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All Journal Jurnal Teknik Informatika (JUTIF) Nuansa Informatika Digitus : Journal of Computer Science Applications
Adiyati, Nita
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Computer Science & IT Decision Sciences, Operations Research & Management

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PENGEMBANGAN APLIKASI AUGMENTED REALITY SEBAGAI MEDIA PEMBELAJARAN UNTUK MENGENAL TATA SURYA PADA SD ISLAMIC VILLAGE Edi Junaedi; Adiyati, Nita; Wulandari, Fithri; Maulana Ramadhan, Adrian
NUANSA INFORMATIKA Vol. 17 No. 2 (2023): Volume 17 No 2 Tahun 2023
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v17i2.29

Abstract

Educators must adapt their teaching methods during the COVID-19 pandemic to ensure effective and captivating learning experiences that incorporate interactivity, challenges, motivation, and allow students to nurture their creativity and independence based on their individual talents and interests. At Islamic Village Elementary School, it is especially important to utilize engaging learning media since elementary students have a natural enthusiasm for new concepts. When selecting learning media, factors such as learning objectives, effectiveness, user-friendliness, and flexibility should be taken into consideration. Augmented Reality (AR) applications have emerged as an exciting advancement in learning media, particularly in helping students comprehend Natural Science subjects. Thus, an AR application has been developed as a learning tool for Natural Science at Islamic Village Elementary School, utilizing the Marker-Based Tracking AR method. The research aims to facilitate students' learning and understanding of the material while making the learning process more captivating and less monotonous. Within the application, objects are visually presented in 3D using animations, sound, and appealing colors. The research methodology encompasses literature review, field study, and sample calculations. The outcome of this research is a mobile application that serves as a supportive tool to enhance the learning experience.
ANDROID-BASED GARBAGE MANAGEMENT APPLICATION USING K-MEANS ALGORITHM ON RT 03/02 KEL. KARAWACI BARU Abid, M. Nur Rois; Artanti, Sarah Camilla; Adiyati, Nita; Junaedi, Edi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2274

Abstract

Technological advances have brought significant changes in human life, one of which is the facilitation of access to information. Despite this, garbage management remains an important issue that requires serious attention. Without practical management efforts, the negative environmental impact will continue to increase. Therefore, the study aims to develop an Android-based waste management application using the Waterfall methodology approach and the K-means algorithm method to allow users to group the type of garbage according to its characteristics so that the waste management process can be done more systematically and efficiently. From the application design and testing results, it was concluded that this waste management application can serve as an effective tool in helping people manage their garbage more efficiently. With this digital platform, it is expected that public awareness of the importance of garbage management can be increased and contribute to the maintenance of hygiene and the health of the environment. Thus, the Android-based waste management application has great potential to be a relevant solution in dealing with the problem of waste management. With this digital approach, it is expected that people can be more effective in regulating and utilizing garbage and actively participate in efforts to maintain environmental sustainability for generations to come.
Early Prediction of At Risk Students Using Minimal Data: A Machine Learning Framework for Higher Education Hamsiah; Adiyati, Nita; Subekti, Rino
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.953

Abstract

Early identification of academically at risk students is essential for timely intervention and improved retention in higher education. This study investigates the effectiveness of using pre admission and early semester LMS data to predict student risk using machine learning models. The objective is to assess whether limited, readily available data from the first four weeks of instruction can reliably support early warning systems. A supervised learning framework was applied using the Open University Learning Analytics Dataset (OULAD), with features derived from student demographics and early LMS activity logs. Models evaluated include Logistic Regression, XGBoost, and CatBoost, with time based validation and SMOTE employed to address class imbalance. Model performance was measured using ROC AUC, F1 Score, and Recall. The CatBoost model achieved the best performance, with an F1 score of 0.770 and ROC AUC of 0.750, significantly outperforming baseline models. Quiz submission behavior, login frequency, and pre admission qualification level emerged as the most predictive features. Results also revealed a steady week by week improvement in model accuracy, confirming the increasing value of LMS engagement data over time. These findings affirm that early stage student data can be used effectively to predict academic risk, enabling institutions to act before major assessments are conducted. The study emphasizes the need for institutional readiness, ethical implementation, and inclusive practices in deploying predictive tools. Future research should expand the feature space and test cross institutional generalizability to refine early warning systems further.
Co-Authors Abid, M. Nur Rois Artanti, Sarah Camilla Edi Junaedi Edi Junaedi Hamsiah Maulana Ramadhan, Adrian Rino Subekti Wulandari, Fithri