting Regressor achieves the best performance with an R² value of 0.7874 and an MSE of 1.6282, supported by consistent RMSE and MAE metrics. The model is implemented in a Flask-based web application that enables practical container count prediction through Microsoft Excel file uploads. The implementation of this predictive system has the potential to help PT XYZ reduce loading discrepancies, minimize additional costs, and optimize logistics planning, while also contributing academically to the application of machine learning in the maritime logistics sector.

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