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All Journal JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH INFORMATIKA Warta Pengabdian Jurnal Informatika Universitas Pamulang JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal ICT : Information Communication & Technology JISKa (Jurnal Informatika Sunan Kalijaga) IJAIT (International Journal of Applied Information Technology) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of Innovation Information Technology and Application (JINITA) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Ilmu Komputer untuk Masyarakat Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Journal of Informatics and Computing
Rizal, Randi
Informatika, Universitas Siliwangi, Tasikmalaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : International Journal of Informatics and Computing

 1 
Advanced Phishing Attack Detection Through Network Forensic Methods and Incident Response Planning Based on Machine Learning Selamat, Siti Rahayu; Rizal, Randi; Nursihab, Cucu; Amien, Nashihun
JICO: International Journal of Informatics and Computing Vol. 1 No. 1 (2025): May 2025
Publisher : IAICO

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Abstract

The widespread use of smartphones has led to an increase in cybercrimes, particularly phishing attacks. Phishing attacks are commonly propagated through email, WhatsApp groups, and other communication channels. The stolen data is then used to commit further crimes, exploiting the victims' personal information. This study addresses the detection of phishing attacks using network forensic methods and incident response planning. Unlike previous approaches that relied solely on Incident Response Plans (IRPs) and Incident Handling methods to react to phishing attacks, this research emphasizes proactive detection. By employing network forensics, suspicious websites can be identified and differentiated from legitimate ones, enabling early detection and prevention of phishing attacks. The results demonstrate that network forensics can significantly enhance the ability to detect phishing sites before they can harm users. In our experiments, we analyzed a dataset of 10,000 websites, identifying 95% of phishing sites with a false positive rate of only 2%. Utilizing the Random Forest machine learning algorithm, we achieved high performance metrics with an accuracy of 96.5%, precision of 97.1%, recall of 95.8%, and an F1-score of 96.4%. This proactive approach not only mitigates the risk of phishing but also provides a robust framework for incident response, ensuring that potential threats are identified and neutralized promptly.
Sentiment Analysis of Application X on The Impact of Social Media Content on Adolescent Mental Well-Being using Naïve Bayes Algorithm Rizal, Randi; Pendit, Ulka Chandini; Ramli, Nuraminah; Annisa, Siti
JICO: International Journal of Informatics and Computing Vol. 1 No. 1 (2025): May 2025
Publisher : IAICO

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Since the pandemic, the use of social media has increased significantly. However, its presence has raised significant concerns about its impact on the mental well-being of teenagers. The pervasive influence of social media has led to substantial changes in the social system within society. Despite this influence, there is currently no comprehensive understanding of the specific impact of social media on mental health. To address this gap, this research proposes the use of sentiment analysis of social media posts with the Naive Bayes algorithm as an approach to identify and classify positive and negative sentiments in these posts related to the mental well-being of teenagers. This solution aims to provide a deeper understanding of the impact of social media content on this vulnerable demographic. In this study, a total of 555,361 social media posts were successfully collected and analyzed using the Naive Bayes algorithm, which was trained with a sample of 27,977 test data. The research results demonstrate that sentiment analysis with the Naive Bayes algorithm is effective in classifying social media sentiment, with 50.55% of the posts classified as positive and 46.97% classified as negative. The identified sentiment patterns have provided valuable insights into the positive and negative impact of social media content on the mental well-being of teenagers.
Improving Emotion Recognition Accuracy with Combination of Bidirectional and Long Short-Term Memory Models Haerani, Erna; Rahmatulloh, Alam; Rizal, Randi
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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Emotions play a vital role in shaping human behavior and mental health, making accurate emotion recognition essential for mitigating potential negative impacts. This study explores the application of Bidirectional Long Short-Term Memory (Bi-LSTM) for recognizing emotions from text-based data. Bi-LSTM extends the standard LSTM by enabling the model to process input sequences in both forward and backward directions, thereby capturing contextual dependencies more effectively. The research methodology consists of data collection, manual emotion labeling, and pre-processing techniques, including stemming, tokenization, and one-hot encoding. Visualization of the dataset and the distribution of labeled emotions was conducted to gain deeper insights into the data. The Bi-LSTM model was trained for 25 epochs, achieving a training accuracy of 0.9954 and validation accuracy of 0.8790, along with a training loss of 0.0133 and validation loss of 0.658. A confusion matrix was used to further evaluate model performance and classification accuracy across various emotion categories. The experimental results confirm that the Bi-LSTM model is highly effective in recognizing emotions from textual input. Its ability to capture long-term dependencies in both directions contribute to improved learning and prediction. However, opportunities for enhancement remain, particularly in refining the model architecture, expanding the dataset, and exploring additional feature extraction techniques. This research demonstrates the potential of Bi-LSTM in building intelligent emotion-aware systems for applications in mental health monitoring, customer feedback analysis, and human-computer interaction.
Co-Authors Al Husaini, Muhammad Al-Husaini, Muhammad Alam Rahmatulloh Aldya, Aldy Putra Althaf Pramasetya Perkasa, Mochamad Amien, Nashihun Candra, Fikri Chairat, Arief Suardi Nur Darmawan, Muhamad Aditya Dina Lestari, Dina El-Akbar, R Reza Faisal Muhammad Dzikry Faiz, Muhammad Fatchul Arifin Genta Nazwar Tarempa Gunawan, Rohmat Haerani, Erna Herman Dwi Surjono Hidayatulah Himawan Impron, Ali Indra Karyana Irani Hoeronis Komarudin, Arif Maulana Linda Sutriani Lukmana, Hen Hen M, Meto Rizki Mas’ud, Mohd. Zaki Nugraha, Cindera Syaiful Nur Widiyasono, Nur Nursamsi, Dede Rizal Nursihab, Cucu Nusamsi, Dede Rizal Oumarou, Hayatou Pendit, Ulka Chandini Purwayoga, Vega Puspareni, Luh Desi R. Reza El Akbar Rahmi Nur Shofa Ramli, Nuraminah Rasi Nuraeni Rianto, Rianto Rifki Maulana Insan Rizal Kurniawan Rochim, Rachma Verina Ruuhwan Ruuhwan Selamat, Siti Rahayu Septian Chandra Siti Annisa, Siti Siti Yuliyanti, Siti Sudiarjo, Aso Syadza Anggraini Tiara Komala Sutra, Melanda Yahdi Siradj Yusuf Sumaryana