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All Journal VALENSI Voteteknika (Vocational Teknik Elektronika dan Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) Jurnal Kartika Kimia JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology JATI (Jurnal Mahasiswa Teknik Informatika) Informatics and Digital Expert (INDEX) Journal of Applied Computer Science and Technology (JACOST) International Journal of Quantitative Research and Modeling Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Algoritma IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Jurnal Matematika Integratif Jurnal Sains dan Kesehatan
Melina Melina
Jurusan Teknik Informatika, Fakultas Sains Dan Informatika, Universitas Jenderal Achmad Yani
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

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Journal : International Journal of Quantitative Research and Modeling

 1 
Comparative Analysis of K-Means and K-Medoids Clustering in Retail Store Product Grouping Muthmainah, Sekar Ghaida; Hadiana, Asep Id; Melina, Melina
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.753

Abstract

The retail business is growing very rapidly with increasing business competition. The application of information technology is one strategy for understanding consumer product purchasing patterns and grouping sales products. This research aims to analyze and compare the K-Means and K-Medoids Clustering techniques for retail data based on the Davies Bouldin Index value and computing time. K-Means is an algorithm that divides data into k clusters based on centroids, while K-Medoids Clustering uses objects with medoids representing clusters as centroid centers. Clustering in both methods produces an optimal number of clusters of 3 clusters. The results of this research show that K-Means produced 358 data in Cluster 1, 292 data in Cluster 2, and 367 data in Cluster 3 with a DBI of 0.7160. Meanwhile, K-Medoids produced 295 data in Cluster 1, 360 data in Cluster 2, and 362 data in Cluster 3 with a DBI of 0.7153. In addition, this study calculated the average computation from 5 experiments, namely K-Means with an average time of 0.024278/s and K-Medoids of 0.05719/s. Based on the lower DBI, K-Medoids have better results in clustering, but the K-Means method is better in terms of computational efficiency. It is hoped that the results of this research will provide valuable insights for retail business people in analyzing sales data.
Semantic Classification of Sentences Using SMOTE and BiLSTM Tanjung, Irvan; Ilyas, Rid; Melina, Melina
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.750

Abstract

A paraphrase is a sentence that is re-expressed with a different word arrangement without changing its meaning (semantics). To find out the semantic proximity to the pair of citation sentences in the form of paraphrases, a computational model is needed. In doing classification sometimes appears a problem called Imbalance Class, which is a situation in which the distribution of data of each class is uneven. There are class groups that have less data (minorities) and class groups that have more data (majority). Any unbalanced real data can affect and decrease the performance of classification methods. One way to deal with it is using the SMOTE method, which is an over-sampling method that generates synthesis data derived from data replication in the minority class as much as data in the majority class. The study applied SMOTE in the classification of semantic proximity of citation pairs, used Word2Vec to convert words into vectors, and used the BiLSTM model for the learning process. The research was conducted through 8 different scenarios in terms of the data used, the selection of learning models, and the influence of SMOTE. The results showed that scenarios using previous research data with BiLSTM and SMOTE models provided the best accuracy and performance.
Securing Network Log Data Using Advance Encryption Standard Algorithm And Twofish With Common Event Format Ali, Moch. Dzikri Azhari; Hadiana, Asep Id; Melina, Melina
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.757

Abstract

The rapid advancement of information technology demands enhanced security for data exchange in the digital world. Network security threats can arise from various sources, necessitating techniques to protect information transmitted between interconnected networks. Securing network logs is a critical step in strengthening overall network security. Network logs are records of activities within a computer network, including unauthorized access attempts, user activities, and other key events. This research focuses on developing a network log security system by comparing the performance of the Advanced Encryption Standard (AES) and Twofish algorithms, integrated with the Common Event Format (CEF) for encrypting network logs. Tests were conducted on network log datasets to evaluate system functionality and performance. Results indicate that the AES algorithm performs encryption and decryption faster than Twofish. Across five tests with different file sizes, AES took an average of 2.1386 seconds for encryption, while Twofish required 22.8372 seconds. For decryption, AES averaged 2.451 seconds compared to Twofish’s 26.140 seconds. The file sizes after encryption were similar for both algorithms. Regarding CPU usage, AES demonstrated higher efficiency. The average CPU usage during AES encryption was 0.5558%, whereas Twofish used 23.2904%. For decryption, AES consumed 0.4682% of CPU resources, while Twofish required 13.7598%. These findings confirm that AES is not only faster in both encryption and decryption but also more efficient in terms of CPU usage. This research provides valuable insights for optimizing network log security by integrating standardized log formats, like CEF, with appropriate encryption techniques, helping to safeguard against cyber threats.
Enhancing Email Client Security with HMAC and PGP Integration to Mitigate Cyberattack Risks Oktaviani, Ayu Nur; Hadiana, Asep Id; Melina, Melina
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.758

Abstract

The rapid advancement of technology in the modern era has significantly increased the risk of data breaches and misuse, particularly in email communications. Ensuring data privacy and security is crucial to preventing information theft and mitigating cyberattack risks. This research focuses on enhancing email client security through the integration of Hash-Based Message Authentication Code (HMAC) and Pretty Good Privacy (PGP). HMAC is employed as a message authentication mechanism to ensure the integrity and authenticity of email messages, while PGP is utilized to generate public and private key pairs, enabling secure encryption and decryption processes. By integrating these two security methods into the email client system, we aim to enhance its resilience against cyber threats. The system's effectiveness was evaluated through black-box testing, demonstrating its capability to secure the email delivery process. Additionally, an analysis of key randomness using the entropy method revealed a maximum value of 6 bits, indicating a relatively high level of randomness and further strengthening the encryption process. The results of this study indicate that the combined use of HMAC and PGP provides a robust security solution for enhancing email client security and mitigating potential cyberattack risks.
Co-Authors Abdillah, Fajrul Agus Komarudin Agus Komarudin Ahmad Marjan Akbar Mulya Hadi Ali, Moch. Dzikri Azhari Ardiyansyah, Muhamad Salman Asep Rizaludin Azhari, Moch Dzikri Benedictus Benny Sihotang Cecep Jamaludin Devitri Amisa Dewi Novianti Diana Lupitasari Dudi Darmawan Eddie Khrisna Putra Eina, Muhammad Fikri Emia Rosta Br. Sebayang Endang Rosdiana Fahmi Yusron Fiddin Fajri Rakhmat Umbara Farhan Fadilah Fuji Astari, Dhea Hadiana, Asep Id Herlina Napitupulu Hernandi Sujono Illawati, Adinda Rahma Ilyas, Rid Kasyidi, Fatan Komarudin, Agus Levi Sabili, Naufal Lia Dewi Juliawaty Lia Dewi Juliawaty Monica, Taris Muthmainah, Sekar Ghaida Nabilla, Ulya Nia Sri Hardianti Norizan Mohamed Octo Emerald Siregar Oktaviani, Ayu Nur Putra, Eddie Krishna Rahmat Awaludin Salam Razaki, Adam Restianim, Vivien Reza Fauzi Iskandar Rezky Yuniarti Ridwan Ilyas Senadi Budiman Sudrajat, Risqi Sukono Sukono Sukrido Sukrido Sumantri, Fithra Aditya Tanjung, Irvan Tia Setiawati Valentina Adimurti Kusumaningtyas Wina Witanti Yana Maolana Syah Yana Maolana Syah Yuliana, Trisna Yulison Herry Chrisnanto