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All Journal Jurnal Teknologi Informasi dan Multimedia Jurnal Bumigora Information Technology (BITe) International Journal of Electronics and Communications Systems
Naufal Hanif
Magister Ilmu Komputer, Universitas Bumigora
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Application of Domain Keys Identified Mail, Sender Policy Framework, Anti-Spam, and Anti-Virus: The Analysis on Mail Servers Marzuki, Khairan; Hanif, Naufal; Hariyadi, I Putu
International Journal of Electronics and Communications Systems Vol. 2 No. 2 (2022): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v2i2.13543

Abstract

Viruses spread through email are often sent by irresponsible parties that aim to infect email users' servers. This background encouraged the author to analyze the application of DKIM, SPF, anti-spam, and anti-virus to avoid spam, viruses, and spoofing activities. The goal is for the server to prevent spam, spoofing, and viruses to ensure the security and convenience of email users and prevent the impact of losses caused by them. The design and analysis of DKIM, SPF, anti-spam, and anti-virus applications use the NDLC methodology. The process includes designing spam, spoofing, and virus filtering systems and performing installation and configuration simulations. The next stage is implementation, during which the previously developed system is tested on the spam filtering system, spoofing, and viruses. The last stage is the monitoring stage, where supervision is conducted on the approach to determine its success level. This study concludes that applying the DKIM protocol can prevent spoofing through private and public key-matching methods for authentication. Meanwhile, the application of the SPF protocol can prevent spoofing by authorizing the IP address of the sending server. Additionally, SpamAssassin, ClamAV and Amavisd-New can prevent spam and viruses from entering by checking email headers, bodies, and attachments.
Prediksi Beban Kerja Server Secara Real-Time pada Pusat Data Cloud dengan Pendekatan Gabungan Long Short-Term Memory (LSTM) dan Fuzzy Logic Naufal Hanif; Dadang Priyanto; Neny Sulistianingsih
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.731

Abstract

Efficient resource management in Cloud Data Centers is essential to reduce energy waste and maintain optimal system performance. This study aims to predict server workload in real time using a hybrid approach that combines Long Short-Term Memory (LSTM) and Fuzzy Logic. CPU and RAM usage data were collected every second from a Proxmox Cluster using its API, then normalized and processed using an LSTM model to forecast future workloads. The predicted results were then classified using Fuzzy Logic into three workload categories: light, medium, and heavy. The model was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), where the results showed an MAE of 2.48 on the training data and 3.09 on the testing data, as well as RMSE values of 5.15 and 5.57, respectively. Based on these evaluation results, the prediction system achieved an accuracy of 97.52% on the training data and 96.91% on the testing data, indicating that the model can generate accurate and stable predictions. This method enables automated decision-making such as workload-based power management, thereby improving energy efficiency and overall system performance.
Peningkatan Kinerja Klasifikasi Scabies Sapi MenggunakanEdited Nearest Neighbours (ENN) pada Model Random Forestdan XGBoost Ihsan, M. Khaerul; Maulana, Muhammad; Tanwir, Tanwir; Mas’ud, Abi; Hanif, Naufal; Resmiranta, Dading Oktaviadi
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.6055

Abstract

Background: Scabies disease in cattle causes significant economic losses for farmers due to declines in the animals’physical condition and productivity.Objective: This study aims to evaluate the effectiveness of the Edited Nearest Neighbours (ENN) method in improvingclassification performance for scabies in cattle.Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600clinical symptom samples was converted to numerical data and cleaned of noise using the ENN technique.Result: Applying ENN significantly improved the accuracy of both the Random Forest and XGBoost models, increasing itfrom around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability todetect positive cases.Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnosticsystem. This method is highly recommended to optimize the performance of classification algorithms on animal clinicaldata with high levels of inconsistency. 
Co-Authors Dadang Priyanto Hariyadi, I Putu Ihsan, M. Khaerul Khairan marzuki Mas’ud, Abi Muhammad Maulana Neny Sulistianingsih Resmiranta, Dading Oktaviadi Tanwir, Tanwir