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All Journal Jurnal Simetris Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma Moneter : Jurnal Keuangan dan Perbankan Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Chaerul Umam
Jurusan Teknik Kimia, Universitas Sultan Ageng Tirtayasa, Cilegon-Banten, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Other

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Deteksi dan Pencegahan Web Defacing Judi Online dengan Wazuh SIEM dan Snort IDS Berbasis Signature Reza Pahlevi, Mohammad Rizky; Umam, Chaerul; Handoko, L. Budi
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2220

Abstract

Web defacing attacks, where websites are replaced with unwanted content, such as online gambling advertisements, pose a serious threat to the integrity and reputation of websites, especially those belonging to government agencies. This research aims to detect and prevent web defacing attacks containing online gambling content by combining Wazuh Security Information and Event Management (SIEM) and Snort signature-based Intrusion Detection System (IDS). Wazuh is used to monitor and collect activity logs in real-time when suspicious activity is detected. Meanwhile, Snort IDS acts as a signature-based intrusion detection system that can recognize web defacing attack patterns through predefined rules for online gambling content. This research was conducted by building a web defacing attack simulation environment on the server, then testing the response and effectiveness of Wazuh and Snort in detecting and preventing attacks. The test results show that the combination of Wazuh SIEM and Snort IDS can detect and prevent web defacing attacks with a very high accuracy rate, namely 100% of attacks can be detected by Wazuh File Integrity Monitoring and 76% for Snort IDS. The implementation of this system is expected to help improve website security, especially those managed by public institutions, from web defacing threats.
DIGITAL SIGNATURE PADA CITRA MENGGUNAKAN RSA DAN VIGENERE CIPHER BERBASIS MD5 Handoko, Lekso Budi; Umam, Chaerul; Setiadi, De Rosal Ignatius Moses; Rachmawanto, Eko Hari
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v10i1.2212

Abstract

Salah satu teknik yang populer untuk mengamankan data dengan tingkat keamanan yang tinggi yaitu kriptografi. Berbagai penelitian telah dilakukan dengan menggabungkan kunci simteris dan kunci asimteris untuk mendapatkan keamanan ganda. Dalam makalah ini, tanda tangan digital diterapkan melalui Rivest Shamir Adleman (RSA) sebagai algoritma kunci asimteris yang akan digabung dengan algoritma kunci simteris Vigenere Cipher. RSA yang tahan terhadap serangan karena menggunakan proses eksponensial dan kuadrat besar dapat menutupi kelemahan Vigenere Cipher, sedangkan Vigenere Cipher dapat mencegah kemunculan huruf yang sama dalam cipher yang mempunyai pola tertentu. Vigenere cipher mudah diimplementasikan dan menggunakan operasi substitusi. Untuk mengkompresi nilai numerik yang dihasilkan secara acak, digunakan fungsi hash yaitu Message Digest 5 (MD5). percobaan dalam makalah ini telah memberikan kontribusi dalam peningkatan kualitas enkripsi dimana citra digital dioperasikan dengan MD5 yang kemudian hasilnya akan diubah menjadi RSA. Fungsi hash awal yaitu 32 karakter diubah menjadi 16 karakter yang akan menjadi inputan untuk proses RSA dan Vigenere Cipher. Pada citra berwarna yang digunakan sebagai media operasi, akan dilakukan pengecekan apakah citra tersebut sudah melalui proses digital signature
Implementation Of Extreme Gradient Boosting Algorithm For Predicting The Red Onion Prices Saputri, Pungky Nabella; Alzami, Farrikh; Saputra, Filmada Ocky; Andono, Pulung Nurtantio; Megantara, Rama Aria; Handoko, L Budi; Umam, Chaerul; Wahyudi, Firman
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023): APRIL
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.456 KB) | DOI: 10.32832/moneter.v11i1.55

Abstract

Red Onion or the Latin name Allium Cepa is included in the group of vegetable plants that are needed by the public for food needs. Red Onions are one of the seasonal crops so their availability can change in the market which causes price instability due to a lack of supply of production by several factors: 1) not yet it's harvest time, 2) crop attacked disease pests and fungi, and 3) weather factor. Therefore, a study is needed to predict red onion prices, so that it can be used as information for the government to stabilize red onion prices. The method used in this study is CRISP-DM and the Extreme Gradient Boosting algorithm to predict the price of red onions by taking data samples from Tegal and Pati Cities. The results of this study are that the Extreme Gradient Boosting algorithm is able to produce Tegal District Root Mean Square Error (RMSE) values of 5107.97% and Mean Absolute Percentage Error (MAPE) values of 0.17%. For prediction results with Pati Regency data samples, it produces a Root Mean Square Error (RMSE) value of 6049.74% and a Mean Absolute Percentage Error (MAPE) of 0.17%.
PREDIKSI EMAIL PHISING MENGGUNAKAN SUPPORT VECTOR MACHINE Umam, Chaerul; Handoko, L. Budi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 8, No 01 (2024): SEMNAS RISTEK 2024
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v8i01.7138

