Digitalization of Microscopic Cell Analysis Using ImageJ and Micrometers as an Innovation in Science Experiments in the Era of Industrial Revolution 4.0

Litasari Aldila Aribowo(1), Ni Luh Tirtasari(2), Prasetyo Listiaji(3), Nanik Wulan Sari(4), Inayah Inayah(5),

DOI: https://doi.org/10.46843/jpm.v5i3.843


(1) Department of Science Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang, Indonesia
(2) Department of Science Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang, Indonesia
(3) University of Szeged, Szeged, Hungary
(4) Department of Science Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang, Indonesia
(5) Department of Science Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang, Indonesia

Abstract

Cell measurement and analysis are fundamental skills in biology and science education microscopic practicums. The purpose of this study is to compare the accuracy, consistency, and processing time of onion cell measurements using an ocular micrometer as a traditional method and ImageJ software as a digital method. The digitalization of microscopic cell analysis as an innovation in science education supports the 4.0 Industrial Revolution. The research employed a comparative-experimental design. This study's sample consisted of 270 undergraduate students from a university with basic experience using micrometers and microscopes, ensuring that the participants' technical abilities were relatively homogeneous. The data analysis for this study focuses on comparing cell size measurements and processing time between the two methods. The results show a non-significant difference in cell size measurements and significant differences in processing time between the traditional and digital methods, corresponding to a large effect size and a reduction in processing time for the digital method. The digital method is more efficient. The results of this study suggest that the digitalization of microscopic cell analysis not only empowers technology integration in science education but also supports the development of students' digital literacy and analytical skills.

Keywords

Cell Measurement, Digital Microscopy, ImageJ, , Industry 4.0 Education, Science Practicum

Full Text:

PDF

References

Amien, F. Q., & Basari, B. (2023). Visualisasi Bioimaging dengan Menggunakan Machine Learning. Al Qalam: Jurnal Ilmiah Keagamaan Dan Kemasyarakatan, 17(1), 98. https://doi.org/10.35931/aq.v17i1.1787

Andrian, R., Agustiansyah, A., Junaidi, A., & Lestari, D. I. (2022). Aplikasi pengukuran luas daun tanaman menggunakan pengolahan citra digital berbasis android. Jurnal Agrotropika, 21(2), 115-123.

Anindita, R., Nathalia, D. D., & Rahmadi, C. (2024). Digitalisasi Preparat Mikroskopis Plasmodium falciparum dan Plasmodium vivax Sebagai Media Pembelajaran Protozoologi. Bioscientist : Jurnal Ilmiah Biologi, 12(2), 2290. https://doi.org/10.33394/bioscientist.v12i2.13803

Ba?er, A., & Büyük, B. (2024). Bridging the gap in medical education: comparing analysis of light microscopy and virtual microscopy in histology. PeerJ, 12(7). https://doi.org/10.7717/peerj.17695

Bourn, M. D., Daly, L. F., Huggett, J. F., Braybrook, J., & Rivera, J. F. (2025). Evaluation of Image Analysis Tools for The Measurement of Cellular Morphology. Frontiers in Cell and Developmental Biology, 13, 1572212. https://doi.org/10.3389/fcell.2025.1572212

Britten-Jones, A. C., Rajan, R., Craig, J. P., & Downie, L. E. (2022). Quantifying Corneal Immune Cells from Human in Vivo Confocal Microscopy Images: Can Manual Quantification be Improved with Observer Training? Experimental Eye Research, 216. https://doi.org/10.1016/j.exer.2022.108950

Chintalapati, P., Javvaji, S., & Gururaj, K. (2017). Measurement of Damaged Leaf Area Caused by Leaffolder in Rice. J Entomol Zool Stud, 5, 415–417.

