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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) SKANIKA: Sistem Komputer dan Teknik Informatika J-Intech (Journal of Information and Technology) Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal Sistem Informasi dan Manajemen JoDENS: Journal of Digital Ecosystem for Natural Sustainability Journal Collabits
Roza, Yuni
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Journal : Journal Collabits

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Linear Regression Algorithm in Pulse Purchase System Simple Using Python Afiyati, Afiyati; Ayu, Kurnia Gusti; Roza, Yuni; Sakhrassalam, Haytsam; Syafiq, Nur Muhammad Zihni
Journal Collabits Vol 1, No 2 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i2.27256

Abstract

In today's digital era, the online credit purchase system has become an integral part of everyday life. The use of linear regression algorithms in this context is becoming increasingly relevant, as it provides a powerful approach to analyzing and predicting pulse buying patterns. This research proposes a simple pulse purchase system that implements a linear regression algorithm, using the Python programming language. The purpose of this study is to develop a predictive model that can estimate the amount of credit to be purchased based on certain variables, such as the time of purchase, the number of previous transactions, and the value of prior purchases. By analyzing historical transaction data, the system can take into account possible purchase patterns and estimate future credit needs with an adequate level of accuracy. The implementation of linear regression algorithms in Python allows users to easily access and use this pulse purchase system. Through a simple but intuitive interface, users can enter their transaction parameters and the system will predict the required number of pulses. Experiments were conducted to test the performance of this system in producing accurate predictions. The results of the experiment show that this system can provide estimates close to the real value, with a high degree of accuracy. This indicates that the use of linear regression algorithms in pulse purchase systems has great potential to improve efficiency and reliability in online transactions. In addition, the implementation of this algorithm also has a positive impact on transaction security. By analyzing purchasing patterns, the system can detect anomalies or suspicious activities that may occur, thereby increasing the level of security in the process of buying credit online. Overall, this study shows that the use of linear regression algorithms in pulse purchase systems has significant benefits in improving the efficiency, reliability, and security of online transactions. The practical implementation of this algorithm in the Python programming language opens the door for further development in the analysis and optimization of future pulse purchase system.
DevelopmentUser Interface and Experience of Web-Based Psychological Tests (Case Study: Universal University) Roza, Yuni; Maesaroh, Siti
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25562

Abstract

Developing a user interface and user experience for a web-based psychological test application at Universal University, especially the Uvers Career Center (UCC), is a technology that is needed to make the appearance of an application non-monotonous and dynamic. Previously, psychological test applications that had been developed by previous research did not meet the UCC's own standard requirements. The data source that will be used is qualitative data and uses a usability testing method with the aim of making it easier for users to carry out psychological tests with a display that is in accordance with what is required by the UCC. The final result of the research is designing the user interface and experience of the MindQuiz application which has a usability score of 83.11.
Analysis and Prediction of Customer Churn in the Telecommunications Industry Using Logistic Regression and Random Forest Nabila, Celsi Alisa; Santoso, Ryno Julian; Nafisa, Sabila Alya; Roza, Yuni
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37599

Abstract

Customer churn represents a major challenge for telecommunication companies because of its significant influence on revenue stability and customer retention efforts. Intense competition among service providers has increased the need for reliable predictive models capable of identifying customers with a high probability of terminating their subscriptions. This study focuses on the analysis and prediction of customer churn by applying machine learning techniques to the Telco Customer Churn dataset. The research workflow includes data preprocessing stages such as duplicate removal, treatment of missing values, and transformation of both categorical and numerical features. Exploratory data analysis supported by visualization techniques is employed to examine customer behavior and feature relationships. Subsequently, the dataset is partitioned into training and testing subsets using an 80:20 stratified split. A preprocessing pipeline is applied, incorporating feature scaling for numerical variables and one-hot encoding for categorical variables. Predictive models are developed using Logistic Regression and Random Forest algorithms, and their performance is assessed through accuracy measurements and classification reports. The results indicate that the Random Forest model delivers better predictive performance than Logistic Regression, demonstrating its effectiveness in modeling complex data patterns. Overall, the study confirms that machine learning-based approaches can serve as effective tools for churn prediction and offer meaningful insights to support strategic decision-making in customer retention within the telecommunication sector.
Co-Authors Afiyati, Afiyati Ayu, Kurnia Gusti Dody, Dody Febrianti, Eka Lia Ilwan Syafrinal Ismail, Asriyani Kaharuddin Kaharuddin Musliadi K H Nabila, Celsi Alisa Nafisa, Sabila Alya Nasril Sany Novia Dewi Pernando, Yonky Pramudiya, Widi Purnajaya, Akhmad Rezki Sakhrassalam, Haytsam Santoso, Ryno Julian Saragih, Raymond Erz Siti Maesaroh Syafiq, Nur Muhammad Zihni Verdian, Ihsan