Analisis Komparatif Algoritma Process Mining untuk Pemetaan Navigasi dan Deteksi Bottleneck E-Commerce
Abstract
E-commerce digitalization generates massive clickstream data, complicating customer journey mapping and bottleneck detection. The unstructured nature of web logs often leads to modeling failures. This study evaluates the performance of Alpha Miner, Heuristic Miner, and Inductive Miner algorithms in mapping user navigation routes and detecting interface inefficiencies using a public e-commerce clickstream dataset. Through Token-Based Replay evaluation, the research shows that Alpha Miner is inefficient for dynamic data and prone to Out of Memory errors. Conversely, Inductive Miner proved superior with a perfect fitness level (0.999), while Heuristic Miner was optimal in filtering noise (0.887). Further evaluations using the Performance Directly-Follows Graph localized the main bottleneck at the post-login transition to the shopping cart addition, which took the longest interface delay (42 minutes). These empirical findings provide a benchmark to optimize user interfaces and boost digital transaction conversions.
Keywords: Bottleneck; Conformance Checking; Customer Journey; E-Commerce; Process Mining.
Abstrak
Digitalisasi e-commerce menghasilkan data clickstream masif yang menyulitkan pemetaan customer journey dan deteksi bottleneck. Sifat log web yang tidak terstruktur sering kali memicu kegagalan pemodelan. Penelitian ini mengevaluasi kinerja Alpha Miner, Heuristic Miner, dan Inductive Miner untuk memetakan navigasi pengguna serta mendeteksi inefisiensi antarmuka menggunakan dataset publik rekaman clickstream e-commerce. Melalui evaluasi Token-Based Replay, penelitian menunjukkan bahwa Alpha Miner tidak efisien untuk data dinamis dan rentan memicu Out of Memory. Sebaliknya, Inductive Miner terbukti paling unggul dengan tingkat kecocokan sempurna (0.999), sedangkan Heuristic Miner optimal dalam menyaring derau (0.887). Evaluasi lanjutan berbasis Performance Directly-Follows Graph melokalisasi bottleneck utama pada transisi pasca-login menuju penambahan keranjang belanja dengan jeda waktu antarmuka terlama (42 menit). Temuan empiris ini menjadi acuan untuk mengoptimalkan rekayasa antarmuka pengguna demi mendongkrak konversi transaksi digital.
Keywords
References
B. Topaloglu, B. Oztaysi, and O. Dogan, “Cart-State-Aware Discovery of E-Commerce Visitor Journeys with Process Mining,” J. Theor. Appl. Electron. Commer. Res., vol. 19, no. 4, pp. 2851–2879, 2024, doi: 10.3390/jtaer19040138.
D. Weijs and E. Caron, “Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining,” Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, vol. 1, pp. 418–424, 2022, doi: 10.5220/0011263900003266.
Y. P. Putra, O. A. Putra, and W. Novrian, “Systematic Literature Review ( SLR ) pada Aplikasi Process Mining dalam Transformasi Digital Proses Bisnis,” Arcitech J. Comput. Sci. Artif. Intell., vol. 5, no. 1, pp. 90–107, 2025, doi: 10.29240/arcitech.v5i1.13888.
R. Halvorsrud, F. Mannhardt, O. Prillard, and C. Boletsis, “Customer journeys and process mining – challenges and opportunities,” ITM Web Conf., vol. 62, no. 1, p. 05002, 2024, doi: 10.1051/itmconf/20246205002.
I. Mandic, “Enhancing Customer Journey Through Process Mining: A Literature Review and Future Research Directions,” Proceedings of the 10th International Conference on Future Bioengineering and Medical Engineering (FUTURE-BME), vol. 1, no. 1, pp. 1–10, 2024, doi: 10.24867/FUTURE-BME-2024-108.
H. S. Husin and S. Ismail, “Exploring process mining for analyzing user navigation behavior,” AIP Conference Proceedings, vol. 2643, no. 1, p. 050017, 2022, doi: 10.1063/5.0119668.
