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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Innovative and Creativity
Iqbal Firdaus
Institut Bisnis dan Teknologi Kalimantan
Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Penggunaan Aplikasi ThinkListen untuk Peningkatan Berpikir Kritis melalui Pertanyaan HOTS dalam Pembelajaran Listening EFL Iqbal Firdaus; Diba Ramadhana
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.3369

Abstract

Penelitian ini bertujuan menganalisis efektivitas aplikasi ThinkListen berbasis Higher-Order Thinking Skills (HOTS) dalam meningkatkan keterampilan berpikir kritis mahasiswa pada pembelajaran listening (Listening for Academic Purposes) di konteks EFL. Masalah yang diangkat berawal dari kurangnya penerapan aktivitas berpikir tingkat tinggi dalam pengajaran listening, yang umumnya masih menekankan keterampilan pemahaman tingkat rendah. Penelitian menggunakan desain Classroom Action Research (CAR) dengan dua siklus, masing-masing mencakup perencanaan, tindakan, observasi, dan refleksi. Aplikasi ThinkListen dimanfaatkan untuk menyajikan materi audio akademik, memberikan pertanyaan HOTS (analisis, evaluasi, kreasi), serta memfasilitasi diskusi interaktif dan pelacakan kemajuan belajar. Subjek penelitian terdiri atas enam mahasiswa tingkat menengah di sebuah universitas swasta di Banjarmasin. Data diperoleh melalui lembar observasi, refleksi mahasiswa, serta pre-test dan post-test berbasis aplikasi. Hasil menunjukkan peningkatan skor rata-rata dari 62 pada pre-test menjadi 78 pada post-test, disertai keterlibatan mahasiswa yang lebih aktif dalam menjawab pertanyaan analitis dan evaluatif. Temuan ini menegaskan bahwa integrasi aplikasi digital berbasis HOTS efektif dalam mengembangkan keterampilan berpikir kritis sekaligus meningkatkan partisipasi mahasiswa pada pembelajaran listening EFL.
The Impact of Squeeze-and-Excitation Blocks on CNN Models and Transfer Learning for Pneumonia Classification Using Chest X-ray Images Muhammad Yunan; Aris Marjuni; Affandy Affandy; Mochamad Arief Soeleman; Iqbal Firdaus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6693

Abstract

Pneumonia is one of the leading causes of death due to respiratory tract infections, especially in children and the elderly. Early detection using chest X-ray images is crucial to accelerate diagnosis and treatment, but manual interpretation is often subjective and error-prone. This study evaluates the effect of Squeeze-and-Excitation (SE) Block integration on the performance of a custom Convolutional Neural Network (CNN) model and three popular transfer learning architectures: MobileNetV2, VGG16, and InceptionV3 in X-ray image-based pneumonia classification. A dataset of 5,856 images, taken from Chest X-ray Images (Pneumonia) on Kaggle, was processed through preprocessing, undersampling, and augmentation. Each model was tested in two configurations: without and with SE Block. Evaluation was performed using accuracy, precision, recall, F1-score, and test loss metrics. The results show that SE Block integration improves the performance of most models. The accuracy of the custom CNN increased from 95.17% to 95.88%, MobileNetV2 from 97.18% to 97.59%, and VGG16 from 96.88% to 97.69%. InceptionV3 also saw an accuracy increase from 94.06% to 94.16%, although accompanied by an increase in test loss. SE Block proved effective in strengthening the model's emphasis on important features through an inter-channel recalibration mechanism, especially on efficient architectures like MobileNetV2 and complex models like VGG16. These findings support the development of a more accurate, efficient, and adaptive deep learning-based pneumonia diagnosis system, especially for implementation in healthcare facilities with limited resources.
Co-Authors Affandy Affandy Aris Marjuni Diba Ramadhana Mochamad Arief Soeleman Muhammad Yunan