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The Effect of Self-Regulated Learning Model Assisted by The Brainly Application on Students' Learning Outcomes in History Pratiwi, Feby Dewi; Umamah, Nurul; Marjono, Marjono; Sumardi, Sumardi; Hilmiah, Anis Syatul; Prasky Hartono, Fernanda; Zulfikar, Fahcri
JURNAL HISTORICA Vol. 8 No. 2 (2024): December 2024
Publisher : History Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jh.v8i2.50948

Abstract

This research aims to see whether there is a significant influence with the application of the Self Regulated Learning model assisted by the Brainly application on learning outcomes in history learning. This research uses a quantitative approach with a quasi-experimental design type, then hypothesis testing is carried out using the ANCOVA test (covariance analysis). The population in this study were all class XI students of SMAN Balung for the 2023/2024 academic year. The samples in this research were students in classes XI 7, XI 9, XI 8 and XI 11. Data collection in this research used documentation, observation and questionnaire techniques. This research shows that there is an influence of the Self Regulated Learning learning model assisted by the Brainly application on learning outcomes, showing the results of the ANCOVA test with a significance value of 0.000<0.05 and a partial eta squared value of 0.221 including a large influence in the application of this model. The average score of the control class taught using the Project Based Learning model was 83.278, while the experimental class taught using the Self Regulated Learning model assisted by Brainly had an average score of 85.595. Based on the average score results, it can be seen that the experimental class is superior to the control class. The conclusion that can be drawn from this research is that a significant influence was obtained from the application of the Self Regulated Learning model assisted by the Brainly application on students' learning outcomes in history learning.
Hybrid VGG16–LSTM Classification of Microscopic Bacterial Images for Environmental Microbiology Screening Yanti, Indah; Marjono, Marjono; Antariksa, Antariksa; Kurniawan, Andi; Anam, Syaiful; Bukhori, Hilmi Aziz
The Journal of Experimental Life Science Vol. 16 No. 2 (2026)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2026.016.02.02

