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All Journal Faktor Exacta Jurnal Riset Informatika
Andri Agung Riyadi
Universitas Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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COMPARATIVE ANALYSIS OF THE K-NEAREST NEIGHBOR ALGORITHM ON VARIOUS INTRUSION DETECTION DATASETS Andri Agung Riyadi; Fachri Amsury; Irwansyah Saputra; Tiska Pattiasina; Jupriyanto Jupriyanto
Jurnal Riset Informatika Vol 4 No 1 (2021): Period of December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (945.029 KB) | DOI: 10.34288/jri.v4i1.341

Abstract

Security in computer networks can be vulnerable, this is because we have weaknesses in making security policies, weak computer system configurations, or software bugs. Intrusion detection is a mechanism for securing computer networks by detecting, preventing, and blocking illegal attempts to access confidential information. The IDS mechanism is designed to protect the system and reduce the impact of damage from any attack on a computer network for violating computer security policies including availability, confidentiality, and integrity. Data mining techniques have been used to obtain useful knowledge from the use of IDS datasets. Some IDS datasets that are commonly used are Full KDD, Corrected KDD99, NSL-KDD, 10% KDD, UNSW-NB15, Caida, ADFA Windows, and UNM have been used to get the accuracy rate using the k-Nearest Neighbors algorithm (k-NN). The latest IDS dataset provided by the Canadian Institute of Cybersecurity contains most of the latest attack scenarios named the CICIDS2017 dataset. A preliminary experiment shows that the approach using the k-NN method on the CICIDS2017 dataset successfully produces the highest average value of intrusion detection accuracy than other IDS datasets.
Text Mining of PeduliLindungi Application Reviews on Google Play Store Irwansyah Saputra; Taufik Djatna; Riki Ruli A. Siregar; Dinar Ajeng Kristiyanti; Hasbi Rahma Yani; Andri Agung Riyadi
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.10629

Abstract

Aplikasi PeduliLindungi merupakan aplikasi buatan pemerintah indonesia  untuk melakukan pelacakan dan penghentian  penyebaran Covid-19. Ulasan terkait aplikasi tersebut tidak seluruhnya baik, hal ini dibuktikan dengan beragamnya peringkat bintang yang diberikan pengguna sehingga terjadinya kesulitan dalam melihat sentimen positif atau negatif terkait aplikasi tersebut. Penelitian ini bertujuan untuk mengklasifikasi ulasan mengenai aplikasi PeduliLindungi kepada dua kelas, yakni sentimen positif dan sentimen negatif. Algoritma klasifikasi yang digunakan adalah klasifikasi Naive Bayes Classifier (NBC). Hasil Menunjukkan Accuracy  85%, Precision 77,7%, Recall 98%, dan F1-Score 86,7%.
Implementation of the Association Method in the Analysis of Sales Data from Manufacturing Companies Andri Agung Riyadi; Fachri Amsury; Nanang Ruhyana; Ihsan Aulia Rahman
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.491

Abstract

The company produces sales data every day. Over time, the data increases, and the amount becomes very large. The data is only stored without understanding the benefits that exist from these data due to limitations in proper knowledge in analyzing the data, especially transaction data. Sale. To overcome these problems, a study focused on reprocessing sales transaction data in 2018 with a data mining technique approach using the Knowledge Discovery in Database (KDD) concept using the association method and apriori algorithm and a supporting application, namely RapidMiner. This study aims to help companies find customer buying habits or patterns based on 2018 sales transaction data. The results of this study produce 316 association rules where the best rules are generated on record 309 with PRO 889 & PRO 868 PRO 869 rules.
COMPARATIVE ANALYSIS OF THE K-NEAREST NEIGHBOR ALGORITHM ON VARIOUS INTRUSION DETECTION DATASETS Andri Agung Riyadi; Fachri Amsury; Tiska Pattiasina; Jupriyanto Jupriyanto
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i1.147

Abstract

Because we have flaws in developing security rules, inadequate computer system settings, or software defects, security in computer networks can be vulnerable. Intrusion detection is a computer network security method that detects, prevents, and blocks unauthorized access to confidential information. The IDS method is intended to defend the system and minimize the harm caused by any attack on a computer network that violates computer security policies such as availability, confidentiality, and integrity. Data mining techniques were utilized to extract relevant information from IDS databases. The following are some of the most widely utilized IDS datasets NSL-KDD, 10% KDD, Full KDD, Corrected KDD99, UNSW-NB15, ADFA Windows, Caida, dan UNM have been used to get the accuracy rate using the k-Nearest Neighbors algorithm (k-NN). The latest IDS dataset provided by the Canadian Institute of Cybersecurity contains most of the latest attack scenarios named the CICIDS2017 dataset. Preliminary experiment shows that the approach using the k-NN method on the CICIDS2017 dataset successfully produces the highest average value of intrusion detection accuracy than other IDS datasets.
Co-Authors Dinar Ajeng Kristiyanti Fachri Amsury Hasbi Rahma Yani Ihsan Aulia Rahman Irwansyah Saputra Irwansyah Saputra Jupriyanto Nanang Ruhyana Riki Ruli A. Siregar Taufik Djatna Tiska Pattiasina