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All Journal Computer Science (CO-SCIENCE) YASIN: Jurnal Pendidikan dan Sosial Budaya
Ramadhan, Angga
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Computer Science & IT Social Sciences Other

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Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung Masruriyah, Anis; Novita, Hilda; Sukmawati, Cici; Ramadhan, Angga; Arif, Siti; Dermawan, Budi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2389

Abstract

Heart disease remains a leading cause of death in Indonesia and worldwide. In the realm of data mining, class imbalance between heart disease and normal samples within datasets presents a significant challenge. This disparity can lead to model bias toward the majority class, resulting in suboptimal performance in identifying instances of heart disease. This study addresses this issue by implementing oversampling techniques, particularly Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). The findings reveal that models without oversampling achieve accuracy and precision exceeding 80%, but exhibit poor class separation performance. In contrast, models employing oversampling, despite experiencing reductions in accuracy and precision, enhance their ability to distinguish between heart disease and normal classes. The top-performing model utilizing the Random forest algorithm with SMOTE attains an AUC value of 0.868, signifying a significant improvement in class separation. These discoveries provide essential guidance for the development of more effective and accurate heart disease classification models. The utilization of oversampling techniques, such as SMOTE, proves to be an effective strategy for mitigating class imbalances in heart disease data mining. While accuracy and precision may decrease, the model's capability to identify heart disease becomes more reliable, with notable outcomes assessed using AUC. This research contributes significantly to enhancing efforts in heart disease prevention and treatment through sophisticated and sustainable data mining techniques.  
Pengukuran Kinerja Model Klasifikasi dengan Data Oversampling pada Algoritma Supervised Learning untuk Penyakit Jantung Masruriyah, Anis; Novita, Hilda; Sukmawati, Cici; Ramadhan, Angga; Arif, Siti; Dermawan, Budi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2389

Abstract

Heart disease remains a leading cause of death in Indonesia and worldwide. In the realm of data mining, class imbalance between heart disease and normal samples within datasets presents a significant challenge. This disparity can lead to model bias toward the majority class, resulting in suboptimal performance in identifying instances of heart disease. This study addresses this issue by implementing oversampling techniques, particularly Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). The findings reveal that models without oversampling achieve accuracy and precision exceeding 80%, but exhibit poor class separation performance. In contrast, models employing oversampling, despite experiencing reductions in accuracy and precision, enhance their ability to distinguish between heart disease and normal classes. The top-performing model utilizing the Random forest algorithm with SMOTE attains an AUC value of 0.868, signifying a significant improvement in class separation. These discoveries provide essential guidance for the development of more effective and accurate heart disease classification models. The utilization of oversampling techniques, such as SMOTE, proves to be an effective strategy for mitigating class imbalances in heart disease data mining. While accuracy and precision may decrease, the model's capability to identify heart disease becomes more reliable, with notable outcomes assessed using AUC. This research contributes significantly to enhancing efforts in heart disease prevention and treatment through sophisticated and sustainable data mining techniques.  
Model Teacher Centric Branding sebagai Strategi Membangun Reputasi Sekolah Islam Terpadu: Studi Kualitatif Deskriptif di Indonesia Ramadhan, Angga; Karim, Hamdi Abdul
YASIN Vol 5 No 6 (2025): DESEMBER
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/yasin.v5i6.8261

Abstract

Although educational branding has received attention in previous studies, research that specifically examines Teacher Centric Branding (TCB) as a teacher-based value approach to building the reputation of Sekolah Islam Terpadu (SIT) remains very limited. This study aims to conduct an in-depth analysis of the mechanisms through which institutional reputation is formed via the internalization of Islamic values, exemplary practice, and the support of teachers’ spiritual leadership as a living brand. The study employs a qualitative approach with an interpretative phenomenological design, involving 12 participants selected through purposive and snowball sampling. Data were collected through in-depth interviews, participatory observation, and document analysis, and were analyzed using Interpretative Phenomenological Analysis (IPA). The findings show that value internalization through structured spiritual development serves as the foundation of teachers’ professional identity; teacher role modeling in interactions with students and parents emerges as the most influential factor in shaping public perceptions; and spiritual leadership plays a crucial role in ensuring value consistency and institutional cultural stability. These findings expand theoretical understanding of internal branding, the human brand, and value-based leadership in the context of Islamic education, and affirm that SIT reputation is more strongly built through the quality of teachers’ values and behaviors than through external promotional strategies. The implications of this study include strengthening teacher development programs, enhancing communication competencies, and developing indicators of role modeling as an integral part of school branding strategies, while also opening opportunities for further research in other SIT contexts by involving more stakeholders and employing longitudinal designs to more comprehensively understand the dynamics of TCB formation.
Co-Authors Arif, Siti Budi Dermawan, Budi Hamdi Abdul Karim, Hamdi Abdul Masruriyah, Anis Novita, Hilda Sukmawati, Cici