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All Journal Jurnal Teknologi Informasi dan Multimedia International Journal of Electronics and Communications Systems SWARNA Jurnal Kecerdasan Buatan dan Teknologi Informasi
Naufal Hanif
Program Studi Teknologi Informasi, Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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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.
COMPARATIVE ANALYSIS OF RANDOM FOREST AND SUPPORT VECTOR MACHINE FOR FOOD CALORIE LEVEL CLASSIFICATION Dading Oktaviadi Resmiranta; Tanwir; I Gede Yogi Pratama; Naufal Hanif; Azral Satriani; Khairan Marzuki
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.450

Abstract

The rapid escalation of global metabolic health concerns emphasizes the critical urgency for advanced technological solutions that facilitate precise and automated monitoring of daily caloric intake. This research conducts a rigorous comparative analysis to evaluate the predictive performance and computational efficiency of Random Forest (RF) and Support Vector Machine (SVM) algorithms in classifying food calorie levels. The methodology commenced with a comprehensive data preprocessing phase involving multi-strategy missing value imputation and the discretization of caloric values into ordinal categories. Feature selection was meticulously executed using linear regression coefficients to identify high-impact nutritional variables. To ensure a robust evaluation, the dataset was partitioned using an 80:20 ratio for training and testing, complemented by cross-validation to minimize bias and variance. Experimental results indicated that the Random Forest (RF) demonstrated superior classification capabilities, achieving a peak accuracy of 94.8% alongside balanced precision and recall scores. Statistical evaluation via confusion matrices further revealed that Random Forest exhibited enhanced generalization across high-dimensional nutritional features compared to the geometric approach of Support Vector Machine (SVM). Furthermore, the analysis of computational overhead provided critical insights into the real-time deployment feasibility of each model. Ultimately, the findings suggest that the Random Forest serves as a robust engine for personalized dietary management systems, offering a reliable framework for future developments in preventive digital healthcare. By successfully bridging machine learning with nutritional science, this study establishes a benchmark for high-accuracy food classification essential for modern health-centric mobile applications.
Branding and Digital Promotion to Increase Kub Ziae's Competitiveness in the Sembalun Tourism Area Ondi Asroni; Angga Radlisa Samsudin; Dading Oktaviadi Resmiranta; Muhammad Innuddin; Naufal Hanif; Muhammad Tahir
SWARNA: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 4 (2026): SWARNA : Jurnal Pengabdian Kepada Masyarakat, April, 2026
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/swarna.v5i4.2000

Abstract

KUB ZIAE Sembalun Bumbung is a souvenir center located on the main tourist route of Sembalun, offering products such as black garlic, organic red rice, Rinjani coffee, and dried beans. However, this business faces challenges including weak brand identity, low visibility, and the absence of digital presence. Limited digital literacy among managers also results in reliance on conventional promotion methods. This community service program aimed to enhance KUB ZIAE’s competitiveness through two key strategies: (1) strengthening offline visual identity with signboards, banners, and product labels; and (2) developing digital assets such as Google Business Profile, Instagram, and Facebook accounts, along with content creation training. A participatory approach was applied, including needs assessment, co-design, intensive training, and on-site mentoring. The results showed improved business visibility with the installation of signboards and banners, while new product labels increased consumer trust. On the digital side, the Google Business account successfully appeared in search results, Instagram posts reached hundreds of accounts, and members gained skills in creating and managing promotional content. Although challenges remain, such as limited digital literacy and internet connectivity, the intervention effectively enhanced visibility, expanded market reach, and strengthened KUB ZIAE’s image as a leading souvenir center in the Rinjani tourism area
Optimization of Support Vector Machine Using SMOTE and Grid Search for Kidney Health Data Classification Muhammad Maulana; Zulkipli Zulkipli; Tanwir Tanwir; Dading Oktaviadi Resmiranta; Naufal Hanif; Raisul Azhar
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.993

Abstract

Kidney disease is a highly prevalent health problem that can seriously impact the quality of life of those affected. To improve diagnostic accuracy, machine learning methods are widely used to classify patient data. Class imbalance (imbalanced data) is one of the problems that often occurs in the classification process and can affect the performance of machine learning models, especially in detecting minority classes. This study aims to improve the performance of the Support Vector Machine (SVM) algorithm by applying the SMOTE (Synthetic Minority Over-sampling Tech-nique) and Grid Search methods in the data classification process. SMOTE is used to balance the class distribution by adding synthetic data to the minority class, while Grid Search is used to ob-tain optimal model parameters. The results show that the SVM model without handling data im-balance produces relatively low performance with an accuracy value of 51%, precision 17%, re-call 33%, and F1-score 23%. After applying the SMOTE method, the model performance increases significantly to 81% accuracy, 81% precision, 80% recall, and 81% F1-score. Furthermore, the ap-plication of Grid Search to the SVM + SMOTE model provides the best results with an accuracy of 84%, precision 82%, recall 81%, and F1-score 81% with an AUC value of 0,92. The findings of this study indicate that the combination of SMOTE and Grid Search is effective in improving the per-formance of the SVM algorithm in data classification. The novelty of this study demonstrates that data imbalance management and hyperparameter optimization play a crucial role in producing more accurate and optimal classification models.
Co-Authors Angga Radlisa Samsudin Azhar, Raisul Azral Satriani Dadang Priyanto Dading Oktaviadi Resmiranta Dading Oktaviadi Resmiranta Hariyadi, I Putu I Gede Yogi Pratama I Putu Hariyadi Khairan marzuki Muhammad Innuddin Muhammad Maulana Muhammad Tahir Neny Sulistianingsih Ondi Asroni Tanwir Tanwir Tanwir Zulkipli Zulkipli