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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) 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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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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Abstract

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.
PERANCANGAN SISTEM INFORMASI MANAJEMEN SKRIPSI MENGGUNAKAN BUSINESS SYSTEM PLANNING Ruuhwan, Ruuhwan; Rizal, Randi; Sudiarjo, Aso
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 6, No 1 (2022): SEMNAS RISTEK 2022
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v6i1.5835

Abstract

Sistem informasi pada suatu organisasi menjadi sangat penting untuk guna mengolah data dengan cepat untuk pengguna. Hal yang perlu dilakukan untuk pertama kali dalam membuat sistem informasi adalah membuat perencanaan. Metodologi yang digunakan untuk perencanaan sistem informasi adalah melakukan penyusunan rencana strategis dengan menggunakan Business System Planning (BSP). Metodologi ini bertujuan untuk upaya bagaimana sistem informasi harus terstruktur, terpadu, dan dilaksanakan oleh organisasi dalam jangka waktu lama. Universitas Perjuangan adalah salah satu universitas di Tasikmalaya. Manajemen pendaftaran dan pengelolaan skripsi di Universitas Perjuangan masih dilakukan secara manual. Selain itu tidak adanya sistem informasi untuk melihat kuota calon dosen pembimbing dan progres pengajuan tugas akhir menjadi kendala tersendiri dalam penentuan calon dosen pembimbing dan batas akhir proses pengerjaan skripsi. Dengan demikian diperlukan sebuah perencanaan untuk membangun sebuah sistem informasi manajemen skripsi yang nantinya akan bermanfaat untuk mengelola data skripsi dan memudahkan dalam hal pengajuan skripsi, penentuan pembimbing, penentuan penguji dan penentuan jadwal sidang. Langkah yang dilakukan dengan metodologi ini business system planning ini adalah mendefinisikan tujuan bisnis, proses bisnis , kelas data, arsitektur informasi dan integrasi.Kata Kunci: Business System Planning, Skripsi
Artificial Intelligence (AI) for Classification of Cyber Attacks on Internet of Things (IoT) Network Traffic Rizal, Randi; Widiyasono, Nur; Yuliyanti, Siti
JUMANJI (Jurnal Masyarakat Informatika Unjani) Vol 7 No 2 (2023): Jurnal Masyarakat Informatika Unjani
Publisher : Jurusan Informatika Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jumanji.v7i2.325

Abstract

Internet of Things (IoT) is an architecture that connects large numbers of smart devices in today's modern global network system. Distributed denial of services (DDoS) attacks are one of the most common types of cyber attacks, targeting servers or networks with the aim of disrupting their normal activities. Although real-time detection and mitigation of DDoS attacks is difficult to achieve, the solution would be invaluable as attacks can cause significant damage. This research utilizes artificial intelligence (AI) to classify attacks on Internet of Things (IoT) network traffic. The resulting classification of DDOS attacks from all types of attacks, namely SYN, ACK, UDP, and UDPplain. The application of a deep learning model with the Convolutional Neural Network (CNN) algorithm is used to classify normal traffic from DDoS cyber attacks. The CNN algorithm performs very well in the classification process with an accuracy of 99%. Next, we plan to build a new model to block or mitigate DDoS attacks based on the output of the CNN classification algorithm used in this research.
VISUAL ENTITY OBJECT DETECTION SYSTEM IN SOCCER MATCHES BASED ON VARIOUS YOLO ARCHITECTURE Althaf Pramasetya Perkasa, Mochamad; El Akbar, R. Reza; Al Husaini, Muhammad; Rizal, Randi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2015

Abstract

In this study, a performance comparison between the YOLOv7, YOLOv8, and YOLOv9 models in identifying objects in soccer matches is conducted. Parameter adjustments based on GPU storage capacity were also evaluated. The results show that YOLOv8 performs better, with higher precision, recall, and F1-score values, especially in the "Ball" class, and an overall accuracy (mAP@0.5) of 87.4%. YOLOv9 also performs similarly to YOLOv8, but YOLOv8's higher mAP@0.5 value shows its superiority in detecting objects with varying degrees of confidence. Both models show significant improvement compared to YOLOv7 in overall object detection performance. Therefore, based on these results, YOLOv8 can be considered as the model that is close to the best performance in detecting objects in the dataset used. This study not only provides insights into the performance and characteristics of the YOLOv7, YOLOv8, and YOLOv9 models in the context of object detection in soccer matches but also results in a dataset ready for additional analysis or for training deep learning models.
ENSEMBLE MACHINE LEARNING WITH NEURAL NETWORK STUNTING PREDICTION AT PURBARATU TASIKMALAYA Al-Husaini, Muhammad; Lukmana, Hen Hen; Rizal, Randi; Puspareni, Luh Desi; Hoeronis, Irani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2421

