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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Peternakan Integratif Elkom: Jurnal Elektronika dan Komputer Journal of Education and Learning (EduLearn) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Scientific Journal of Informatics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization AdBispreneur Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Informatika Aptisi Transactions on Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mnemonic Journal Sensi: Strategic of Education in Information System Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Jurnal Bumigora Information Technology (BITe) Aiti: Jurnal Teknologi Informasi Infotech: Journal of Technology Information Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Applied Research (IJAR) Journal of Applied Data Sciences JOINTER : Journal of Informatics Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Information Technology (JIfoTech) Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Nusantara of Engineering (NOE) Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi Blockchain Frontier Technology (BFRONT) Scientific Journal of Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Irwan Sembiring
Universitas Kristen Satya Wacana
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Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic Wibowo, Mars Caroline; Setyawan, Iwan; Setiawan, Adi; Sembiring, Irwan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6717

Abstract

Despite the growing sophistication of cyberattacks, current network traffic analysis tools often lack intuitive visual support, limiting human analysts’ ability to interpret complex threat behaviors. To address this gap, this study proposes a novel deep learning-based visualization framework using a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize threat-specific infographics from structured numerical features in the CICIDS 2017 dataset. Unlike conventional methods, such as PCA or static dashboards, which often result in abstract or non-adaptive visuals, our approach generates class-distinct grayscale images that preserve the behavioral patterns of various attacks, including denial-of-service, brute force, and port scanning. The preprocessing pipeline reshapes the selected flow-based features into 28×28 matrices to train the generative model. Evaluation using the Frechet Inception Distance (FID) yielded a score of 28.4, whereas a CNN classifier trained on the generated images achieved 91.2% accuracy, confirming visual fidelity and semantic integrity. Additionally, a panel of human experts rated the interpretability of the generated images at 4.3 out of 5.0. These findings demonstrate that generative visualization can enhance human-centered threat analysis by bridging raw data with interpretable imagery, thereby offering a scalable and explainable approach for integrating AI into real-time security workflows.
Optimizing Automated Machine Learning for Ensemble Performance and Overfitting Mitigation Migunani, Migunani; Setiawan, Adi; Sembiring, Irwan
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 3 (2025): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i3.763

Abstract

Automated Machine Learning (AutoML) has revolutionized model development, but its impact on ensemble diversity and overfitting reduction remains underexplored. This Systematic Literature Review (SLR) analyzes 107 studies published between 2020 and 2024 to explore how AutoML enhances ensemble diversity, mitigates overfitting, and the challenges hindering its integration. Unlike previous reviews focusing on AutoML or ensemble methods independently, this study synthesizes their intersection and identifies key research trends. The findings reveal that AutoML improves ensemble robustness through automated hyperparameter tuning, meta-learning, and algorithmic blending while facing trade-offs in computational cost and interpretability. Four main themes emerge, integration mechanisms (19.6%), overfitting mitigation (26.2%), performance trade-offs (28.6%), and integration barriers (26.2%). Empirical results indicate that AutoML ensembles outperform traditional models by 22–41% in accuracy but require approximately 3.2 times higher computational resources. Hybrid AutoML and Explainable AI frameworks are recommended to balance accuracy and transparency. Theoretically, this study advances understanding of the synergy between AutoML and ensemble learning, while practically providing guidance for deploying reliable AI systems in sectors like healthcare, finance, and digital business. Policy implications align with the EU AI Act and the US Executive Order on trustworthy AI, supporting Sustainable Development Goals 9 and 8.
Implementation of Tensor Flow in Air Quality Monitoring Based on Artificial Intelligence Rahardja, Untung; Aini, Qurotul; Manongga, Danny; Sembiring, Irwan; Girinzio, Iqbal Desam
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.430

