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All Journal Jurnal ICT : Information Communication & Technology Teknik: Jurnal Ilmu Teknik dan Informatika Jurnal Ilmiah Research Student
Wahyu Nugraha
Unknown Affiliation
Religion Computer Science & IT Economics, Econometrics & Finance Mathematics Medicine & Pharmacology

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ETIKA DALAM KEGIATAN KEMASYARAKATAN: DENGAN KEGIATAN BERBAGI NASI Wahyu Nugraha
JURNAL ILMIAH RESEARCH STUDENT Vol. 1 No. 3 (2024): Januari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jirs.v1i3.779

Abstract

Business ethics in a societal context refers to a company's moral behavior that takes into account its impact on society as a whole. In the era of globalization and increasingly close connectivity, companies have a responsibility not only to achieve economic profits, but also to pay attention to their contribution to social welfare. This abstract discusses the implications and challenges of business ethics in society, highlighting the role of companies as active members in shaping and supporting the sustainability of society. The implications of business ethics in society include the obligation of companies to ensure that their operational activities comply with moral norms and that their contribution to society is more than just creating financial gain. Society-centered business ethics requires companies to pay attention to their impact on the environment, human rights, and social justice. The challenges facing business ethics in society involve frequent conflicts between corporate profits and the general welfare. Economic globalization can create increasing inequality, and companies are faced with pressure to meet shareholder demands while maintaining ethical integrity. Managing this balance requires business policies and practices that focus on transparency, accountability and sustainable development. Thus, this research underlines the need for a paradigm shift in the view of business, where business ethics is not only understood as a moral obligation, but also as a foundation for growth sustainable and inclusive economy in society.
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.
Analisis Komparatif Algoritma Machine Learning dan Metode Feature Selection Untuk Prediksi Risiko Serangan Jantung Wahyu Nugraha; Muhamad Syarif
Jurnal ICT: Information Communication & Technology Vol. 26 No. 1 (2026): JICT-IKMI, July , 2026
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v26i1.360

Abstract

Cardiovascular disease remains one of the leading causes of mortality worldwide. The application of Machine Learning (ML) provides a promising approach for the early detection of heart attack risk, yet the predictive modeling process is frequently hindered by imbalanced data and the presence of irrelevant features. This study evaluates the performance of eight classification algorithms in predicting heart attack risk using a dataset of 8,763 patient records. To address the bias caused by the majority class dominance (64.1% healthy patients), the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training dataset. The evaluation was conducted comparatively across three data dimensionality scenarios, that is Baseline Data (all features), the top five features derived from Random Forest Feature Importance, and a subset of five features selected via Recursive Feature Elimination (RFE). The results demonstrated that the Gradient Boosting Machine (GBM) algorithm achieved the highest functional accuracy of 60.4% under the Baseline scenario. The most critical finding of this experiment was the empirical evidence of the Accuracy Paradox in the RFE feature scenario. Although linear models such as Logistic Regression and probabilistic models like Naive Bayes achieved the highest “accuracy” at 64.3%, further evaluation revealed that their Precision and Recall scores for detecting heart attack risk were exactly 0.00. These models completely failed to identify the positive class, merely predicting the majority class for all test samples. In conclusion, demographic and lifestyle features independently possess only moderate predictive power (~60%), and relying solely on Accuracy as an evaluation metric in clinical predictive modeling can lead to highly misleading conclusions.
Co-Authors Muhamad Syarif Raja Sabaruddin