Abstract

Email phising merupakan salah satu bentuk kejahatan di internet yang dapat merugikan banyak orang. Ketika seseorang sudah terkena phising maka data data orang tersebut dapat hilang dan digunakan oleh orang yang tidak bertanggung jawab. Pada penelitian ini, akan melakukan proses klasiifkasi email phising dengan menggunakan bantuan machine learning yaitu algoritma SVM. Dataset yang digunakan pada penelitian ini yaiitu merupakan dataset yang berisi body email yang terdiri dari total 18650 data yang terdiri dari 11322 data safe email dan 7328 data phising email. Dari data tersebut, akan dibagi menjadi 70% data pelatihan dan 30% data pengujian. Setelah dilakukan proses pengujian pada penelitian ini, algoritma SVM yang digunakan mendapatkan akurasi pengujian sebesar 84.56%.
KOMBINASI AUTOKEY CIPHER DAN TRANSPOSISI KOLOM DALAM MODEL SUPER ENKRIPSI Handoko, L. Budi; Umam, Chaerul
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 8, No 01 (2024): SEMNAS RISTEK 2024
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v8i01.7132

Abstract

Kriptografi modern dibangun berdasarkan banyak konsep yang diperkenalkan dalam kriptografi klasik. Penelitian ini mengevaluasi efektivitas penggunaan metode Autokey Cipher dan Transformasi Kolom dalam melindungi keamanan data sensitive. Tranposisi kolom merupakan jenis trabnsposisi cipher yang mudah dan sederhana. Dengan menerapkan metode enkripsi Autokey Cipher menggunakan kunci 'FIKUNGGUL' dan transformasi kolom dengan kunci 'JAYA', teks asli 'UDINUSSMG' berhasil diubah menjadi teks sandi yang kompleks dan sulit diprediksi. Hasil penelitian menunjukkan bahwa penggunaan kedua teknik kriptografi ini secara signifikan meningkatkan tingkat keamanan data terhadap serangan brute force dan akses tidak sah. Proses enkripsi dan dekripsi yang kompleks dari kedua metode kriptografi tersebut berhasil mencegah penyerang untuk dengan mudah mendapatkan akses ke informasi yang dilindungi, serta memberikan lapisan keamanan tambahan yang efektif.
IMPLEMENTATION OF LSTM (LONG SHORT TERM MEMORY) ALGORITHM TO PREDICT WEATHER IN CENTRAL JAVA Irwan, Rhedy; Andono, Pulung Nurtantio; Al Zami, Farrikh; Ocky Saputra, Filmada; Megantara, Rama Aria; Handoko, L. Budi; Umam, Chaerul
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1118

Abstract

Agro-indutrial agricultural production such as red onions in Indonesia has a very important share in driving Indonesia's economic growth, especially in Central Java province which contributed 28.15% of the total national red onion production in 2021. Weather conditions have a major influence on the red onion planting process until the red onions are ready to be harvested. In this study, the objective is to predict various types of weather such as rainfall, air temperature, and air humidity in seven districts in Central Java, namely Brebes, Temanggung, Demak, Boyolali, Kendal, Pati, and Tegal. To do this, the use of the LSTM (Long Short Term Memory) algorithm with its ability to store memory longer than RNN will be reliable for predicting various types of weather in the future. This research was developed with the CRISP-DM (Cross Industry Process Model for Data Mining) method which has a goal-oriented approach, this method is a mature and widely accepted method in Data Mining with various applications in Machine Learning. With the final results from 39 models by using the evaluation of the average value of train MSE 0.013, test RMSE 0.11, test MSE of 0.02, test RMSE 0.12 and succeed to predict 5 days or months ahead from the last data that is provided.
Imperceptible Watermarking Using Discrete Wavelet Transform and Daisy Descriptor for Hiding Noisy Watermark Abdussalam, Abdussalam; Umam, Chaerul; Sari, Wellia Shinta; Rachmawanto, Eko Hari; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Lestiawan, Heru; Islam, Hussain Md Mehedul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4423