Abstract

Chemicals that cannot be controlled today can pollute resources and the environment. Common sources of pollutants are due to public transportation, cigarette smoke, volcanic activity that emits volcanic ash, factory smoke, forest fires, biogas, or carbon dioxide. The purpose of this paper is to monitor air quality, detect air and anticipate pollution levels. With the specified algorithms, three algorithms will be used to create a good and accurate model where four different gasses are predicted: carbon dioxide, sulfur dioxide, and nitrogen dioxide, in this paper, there are four algorithms used for the Air Qualification Index which are Support Vector Regression, Linear Regression, and Ensemble Gradient Boosted Decision Tree. This research also includes quantitative research which is hypothesized to be evaluated against Root Mean Squared Error, Mean Squared Error, and Mean Absolute error, depending on the performance of the measurements made by artificial intelligence, and the lower error value is selected. Based on the algorithm to be predicted in this air quality monitoring, there are 5 air pollutants like Carbon dioxide, Sulfur dioxide, and Nitrogen dioxide, and the sensors to be used are two sensors like PM2.5 and PM10 that can be predicted.
Ekstraksi Knowledge tentang Penyebaran #Ratnamiliksiapa pada Jejaring Sosial (Twitter) menggunakan Social Network Analysis (SNA) Tomasoa, Lyonly; Iriani, Ade; Sembiring, Irwan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 6: Desember 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4200.212 KB) | DOI: 10.25126/jtiik.2019661710

Abstract

Memasuki tahun politik 2018-2019, Indonesia mengalami darurat hoax  dimana isu-isu politik menyebar dengan sangat cepat terutama pada jejaring sosial yang merupakan wadah untuk menghubungkan setiap individu di seluruh dunia. Twitter sebagai salah satu jejaring sosial yang sering dipakai masyarakat Indonesia, menyebabkan isu-isu politik pun ikut terbawa dalam bentuk tagar (#). Tagar #RatnaMilikSiapa yang merupakan isu politik dari kasus hoax kebohongan penganiayaan RS di kota Bandung dijadikan sebagai tunggangan para pengguna twitter untuk membangun opini di masyarakat sehinga menjadikan hal tersebut sebagai hoax menjelang pemilihan Presiden 2019. Opini-opini dari setiap pengguna twitter tersebut telah menciptakan jaringan-jaringan komunikasi yang membahas tentang kasus politik penganiyayaan RS dengan tagar #RatnaMilikSiapa . Penelitian ini bertujuan untuk mengidentifikasi aktivitas penyebaran tagar #RatnaMilikSiapa dengan menggunakan metode Social Network Analyis (SNA) pada jejaring sosial twitter. Dalam penelitian ini, dilakukan identifikasi terhadap aktor utama dengan melakukan perhitungan sentralitas tingkatan atau degree Centrality (BC) sehingga dapat ditemukan aktor yang berpengaruh dalam terbentuknya kelompok-kelompok jaringan tweet #RatnaMilikSiapa pada jejaring sosial twitter. Hasil dari penelitan ini adalah ditemukannya 3 aktor  kunci (creator & influencers) yang berasal dari 5 aktor utama penyebaran tweet  #RatnaMilikSiapa dengan mengidentifikasi adanya pertukaran berita yang dilakukan oleh para aktor utama dan didukung dengan perhitungan nilai sentralitas keperantaraan atau betweenness Centrality (BC). Kemudian juga ditemukannya 32 aktor boundary spanner yang merupakan dampak dari aktivitas pertukaran berita atau information exchange  yang dilakukan oleh aktor kunci pada jaringan komunikasi dalam jejaring sosial twitter.AbstractEntering the political year of 2018-2019, Indonesia is facing a hoax crisis where political issues spread rapidly, especially on social media as a place for connecting people all over the world. Twitter as one of the popular social media, which is frequently used by the society of Indonesian, leads the political issues spread widely through the hashtag (#).  #RatnaMilikSiapa which was a hoax case about RS persecution in Bandung turned as a way for Twitter users creating a judgment in the society so that that issue became a hoax approaching the Presidential Election of 2019. The opinions of Twitter users had created a communication network discussed RS persecution as a political issue with #RatnaMilikSiapa.  This research intends to identify the #RatnaMilikSiapa deployment activity with the using of Social Network Analyis (SNA) method on Twitter. This research conducts the identification toward the main actors with degree Centrality (BC) calculation until the actor who influenced the establishment of #RatnaMilikSiapa tweet network groups on Twitter can be found. The results of this research are the researcher had found the three key actors (creator and influencers) which originated from 5 main actors who spread #RatnaMilikSiapa tweet. The researcher identifies the information exchange which had been done by the main actors and the results supports by the value of betweenness Centrality (BC) calculation. Later, the researcher had found 32 actors of boundary spanner which was the impact of information exchange done by the key actors on the communication network of Twitter.
Analisis Klasifikasi Sentimen Ulasan pada E-Commerce Shopee Berbasis Word Cloud dengan Metode Naive Bayes dan K-Nearest Neighbor Limbong, Josua Josen Alexander; Sembiring, Irwan; hartomo, kristoko dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 2: April 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022924960