Cizkova, K., Foltynkova, T., Gachechiladze, M., & Tauber, Z. (2021). Comparative Analysis of Immunohistochemical Staining Intensity Determined by Light Microscopy, ImageJ and Qupath in Placental Hofbauer Cells. Acta Histochemica et Cytochemica, 54(1), 21–29. https://doi.org/10.1267/ahc.20-00032

Culley, S., Caballero, A. C., Burden, J. J., & Uhlmann, V. (2024). Made to measure: an introduction to quantifying microscopy data in the life sciences. Journal of Microscopy, 295(1), 61-82. https://doi.org/10.1111/jmi.13208

Dewantara, B. S. B., Pramadihanto, D., Sesulihatien, W. T., Sudarsono, A., Oktavianto, H., Sumantri, B., & Winarno, I. (2022). Pembuatan Sistem Mikroskop Digital Terintegrasi dengan Pengolahan Citra untuk Pengembangan Perangkat Pembelajaran IPA di SMPIT Al Uswah Surabaya. J-Dinamika: Jurnal Pengabdian Masyarakat, 7(2), 173-180. https://doi.org/10.25047/j-dinamika.v7i2.2413

Djunaid, U., & Bannang, A. (2025). Optimalisasi Pengamatan Mikroskopis: Panduan dan Teknik Penggunaan Mikroskop dalam Praktikum Laboratorium. JBB: Jurnal Biologi Babasal, 4(1), 77–86.

Elliott, K., Berry, J. C., Kim, H., & Bart, R. S. (2022). A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity. Plant Methods, 18(1). https://doi.org/10.1186/s13007-022-00906-x

Kurniatin, L., Ariyanto, D. K. A., & Saputra, B. A. (2023). Analisis Perbandingan Hasil Pengolahan Citra Asli Dan Cropping Untuk Mengidentifikasi Karakteristik Tanaman Selada Menggunakan Metode Morfologi Dan Ekstrasi Ciri. Jurnal Ilmiah SINUS, 21(1), 73-82. https://doi.org/10.30646/sinus.v21i1.664

Feng, J., Li, Z., Zhang, S., Bao, C., Fang, J., Yin, Y., ... & Zheng, Y. (2023). A Microimage-Processing-Based technique for detecting qualitative and quantitative characteristics of plant cells. Agriculture, 13(9), 1816. https://doi.org/10.3390/agriculture13091816

Gabdulinova, K. G., & Kovrova, M. A. (2021). The Use of A Digital Microscope for The Development of Young Schoolchildren's Ideas about Plants, Animals and Fungi. Information Technologies and Learning Tools, 86(6), 19–29. https://doi.org/10.33407/itlt.v86i6.4320

García Ramos, J., de Souza Júnior, R. S., & Borges, E. M. (2025). How Digital Images Are Transforming Chemical Education: A Review of Laboratory-Based Applications. ACS Omega, 10(30), 32651–32672. https://doi.org/10.1021/acsomega.5c05125

Harefa, A. R., Gulo, H., Waruwu, T., Telaumbanua, D., Zega, N. A., Telaumbanua, F., & Telaumbanua, Y. F. (2025). Pelatihan Penggunaan Mikroskop untuk Identifikasi Sel dan Jaringan pada Preparat di SMP Negeri 3 Sogae’adu Kabupaten Nias. Journal of Community Service, 03, 157–168. https://doi.org/10.56855/jcos.v3i3.1623

Helmy, I. M., & Abdel Azim, A. M. (2012). Efficacy of ImageJ in the assessment of apoptosis. Diagnostic Pathology, 7(1), 15. https://doi.org/10.1186/1746-1596-7-15

Herawati, E., Listyawati, S., Widiyani, T., Budiharjo, A., & Parama Astirin, O. (2024). Pendampingan dan Peningkatan Kompetensi Praktek Biologi Mikroskopis bagi Siswa SMP Djama’atul Ichwan, Surakarta. Jurnal Ilmu Pengetahuan, Teknologi, Dan Seni Bagi Masyarakat, 13(2), 2745–4223.

