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All Journal BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer JOINTECOMS (Journal of Information Technology and Computer Science)
Ashshidiq, Muhammad Faisal
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Computer Science & IT

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Penerapan Algoritma String Matching dan Regular Expression pada Aplikasi Kamus Besar Bahasa Indonesia (KBBI) Bintang, Jasmine Mutiara; Ashshidiq, Muhammad Faisal; Dzakwan, Hilal Fakhri
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 4 No 1 (2023): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v4i1.57

Abstract

The Kamus Besar Bahasa Indonesia (KBBI) application is a software or application that allows users to access and search for the meaning of a single word in Indonesian along with its spelling. Indonesian has a complex morphological structure, where words can change form through affixation (addition of prefixes or suffixes), reduplication, or internal changes that create many dialectal variations and spelling variations that can affect word forms and writing patterns. This causes a variety of words that are similar but different in writing. On the other hand, that the Indonesian Dictionary application needs speed and accuracy of word searches then the program makes adjustments to those in the dictionary. Therefore, researchers analyze based on the data that has been obtained by the KBBI application using string matching (brute force) and regular expression algorithms. Researchers hope that the algorithms that have been analyzed can help solve problems in the KBBI application in conducting word searches to find the meaning of spelling funds.
Perbandingan Kinerja Algoritma Unsupervised Machine Learning untuk Deteksi Anomali dalam Proses ETL Ashshidiq, Muhammad Faisal; Fauzan, Mohammad Nurkamal; Nuraini, RN
Journal of Information Technology and Computer Science Vol. 5 No. 3 (2025): JOINTECOMS : Journal of Information Technology and Computer Science
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jointecoms.v5i3.22586

Abstract

Penelitian ini melakukan perbandingan komprehensif terhadap tiga algoritma unsupervised machine learning untuk deteksi anomal dalam proses Extract, Transformasi, Load (ETL). Algoritma yang dibandignkan adalah Isolation Forest, Local Outlier Factor, dan One-Class Support Vector Machine (OC-SVM). Penelitian ini menggunakan dataset dengan struktur nested array ayng umum ditemukan pada aplikasi berbasis web dan Internet of Things (IoT). Hasil penelitian menunjukkan bahwa Isolation Forest memberiikan performa terbaik dengan nilai F1-Score 0.723, accuracy 0.935, precision 0.567 dan recall 1.00. Local Outlier Factor menunjukkan performa terendah dengan F1-Score 0.221, dan One-Class SVM memberikan performa moderat dengan F1-Score 0.488. Hasil visualisasi menggunakan Principal Component Analysis (PCA) untuk memperkuat temuan dalam memisahkan data normal dan anomali. penelitian ini memberikan kontribusi penting dalam pemilihan algoritma deteksi anomaly yang tepat untuk menjaga kualitas data setelah proses ETL.
Co-Authors Bintang, Jasmine Mutiara Dzakwan, Hilal Fakhri Fauzan, Mohammad Nurkamal Nuraini, RN