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Analisis Keamanan dan Kenyamanan pada Cloud Computing Dwina Satrinia; Syifa Nurgaida Yutia; Iik Muhamad Malik Matin
Journal of Informatics and Communication Technology (JICT) Vol 4 No 1 (2022)
Publisher : PPM Institut Teknologi Telkom Telkom Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v4i1.111

Cloud computing atau ‘komputasi awan’ menyajikan berbagai kemudahan untuk organisasi maupun individu dalam mengakses data dimanapun dan kapanpun. Cloud computing memiliki kelebihan seperti memberikan berbagai pilihan model layanan, jenis penyimpanan data, serta kustomisasi komputasi sehingga memberikan manfaat yang menarik yaitu efisiensi, efektif dan hemat biaya, tetapi hal tersebut tidak membuat cloud computing aman dari ancaman serangan keamanan. Konsep keamanan dibutuhkan untuk membantu manajemen pada organisasi maupun individu dalam melindungi dan melakukan pengamanan data pada layanan cloud. Selain itu, model kenyamanan juga diperlukan untuk membantu pengguna dalam menggunakan layanan cloud.

Analisis Keamanan dan Kenyamanan pada Cloud Computing Dwina Satrinia; Syifa Nurgaida Yutia; Iik Muhamad Malik Matin
Journal of Informatics and Communication Technology (JICT) Vol. 4 No. 1 (2022)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v4i1.111

Cloud computing atau ‘komputasi awan’ menyajikan berbagai kemudahan untuk organisasi maupun individu dalam mengakses data dimanapun dan kapanpun. Cloud computing memiliki kelebihan seperti memberikan berbagai pilihan model layanan, jenis penyimpanan data, serta kustomisasi komputasi sehingga memberikan manfaat yang menarik yaitu efisiensi, efektif dan hemat biaya, tetapi hal tersebut tidak membuat cloud computing aman dari ancaman serangan keamanan. Konsep keamanan dibutuhkan untuk membantu manajemen pada organisasi maupun individu dalam melindungi dan melakukan pengamanan data pada layanan cloud. Selain itu, model kenyamanan juga diperlukan untuk membantu pengguna dalam menggunakan layanan cloud.

Impact of Hyperparameter Optimizer for Image Malware Detection Iik Muhamad Malik Matin
International Conference on Education, Science, Technology and Health (ICONESTH) 2024: The 2nd ICONESTH
Publisher : International Conference on Education, Science, Technology and Health (ICONESTH)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46244/iconesth.vi.367

Image-based malware detection has become an area of further research in dealing with image-based malware attacks. Various deep learning models have been used to improve detection accuracy. One popular architecture is VGG16, a convolutional network widely used in image classification. In this study, we explore the impact of hyperparameter tuning on the optimization of the VGG16 model for image-based malware detection. The hyperparameter experiments conducted in this study are optimizer, and the number of epochs. Through 6 experiments with parameter variations, we evaluate the performance of the VGG16 model using several SGD, and Adam optimizers and the number of epochs consisting of 100, 250 and 500 epochs. The experimental results show that the selection and tuning of the optimizer can affect the performance of the model in terms of accuracy and training efficiency. The optimized Adam optimizer gives the best results, with higher detection accuracy than the SGD optimizer. The results show that the Adam optimizer has the highest accuracy reaching 85%.

Sistem Monitoring Keamanan Pada Data Center Berbasis Security Information And Event Management (Siem) Dengan Wazuh dan IDS Iik Muhamad Malik Matin; Fadhilrahman; Asep Kurniawan; Ayu Rosyida Zain; Maria Agustin
Journal of Innovative and Creativity Vol. 5 No. 1 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i1.1761