Abstract

This research aims at overcoming the challenge of improving security and robustness in digital image watermarking, a critical activity in protecting intellectual property against misuse and manipulation. In a move to overcome such a challenge, this work introduces a new form of watermarking that incorporates Discrete Wavelet Transform (DWT) and Daisy Descriptor, with a view to enhancing both durability and invisibility of the watermark. The proposed method embeds a noise-variant watermark into selected frequency sub-bands using DWT, while the Daisy Descriptor enhances resistance to noise-based attacks. Testing conducted with three grayscale images, namely Lena, Cameraman, and Lion, each with a resolution of 512 × 512 pixels, showed that the proposed DWT-Daisy Descriptor outperforms current methodologies, producing high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values. In fact, in Lena, a PSNR value of 63.71 dB and an SSIM value of 1 were attained, with Cameraman having a PSNR value of 68.33 dB and an SSIM value of 1. As for attack resistivity, a high PSNR value of 50.11 dB under Gaussian attack and 55.70 dB under Salt-and-Pepper attack, with SSIM values approaching 1, confirm the robustness of the proposed scheme. This study highlights the significance of an efficient and secure watermarking technique that not only preserves image quality but also withstands various distortions, making it highly relevant for digital content protection in modern multimedia applications.
Performance Analysis of Support Vector Classification and Random Forest in Phishing Email Classification Umam, Chaerul; Handoko, Lekso Budi; Isinkaye, Folasade Olubusola
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3301

Abstract

Purpose: This study aims to conduct a performance analysis of phishing email classification system using machine learning algorithms, specifically Random Forest and Support Vector Classification (SVC). Methods/Study design/approach: The study employed a systematic approach to develop a phishing email classification system utilizing machine learning algorithms. Implementation of the system was conducted within the Jupyter Notebook IDE using the Python programming language. The dataset, sourced from kaggle.com, comprised 18,650 email samples categorized into secure and phishing emails. Prior to model training, the dataset was divided into training and testing sets using three distinct split percentages: 60:40, 70:30, and 80:20. Subsequently, parameters for both the Random Forest and Support Vector Classification models were carefully selected to optimize performance. The TF-IDF Vectorizer method was employed to convert text data into vector form, facilitating structured data processing. Result/Findings: The study's findings reveal notable performance accuracies for both the Random Forest model and Support Vector Classification across varying data split percentages. Specifically, the Support Vector Classification consistently outperforms the Random Forest model, achieving higher accuracy rates. At a 70:30 split percentage, the Support Vector Classification attains the highest accuracy of 97.52%, followed closely by 97.37% at a 60:40 split percentage. Novelty/Originality/Value: Comparisons with previous studies underscored the superiority of the Support Vector Classification model. Therefore, this research contributes novel insights into the effectiveness of this machine learning algorithms in phishing email classification, emphasizing its potential in enhancing cybersecurity measures.
Analisis Tripartit Keamanan Docker: Evaluasi Metode Deteksi Kerentanan, Registry, dan Layanan Widyanto Utomo, Arya; Ghozi, Wildanil; Umam, Chaerul
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2983

Abstract

The adoption of Docker as the standard container platform poses new security challenges, particularly regarding vulnerabilities in public images. This study evaluates the effectiveness of three vulnerability scanning methods for Docker images: direct scanning, vendor-integrated SBOM scanning, and cross-vendor SBOM scanning, using Trivy and Grype on 36 images from three major registries (Docker Official, Bitnami, Chainguard). The results show that direct scanning and vendor-integrated SBOM scanning produce identical detections (12,023 vulnerabilities with Trivy; 8,950 with Grype), while cross-vendor SBOM scanning decreases dramatically by more than 90% (only 800–790 findings). Chainguard proved to be the most secure, while Docker Official was the most vulnerable (e.g., python:latest had 2,053 vulnerabilities). Programming language-based images (Rust: 3,825; Node.js: 3,816) were also riskier than specialized services (Redis: 341; MongoDB: 351). This research developed a framework for evaluating the effectiveness of cross-approach vulnerability scanning and strengthened the theory of software supply chain security through the concept of SBOM provenance dependency, which became the basis for the development of a multi-phase vulnerability scanning framework and recommendations for secure container implementation.
Co-Authors Abdussalam Abdussalam, Abdussalam Al zami, Farrikh Alzami, Farrikh ARIANTO, EKO De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Ervikhan, Muhammad Ibnu Firman Wahyudi, Firman Guruh Fajar Shidik Heru Lestiawan Irwan, Rhedy Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul L. Budi Handoko Megantara, Rama Aria Nur Hariyanto, Mohammad Malik Ocky Saputra, Filmada Pulung Nurtantio Andono Reza Pahlevi, Mohammad Rizky Saputra, Filmada Ocky Saputra, Maulana Bagus Saputri, Pungky Nabella Sinaga, Daurat Sinaga, Daurat Sya'bani, Hafiidh Akbar Wellia Shinta Sari Widyanto Utomo, Arya Wildanil Ghozi