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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Sistemasi: Jurnal Sistem Informasi PINISI Discretion Review
Resky Ayu Dewi Talasari
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Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Library & Information Science Social Sciences

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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 Tiara Rahmania Hadiningrum; Resky Ayu Dewi Talasari; Karina Fitriwulandari Ilham; Royyana Muslim Ijtihadie
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.
Feature Selection and Explainable AI for Heart Disease Detection using Machine Learning Resky Ayu Dewi Talasari; Ayutri Wahyuni; Clara Diva; Muhammad Nur Alamsyah Rajab
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6491

Abstract

Early detection of heart disease is essential for supporting timely clinical intervention, improving treatment outcomes, and enhancing the quality of patient care. This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—combined with two feature selection methods, Chi-Square and Recursive Feature Elimination (RFE), using the UCI Heart Disease dataset. Six modeling scenarios were evaluated based on accuracy, precision, recall, and F1-score. The experimental results demonstrate that the Random Forest model achieved the best overall performance, with an accuracy of 85.2% and a recall of 97.0%, indicating a strong capability to identify patients with potential heart disease. To enhance model transparency and interpretability, SHAP (SHapley Additive exPlanations) was employed as an Explainable AI (XAI) technique and integrated into a web-based decision support system to provide intuitive explanations of prediction outcomes. The proposed system is intended to serve as an initial clinical decision-support tool and is not designed to replace diagnosis or clinical judgment by healthcare professionals.
Co-Authors Ayutri Wahyuni Clara Diva Ilham, Karina Fitriwulandari Karina Fitriwulandari Ilham Muhammad Nur Alamsyah Rajab Royyana Muslim Ijtihadie Tiara Rahmania Hadiningrum Umi Laili Yuhana