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All Journal Teknik: Jurnal Ilmu Teknik dan Informatika SOROT: Jurnal Pengabdian Kepada Masyarakat
Raja Sabaruddin
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Mechanical Engineering Other

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Pemanfaatan Transformasi Digital Mindset dalam Kewirausahaan UMKM untuk Pengembangan Ekonomi Lokal Riski Annisa; Raja Sabaruddin; Panny Agustia Rahayuningsih; Monikka Nur Winnarto
SOROT : Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2 (2023): Juli
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/sorot.v2i2.4855

Abstract

This study aims to explore the utilization of digital mindset transformation in SME entrepreneurship through community engagement activities. In the rapidly evolving digital era, SMEs face complex challenges in adopting digital technologies and transforming their business behaviors. This community engagement involves training, guidance, and collaboration with SMEs to strengthen their understanding of the importance of digital mindset transformation. The main findings of this community engagement initiative demonstrate that digital mindset transformation can have positive impacts on SME entrepreneurship. SMEs that embrace digital mindset transformation tend to achieve better business growth, enhance operational efficiency, adapt to market changes, and make better strategic decisions. Furthermore, community engagement activities significantly contribute to the development of SME entrepreneurship, community development, and local economic empowerment. The potential implications of this initiative include improved quality and competitiveness of SMEs in the digital era. Therefore, recommendations for further development involve expanding collaborations among universities, governments, and relevant stakeholders, intensifying efforts in digital infrastructure development, and exploring other aspects of digital transformation such as cyber security and e-commerce.
Evaluasi Performa Algoritma Klasifikasi dalam Prediksi Kekambuhan Kanker Tiroid Pasca Terapi RAI: Studi Kasus Dataset RAI Therapy Wahyu Nugraha; Raja Sabaruddin
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 1 (2025): Mei: Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i1.717

Abstract

Thyroid cancer is the most common endocrine malignancy, with a steadily increasing incidence rate. Although the overall survival rate is relatively high, the risk of recurrence after definitive treatment such as Radioactive Iodine (RAI) therapy remains a significant clinical challenge. Predicting recurrence risk is crucial for optimizing monitoring strategies and interventions. With advances in technology, machine learning (ML) approaches are increasingly utilized to support medical predictions, including the recurrence of thyroid cancer. This study aims to evaluate the performance of four classification algorithms—Logistic Regression, XGBClassifier, Random Forest Classifier, and Voting Classifier—in predicting thyroid cancer recurrence using the Thyroid Cancer Recurrence After RAI Therapy dataset, which consists of 383 patient records and 13 key clinical attributes. The evaluation was conducted using accuracy, precision, recall, F1-score, and area under the curve (AUC) metrics. The results show that the XGBClassifier is the best-performing model with an accuracy of 97.4% and an AUC of 0.95, demonstrating superior performance in handling the minority class. This research is expected to contribute to the development of more effective machine learning–based clinical decision support systems for predicting thyroid cancer recurrence after therapy.
Co-Authors Monikka Nur Winnarto Panny Agustia Rahayuningsih Riski Annisa Wahyu Nugraha