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International Journal of Financial, Accounting, and Management
Published by Goodwood Publishing
ISSN : -     EISSN : 26563355     DOI : https://doi.org/10.35912/ijfam
Core Subject : Science,
Decision Sciences, Operations Research & Management
This journal is the leading international journal in the field of Financial, Accounting, and Management. International Journal of Financial, Accounting, and Management (IJFAM) comprises a multitude of activities which together form one of the world's fastest-growing international sectors. This journal takes an interdisciplinary approach and includes all aspects of financial, accounting, and management studies. The journal's contents reflect its integrative approach - including primary research articles, discussion of current issues, case studies, reports, book reviews, and forthcoming meetings.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 412 Documents
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EID Al-Fitr Homecoming Traffic Prediction to Anticipate Continuity on the Jakarta-Cikampek Toll Road Rahmawati, Ayu Dwi; Sinaga, Saut Panggabean; Earlyanti, Novi Indah
International Journal of Financial, Accounting, and Management Vol. 7 No. 3 (2025): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijfam.v7i3.2876

Abstract

Purpose: This study focuses on homecoming traffic flow prediction for sustainable anticipation to overcome the surge in traffic flow on the Jakarta-Cikampek Toll Road. This study aims to identify traffic patterns based on historical data, develop a time-series prediction model (ARIMA), and evaluate congestion levels using the Volume Capacity Ratio (VCR). The main issue is the high traffic flow during homecoming, which requires predictive and proactive approaches. This study uses concepts and theories such as traffic management, traffic flow characteristics, and time series prediction models (ARIMA and decomposition). This quantitative study analyzed historical data from Jasa Marga (2019–2024). Research Methodology: This quantitative study analyzed historical data from Jasa Marga (2019–2024). Analytical techniques included in this study, such as stationarity tests, ARIMA parameter identification, and VCR calculations, were used to assess congestion. Results: The results indicate that peak homecoming traffic occurs from H-5 to H-1, whereas returning traffic peaks from H+2 to H+5. The SARIMA (1,1,2) (2,1,2)²² model was more accurate in capturing seasonal patterns than the decomposition model. The VCR indicator is more than 1.0 during peak days, which indicates a congested road. These findings support traffic management strategies, such as contraflow and one-way systems. In conclusion, historical data-based prediction models can be used to effectively anticipate future traffic congestion. Conclusions: Historical data models can effectively anticipate congestion and support contraflows in urban traffic. Limitations: Stakeholders must enhance their data, infrastructure, awareness, and sustainable transportation. Contributions: This study used the Jakarta–Cikampek Toll Road for the results.
Analysis of the impact of Customer Value Management (CVM) on increasing Cellular Packet Telkomsel (Study case: PT Telkomsel) Syahputra, Indra; Nugroho, Agung
International Journal of Financial, Accounting, and Management Vol. 7 No. 3 (2025): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijfam.v7i3.3720

Abstract

Purpose: This study aims to analyze the impact of the Customer Value Management (CVM) program supported by machine learning on increasing customer purchases of Telkomsel cellular data packages, as well as identifying the key behavioral factors influencing purchasing decision. Methodology/approach: This research employs a quantitative explanatory approach using big data analytics. The dataset consists of customer transaction records over a three-month period (January–March 2023), involving 5.7 million customer data points. A supervised machine learning classification model was developed using the CatBoost Gradient Boosting Decision Tree (GBDT) algorithm to predict customer purchasing propensity, supported by Focus Group Discussions (FGD) with subject-matter experts. Results/findings: The CatBoost model achieved an accuracy of 86% in predicting potential lapsers. The test-and-learn campaign based on CVM personalization resulted in a 6.55% increase in take-up rate and generated a revenue uplift of IDR 141.6 million. The most significant factors influencing purchases were monthly data package revenue, frequency of data usage within specific price ranges, and total monthly data revenue. Conclusion: The findings confirm that CVM implementation supported by machine learning effectively enhances personalized marketing, improves customer targeting, and increases purchasing performance at PT Telkomsel. Limitations: This study is limited to a single company, a three-month observation period, and the use of one machine learning algorithm. Contribution: This study contributes empirical evidence on the effectiveness of integrating CVM and CatBoost-based machine learning in large-scale telecom marketing to optimize customer value and revenue growth.

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