Hu, Y., Yu, J., Cui, X., Zhang, Z., Li, Q., Guo, W., ... & Gu, X. (2021). Combination usage of AdipoCount and Image-Pro Plus/ImageJ software for quantification of adipocyte sizes. Frontiers in endocrinology, 12, 642000. https://doi.org/10.3389/fendo.2021.642000

Jadzic, J., Cvetkovic, D., Tomanovic, N., Zivkovic, V., Nikolic, S., Milovanovic, P., ... & Djonic, D. (2021). The severity of hepatic disorder is related to vertebral microstructure deterioration in cadaveric donors with liver cirrhosis. Microscopy Research and Technique, 84(5), 840-849. https://doi.org/10.1002/jemt.23642

Jayakody, H., Liu, S., Whitty, M., & Petrie, P. (2017). Microscope Image Based Fully Automated Stomata Detection and Pore Measurement Method for Grapevines. Plant Methods, 13(1). https://doi.org/10.1186/s13007-017-0244-9

Jumadi, J., Yupianti, & Sartika, D. (2021). Pengolahan Citra Digital untuk Identifikasi Objek Menggunakan Metode Hierarchical Agglomerative Clustering. Jurnal Sains Dan Teknologi, 10(2), 148–156.

Kadam, K., Sowjanya, P. R., Kalshetti, O., Sangnure, V., Kadam, A. R., Madave, A., ... & Marathe, R. A. (2024). Evaluation of Leaf Area Parameter of Pomegranate (Punica granatum L.) Germplasm by using ImageJ in Comparison with Manual Method. Journal of Agriculture Research and Technology, 49(01), 90–94. https://doi.org/10.56228/jart.2024.49112

Kurniawan, R. (2025). Analisis Kendala dan Strategi Guru dalam Praktikum Biologi. Jurnal Ilmiah IPA Dan Matematika, 3(1), 19–24.

Li, Z., Ji, C., & Liu, J. (2007). Leaf Area Calculating Based on Digital Image. International Conference on Computer and Computing Technologies in Agriculture, 1427–1433.

Makin, F. M. P. R., Wiguna, G. A., & Welsiliana. (2023). Pelatihan Penerapan Teknologi Mikroskop Digital Untuk Pembelajaran Berbasis Praktikum di SMA Negeri Taekas. Jurnal Pengabdian Kepada Masyarakat Indonesia, 4(2), 117. https://doi.org/10.36596/jpkmi.v4i2.654

Mendes, G. N., Floresta, L. G., Takeshita, W. M., Brasileiro, B. F., & Trento, C. L. (2025). The Application of ImageJ Software for Roughness Analysis of Dental Implants. Journal of Imaging Informatics in Medicine, 38(3), 1812–1819. https://doi.org/10.1007/s10278-024-01298-1

Mohamed, M. M. A., Suliman, A., Puchkov, M., & Loktionova, E. (2019). Applying a Digital Method for Measuring Leaf Area Index of Tomato Plants. Digital Agriculture-Development Strategy, 167, 5–8.

Negi, N. S., & Singh, M. C. (2023). An Image Analysis Based System (ImageJ) for Determination of Leaf Area in Seven Chrysanthemum Varieties. Pharma Innovation Journal, 12(10), 2275–2281. http://imagej.nih.gov/ij/

Nurjannah, N., Zainura, S., Iswardy, E., & Yusibani, E. (2024). Volume Estimation of Lung Cancer using Image-J For CT-Scan Image. INDONESIAN JOURNAL OF APPLIED PHYSICS, 14(1), 13. https://doi.org/10.13057/ijap.v14i1.67723

Out, W. A., Evett, R., Hošková, K., Power, R. C., Ruiz-Pérez, J., Tromp, M., ... & Hasler, M. (2025). Inter- and Intra-observer Variation in Phytolith Morphometry. Annals of Botany, 135(5), 851–866. https://doi.org/10.1093/aob/mcae116

Panda, A. K., Verma, V., Srivastav, A., Badola, R., & Hussain, S. A. (2024). Digital Image Processing: A New Tool for Morphological Measurements of Freshwater Turtles Under Rehabilitation. PLoS ONE, 19(3). https://doi.org/10.1371/journal.pone.0300253

Pratama, R., & Cahaya, M. A. (2025). Hubungan Antara Motivasi Mahasiswa dan Pengalaman Praktikum di Laboratorium Biologi. Indonesian Research Journal on Education Web Jurnal Indonesian Research Journal on Education, 5, 2775–9582.

