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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Literasiologi Teaching English as a Foreign Language Overseas Journal (TEFLO)
Risal, Syamsul
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Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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INTEGRATIVE LEARNING MODEL TYPE A SEQUENCED IN LEARNING WRITING IN CLASS VII STUDENTS OF UPTD SMP NEGERI 34 SINJAI Risal, Syamsul
Teaching English as a Foreign Language Overseas Journal Vol. 10 No. 3 (2022): Teaching English as a Foreign Language Overseas Journal
Publisher : Publikasi dan UKI Press UKI Toraja.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47178/wjtq0s67

Abstract

The aim of this research is to identify the effectiveness of the sequenced type integrative learning model in learning to write for class VII students at UPTD SMP Negeri 34 Sinjai. The researcher was applied pre-experimental design the population is the second-grade students of UPTD SMP Negeri 34 Sinjai in academic year 2021/2022. In analyzing the numerical data, the writer was used SPSS for windows. Basically, all learning models that are implemented are designed in the learning process, if they are implemented with a full sense of responsibility, the results achieved can be meaningful for students and teachers as well as all existing components, including the government and society in general. The improvement in students' writing skills in research can be seen from the increase in the number of words in each sentence produced when students write. The data from this research indicates that the application of integrated learning with a sequenced model through deep thinking skills strategies can increase children's vocabulary and understanding of good behavior related to peaceful values.
IMPLEMENTASI TEORI BELAJAR SOSIAL MENURUT ALBERT BANDURA DALAM PEMBELAJARAN PAI Irama, Debi Irama; Sutarto, Sutarto; RISAL, Syamsul
Jurnal Literasiologi Vol 12 No 4 (2024): Jurnal Literasiologi
Publisher : Yayasan Literasi Kita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47783/literasiologi.v12i4.819

Abstract

pembelajaran Pendidikan Agama Islam (PAI). Teori belajar sosial menekankan pentingnya proses observasi, imitasi, dan penguatan sosial dalam pembentukan perilaku. Dalam konteks pembelajaran PAI, teori ini relevan untuk membentuk karakter siswa melalui keteladanan guru, interaksi sosial, dan penerapan nilai-nilai agama. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi kasus pada lingkungan pendidikan formal. Data diperoleh melalui observasi, wawancara, dan analisis dokumen. Hasil penelitian menunjukkan bahwa penerapan teori ini dalam pembelajaran PAI melibatkan strategi seperti memberikan contoh perilaku islami, melibatkan siswa dalam diskusi interaktif, dan memberikan penguatan positif untuk mendorong perilaku baik. Faktor-faktor seperti kompetensi guru, lingkungan sekolah, dan dukungan orang tua turut memengaruhi keberhasilan implementasi teori ini. Penelitian ini menyimpulkan bahwa teori belajar sosial Albert Bandura dapat meningkatkan efektivitas pembelajaran PAI dengan Penelitian ini bertujuan untuk menganalisis implementasi teori belajar sosial menurut Albert Bandura dalam menciptakan lingkungan belajar yang mendukung pengembangan sikap dan perilaku sesuai nilai-nilai Islam.
Enhancing Stroke Prediction with Logistic Regression and Support Vector Machine Using Oversampling Techniques Risal, Syamsul; Fajar Apriyadi; A. Sumardin; Andini Dani Achmad; Annisa Nurul Puteri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6431

Abstract

Stroke is a significant health concern that can result in both death and disability, making the early identification of risk factors crucial. Previous studies on stroke prediction have been limited by inadequate handling of class imbalance, lack of comprehensive feature selection, and parameter optimization, with accuracy rates usually below 80%. This study compares the performance of Logistic Regression (LR) and Support Vector Machine (SVM) algorithms combined with different oversampling methods—SMOTE, Borderline-SMOTE, ADASYN, Random Over Sampling (ROS), and Random Under Sampling (RUS)—on a stroke prediction dataset. Correlation-based feature selection identified age, hypertension, and heart disease as significant predictors. GridSearchCV with 10-fold cross-validation was used for hyperparameter optimization, and performance was evaluated using precision, recall, accuracy, and ROC curves. The results showed that SVM significantly outperformed Logistic Regression across all sampling methods. SVM+ROS achieved the highest performance with perfect recall (100%), precision of 97.18%, and accuracy of 98.56% (AUC: 0.9857), whereas SVM + Borderline-SMOTE offered balanced performance with a recall of 94.99%, precision of 95.06%, and accuracy of 95.17% (AUC: 0.9512). LR + Borderline-SMOTE performed the best with an accuracy of 84.98% (AUC: 0.8503), significantly better than previous studies. This improved accuracy shows significant clinical benefits, potentially reducing missed stroke diagnoses by identifying thousands of additional at-risk patients in large-scale screening programs. Healthcare providers should consider implementing SVM with ROS in critical care settings, where potentially missed stroke cases have severe consequences. Simultaneously, SVM with Borderline-SMOTE may be more appropriate for resource-constrained environments.
Co-Authors A. Sumardin Andini Dani Achmad Annisa Nurul Puteri Fajar Apriyadi Irama, Debi Irama Sutarto Sutarto