Abstract

Saat ini internet memungkinkan pengguna untuk membuat ulasan secara online diberbagai jenis platform. Salah satunya aplikasi e-commerce Shopee pada website google play store dimana kelas sentimen positif dan negatif yang terdapat pada ulasan online jelas mencerminkan persepsi pengguna tentang berbagai jenis layanan dan produk yang ada. Selain itu, pelanggan berpotensial yang membaca ulasan online dapat secara signifikan terpengaruh oleh sentimen dari ulasan yang tertera pada kolom ulasan. Hal ini menandakan ulasan yang bersentimen positif ataupun negatif yang ditinggalkan oleh pengguna sangat mempengaruhi pengguna lainnya dalam memilih layanan maupun produk yang dicari. Oleh karena itu perlunya analisis sentimen untuk mengklasifikasi dataset yang begitu banyak sehingga dapat dengan mudah mengetahui apa saja sentimen pelanggan. penelitian ini menggunakan data ulasan sebanyak 500 ulasan . Kemudian ulasan tersebut diklasifikasi menggunakan aplikasi orange dengan metode Naïve Bayes dan K-Nearest Neighbor (KNN). Kemudian selanjutnya menggunakan metode word cloud untuk mengetahui topik-topik yang sering diulas oleh pelanggan. Hasilnya setelah menggunakan metode Naive Bayes memperoleh hasil nilai accuracy 0,914, precision 0,915, recall 0,914 dan F1 score 0,916. Sedangkan metode KNN memperoleh nilai accuracy 0,928,  precision 0,929,  recall 0,928, dan F1 score 0,926. Hal ini membuktikan bahwa dalam penelitian ini kinerja metode KNN lebih baik. Kemudian berdasarkan hasil word cloud yang diperoleh didapatkan informasi kata dengan sentimen positif yang paling sering diulas oleh pelanggan diantaranya terkait kata: gratis, bagus, suka, murah, mudah, dan cepat. Sedangkan informasi sentimen negatif yang diperoleh seperti kata : kecewa, jelek, mahal, bohong, ribet, dan perbaiki. AbstractToday the internet allows users to create online reviews on various types of platforms. One of them is the Shopee e-commerce application on the google play store website, where the positive and negative sentiment classes contained in online reviews reflect user perceptions about the various types of services and products available. Also besides, potential customers who read online reviews can be significantly affected by the sentiment of the reviews listed in the review column. This indicates that positive or negative reviews left by users greatly influence other users in choosing the services or products they are looking for. Therefore the need for sentiment analysis to classify such a large dataset so that you can easily find out what customer sentiments are. This study uses a dataset of 500 reviews. Then the reviews are classified using the orange application with the Naïve Bayes and K-Nearest Neighbor (KNN) methods. Then use the word cloud method to find out topics that are frequently reviewed by customers. The results, after using the Naïve Bayes method, get the accuracy value of 0.914, precision 0.915, recall 0.914, and F1 score 0.916. Meanwhile, the KNN method obtained an accuracy value of 0.928, precision 0.929, recall 0.928, and F1 score 0.926. This proves that in this study the performance of the KNN method is better. Then based on the word cloud results obtained word information with positive sentiments that are most often shared by customers related to words: free, good, like, cheap, easy, and fast. Meanwhile, the negative sentiment information obtained includes the words: disappointed, ugly, expensive, lying, complicated, and fix. 
Pengaruh E-Payment Trust terhadap Minat Transaksi pada E-Marketplace Menggunakan Framework Technology Acceptance Model (TAM) 3 Lestari, Merryana; Purnomo, Hindriyanto Dwi; Sembiring, Irwan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021855212

