Exploring Classification Algorithms for Detecting Learning Loss in Islamic Religious Education: A Comparative Study

Rohmat Sapdi - UIN Sunan Gunung Djati Bandung, 40614, Indonesia
Dian Maylawati - UIN Sunan Gunung Djati Bandung, 40614, Indonesia
Diena Ramdania - UIN Sunan Gunung Djati Bandung, 40614, Indonesia
Ichsan Budiman - UIN Sunan Gunung Djati Bandung, 40614, Indonesia
Muhammad Al-Amin - UIN Sunan Gunung Djati Bandung, 40614, Indonesia
Mi'raj Fuadi - Universitas Mataram, 83115, Indonesia

Citation Format:

DOI: http://dx.doi.org/10.62527/joiv.8.2.1823


This study investigates the detection of learning loss in Islamic religious education subjects in Indonesia. Focusing on the effectiveness of multiple classification algorithms, the research assesses learning loss across literacy, numeracy, writing, and science domains. While education traditionally involves knowledge transmission, it also seeks to instill values. Given Indonesia's predominantly Islamic demographic, Islamic Religious Education (IRE) is pivotal in disseminating moral and cultural values, encompassing teachings from the Koran, Hadith, Aqedah, morality, Fiqh, and Islamic history. The study's central aim is to discern learning loss in IRE in Islamic schools, utilizing the Gradient Boosting Classifier as its primary analytical tool. Various classification algorithms, including the Cat Boost Classifier, