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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal Of Computer Science And Technology
Laina tussifah Santoso
AMIK Polibisnis
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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PENGARUH FREKUENSI PENGGUNAAN MEDIA DIGITAL GOOGLE CLASSROOM DAN MOTIVASI BELAJAR TERHADAP SELF-EFFICACY KEMAMPUAN BAHASA INGGRIS MAHASISWA AKADEMI MANAJEMEN INFORMATIKA DAN KOMPUTER POLIBISNIS Karyawaty Gultom; Hanafi Asnan; Laina tussifah Santoso
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6405

Abstract

Abstract: This research aims to investigate the way learning motivation and the intensity of utilizing the digital platform Google Classroom influence the English language self-efficacy of AMIK Polibisnis students. The study applied a quantitative approach with an ex-post facto causal design. A total of 30 students were selected as the sample through purposive sampling. The instrument was a five-point Likert scale questionnaire that had undergone validity and reliability testing. Data were analyzed using simple linear regression and multiple linear regression. The findings revealed that learning motivation had a positive and significant effect (sig. 0.001), the level of Google Classroom use also positively and significantly impacted self-efficacy (sig. 0.000), and both variables together exerted a significant influence (sig. 0.000183) with a 32.2% contribution. The results indicate that students' confidence in their English competence grows as the frequency of accessing Google Classroom and their learning drive increase. Consequently, continuously optimizing the use of Google Classroom and fostering learning enthusiasm are crucial steps to strengthen students' self-belief in English learning within higher education institutions.  Keywords: Google Classroom; Learning Motivation; Self-Efficacy; English Ability   Abstrak: Tujuan studi ini adalah untuk mengkaji bagaimana motivasi belajar dan intensitas pemanfaatan media digital Google Classroom memengaruhi keyakinan diri (self-efficacy) berbahasa Inggris mahasiswa AMIK Polibisnis. Riset ini menggunakan pendekatan kuantitatif dengan desain kausalitas ex-post facto. Sampel terdiri dari 30 mahasiswa yang dipilih melalui teknik purposive sampling. Instrumen yang digunakan berupa angket berskala Likert lima tingkat yang telah melewati pengujian validitas dan reliabilitas. Analisis data dilakukan dengan regresi linier sederhana dan regresi linier berganda. Hasil penelitian memperlihatkan bahwa motivasi belajar memberikan dampak positif dan signifikan (sig. 0,001), intensitas penggunaan Google Classroom juga berpengaruh positif dan signifikan terhadap efikasi diri (sig. 0,000), dan kedua variabel secara bersamaan memberikan pengaruh yang signifikan (sig. 0,000183) dengan kontribusi sebesar 32,2%. Temuan studi memperlihatkan bahwa keyakinan diri mahasiswa terhadap kemampuan bahasa Inggris mereka bertambah seiring dengan meningkatnya frekuensi akses Google Classroom dan tingginya dorongan belajar. Karenanya, upaya mengoptimalkan pemanfaatan Google Classroom secara terus-menerus serta mendorong semangat belajar menjadi langkah penting guna memperkuat rasa percaya diri mahasiswa dalam mempelajari bahasa Inggris di lingkungan perguruan tinggi. Kata Kunci: Google Classroom; Motivasi Belajar; Self-Efficacy; Kemampuan Bahasa Inggris
Comparative Analysis Of Ensemble Learning Algorithms For Student Mental Health Classification: Balancing Predictive Performance And Computational Efficiency Lia Umbari Putri; Laina Tussifah Santoso; Masri Wahyuni
Journal of Computer Science and Technology (JOCSTEC) Vol 4 No 3 (2026): JOCSTEC - September
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jocstec.v4i3.764

Abstract

Student mental health disorders have become an increasingly important issue due to their significant impact on academic performance and psychological well-being. Machine learning-based approaches offer a promising alternative for supporting early detection; however, only a limited number of studies have conducted comprehensive evaluations of various ensemble learning algorithms by simultaneously considering predictive performance, computational efficiency, and statistical significance. This study aims to compare ten classification algorithms, namely Random Forest, Extra Trees, Gradient Boosting, Hist Gradient Boosting, AdaBoost, XGBoost, LightGBM, CatBoost, Logistic Regression, and Stacking Ensemble, using 5-fold Stratified Cross-Validation. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, Receiver Operating Characteristic–Area Under the Curve (ROC-AUC), training time, inference time, confusion matrix, feature importance, as well as One-Way Analysis of Variance (ANOVA) and post-hoc Tukey’s Honestly Significant Difference (Tukey HSD) tests. The experimental results show that CatBoost achieved the highest classification performance, with an Accuracy of 84.86%, an F1-Score of 87.19%, a Recall of 88.03%, and an ROC-AUC of 0.9220. XGBoost delivered comparable predictive performance with a substantially shorter training time (3.92 seconds), while LightGBM was the most computationally efficient algorithm, requiring an average training time of only 0.85 seconds. In contrast, the Stacking Ensemble required the longest training time (215.41 seconds) without providing a statistically meaningful performance improvement over the boosting-based models. The ANOVA results revealed significant differences in ROC-AUC among the evaluated models (p < 0.001), whereas the Tukey HSD test indicated that most boosting algorithms did not differ significantly from one another. These findings provide practical guidance for selecting classification algorithms by balancing predictive performance and computational efficiency to support early mental health screening systems for university students.
Co-Authors Hanafi Asnan Karyawaty Gultom Lia Umbari Putri Masri Wahyuni