Abstract

Transaksi melalui e-Marketplace dilakukan menggunakan transaksi pembayaran secara digital yang disebut sebagai layanan e-Payment. Oleh karena itu, e-Payment memegang peranan penting dalam proses transaksi jual beli pada e-Marketplace khususnya dalam transaksi pembayaran. Seringkali pengguna memiliki kekhawatiran tersendiri dalam melakukan transaksi pembayaran menggunakan e-Payment, salah satu kekhawatiran paling mendasar adalah mengenai jaminan integritas keamanan data dan privasi data pelanggan. Kepercayaan pengguna dipandang menjadi suatu resiko besar yang dapat memberikan pengaruh terhadap minat pembelian pada e-Marketplace. Melalui penelitian ini, akan dianalisis bagaimana tingkat pengaruh kepercayaan pengguna pada e-Payment di Indonesia terhadap transaksi pada e-Marketplace memakai metode Technology Acceptance Model (TAM) versi 3. Hasil penelitian ini merupakan bahan evaluasi bagi vendor e-Marketplace guna melakukan analisis seberapa sering pengguna melakukan transaksi di dalam e-Marketplace sehingga semakin memberikan kepercayaan pengguna untuk melakukan transaksi menggunakan layanan e-Payment. AbstractTransactions through e-Marketplace are carried out using digital payment transactions or commonly referred to as e-Payments. Therefore, e-Payment plays an important role in the process of buying and selling transactions on the e-Marketplace, especially in payment transactions. Often users have their own concerns in making payment transactions using e-Payment, one of the most basic concerns is about guaranteeing data security integrity and customer data privacy. User trust is seen as a big risk that can have an influence on buying interest in the e-Marketplace. Through this research, it will be analyzed how the level of influence of user trust in e-Payment in Indonesia on the impact of purchases on e-Marketplace using the Technology Acceptance Model 3 (TAM 3) framework. The results of this study can be used as evaluation material for e-Marketplace vendors to analyze how often users make transactions in the e-Marketplace so that it gives more confidence to users to make transactions using e-Payment services.
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning Kurniati, Florentina Tatrin; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan; Huizen, Roy Rudolf
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27842

Abstract

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.
Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators Siswanto, Joko; Manongga, Danny; Sembiring, Irwan; Wijono, Sutarto
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1245

Abstract

The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models.
Analisis Verifikasi Proof of Stake (POS) NFT dengan Teknologi Smart Contract Sumampouw, Eleazer Gottlieb Julio; Sembiring, Irwan
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2024): EduTIK : Februari 2024
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/edutik.v4i1.9214

