Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal JSI: Jurnal Sistem Informasi (E-Journal) JOISIE (Journal Of Information Systems And Informatics Engineering)
Fatimah, Aisyah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Perbandingan Query Response Time MariaDB Pada Sistem Operasi Kali Linux dan Ubuntu Fatimah, Aisyah; Afifah, Nurul; Septiani Kusuma Ningrum
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.230

Abstract

Data is a vital resource for organizations that demand unparalleled access speeds and adequate levels of security. Critical components in the data need to be managed effectively and efficiently to enable timely decision-making. This study proposed a comparative analysis of query execution time using MariaDB Database Management System (DBMS) on Kali Linux and Ubuntu operating systems. Researchers identified response times for select, update, and delete operations on variations in data size. This experiment was carried out three times to ensure the consistency of the results. As a result, Ubuntu exhibits a more stable pattern of increasing response time, although it is slower than Kali Linux. Ubuntu performs better in terms of scalability, especially when handling large datasets. Based on this study, the selection of an operating system for database management not only be based solely on query execution speed, but also consider other important factors, such as the operating system's security level, ease of use, and user preferences.
ANALISIS KOMPARATIF MODEL DATA MINING DALAM PREDIKSI KETEPATAN PENYELESAIAN SERVICE LEVEL AGREEMENT Fatimah, Aisyah; Tania, Ken Ditha; Meiriza, Allsela
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 9 No 1 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i1.4943

Abstract

Kepatuhan terhadap Service Level Agreement (SLA) sangat penting dalam manajemen layanan teknologi informasi untuk menjamin kualitas layanan dan mengatur ekspektasi penyelesaian insiden. Namun, banyak organisasi kesulitan memprediksi apakah tiket insiden akan memenuhi SLA, yang dapat menyebabkan ketidakpuasan pengguna, eskalasi masalah, dan beban kerja tinggi bagi tim IT. Tingginya volume tiket harian membuat identifikasi manual terhadap tiket berisiko tinggi menjadi tidak efektif. Penelitian ini bertujuan mengembangkan model klasifikasi berbasis machine learning untuk memprediksi kepatuhan tiket terhadap SLA. Empat algoritma dievaluasi: XGBoost, Random Forest, Decision Tree, dan Logistic Regression. Tahapan mencakup preprocessing, encoding fitur kategorikal, seleksi fitur berbasis Random Forest, penyeimbangan data menggunakan SMOTE, dan hyperparameter tuning dengan Optuna. Hasil menunjukkan XGBoost memiliki performa terbaik dengan akurasi 99,98%, precision 0,9437, recall 0,9710, dan F1-score 0,9571. Selain akurat dan efisien, model ini unggul secara interpretatif melalui SHAP, yang menjelaskan kontribusi tiap fitur. Kesimpulannya, XGBoost direkomendasikan sebagai model paling andal untuk menjadi alat bantu strategis bagi manajer layanan TI dalam mengidentifikasi insiden yang berisiko tinggi gagal memenuhi SLA.
Co-Authors Allsela Meiriza, Allsela Ken Ditha Tania Muhammad Afif Septiani Kusuma Ningrum