A data center is a center for managing, storing and processing important data which includes sensitive information, business applications and technology infrastructure. Due to the importance of data centers in supporting organizational operations, their vulnerability to information security threats such as cyber attacks, unauthorized access has a serious impact on daily operations. The implementation of IDS is currently not optimal because in-depth analysis is difficult and requires experts to observe it. For this reason, an effective security monitoring system is needed on an Intrusion Detection System (IDS) based on Security Information and Event Management (SIEM) using the Wazuh platform. IDS plays an important role in detecting network security threats, while SIEM provides the ability to integrate and analyze security data from various sources. This research designs and implements Wazuh integration with SIEM to strengthen detection and response capabilities against security threats. Experimental methodology is used to evaluate the performance of the developed system, with a focus on intrusion detection, log analysis and security event management. The research results show that the integration of Wazuh with SIEM provides significant improvements in monitoring capabilities and response to security threats, which will be a valuable contribution in ensuring the security of networks and sensitive data.

Deteksi PE Ransomware Menggunakan Shallow Learning Iik Muhamad Malik Matin; Zahra Azizah; Ihsan Alamal Ahmad
Prosiding Sains dan Teknologi Vol. 5 No. 1 (2026): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 5 - Februari 2026
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Ransomware merupakan salah satu ancaman keamanan siber yang berkembang pesat dalam satu dekade terakhir. Serangan jenis ini tidak hanya mengakibatkan kerugian finansial, tetapi juga gangguan pada layanan publik dan infrastruktur digital. Deteksi dini terhadap aktivitas ransomware menjadi tantangan utama karena pola serangan yang cepat dan adaptif. Penelitian ini bertujuan untuk mengimplementasikan metode Shallow Learning dalam mendeteksi ransomware menggunakan dataset RanSAP. Dataset ini memuat pola akses penyimpanan dari aktivitas ransomware dan aplikasi normal (benign). Empat algoritma yang digunakan yaitu Support Vector Machine (SVM), Random Forest (RF), Decision Tree dan Logistic Regression (LR). Evaluasi dilakukan dengan confusion matrix untuk mengukur akurasi, presisi, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa model SVM memiliki kinerja terbaik dengan akurasi 95%, diikuti RF dengan 93%, Desicion Tree 91% dan LR dengan 89%. Penelitian ini menunjukan bahwa Shallow Learning cukup efektif dalam mendeteksi pola perilaku ransomware.

A Rule-Based Data-Driven Framework for Partner Selection in Digital Agribusiness Zahra Azizah; Iik Muhamad Malik Matin; Okta Gabriel Sinsaku Sinaga; Faiz Akbar; Asiwidia Simanjuntak
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3359

Digital transformation has reshaped partner evaluation in agribusiness business-to-business (B2B) networks, shifting decision-making from intuition-based judgments to transparent, data-driven assessments. Addressing the need for scalable and trustworthy selection mechanisms, this study introduces a novel hybrid anomaly detection framework that sequentially combines rule-based z-score normalization with the Local Outlier Factor (LOF) algorithm to evaluate digital business credibility. The framework leverages Google Maps data, a widely accessible, user-generated information source that reflects real customer experiences, to assess 6,237 hospitality, restaurant, and café (HORECA) businesses in Indonesia’s Jabodetabek region, a growing hub in the agribusiness supply chain. Using structured data collected through the Google Places API, the rule-based method identified 47.06% of businesses as anomalies, predominantly those with disproportionately high ratings relative to customer engagement. Meanwhile, LOF detected 5.02% of density-based outliers, capturing irregularities that only emerge in local spatial and contextual comparisons. A statistical comparison (χ² = 195.10, p < 0.001) revealed a 56.52% overlap between the two methods, emphasizing their complementary strengths: rule-based thresholds provide interpretability and efficiency, whereas LOF offers sensitivity to nuanced, neighborhood-level deviations. These findings show that no single technique fully captures the complexity of digital credibility anomalies; however, their combination enables more balanced and context-aware evaluations. This approach enhances the accuracy and fairness of credibility assessments, which is crucial for partner selection in digital agribusiness ecosystems. Overall, the study provides practical and methodological contributions for building transparent, reproducible, and equitable anomaly-detection systems for emerging digital markets

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