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All Journal COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Mustakim Mustakim
Universitas Muhammadiyah Papua
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Analisis Penggunaan Teknologi Cloud Computing Dalam Manajemen Data di Perusahaan Mustakim Mustakim
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 4 No. 5 (2024): COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v4i5.1557

Abstract

Di era digital, jumlah data yang dihasilkan oleh perusahaan meningkat dengan sangat cepat, sehingga pengelolaan data yang efisien menjadi kebutuhan yang mendesak. Cloud computing muncul sebagai solusi teknologi yang menyediakan kemampuan untuk meningkatkan skala, efisiensi biaya, fleksibilitas, serta akses mudah dalam manajemen data. Penelitian ini bertujuan untuk mengeval_uasi penggunaan teknologi cloud computing dalam pengelolaan data di perusahaan, dengan studi kasus pada perusahaan di Papua. Penelitian ini menggunakan metode kualitatif dengan mengandalkan data sekunder. Teknik pengumpulan data mencakup observasi, wawancara, dan studi literatur. Setelah data dikumpulkan dianalisis menggunakan tiga langkah, yaitu penyederhanaan data, presentasi data, dan membuat kesimpulan. Hasil penelitian mengungkapkan bahwa cloud computing menjadi komponen penting dalam pengelolaan data perusahaan. Teknologi ini memfasilitasi perusahaan untuk menyimpan dan mengolah data secara online tanpa memerlukan infrastruktur fisik yang mahal dan terbatas. Beberapa keunggulan berupa fleksibilitas, skalabilitas, keamanan, serta efisiensi biaya, membuat cloud computing membantu perusahaan untuk meningkatkan pengelolaan data. Sehingga teknologi ini dapat membuka peluang bagi perusahaan untuk berinovasi, meningkatkan produktivitas, dan beradaptasi lebih cepat terhadap perubahan pasar.
Comparative Analysis of Classification Methods for Cyberattack Detection on Computer Networks Sherly Agustini; Mustakim Mustakim; Okta Veza
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1516

Abstract

The rapid growth of computer networks has increased the complexity and intensity of cyber threats, making machine learning based intrusion detection one of the most widely studied defense mechanisms. This study compares the performance of five classification methods Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes in detecting five categories of network activity: Normal, Denial of Service (DoS), Probing, Remote to Local (R2L), and User to Root (U2R). Due to limited access to sensitive real-world network traffic data, this study uses a small-scale simulated dataset of 500 samples generated programmatically using controlled statistical distributions to represent the characteristics of each category, including the class imbalance condition commonly found in real network traffic. The data was split into 70% training and 30% testing using a stratified scheme and evaluated using accuracy, precision, recall, F1-score, and computation time metrics. Results show that Decision Tree achieved the highest macro F1-score (85.96%) with 94.00% accuracy, slightly ahead of Naive Bayes (84.52% macro F1-score, 95.33% accuracy). Random Forest recorded the highest overall accuracy (96.67%), but its macro F1-score (84.06%) lagged due to low recall on the U2R class, which has very few samples. Feature-importance analysis indicates that srv_count, dst_host_count, and count are the main determinants for distinguishing attack categories. Naive Bayes and KNN recorded the fastest computation times, while Random Forest required the longest training time. The small dataset size causes performance estimates on minority classes (R2L and U2R) to be prone to fluctuation, so these findings should be regarded as a preliminary proof-of-concept study requiring further validation using a larger dataset or real world network traffic data.
Comparative Analysis of Classification Methods for Cyberattack Detection on Computer Networks Sherly Agustini; Mustakim Mustakim; Okta Veza
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1516

Abstract

The rapid growth of computer networks has increased the complexity and intensity of cyber threats, making machine learning based intrusion detection one of the most widely studied defense mechanisms. This study compares the performance of five classification methods Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes in detecting five categories of network activity: Normal, Denial of Service (DoS), Probing, Remote to Local (R2L), and User to Root (U2R). Due to limited access to sensitive real-world network traffic data, this study uses a small-scale simulated dataset of 500 samples generated programmatically using controlled statistical distributions to represent the characteristics of each category, including the class imbalance condition commonly found in real network traffic. The data was split into 70% training and 30% testing using a stratified scheme and evaluated using accuracy, precision, recall, F1-score, and computation time metrics. Results show that Decision Tree achieved the highest macro F1-score (85.96%) with 94.00% accuracy, slightly ahead of Naive Bayes (84.52% macro F1-score, 95.33% accuracy). Random Forest recorded the highest overall accuracy (96.67%), but its macro F1-score (84.06%) lagged due to low recall on the U2R class, which has very few samples. Feature-importance analysis indicates that srv_count, dst_host_count, and count are the main determinants for distinguishing attack categories. Naive Bayes and KNN recorded the fastest computation times, while Random Forest required the longest training time. The small dataset size causes performance estimates on minority classes (R2L and U2R) to be prone to fluctuation, so these findings should be regarded as a preliminary proof-of-concept study requiring further validation using a larger dataset or real world network traffic data.
Co-Authors Okta Veza sherly agustini