Abstract

ABSTRAK Penelitian mengenai Analisis Verifikasi Proof of Stake (PoS) NFT dengan Teknologi Smart Contract, yang dilakukan melalui metode eksperimental, menghasilkan pencapaian yang sesuai dengan tujuan penelitian. Peneliti berhasil mengembangkan dan menjalankan sistem sesuai dengan tujuan yang diinginkan. Beberapa pencapaian utama mencakup implementasi berhasil dari proses verifikasi PoS, serta proses Stake, Unstake, dan Claim yang menggunakan integrasi Web3 dan dompet Metamask. Rekam transaksi dengan akurat mencatat waktu pengirim dan penerima bersama dengan prosedur verifikasi pemilik. Lebih lanjut, penelitian ini menyajikan analisis perbandingan antara Proof of Work (PoW) dan Proof of Stake (PoS). Temuan penelitian menunjukkan keunggulan Proof of Stake (PoS) dalam efisiensi waktu transaksi, biaya transaksi yang lebih rendah, peningkatan keamanan melalui pemilihan validator yang cermat, dan ketahanan terhadap berbagai jenis serangan. Secara keseluruhan, penelitian ini mengukuhkan keefektifan dan keunggulan implementasi Proof of Stake (PoS) dalam konteks Non-Fungible Tokens (NFTs) menggunakan Smart Contract. ABSTRACT The research on the Analysis Verification of Proof of Stake (PoS) NFT Smart Contract Technology, conducted through experimental methods, has yielded successful outcomes aligning with the research objectives. The researcher has successfully developed and executed the system, achieving the intended goals. Key accomplishments include the successful implementation of the PoS verification process, as well as the Stake, Unstake, and Claim processes, utilizing Web3 and Metamask wallet integration. Transaction records accurately capture the timing of sender and receiver actions, alongside owner verification procedures. Furthermore, the research presents a comparative analysis between Proof of Work (PoW) and Proof of Stake (PoS). The findings underscore the superiority of Proof of Stake (PoS) in terms of transaction time efficiency, lower transaction costs, enhanced security through meticulous validator selection, and resilience against various types of attacks. Overall, the research substantiates the efficacy and advantages of implementing Proof of Stake (PoS) in the context of Non-Fungible Tokens (NFTs) using Smart Contracts.
Implementasi dan Analisis Deteksi Serangan Jaringan pada Web Server NFT Menggunakan Suricata Pinontoan, Phillnov Yohanes; Sembiring, Irwan
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2024): EduTIK : Februari 2024
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/edutik.v4i1.9428

