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All Journal Infotech Journal Jurnal Ilmiah Universitas Batanghari Jambi Faktor Exacta Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Riset Informatika INTI Nusa Mandiri Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Pendidikan Teknologi Informasi dan Vokasional Jurnal Infortech JIRA: Jurnal Inovasi dan Riset Akademik Jurnal AbdiMas Nusa Mandiri Dedikasi: Jurnal Pengabdian kepada Masyarakat TIERS Information Technology Journal Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Teknologi Sistem Informasi Abdi Teknoyasa Journal of Artificial Intelligence and Digital Business COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal Komputer Antartika Jurnal Pengabdian Masyarakat J-CEKI

Fachri Amsury

Universitas Nusa Mandiri

Author-ID : 3447608

Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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

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.

COMPARISON OF CLASSIFICATION ALGORITHMS FOR ANALYSIS SENTIMENT OF FORMULA E IMPLEMENTATION IN INDONESIA Fachri Amsury; Nanang Ruhyana; Tati Mardiana
Jurnal Riset Informatika Vol 4 No 3 (2022): Period of June 2022
Publisher : Kresnamedia Publisher

The Formula E racing series has become one of the world's most prestigious competitions. In 2022, Indonesia hosted the famous Formula E race. The event possesses the potential for economic benefits for Indonesia worth 78 million euros through the arrival of 35,000 spectators. Indonesians are enthusiastic about Formula E since it allows their nation to encourage tourists and gain international prominence. However, some people do not support this event. Since they regard that amid the COVID-19 pandemic, it is preferable for the government to focus on people affected by the pandemic rather than support a Formula E event. This study compares the Support Vector Machine and Naive Bayes algorithms in classifying public opinion in the Formula E race. This study gets its information from user comments on social media platforms, especially Twitter. The stages start with text preprocessing and include cleaning, case folding, tokenization, filtering, and stemming. Proceed with weighting using the TF-IDF approach. Data testing uses a confusion matrix to evaluate the classification results by testing accuracy, precision, and recall. Categorizing public opinion using the SVM algorithm has an accuracy of 82 percent, a precision of 97.86 percent, and a recall of 77.90 percent. On the other hand, the accuracy of the Naive Bayes technique is more limited, at 87.54 percent. Society's opinion on Twitter shows positive sentiment towards implementing Formula E.

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

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.

CLASSIFICATION OF STUDENT SATISFACTION WITH ONLINE LECTURE Nanang Ruhyana; Tati Mardiana; Fachri Amsury; Daning Nur Sulistyowati
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Abstra Covid-19 has had a significant impact on people's lives, resulting in the paralysis of almost the entire economy and education, especially in the education sector, resulting in many students being unable to carry out teaching and learning activities at schools or universities. Based on this, the Ministry of Education and Culture has issued an appeal to stop face-to-face teaching and learning activities at schools and universities and replace them with distance or online learning. Resulting in teaching and learning activities to be less than optimal for students or students, there is dissatisfaction with the distance or online learning system, the purpose of this study is to measure the level of student satisfaction with online lectures by applying data mining techniques, classifying the level of online learning satisfaction using an online learning approach. k-NN algorithm and Decision Tree with 100 questionnaire data that has been collected from active students who carry out online lectures with an accuracy rate of 96.00% from the k-NN algorithm and a satisfied precision value of 95.51%, a satisfied recall value of 98.84% on a precision value the dissatisfied class is 90.91%, the recall value of the dissatisfied class is 71.43%. While the accuracy results using the Decision Tree algorithm approach is lower with an accuracy of 95.00%. based on research results that the level of student satisfaction with distance learning or online is quite high.

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

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.

COMPARISON OF CLASSIFICATION ALGORITHMS FOR ANALYSIS SENTIMENT OF FORMULA E IMPLEMENTATION IN INDONESIA Fachri Amsury; Nanang Ruhyana; Tati Mardiana
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Implementation of the Association Method in the Analysis of Sales Data From Manufacturing Companies Fachri Amsury; Nanang Ruhyana; Andry Agung Riyadi; Ihsan Aulia Rahman
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

The company produces sales data every day. Over time, the data increases, and the amount becomes very large, and 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. In order 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 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.

