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All Journal Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Advanced Computer Knowledge and Algorithms
Rijal, Himmatur
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

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ANALISA PENGARUH UKURAN BUFFER UNTUK SISTEM DUPLIKASI FILE BERSKALA BESAR PADA TEMPORARY FILE SYSTEM Rijal, Himmatur; Yafis, Balqis
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 2 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i2.10295

Abstract

Dalam penelitian mengidentifikasi pengaruh ukuran buffer dalam proses duplikasi file dengan ukuran file 1 gigabyte (GB) dan menggunakan sistem file tmpfs. Baseline code menggunakan operasi tingkat-rendah (low-level operation) seperti open(), creat(), read(), dan write(). Penelitian ini bertujuan untuk mengoptimasi proses duplikasi file menggunakan tiga metode. Pada tahap awal, penulis mengidenfikasi beberapa isu yang terdapat pada operasi read/write pada sistem file linux. Terdapat tiga metode yang akan diinvestgasi dalam penelitian ini, pertama, meningkatkan ukuran buffer, kedua membandingkan fungsi  creat() dan open(), ketiga menganalisa fungsi O_Direct() pada tmpfs. Untuk duplikasi file, ukuran buffer menjadi hambatan kinerja pada proses sistem file ini. Karena jumlah read() tergantung pada ukuran buffer, sehingga operasi baca-tulis terus beralih antara mode pengguna dan mode kernel.
Implement the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) Algorithms for Sales Classification Husna, Asmaul; Retno, Sujacka; Rijal, Himmatur
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17819

Abstract

The Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) algorithms are two algorithms that have proven efficient in various classification and prediction applications. This research examines the application of these two algorithms in the context of selling goods in PIM supermarkets. In this research, AHP and KNN are used to classify goods sold based on various criteria such as price, number of stock items sold, total sales amount. The research results show that KNN outperforms AHP in predicting the best-selling, best-selling and least-selling items based on sales in 2022 at PIM supermarkets. Based on this research, it can be concluded that the KNN algorithm is suitable for predicting the classification of goods sales in PIM Supermarkets. This research classifies sales of goods using the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) methods. This research uses 3 criteria. By using the value K=1, the experimental results show that the highest KNN has an accuracy of 38%, while AHP has an accuracy of 32%. Differences in accuracy results can be influenced by parameter settings and characteristics of the dataset used. Therefore, further analysis of these factors is needed to understand the performance differences between the two methods.
Co-Authors Asmaul Husna Sujacka Retno Yafis, Balqis