ANALISIS PHISHING PADA PESAN WHATSAPP MENGGUNAKAN LSTM

Alison Dejesus; Petrus Katemba; Ernarosani Nubatonis

doi:10.46880/mtk.v12i2.6268

Authors

Alison Dejesus Universitas Uyelindo
Petrus Katemba Universitas Uyelindo
Ernarosani Nubatonis Universitas Uyelindo

DOI:

https://doi.org/10.46880/mtk.v12i2.6268

Keywords:

WhatsApp Phishing, LSTM, NLP Indonesia, Deteksi Phishing, Text Preprocessing

Abstract

Phishing messages on instant messaging applications such as WhatsApp pose a cybersecurity threat, particularly through social engineering techniques, making automated detection of Indonesian-language phishing messages important. This study aims to develop a phishing detection system for Indonesian-language WhatsApp messages using the Long Short-Term Memory (LSTM) model. The dataset consisted of 1,162 messages, comprising 817 non-phishing and 345 phishing messages, which were processed through cleaning, case folding, normalization, tokenization, stopword removal, stemming using Sastrawi, sequencing, and padding before model training. The LSTM model was trained using 5 epochs, a batch size of 32, a learning rate of 0.001, and the Adam optimizer, and evaluation using a confusion matrix achieved an accuracy of 88.0%, precision of 72.50%, recall of 96.67%, and F1-score of 82.86%. These results indicate that the model can support the initial identification of potentially phishing messages, particularly due to its high recall in detecting phishing messages, although the imbalanced class distribution remains a limitation that may affect classification performance.

References

F. A. Manurung, Munawir, and D. Pradeka, “Spam and Phishing Whatsapp Message Filtering Application Using TF - IDF and Machine Learning Methods,” Green Intell. Syst. Appl., vol. 5, no. 1, pp. 1–13, 2025, doi: 10.53623/gisa.v5i1.551.

G. A.-S. Alliance, “State of Scams in Indonesia 2025,” Global Anti-Scam Alliance, 2025. [Online]. Available: https://gasa.org/knowledge-base/reports/state-of-scams-in-indonesia-2025

A. M. Chougule, K. S. Oza, V. T. Patil, and R. B. Diwane, “Real-World Phishing and Smishing Detection Using Deep Learning: A Comparative Study of LSTM, GRU, and GloVe Embeddings,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 14, no. 11, 2025, doi: 10.17148/IJARCCE.2025.1411149.

M. Al Kautsar, G. Guntoro Setiaji, and A. Rifa, “Analisis Komparasi Kinerja Lstm dan CNN dalam Deteksi Spam Email Berbasis Deep learning,” Bull. Comput. Sci. Res., vol. 5, no. 4, pp. 584–593, 2025, doi: 10.47065/bulletincsr.v5i4.572.

K. Rangaswamy, “Phishing Website Detection using Machine Learning and Deep Learning Techniques,” Power Syst. Technol., vol. 49, no. 1, pp. 947–959, 2025, doi: 10.52783/pst.1643.

R. Lestari and D. Angela, “Pengembangan Aplikasi Chatbot Untuk Pemasaran Digital Perguruan Tinggi Menggunakan Long Short Term Memory (LSTM) (Studi Kasus: Marketing ITHB),” 2022, [Online]. Available: http://www.ithb.ac.id

G. Mahesh and S. K. Duvvuri, “A deep learning framework for SMS spam detection: Comparative performance of LSTM and BiLSTM Models,” Glob. J. Eng. Technol. Adv., vol. 27, no. 01, pp. 50–63, 2026, doi: 10.30574/gjeta.2026.27.1.0086.

A. Prayogi, N. Novriyenny, and I. G. Prahmana, “Dinamika Sentimen Komunikasi Mahasiswa dan Dosen dengan Pemanfaatan Analisis Pesan Whatsapp Akademis Menggunakan Machine Learning,” Repeater Publ. Tek. Inform. Dan Jar., vol. 3, no. 2, pp. 45–54, 2025, doi: 10.62951/repeater.v3i2.404.

E. Setiawan and W. Widji Pamungkas, “Pengujian Algoritma Deep Learning Pada Analisis Terhadap Layanan Gojek Menggunakan Data Media Sosial Twitter,” J. Inf., vol. 10, no. 2, pp. 53–67, 2024, [Online]. Available: https://ojs.stmik-im.ac.id/index.php/Informasi/article/view/273

R. Cahyadi, A. Damayanti, and D. Aryadani, “Recurrent Neural Network (Rnn) Dengan Long Short Term Memory (Lstm) Untuk Analisis Sentimen Data Instagram,” J. Inform. Dan Komput., vol. 5, no. 1, pp. 1–9, 2020, doi: 10.22236/jutikom.v5i1.

A. R. Gunawan and R. F. Alfa Aziza, “Sentiment Analysis Using LSTM Algorithm Regarding Grab Application Services in Indonesia,” J. Appl. Informatics Comput., vol. 9, no. 2, pp. 322–332, 2025, doi: 10.30871/jaic.v9i2.8696.

A. Trianurahmah, A. Fauzi, E. N. Tyas, M. A. Suryanto, M. Rizky, and P. Wibisono, “Analisis Ancaman Pishing Melalui Aplikasi WhatsApp: Studi Kasus Manajemen Sekuriti Waspadai Maraknya Kejahatan Phising Dengan Modus Berbasis Link,” Orbit J. Ilmu Multidisiplin Nusant., vol. 1, no. 2, pp. 74–88, 2025, doi: 10.63217/orbit.v1i2.81.

H. S. Wicaksana, U. Ependi, and A. Muzakir, “URL-Based Phishing Detection Using a BERT-LSTM Model,” J. Inf. Syst. Informatics, vol. 8, no. 1, pp. 1344–1367, 2026, doi: 10.63158/journalisi.v8i1.1543.

S. Verma, V. Ayala-Rivera, and A. O. Portillo-Dominguez, “Detection of Phishing in Mobile Instant Messaging Using Natural Language Processing and Machine Learning,” in Proceedings - 2023 11th International Conference in Software Engineering Research and Innovation, CONISOFT 2023, 2023, pp. 159–168. doi: https://doi.org/10.1109/CONISOFT58849.2023.00029.

D. Aditya, “Klasifikasi Teks Data Short Message Services (SMS) Berbahasa Indonesia Menggunakan Artificial Neural Network,” 2024. [Online]. Available: https://repository.unsri.ac.id/147874/3/RAMA_55201_09021282025099_0008118205_0018048003_01_front_ref.pdf

M. W. Sampurno Utomo, H. Wisnu Murti, A. Widya Indah Sujatmoko, and A. Puspita Sari, “Deteksi Spam Email Menggunakan Metode Lstm (Long Short Term Memory),” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 6, pp. 11406–11411, 2024, doi: 10.36040/jati.v8i6.11474.