Pratama, R., Cahaya, M. A., Hartati, M. S., Wulandari, R., & Yusdiarti, M. (2025). Peningkatan Kompetensi Sains melalui Pengenalan dan Pemanfaatan Alat Laboratorium Biologi Siswa Sekolah Alam Langit Biru. Jurnal Abdimas ADPI Sains Dan Teknologi, 6(2), 68–74.

Pratama, S., Ashari, M., Zulkarnain, S. A. B., & Sabrina, E. (2025). Pentingnya Literasi Digital dalam Dunia Pendidikan: Transformasi Pembelajaran di Era Digital. JKIP : Jurnal Kajian Ilmu Pendidikan, 6(2), 554–561. http://journal.al-matani.com/index.php/jkip/index

Prodanov, D., & Verstreken, K. (2012). Automated Segmentation and Morphometry of Cell and Tissue Structures. Selected Algorithms in ImageJ. Molecular Imaging, 10.

Rahimi, M., Khameneh, E. A., Riazi-Esfahani, H., Mahmoudi, T., Pour, E. K., & Kafieh, R. (2023). Application of ImageJ in optical coherence tomography angiography (OCT?A): A literature review. Journal of Ophthalmology, 2023(1), 9479183. https://doi.org/10.1155/2023/9479183

Ramm, S., Odefey, U., Frahm, B., & Pein-Hackelbusch, M. (2024). Semi-automated vs. Manual: Comparative Study of Cell Culture Counting Methods Using Validation Parameters. BioRxiv, 05. https://doi.org/10.1101/2024.05.30.596619

Sakdiah. (2024). Implementasi Segmentasi Pada Citra Paru Menggunakan Metode Thresholding dan Fuzzy Possibility Distribution. Teknologi Dan Informasi, 2(2), 48–56. https://journal.grahamitra.id/index.php/jurikti

Satriorini, M. G. R., Sumiharto, R., & Hujja, R. M. (2022). Sistem Pemantauan Pertumbuhan Anggrek Berdasarkan Pengolahan Citra Digital. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 12(2), 203–212. https://doi.org/10.22146/ijeis.77893

Schindelin, J., Rueden, C. T., Hiner, M. C., & Eliceiri, K. W. (2015). The ImageJ ecosystem: An open platform for biomedical image analysis. Molecular Reproduction and Development, 82(7–8), 518–529. https://doi.org/10.1002/mrd.22489

Schroeder, A. B., Dobson, E. T. A., Rueden, C. T., Tomancak, P., Jug, F., & Eliceiri, K. W. (2021). The ImageJ Ecosystem: Open-source Software for Image Visualization, Processing, and Analysis. Protein Science, 30(1), 234–249. https://doi.org/10.1002/pro.3993

Stenkula, K. G., & Erlanson-Albertsson, C. (2018). Adipose Cell Size: Importance in Health and Disease. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 315, 284–295. https://doi.org/10.1152/ajpregu.00257.2017.-Adipose

Umam, C., Syafii, M., Damayanti, E. N., Dermawan, D. A., & Supyanto, A. (2023). Penerapan metode digital untuk mengukur indeks luas daun tanaman sawi caisim (Brassica juncae L.). Jurnal Pengelolaan Perkebunan (JPP), 4(1), 8-15. https://doi.org/10.54387/jpp.v4i1.30

Yoda, T. (2022). Measurement and Characterization of Yeast Cell Size Using a Digital Optical Microscope. Processes, 10(11), 2396. https://doi.org/10.3390/pr10112396

Refbacks

  • There are currently no refbacks.