Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization International Journal of Advanced Science Computing and Engineering
Yulherniwati, -
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

 1 
Systematic Literature Review on Persuasive System Design Framework for Managing Curriculum Performance Saifunnizam, Syamir Thaqif; Md Fudzee, Mohd Farhan; Hanif Jofri, Muhamad; Kasim, Shahreen; Arrova Dewi, Deshinta; Arshad, Mohamad Safwan; Yulherniwati, -
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3663

Abstract

Integrating digital resources into educational assessment has led to the widespread adoption of e-portfolios as tools for documenting and evaluating student achievement, thereby transforming traditional evaluation methods. However, the existing frameworks primarily focus on assessing academic performance, often neglecting the comprehensive monitoring of student’s co-curricular activities. To overcome current gaps in comprehensive student evaluation, this study introduces a conceptual framework incorporating persuasive system design (PSD) into an e-portfolio to facilitate efficient co-curricular performance monitoring in Malaysian secondary schools. To ensure a thorough approach to educational evaluation, it is essential to effectively monitor and manage academic and extracurricular performance to understand student progress comprehensively. By adding Physical Activity, Sports, and Co-curriculum Assessment (PAJSK) – specific categories and key PSD elements- primary task support, dialogue support, system credibility support, and social support- that are all designed to improve user engagement and system dependability in an educational environment, the framework builds on the Oinas-Kukkonen and Harijumaa PSD Model. This study adapts and discusses the persuasive design elements to meet the goals of educational assessment frameworks by comparing PSD implementation in e-health, e-tourism, e-commerce, and e-learning. The results offer an overview of developing a practical, engaging e-portfolio framework that facilitates comprehensive student evaluation, especially in educational environments focusing on co-curricular achievement.
The Implementation of SAW and BORDA Method to Determine the Eligibility of Students’ Final Project Topic Meidelfi, Dwiny; Yulherniwati, -; Sukma, Fanni; Chandra, Dikky; Jonas, Anna Hendri Soleliza
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.5.2.447

Abstract

The fourth-year students of Bachelor of Applied Studies (BAS) Software Engineering Technology Department of Information Technology (IT) Politeknik Negeri Padang (PNP) are required to work on the Final Project Proposal to the Coordinator, to deliver to the expertise group team to assess the eligibility of the topic. The expertise teams consist of the same skill family. The assessment criteria include originality, novelty, target and topic contribution, methodology, and similarity. Therefore, a system to support group decisions is highly needed to get eligibility for the topic. In a pandemic like today, indoor gatherings are severely restricted. The work from home policy also limits the movement of the team to gather together so that the expert team who would judge cannot conduct a meeting to determine the feasibility of the final project topic optimally. The existence of a subjective assessment of a particular topic requires discussion from the team. The simple Additive Weighting (SAW) method was used to rank the final project proposal, and BORDA method was used to Accumulate the assessment score of the expert team. The research revealed the recommendation on students’ final topics. Testing is done by testing the sensitivity of the criteria used in a decision maker's preference. The final result of this research is a recommendation of a final project that is feasible to be implemented by students and recommendation for sensitive assessment criteria. From the ten topics of the final project that were assessed, seven topics could be accepted. The sensitivity test results showed that the weight with criterion 1 and criterion 4 significantly affect the assessment results.
Systematic Literature Review: An Early Detection for Schizophrenia Classification Using Machine Learning Algorithms Azizi, Ainin Sofiya; Kamal, Marnisha Mustafa; Azizan, Nurzarifah; Zawawi, Rohaizaazira Mohd; Zakaria, Noor Hidayah; Salamat, Mohamad Aizi; Yulherniwati, -
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2446

Abstract

Schizophrenia is a complex mental health disorder that poses significant challenges in diagnosis and treatment due to its multifaceted symptoms, such as hallucinations, delusions, and cognitive impairments. Early detection is crucial for effective intervention, yet traditional diagnostic methods often fail in precision and scalability. This systematic literature review investigates the application of machine learning (ML) algorithms in the early detection and classification of schizophrenia. By synthesizing findings from 40 primary studies, the review highlights the effectiveness of diverse ML models, including Random Forests, Support Vector Machines (SVM), and advanced deep learning techniques like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Key datasets such as clinical records, EEG signals, and neuroimaging data were analyzed to evaluate model performance across metrics like accuracy, precision, and sensitivity. Studies demonstrated that hybrid approaches, integrating multiple data sources and deep learning architectures, achieved classification accuracies exceeding 90%, with notable advancements in early-stage diagnosis. However, the review identifies critical challenges, including data quality issues, biases, and limited external validation, which hinder the widespread clinical application of these models. Through a comparative analysis of ML methods and traditional supervised approaches, the study underscores the transformative potential of ML in enhancing diagnostic accuracy and facilitating personalized treatment plans. Addressing current limitations, such as expanding data diversity and improving model interpretability, is essential for translating these findings into practical healthcare solutions. This research contributes to the growing knowledge in ML-driven diagnostics, advocating for its integration into clinical workflows to optimize schizophrenia management.
Co-Authors Arrova Dewi, Deshinta Arshad, Mohamad Safwan Azizan, Nurzarifah Azizi, Ainin Sofiya Dikky Chandra Dwiny Meidelfi Hanif Jofri, Muhamad Indri Rahmayuni Jonas, Anna Hendri Soleliza Kamal, Marnisha Mustafa Mohd Farhan MD Fudzee, Mohd Farhan Saifunnizam, Syamir Thaqif Salamat, Mohamad Aizi Shahreen Kasim, Shahreen Sukma, Fanni Taufik Hidayat Zakaria, Noor Hidayah Zawawi, Rohaizaazira Mohd