Forensic Analysis of WhatsApp Conversations Using NER-Based Information Extraction Techniques

Febrianto Febrianto; Tuga Mauritsius

doi:10.59261/jequi.v8i5.463

Authors

Febrianto Febrianto Universitas Bina Nusantara
Tuga Mauritsius Universitas Bina Nusantara

DOI:

https://doi.org/10.59261/jequi.v8i5.463

Keywords:

Digital Forensics, Whatsapp, Named Entity Recognition, IndoBERT, Narcotics

Abstract

Background: The widespread use of WhatsApp as a primary communication medium in narcotics-related crimes generates a massive volume of digital evidence, making manual forensic analysis inefficient, time-consuming, and prone to human error.

Objective: This study aims to design, develop, and evaluate a custom named entity recognition (NER) model based on IndoBERT Base P1 to automatically extract forensic information from WhatsApp conversations in narcotics-related cases.

Methods: This study adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, which comprises six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The dataset was derived from forensic extractions of WhatsApp conversations conducted by the Indonesian National Police Forensic Laboratory Center (Puslabfor Bareskrim Polri). It comprises 822,943 message rows from 859 pieces of digital evidence. Following annotation using the BIO (Beginning, Inside, Outside) tagging scheme, the dataset yielded 334,576 annotated instances across 18 custom forensic entity categories.

Results: The evaluation results show that the model achieved a precision of 83.44%, a recall of 95.81%, a weighted F1 score of 89.05%, and a macro F1 score of 89.15%. Training was completed in 5,090.05 seconds over three epochs. All aggregate evaluation metrics exceeded the researcher-defined operational criterion of 80% established for this study.

Conclusion: These findings indicate that the IndoBERT-based custom NER model can transform unstructured conversational data into structured investigative information and has the potential to serve as a digital forensic decision-support tool for narcotics crime investigations.

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