Abstract

This research uses an ensemble model and neural network method that combines several machine learning algorithms used in the prediction of stunting and nutritional status children in Purbaratu Tasikmalaya. This ensemble method is complemented by a combination of the prediction results of several algorithms used to improve accuracy. The data used is anthropometry-based calculations of 195 toddlers with 39% of related stunting from 501 total data in Purbaratu Tasikmalaya City; high rates of stunting this research urgent to make a stable model for prediction. The results of this study are significant as they provide a more accurate and efficient method for predicting stunting and nutritional status in children, which can be crucial for early intervention and prevention strategies in public health and nutrition. The best accuracy value for some of these categories is 98, 21% for the Weight/Age category with the xGBoost algorithm, 97.7% of the best accuracy results with the Random Forest and Decision Tree algorithms for the Height/Age category, the Weight/Height category with the best accuracy of 97.4% for the Random Forest and xGBoost algorithms, and the use of neural network models resulted in an accuracy of 99.19% for Weight/Age and Height/Age while for Weight/Height resulted in an accuracy of 91.94%..
Implementasi Load Balancing Dan Failover to Device Mikrotik Router Menggunakan Metode Equal Cost Multi Path (ECMP) Tiara Komala Sutra, Melanda; ruuhwan, Ruuhwan; Rizal, Randi
Informatics and Digital Expert (INDEX) Vol. 4 No. 2 (2022): INDEX, November 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i2.1189

Abstract

Kebutuhan akses internet saat ini sangat tinggi dengan berbagai kegunaan seperti mencari informasi, artikel maupun pengetahuan terbaru. Dengan kebutuhan tinggi akan penggunaan internet di kalangan masyarakat, terutama pelajar sehingga memunculkan alternatif – alternatif agar pengguna dapat mengakses internet dengan mudah terutama ketika kondisi pandemi COVID-19. Permasalahan yang ditemukan pada PT LIBNET yang telah menggunakan dua Internet Service Provider (ISP) yaitu banyaknya permintaan yang melebihi kapasitasi dari klien menyebabkan perusahaan akan terganggu karena harus dilakukan banyak pengaturan dalam memenuhi permintaan klien tersebut. Seperti mempartisi beban trafik yang datang untuk mengatur perangkat gadget sehingga tidak terpaku pada satu ISP. Maka, supaya trafik dapat berjalan secara ideal, harus menambahkan throughput, membatasi waktu reaksi dan berupaya tidak membebani melebihi kapasitas salah satu ISP. Penggunaan teknik load balancing menjadi solusi teknologi yang sangat efektif untuk memanfaatkan internet tanpa harus terjadi ketimpangan request. Pada penelitian ini telah diimplementasikan loading balancing dalam menyelesaikan permasalahan request client dengan memakai dua buah node untuk melakukan redudansi sebagai syarat minimum suatu kluster. Sedangkan pada server memanfaatkan metode distribusi koneksi yang disebut dengan metode ECMP. Equal Cost Multi Path (ECMP) merupakan metode load balancing yang menggunakan metode per address-pair connection load balancing. Hasilnya akhirnya, ECMP memungkinkan router untuk memiliki lebih dari satu gateway untuk satu network tujuan. Karena metodenya adalah per address-pair connection, maka sistem load balancing ini adalah setiap address yang berbeda di koneksi yang berbeda akan berkemungkinan melewati gateway yang berbeda.
Enhancing YOLOv5s with Attention Mechanisms for Object Detection in Complex Backgrounds Environment Impron, Ali; Lestari, Dina; Sutriani, Linda; Anggraini, Syadza; Rizal, Randi
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16833