Abstract

While conventional culture-based, biochemical, and molecular identification methods remain fundamental in microbiology, computational image analysis can serve as an exploratory, supplementary tool for studying bacterial morphology. This proof-of-concept study evaluates a hybrid VGG16-LSTM model for microscopic bacterial image classification. This study utilized a small subset of the DIBaS dataset consisting of six bacterial classes: Acinetobacter baumannii, Escherichia coli, Lactobacillus plantarum, Micrococcus spp., Propionibacterium acnes, and Pseudomonas aeruginosa. The total dataset size is highly constrained at 124 images, with a correspondingly small number of images per class ranging from 20 to 23. The dataset was divided into training, validation, and testing subsets in a 70:20:10 ratio. All microscopic images were resized to 224 × 224 pixels, normalized, and dynamically augmented during training to improve data variability under these limited-sample conditions. A pre-trained VGG16 network was employed to extract spatial image features, and the final convolutional feature map was reshaped into a spatial sequence and processed using an LSTM layer for further feature learning. Three optimization algorithms, namely Adam, RMSprop, and stochastic gradient descent, were compared. Among them, the RMSprop-optimized model exhibited the highest metrics after 50 epochs, achieving 92.86% accuracy, 95.24% precision, 92.86% recall, and a 92.38% F1-score; however, these performance indicators must be interpreted with caution as they are based on a severely limited test set (approximately 12 images). Some misclassifications occurred between P. aeruginosa and E. coli, which may be attributed to their shared gram-negative rod-shaped morphology. This finding highlights the inherent biological challenge of distinguishing visually similar bacterial species from microscopic image analysis alone, underscoring that such models remain strictly experimental and are not suited for clinical-grade diagnosis or field-ready environmental screening.
Co-Authors Achendri M. Kurniawan Agung Riyantomo Aiham Giovani Akhmad Suryadi Alamsyah, Adi Wildan Alanindra Saputra Alvi Rosyidi Aminudin Afandhi Amru Sukmajati, Amru Andi Hidayatul Fadlilah Andi Kurniawan Anggara S, Clip Anggara S, Clip Anisa Zahra Hermayani, Anisa Zahra ANISAK KURNIASIH Antariksa Sudikno Arief Rachmansyah Arif Surono Ayu Octavia, Diah Baehaki, Imam Bagyo Yanuwiadi Bambang Semedi Bambang Soepeno Baskoro Adi Prayitno Bayuaji, Gerardus David Ady Purnama Bukhori, Hilmi Aziz Burhamtoro Burhamtoro, Burhamtoro Cahyo, Dimas Surya Dwi Christine Riani Elisabeth Dandung Novianto Dewi Puspita Diani, Khonita Rahma Dibyo Waskito Guntoro Dinda Awalia Ramadhani Diza, Novia Fara Djumala Machmud, Djumala Dwi Herlindawati Dwi Pertiwi Hapsari Dwi Ratnaningsih, Dwi Dwi Suheryanto Dyah Ayu Rahmawati Cupasindi Ekobelawati, Fransiska Fadillah Putra Fatimah, Imas Fernanda Prasky Hartono Firdaus, RB. Moh. Muslim Gatot Ciptadi Hairur Rahman HANDAYANI, SIN SYIN LU’LU’ Harahap , Subur Harsuko Riniwati Helik Susilo Hidayah, David Hidayahningrum, Syafitri Hikmawati, Viona Faiqoh Hilmiah, Anis Syatul Hulukati , Stephan Adriansyah Ikha Primarinda ikrar hanggara Ina, Ina Indah Dwi Qurbani Indah Yanti Ishak Ishak Iswanto Iswanto Joko Ariyanto Joko Mulyono Karim, Corina Kayan Swastika Khoiro, Nazidatul Koeswardilla, Della Destalia Kurniawan Sekar Angkoso Kusuma, Vista Anindya Laila Sari Laila, Ratu Anna Nazla Latiifani, Choirunnisa Lestari, Cyntia Ayu LITHON SUNYOTO Loso Judijanto M. Ruslin Anwar M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Maridi Maridi Masitoh, Ikhlasun Dwi Meidiana, Rebeka METTI SETYOWATI Milda Istiqomah Moch. Khamim Moch. Sholeh, Moch. Muhammad Tri Aditya Mujianti, Yunita Ika Murni Ramli Mustagfirin Mustagfirin Napiajo Napiajo Noor Hidayat, Noor Novita Anggraini Novita Anggraini Novita Sari Nuddin Harahab Nuhfil Hanani Nurhabib, Asro Nurina, Lia Nurul Umamah Oktaviani, Anik Paparang, Stenly Reinal Prasetyo, Guruh pratama, ahmad ryan Pratiwi, Feby Dewi Pratiwi, Sabtiya Purwati, Magdalena Yuli Purwati, Magdalena Yuli Qomariah Qomariah Qomariyatus Sholihah Quraisyi, Quraisyi Quraisyi, Quraisyi Rahayu, Sindi Mei Rahmawati, Vini Rarasanti, Pramodia Dyah Rely, Gilbert Rismayanti, Fransisca Ayu Rismayanti, Fransisca Ayu Rizki Aruma Nurjannah, Indah Rulianto, Umar Farouk Rully Putri Nirmala Puji S Siswanto Sa'diyah, Ilmatus Safitri, Ni Luh Eka Sahrul, Adi Sambou, Omar Sari, Yeni Novita Setyawati, Laily Sholekhah, Irmadatus SITI MAHMUDAH Siti Rahayu Sony Susanto Sri Dwiastuti Sri Handayani Suciati Suciati Suciati Sudarisman Sudana , I Made Sudarsini, Sudarsini Sugiyanto - Sulthoni, Luqman Jauhar Sumardi . Sumarjono Sumarjono Supardi Supardi Supiyono, Supiyono surya, riza afita Sutanto, Wiji Suwarno Suwarno Syaifudin, Rizky Aziz Syaiful Anam Syam's, Nova Dewi Safitri Tampubolon, Amy Septrina Tutik Fitri Wijayanti Udan Kusmawan Usuluddin, Fachruddin Wanto, Alfi Haris Widiarto, Ony Widiyanto, Wahyu Wijaya Wijaya, Agung Mike Willa Nurhaidar Lestiana Winarno, Novyantika Eka Putri Yudi Rinanto Yulia Agustin Zahro, Mustika Zahro, Mustika Zulfikar, Fahcri