M. C. Permana, A. P. P. Djakaria, A. D. Ginting, M. R. Asjad, and B. S. R. Andreswari, “Bottleneck and Resource Analysis on IT Help Desk with Process Mining,” J. Inf. Technol. Comput. Sci., vol. 9, no. 1, pp. 77–85, 2024, doi: 10.25126/jitecs.202491581.
M. Siek and R. M. G. Mukti, “Business process mining from e-commerce event web logs : Conformance checking and bottleneck identification,” IOP Conference Series: Earth and Environmental Science, vol. 729, no. 1, p. 012133, 2021, doi: 10.1088/1755-1315/729/1/012133.
F. A. Tridalestari, Mustafid, B. Warsito, A. Wibowo, and H. N. Prasetyo, “Analysis of E-Commerce Process in the Downstream Section of Supply Chain Management Based on Process and Data Mining,” Ingénierie des Systèmes d ’ Inf., vol. 27, no. 1, pp. 81–91, 2022, doi: 10.18280/isi.270110.
J. Dioses and L. Cordova, “A Survey of Process Mining for Customer Management,” Eng. Proc., vol. 83, no. 1, p. 7, 2025, doi: 10.3390/engproc2025083007.
F. A. Tridalestari, Mustafid, and F. Jie, “Consumer Behavior Analysis on Sales Process Model using Process Discovery Algorithm for The Omnichannel Distribution System,” IEEE Access, vol. 11, pp. 42619–42630, 2023, doi: 10.1109/ACCESS.2023.3271394.
N. M. El-gharib and D. Amyot, “Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs Cloud-Based Application Event Logs,” Algorithms, vol. 15, no. 6, p. 180, 2022, doi: 10.3390/a15060180.
M. Salehi, R. Khayami, and M. Mirmozaffari, “A novel multi objective grey wolf optimization fuzzy miner for process discovery : Incorporating robustness and explainability in model evaluation,” PLoS One, vol. 21, no. 3, pp. 1–59, 2026, doi: 10.1371/journal.pone.0343119.
W. Xu, Y. Lou, H. Chen, and Z. Shen, “Exploring the interaction of cognition and emotion in small group collaborative discourse by Heuristic Mining Algorithm (HMA) and Inductive Miner Algorithm (IMA),” Educ. Inf. Technol., vol. 28, no. 10, pp. 13153–13178, 2023, doi: 10.1007/s10639-023-11722-8.
Y. A. Effendi and M. Kim, “Leveraging Dual Timestamps to Enhance Process Model Construction Using Time- infused Inductive Miner,” IEEE Access, vol. 13, pp. 88535–88556, 2025, doi: 10.1109/ACCESS.2025.3571441.
M. R. Mujantara and A. P. Kurniati, “Analysis of the Quality of Patient Treatment Data in MIMIC-IV Using Alpha , Heuristic , and Inductive Miner,” J. Indones. Sos. Teknol., vol. 6, no. 1, pp. 373–391, 2025, doi: 10.59141/jist.v6i1.2232.
A. Kumar, S. Patel, and R. Desai, “A Process Mining Approach to Customer Journey Mapping: Evaluating Service Quality and SERVQUAL Gaps in Service Operations,” J. Bus. Res., vol. 172, no. 1, p. 114402, 2024, doi: 10.1016/j.jbusres.2024.114402.
I. N. Okeke and O. Ijiga, “Problem-Oriented Process Mining for Auditable Marketing Automation Lifecycle Control,” Int. J. Inf. Manage., vol. 75, no. 2, p. 102735, 2024, doi: 10.1016/j.ijinfomgt.2024.102735.
R. Sharma and T. Lin, “Evaluating Post-Authentication User Friction in Digital Marketplaces using Event-Driven Architecture,” Decis. Support Syst., vol. 180, no. 3, p. 114190, 2025, doi: 10.1016/j.dss.2024.114190.
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