Abstract

Enhancing performance for object detection in complex environments is essential for real-world applications that represent complexities, such as stacking objects in the same location or environment. Models for detecting objects developed to this day still have difficulties in detecting objects with environments that have complex backgrounds. The reason is that the model often experiences a decrease in accuracy when the object to be detected is occlusion by other objects and is small in size. Therefore, in this study, a model improvement method was carried out in detecting objects in a complex environment. The algorithm used in this study is YOLOv5s. Optimization is carried out by adding a CBAM (Convolutional Block Attention Module) attention mechanism layer which is integrated with the C3 layer (C3CBAM) in the backbone of the YOLOv5s model architecture. In addition, a P2 feature map is also added to the architecture head. The optimization results carried out were quite satisfactory, namely there was an increase in the precision value by 1.6 %, at mAP@0.5 an increase of 1.4 %, and also mAP@50-95 increased by 0.1%. This proves that the enhancement method applied to YOLOv5s in this study can improve the performance of the model. However, with the addition of the attention mechanism layer, it turns out that it can increase the computational load. Therefore, for future research, a method can be applied to reduce computing load, one of the methods is knowledge distillation.
An Overview Diversity Framework for Internet of Things (IoT) Forensic Investigation Rizal, Randi; Selamat, Siti Rahayu; Mas’ud, Mohd. Zaki
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1520

Abstract

The increasing utilization of IoT technology in various fields creates opportunities and risks for investigating all cybercrimes. At the same time, many research studies have concentrated on security and forensic investigations to collect digital evidence on IoT devices. However, until now, the IoT platform has not fully evolved to adjust the tools, methods, and procedures of IoT forensic investigations. The main reasons for investigators are the characteristics and infrastructure of IoT devices. For example, device number variations, heterogeneity, distribution of protocols used, data duplication, complexity, limited memory, etc. As a result, resulting is a tough challenge to identify, collect, examine, analyze, and present potential IoT digital evidence for forensic investigative processes effectively and efficiently. Indeed, there is not fully used and adapted international standard for the perfect IoT forensic investigation framework. In the research method, a literature review has been carried out by producing previous research studies that have contributed to further facing challenges. To keep the quality of the literature review, research questions (RQ) were conducted for all studies related to the IoT forensic investigation framework between 2015-2022. This research results highlight and provides a comprehensive overview of the twenty current IoT forensic investigation framework that has been proposed. Then, a summary or contribution is presented focusing on the latest research, grouping the forensic phases, and evaluating essential frameworks in the IoT forensic investigation process to obtain digital evidence. Finally, open research issues are presented for further research in developing IoT forensic investigative framework.
Web-Based Deepfake Detection Using VERITAS: Integrating Vision-Based Excitation with Transformer-Driven Intelligence Alam Rahmatulloh; Herman Dwi Surjono; Fatchul Arifin; Rohmat Gunawan; Randi Rizal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7320

Abstract

This study proposes a web-based deepfake detection system that integrates Vision-Based Excitation technology and Transformer-based intelligence, called VERITAS (Vision-based Excitation and Robust Intelligence for Transformer-Assisted Deepfake Detection). The system is designed to automatically detect manipulated images and videos by leveraging the Vision Transformer (ViT) model architecture, equipped with the Grad-CAM mechanism for interpretability of detection results. The study conducted a series of tests to measure the system's performance in various scenarios and ensure its reliability in dealing with various types of input. Load testing results showed that up to 30 simultaneous users, the system can operate with good responsiveness (average response time of 130 ms) without experiencing errors. However, when the number of users reaches 40 or more, the system performance drops drastically with a very high error rate, reflecting limitations in handling server load. Real-world testing showed the system can detect deepfakes with an accuracy of 73.61%, with results varying depending on the quality of the tested images. Furthermore, unit functional testing and coverage analysis demonstrated an excellent test pass rate (85%), with all major functions running smoothly and error handling needed to be fixed in some code sections. Overall, the VERITAS system demonstrates strong potential for web-based deepfake detection, with high reliability under low load and adequate performance in functional testing. However, further optimization is needed to handle higher user loads.
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 Fatchul Arifin Genta Nazwar Tarempa Gunawan, Rohmat Gunawan, Rohmat Haerani, Erna Herman Dwi Surjono Hidayatulah Himawan Impron, Ali Indra Karyana Irani Hoeronis Komarudin, Arif Maulana Linda Sutriani Lukmana, Hen Hen Mas’ud, Mohd. Zaki Nugraha, Cindera Syaiful Nur Widiyasono, Nur Nursihab, Cucu 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 Selamat, Siti Rahayu Septian Chandra Siti Annisa, Siti Siti Yuliyanti, Siti Sudiarjo, Aso Syadza Anggraini Tiara Komala Sutra, Melanda Yahdi Siradj Yusuf Sumaryana