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Impact of Principal Communication and Psychological Climate on Enhancing Teacher Performance Nurliana Nasution; Wahyuni Sri
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 1 (2025): MARCH 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i1.6946

Abstract

Effective school leadership relies heavily on communication, which plays a crucial role in shaping teacher performance. Additionally, a school’s psychological climate significantly influences teacher motivation, satisfaction, and professional engagement. While prior studies have explored these factors separately, limited research has examined their combined effect, particularly in the Indonesian context. This study investigates the joint impact of principals’ communication effectiveness and psychological climate on teacher performance in state high schools in Rumbai District, Pekanbaru City. Using a quantitative correlational design, data were collected from 93 teachers across three schools through Likert-scale questionnaires. Variables measured included principal communication effectiveness, psychological climate, and teacher performance. Data analysis employed descriptive statistics, t-tests, F-tests, and regression analysis. Results show that both principal communication (t = 16.631, p 0.001) and psychological climate (t = 4.171, p 0.001) significantly influence teacher performance. Together, these variables account for 86.2% of the variance in teacher performance (Adjusted R² = 0.862), indicating a strong predictive relationship. Descriptive findings also revealed moderate achievement levels (66%–73%) across all indicators. The study concludes that effective communication and a positive psychological climate are essential for fostering teacher engagement, performance, and a collaborative school culture. These findings highlight the strategic role of school principals as communicative leaders and underscore the need to prioritize communication and climate in leadership development programs.
Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang Erlin Erlin; Yenny Desnelita; Nurliana Nasution; Laili Suryati; Fransiskus Zoromi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1726

Abstract

Dalam aplikasi machine learning sangat umum ditemukan kumpulan data dalam berbagai tingkat ketidakseimbangan mulai dari ketidakseimbangan kecil, sedang sampai ekstrim. Sebagian besar model machine learning yang dilatih pada data tidak seimbang akan memiliki bias dengan memberikan tingkat akurasi yang tinggi pada kelas mayoritas dan sebaliknya rendah pada kelas minoritas. Tujuan penelitian ini adalah untuk mengevaluasi dampak dari SMOTE (Synthetic Minority Oversampling Technique) pada pengklasifikasi Random Forest untuk memprediksi penyakit jantung. Data berjumlah 299 berasal dari UCI Machine learning Repository digunakan untuk membangun model prediksi berdasarkan 12 variabel independen dan 1 variabel dependen. Kelas minoritas dalam dataset pelatihan di oversampling menggunakan teknik SMOTE (Synthetic Minority Oversampling Technique). Model dievaluasi tidak hanya menggunakan ukuran kinerja Accuracy dan Precision saja, namun juga menggunakan alternatif ukuran kinerja lainnya seperti Sensitivity, F1-score, Specificity, G-Mean dan Youdens Index yang lebih baik digunakan untuk data yang tidak seimbang. Hasil penelitian menunjukkan bahwa teknik SMOTE (Synthetic Minority Oversampling Technique) mampu mengurangi overfitting sekaligus meningkatkan kinerja model Random Forest pada semua indikator. Peningkatan skor Accuracy sebesar 3.45%, Precision 4.8%, Sensitivity 7.1%, F1-score 4.8%, Specificity 2.1%, G-Mean 4.4%, dan Youdens Index 6.3%. Penelitian ini membuktikan bahwa dalam menentukan pengklasifikasi dengan algoritma machine learning seperti Random Forest, kemiringan kelas dalam data perlu diperhitungkan dan diseimbangkan untuk hasil kinerja yang lebih baik.
The Effectiveness of a STEAM-Integrated Project-Based Learning Model for Teaching Motorcycle CVT Diagnostics in Vocational Schools Adek Irawan; Nurliana Nasution; Rizki Novendra; Yogo Turnandes; Budi Cahya; Nurli Indra Sari
AL-ISHLAH: Jurnal Pendidikan Vol 18, No 1 (2026): MARCH 2026
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v18i1.9456

Abstract

This study examines the effectiveness of integrating Project-Based Learning (PBL) with a STEAM approach to enhance students’ conceptual understanding and diagnostic skills in continuous variable transmission (CVT) systems within vocational education. A quasi-experimental design was employed involving 34 Grade XII students from the Motorcycle Engineering program at SMK Negeri 4 Rambah, Indonesia. Participants were divided into an experimental group receiving PBL-STEAM instruction and a control group receiving traditional teaching. Data were collected through posttests and analyzed using N-gain, independent samples t-tests, and effect size (Cohen’s d). The experimental group achieved a higher mean posttest score in conceptual understanding (84.24) compared to the control group (74.00). The N-gain for the experimental group was 0.68 (high), while the control group showed 0.28 (low). Diagnostic skills also improved significantly, with mean scores of 90.00 (experimental) and 76.00 (control). Statistical analysis revealed significant differences between groups (p 0.05). Effect sizes were large (d = 1.77 for conceptual understanding; d = 2.68 for diagnostic skills). The findings indicate that the PBL-STEAM approach substantially improves both conceptual mastery and diagnostic skills. This model aligns well with vocational education objectives and supports the development of competencies relevant to industry demands.