Implementation of the FP-Growth Algorithm on Spare Parts Supply Requests Amsury, Fachri; Nanang Ruhyana; Riyadi, Andri Agung; Bayhaqy, Achmad
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Manufacturing companies rely on machines for operational activities to produce finished goods. Common factors constraining the demand and supply of spare parts are the high number of spare parts managed and irregular patterns of demand for spare parts. These varying quantities also require investment in spare parts inventory and longer response times than predicted. The research aims to apply the FP-Growth algorithm approach to find association rules and produce patterns of demand and supply of spare parts in lightweight brick manufacturing companies based on transaction data on demand and supply of spare parts from January – March 2023. The approach used is associated with the applied algorithm. In this research, the primary process of the FP-Growth algorithm is to create a combination of each item until no more combinations are formed using minimum support and minimum confidence parameters. Based on the results of making association rules using spare parts demand data from the machine maintenance department, it is stated that the regulations formed from processing the RapidMiner application with a confidence value of 100% recommend FD Regular Bolt spare parts, then the next rating with a confidence value of 94% is Steel Nuts, seven rules recommend Nuts. Steel. Therefore, it is recommended that FD Regular Bolts and Steel Nuts carry out safety stock to maintain stock availability and place them on shelves included in the fast-moving inventory category.

Co-Authors Adiputra, Jason Adiputra, Mahesa Aditya, Tommy Ahmad Fadlil Fauzi Alghifari, Luthfi Adam Andri Agung Riyadi Andry Agung Riyadi Anggi Dian Oktavianingsih ANGGIE ARDIANSYAH Anjani, Mutiara Putri Asrul Azalia, Devina Bayhaqy, Achmad BENNI RAMADHAN Dwiza Riana Fahlapi, Riza Fatihah, Cinta Aprilia Febriyanti, Syafvika Tiara Ferdy Saputra Frieyadie Gunawan, Heru HANAFI EKO DARONO Hanifah, Nida Helmalia Putri Ismayani Heriyanto Heriyanto Heriyanto Heriyanto Heriyanto Heriyanto Heriyanto Hernawati Ibrahim, Akbar Ida Ayu Putu Sri Widnyani Ihsan Aulia Rahman Ika Kurniawati Ika Kurniawati Ika Kurniawati Intan Permatasari Irwansyah Saputra Irwansyah Saputra Irwansyah Saputra Irwansyah Saputra Irwansyah Saputra Jody, Pradithia Juan Immanuel Jupriyanto . Muhammad Ilyas MUHAMMAD RIZKI FAHDIA Muhammad Rizki Fahdia Muhammad Rizki Fahdia Muhammad Rizki Fahdia Nanang Ruhyana nanang ruhyana Nanang Ruhyana Nanang Ruhyana Nanjaya, Ahmad Fadhil Nurajijah Nurajijah Oktavia, Devya Septi Ongki D.Simatupang Pangestu, Ridwan Panggabean, Gempar Galang Al Fallah Pradana, Andreas Eka Prasetyo, Dwi Bagus Putria Pebriana Sitanggang Rachimsah, Wildan RAHMAD SINGGIH AJI PAMBUDI Rasam Rasam Riyadi, Andri Agung Riza Fahlapi Rizki Fahdia, Muhammad Rusdiansyah, Irfandi Saputra, Aden Asywak Saputra, Irwansyah Satria, Fauzan Setiawan, Rizqi Siti Fauziah Siti Fauziah Sucahyo, Muhamad Yusuf Sulistyowati, Daning Nur Syahrur Rhamadan Tati Mardiana Tiska Pattiasina Tue Rebong, Hendrikus Vivi Rahayu Yusnia Budiarti Zhafran, Muhammad Faiz

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