Abstract

ABSTRAK Penelitian ini berfokus pada masalah keamanan jaringan yang menjadi krusial bagi perusahaan teknologi blockchain dan Non-Fungible Token (NFT) yang rentan terhadap serangan siber seperti DDoS, injeksi SQL, dan malware. Serangan ini tidak hanya menyebabkan kerugian finansial tetapi juga merusak reputasi dan kepercayaan pengguna. Suricata, sebagai sistem deteksi dan pencegahan intrusi open-source, menawarkan berbagai fitur untuk memonitor dan menganalisis lalu lintas jaringan secara real-time. Penelitian ini mengevaluasi efektivitas Suricata dalam mendeteksi ancaman pada web server NFT melalui pendekatan eksperimental. Pengujian dilakukan dengan metode scanning port, web penetration testing, DDoS, dan identifikasi kerentanan sistem web server menggunakan alat seperti NMap, Hping3, Nikto, dan Metasploit. Hasil menunjukkan bahwa Suricata mampu mencatat aktivitas mencurigakan dan mencegah anomali dengan integrasi firewall PFsense. Implementasi Suricata memberikan informasi deteksi serangan web scanning, meskipun tidak memiliki aturan shared object seperti perangkat lunak intrusi lainnya. Penelitian ini memberikan rekomendasi bagi pengembang dan operator platform NFT untuk melindungi aset digital mereka dari serangan siber, serta berkontribusi pada peningkatan keamanan jaringan di sektor NFT. ABSTRACT This research focuses on the critical issue of network security for blockchain technology and Non-Fungible Token (NFT) companies, which are vulnerable to cyberattacks such as DDoS, SQL injection, and malware. These attacks not only cause financial losses but also damage reputation and user trust. Suricata, an open-source intrusion detection and prevention system, offers various features to monitor and analyze network traffic in real-time. This study evaluates the effectiveness of Suricata in detecting threats on NFT web servers through an experimental approach. Testing methods include port scanning, web penetration testing, DDoS, and identifying web server vulnerabilities using tools such as NMap, Hping3, Nikto, and Metasploit. The results show that Suricata can log suspicious activities and prevent anomalies when integrated with the PFsense firewall. While Suricata provides information on web scanning attacks, it lacks shared object rules found in other intrusion software. This research offers recommendations for NFT platform developers and operators to protect their digital assets from cyberattacks and contributes to improving network security in the NFT sector. Thus, this study is highly relevant in the digital era, where information and data security are top priorities for business continuity and user privacy protection.
Co-Authors Abas Sunarya, Po Ade Iriani Adi Setiawan Adriyanto Juliastomo Gundo Agus Sugiarto Agustinus, Ari Aji, Bintang Kristianto Alamsyah, Ferry Andriana, Myra April Lia Hananto Apriliasari, Dwi Ardaneswari, Awanda Arthur, Christian Astawa, I Wayan Aswin Dew Ayu Sanjaya, Yulia Putri Bayu Setyanto Pamungkas Bayu, Teguh Indra Budhi Kristianto Budhi Kristianto Budi Santoso Budi, Reza Setya Cahyaningtyas, Christian Daniawan, Benny Danny Manongga Danny Sebastian Dedy Prasetya Kristiadi Dwi Hosanna Bangkalang Dwi Setiawan Edi Suharyadi Efendy, Rifan Eko Sediono Eko Sediyono Eleazer Gottlieb Julio Sumampouw Elmanda, Vonda Erick Alfons Lisangan Esti Zakia Darojat Evangs Mailoa Evi Maria Faturahman, Adam Fauzi Ahmad Muda Fian Yulio Santoso Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gerry Santos Lasatira Ginting, Jusia Amanda Girinzio, Iqbal Desam Gudiato, Candra Hamdan . Hany Makaruku, Yulian Hasnudi . Henderi Henderi . Hendry Hendry, - Henuk, Yusuf Leonard Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil Ignatius Agus Supriyono Ilham Hizbuloh Indrastanti Ratna Widiasari Iwan Setiawan Iwan Setiawan Iwan Setyawan Joko Listiawan Sukowati Joko Siswanto Joko Siswanto Jonas, Dendy Julians, Adhe Ronny Juneth Manuputty Krismiyati Kristoko D Hartomo Kristoko Dwi Hartomo Kusumajaya, Robby Andika Limbong, Josua Josen Alexander Manongga, Daniel H.F Manongga, Daniel H.F. Manongga, Daniel HF Marsyel Sampe Asang Marvelino, Matthew Mau, Stevanus Dwi Istiavan Maya Sari Merryana Lestari Migunani Migunani Mira Mira Mira Mohammad Ridwan Muhamad Yusup Nanle, Zeze Nazmun Nahar Khanom Nina Setiyawati Ninda Lutfiani Nining Fitriani Nugroho, Samuel Danny Nurtino, Tio Nuryadi, Didik Nurzainah Ginting Pamungkas, Bayu Setyanto Phillnov Yohanes Pinontoan Pinontoan, Phillnov Yohanes Priatna , Wowon Purbaratri, Winny Purnama Harahap, Eka Purnomo, Hidriyanto Dwi Putra, Yonathan Rahadi Qurotul Aini Qurotul Aini R. Suharyadi Rahardja.,M.T.I.,MM, Dr. Ir. Untung Raymond Elias Mauboy Rimes Jopmorestho Malioy Roy Rudolf Huizen Saian, Septovan Dwi Suputra Sandry Lanovela Pasaribu Santoso, Nuke Puji Lestari Sediyono, Eko - Setiawan Hakim Sri Ngudi Wahyuni Sri Ngudi Wahyuni, Sri Ngudi Sri Yulianto Joko Prasetyo Suharyadi Sulistio Sulistio Sumampouw, Eleazer Gottlieb Julio Supriadi, Candra Suryantara, I Gusti Ngurah Susanti, Novita Dewi Sutarto Wijono Suwijo Danu Prasetyo Teady Matius Surya Mulyana, Teady Matius Teguh Indra Bayu Teguh Wahyono Theopillus J. H. Wellem Tintien Koerniawati Tirsa Ninia Lina Tomasoa, Lyonly Tri Wahyuningsih Tri Wahyuningsih Tukino, Tukino Untung Rahardja Untung Rahardja Wibowo, Mars Caroline Wijaya, Angga Zakharia Wiwien Hadikurniawati Yerik Afrianto Singgalen Yessica Nataliani Yohan Maurits Indey Yohnes Madawara, Herdin Yulian Hany Makaruku