Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal JUTI: Jurnal Ilmiah Teknologi Informasi PINISI Discretion Review Journal of Embedded Systems, Security and Intelligent Systems
Talasari, Resky Ayu Dewi
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Library & Information Science Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Improving The UI/UX Quality Of The JasBi Application Using UEQ And UCD Talasari, Resky Ayu Dewi; Ilham, Karina Fitriwulandari; Yuhana, Umi Laili
PINISI Discretion Review Volume 7, Issue 2, March 2024
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/pdr.v7i2.54053

Abstract

This research was conducted to analyze and evaluate the User Interface (UI) and User Experience (UX) contained in the JasBi application using a User Experience Questionnaire (UEQ) and User Centered Design (UCD). At the first questionnaire distribution stage, JasBi application users were less satisfied with the existing UI. This research is a quantitative study using a survey method of those who use the JasBi application. Based on the design of solutions using the UCD method results in the following results: interest in the JasBi application UI is excellent (value 2.30), clarity in the JasBi application (averaged 1.98), efficiency in the JasBi application is excellent (value 2.30), simulation in the JasBi application is excellent (averaged 1.88), newness in the JasBi application (2.10).
Survey on Risks Cyber Security in Edge Computing for The Internet of Things Understanding Cyber Attacks Threats and Mitigation Hadiningrum, Tiara Rahmania; Talasari, Resky Ayu Dewi; Ilham, Karina Fitriwulandari; Ijtihadie, Royyana Muslim
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1210

Abstract

Dalam era pesatnya perkembangan teknologi, penggunaan IoT terus meningkat, terutama dalam konteks edge computing. Makalah survei ini secara teliti menjelajahi tantangan keamanan yang muncul dalam implementasi IoT pada edge computing. Fokus utama penelitian ini adalah potensi serangan dan ancaman siber yang dapat mempengaruhi keamanan sistem. Melalui metode survei literatur, makalah ini mengidentifikasi risiko keamanan siber yang mungkin timbul dalam lingkungan IoT di edge computing. Pendekatan metodologi penelitian digunakan untuk mengklasifikasikan serangan berdasarkan dampaknya pada infrastruktur, layanan, dan komunikasi. Keempat dimensi klasifikasi, yaitu Network Bandwidth Consumption Attacks, System Resources Consumption Attacks, Threats to Service Availability, dan Threats to Communication, memberikan dasar untuk memahami dan mengatasi risiko keamanan. Makalah ini diharapkan memberikan landasan pemahaman yang kokoh tentang keamanan pada IoT dalam edge computing, serta kontribusi untuk pengembangan strategi keamanan yang efektif. Dengan fokus pada pemahaman mendalam tentang risiko keamanan, makalah ini mendorong pengembangan solusi keamanan yang adaptif di masa depan untuk mengatasi tantangan keamanan yang berkembang seiring dengan pesatnya adopsi teknologi IoT di edge computing.
Analyzing the Impact of Data Filtering on Anomaly Detection under Distribution Shift Conditions Talasari, Resky Ayu Dewi; Ayutri Wahyuni
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10051

Abstract

One of the main challenges in anomaly detection for Windows Event Logs and Sysmon is distribution shift, where changes in data distribution invalidate the model's learned normality reference. This study evaluates how data filtering setting value boundaries classified as normal affects the model's ability to handle distribution shifts across three experimental scenarios. This research is among the first to systematically quantify the trade-off between filtering efficiency and model adaptability across varying magnitudes of distribution shifts in anomaly detection systems. The experimental design employs three scenarios: Scenario 1 evaluates filtering under complete cross-environment shift using Dataset A for training and Dataset B for testing, Scenario 2 examines filtering with partial Dataset B training data, and Scenario 3 validates model adaptability without filtering constraints. The goal is to determine whether filtering improves performance under small, adaptable shifts and to measure its impact under large shifts that push the distribution far from the initial training data. Shift magnitude is measured using Jensen Shannon Divergence and Hellinger Distance, followed by evaluation of model performance through precision, recall and F1-score. Results show that filtering can help for minor shifts but substantially impairs adaptation under substantial distributional changes: filtered models remain constrained by prior baseline behavior and fail to learn new patterns, while unfiltered models adapt successfully and maintain accurate detection. These findings suggest critical implications for designing adaptive anomaly detection systems in dynamic operational environments where changes frequently alter normal behavior patterns. Future approaches should incorporate adaptive filtering mechanisms that dynamically adjust baseline boundaries rather than relying solely on static training data distributions.
Co-Authors Ayutri Wahyuni Hadiningrum, Tiara Rahmania Ilham, Karina Fitriwulandari Royyana Muslim Ijtihadie Yuhana, Umi Laili