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All Journal Journal of Community Development Jurnal Nasional Teknik Elektro dan Teknologi Informasi International Journal of Healthcare and Information Technology
Intan Sulistyaningrum Sakkinah
Politeknik Negeri Jember
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Nursing Public Health Social Sciences

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Pengembangan Hypermedia Learning Environment (HLE) untuk Meningkatkan Self-Regulated Learning Berdasarkan Kemampuan Self-Monitoring Intan Sulistyaningrum Sakkinah; Rudy Hartanto; Adhistya Erna Permanasari
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1475.507 KB) | DOI: 10.22146/jnteti.v11i2.3480

Abstract

The use of learning media is currently growing rapidly. Today, many studies use computers as adaptive learning media for students; one example is the hypermedia learning environment (HLE). HLE media was developed to assist students in learning, such as the current situation of the Corona Virus Disease 2019 (COVID-19) pandemic which requires all learning activities to be carried out online. One of those affected fields is the education field, where all learning activities are transferred online, so HLE web-based learning can help students to keep learning from home. HLE is currently being developed to improve students’ abilities in the self-regulated learning (SRL) process. In SRL, there is an important component in it, namely self-monitoring. However, in its development, the developed HLE is not based on self-monitoring. In this study, an adaptive HLE was developed based on students’ self-monitoring abilities. In its development, the HLE system used the agile development method, namely Scrum. The initial data collection for student classification was the self-regulatory inventory (SRI). SRI was used as an instrument to measure students’ self-monitoring ability. The data were then processed to classify students into three classes, namely high, medium, and low. Subsequently, the results of the classification of student abilities were used to develop learning aids in HLE. The development assistance provided was in the form of text and videos that were adjusted to the level of student self-monitoring. From the results of the development, it was found that all HLE functions could run well. The system was tested on twelve students to determine the level of usability by using the system usability scale (SUS). The results were classified as good category, with a score of 72.92. Further research can apply this method to students and measure the effectiveness of the system that has been developed.
Penerapan Digitalisasi Layanan Publik di DISPORABUDPAR Kab. Nganjuk Dengan Aplikasi “Nganjuk Elok” Raditya Pratama; Ulfa Emi Rahmawati; Puji Hastuti; Intan Sulistyaningrum Sakkinah; Nina Virgiana; Fadillah Wahyu Nugraha; Amirzan Fikri Prasetyo
Journal of Community Development Vol. 5 No. 3 (2025): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i3.1434

Abstract

One of the essential obligations of higher education institutions is to engage in community service. To maximize the impact of these activities, collaborations with potential beneficiaries are crucial. Public service organizations are ideal partners for such endeavors. This community service project focused on enhancing the efficiency and accessibility of services at the DISPORABUDPAR of Nganjuk Regency, located at Jl. Diponegoro No. 29, Mangundikaran, Nganjuk District, East Java. Key challenges identified included manual processing of artist registration renewals, the necessity of in-person visits for advice letters and performance permits, disorganized building rental administration, and public confusion regarding event promotion. To address these issues, a mobile and web-based application named "Nganjuk Elok" was developed. This digital solution aims to encourage community participation in cultural and tourism activities while improving the efficiency of budget and resource management within the DISPORABUDPAR. Ultimately, "Nganjuk Elok" has the potential to contribute to local economic growth by attracting more tourists and fostering the creative sector.
Intelligent System for Early Detection of Heart Disease Using XGBoost Machine Learning Algorithm on Web Application Intan Sulistyaningrum Sakkinah; Puji Hastuti; Muhammad Ainul Fikri; Ulfa Emi Rahmawati; Raditya Arief Pratama; Maulana Akbar Firdausya; Ratna Indah Anggraini
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6685

Abstract

Heart disease remains a major contributor to global mortality, highlighting the importance of effective early detection systems that can assist both clinicians and general users. This study develops a heart disease prediction model based on the XGBoost algorithm and deploys it within a web-based application to enhance accessibility and practical usability. The research uses a dataset of 918 instances containing 12 demographic and clinical features commonly associated with cardiovascular risk. Pearson correlation analysis was performed to assess feature relevance, revealing that ExerciseAngina, Oldpeak, ST_Slope, Age, and MaxHR exhibit the strongest correlations with the HeartDisease outcome. These findings align with established clinical evidence on exercise-induced angina, ST-segment depression, and cardiac functional capacity. Following preprocessing and feature encoding, the XGBoost model was trained and evaluated. The model achieved strong predictive performance, with 88.26% accuracy, 88.32% precision, 91.66% recall, an F1-score of 89.96%, and an ROC-AUC of 0.93. The results demonstrate that XGBoost effectively discriminates between positive and negative cases and provides a good balance between sensitivity and precision. To enable real-world applicability, the final model was deployed on a Flask backend and integrated into a web application that allows users to input clinical parameters and receive real-time predictions. System testing confirmed that the application accurately delivers outputs and functions reliably across different input conditions. Overall, this study shows the feasibility of combining machine learning with web technologies to support early, accessible heart disease screening. Future work will involve usability testing and validation using real patient data to further strengthen the system’s clinical relevance.
Co-Authors Adhistya Erna Permanasari Amirzan Fikri Prasetyo Fadillah Wahyu Nugraha Maulana Akbar Firdausya Muhammad Ainul Fikri Nina Virgiana Puji Hastuti Raditya Arief Pratama Raditya Pratama Ratna Indah Anggraini Rudy Hartanto Ulfa Emi Rahmawati