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Alex Fernando Hasahatan
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Humanities Social Sciences

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An Ethnography Study Of Shared Values Woman As Mining Blast Engineer Pratama, Renaldo; Enny, Enny; Alex Fernando Hasahatan; Jerry Heikal
J-CEKI : Jurnal Cendekia Ilmiah Vol. 4 No. 4: Juni 2025
Publisher : CV. ULIL ALBAB CORP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jceki.v4i4.9043

Abstract

This research examines the shared values of women working as mining blast engineers in the mining industry. Through a qualitative approach with a realist ethnographic design, this study analyzes the experiences, perspectives, and adaptation strategies of three female mining blast engineers. The shared values owned by the respondents before entering the blast engineer job are independent character, confidence, courage, toughness, assertiveness, strong faith, respect, professionalism and trustworthiness. Pain values include long work schedules, distant work locations, male-dominated work environments, and fatigue due to work and field conditions. Meanwhile, the shared values formed from these pain values are characters who are brave, tough, assertive, respectful, environmentally sound, professional and trustworthy. These values enable them to adapt, survive and thrive in their field of work. The research also found that the implementation of gender diversity policies has a positive impact on the availability of a safe and conducive working atmosphere and environment. This policy allows all workers, regardless of gender, to have the same duties and responsibilities. It also has an impact on more conducive internal and external work processes, with a focus on work processes and outcomes, rather than on who is working on the project. This research concludes that women have great potential to become mining blast engineers in the mining industry. And the wider implementation of gender diversity policies in mining companies can help create a more inclusive work environment and encourage women's participation in important positions such as mining blast engineer.
Credit Card Customer Churn Prediction With Binary Logistic Regression Enny, Enny; Pratama, Renaldo; Alex Fernando Hasahatan; Jerry Heikal
J-CEKI : Jurnal Cendekia Ilmiah Vol. 4 No. 6: Oktober 2025
Publisher : CV. ULIL ALBAB CORP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jceki.v4i6.11772

Abstract

The increasing prevalence of credit card usage in Indonesia has brought significant benefits to the national economy but also presents challenges for the banking industry, particularly regarding customer churn. This research aims to analyze the factors influencing credit card customer churn using the Binary Logistic Regression method. Utilizing a dataset of 5,000 entries from Kaggle, the research incorporates demographic and behavioral variables such as age, marital status, product ownership, inactivity periods, and transaction patterns. Data preprocessing steps included handling missing values, encoding categorical variables, and feature scaling. The model was trained with 80% of the data and tested on 20%, with variable selection based on p-values (< 0.05). The results indicate that eight key factors significantly affect churn likelihood, including number of dependents, marital status, number of products, inactive months, number of contacts, total transactions, transaction count, and the ratio of Q4 to Q1 transaction amounts. The model achieved an accuracy of 87.10%, demonstrating strong predictive performance, especially for non-churn cases. These findings suggest that classical statistical approaches remain effective for churn prediction when supported by comprehensive data processing and relevant variable selection. The study contributes valuable insights for financial institutions to develop targeted retention strategies and enhance customer loyalty.
Co-Authors Enny Enny Jerry Heikal Pratama, Renaldo