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All Journal G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal
Detin Sofia
Institut Teknologi dan Bisnis Bina Sarana Global, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Public Health Social Sciences Other

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Pengenalan Media Pembelajaran Augmented Reality untuk Anak Berkebutuhan Khusus Ferawati Ferawati; Detin Sofia; Petty Aprilia Sari
I-Com: Indonesian Community Journal Vol 3 No 4 (2023): I-Com: Indonesian Community Journal (Desember 2023)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v3i4.3340

Abstract

The use of digital technology is the main factor in encouraging learning activities. Educators with expertise in the field can apply the principles of successful and efficient pedagogy when designing and implementing teaching strategies. Educators need increased capacity to strategize, organize, and produce open materials and face challenges when tasked with developing their own learning media. Children with special needs are those who have limited physical and psychological abilities. Therefore, the presence of versatile learning tools is significant. This activity aims to introduce Android-based learning media, namely augmented reality learning media, which is crucial in facilitating educational efforts for educators and students. Teachers and students of SKH YKDW 01 attended this service activity. The final results of the activities that have been carried out are proven by the enthusiasm of the participants after the end of the service activities and the questionnaire score of 61%, showing that students and teachers gave excellent scores to the augmented reality application.
Diabetes Disease Prediction on Unbalanced Data Using SMOTE-Tomek Links and Random Forest Algorithm Titis Fatmah Sukamto; Cathy Lintang Prameswary; Dedi Royadi; Detin Sofia
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7164

Abstract

Diabetes, a chronic condition caused by the body’s incapacity to generate or apply insulin as it should, is characterized by elevated blood sugar. If not treated early, the disease can lead to serious complications. This research aims to implement a machine learning-based classification model to predict diabetes, applying the methodology known as CRISP-DM (Cross-Industry Standard Process for Data Mining). The dataset was obtained from the Health Center of Sukatani Village, Rajeg, with a total of 2,075 records and 21 columns. The SMOTE-Tomek Links resampling technique was used to resolve the data’s class imbalance. Five classification algorithms, Naive Bayes, Random Forest, Logistic Regression, Decision Tree, and K-Nearest Neighbor (KNN), were compared in this study. Experiments revealed that the Random Forest algorithm performed the best with 97% accuracy, which increased to 99.64% after the application of SMOTE-Tomek Links. This best model was implemented in a web-based application using the Streamlit framework. The combination of the CRISP-DM approach, Random Forest algorithm, and SMOTE-Tomek Links proved to be effective in predicting diabetes, so that it can help medical personnel and the community in preventing, managing, and monitoring diabetes optimally.
Co-Authors Cathy Lintang Prameswary Dedi Dedi Ferawati Ferawati Petty Aprilia Sari Titis Fatmah Sukamto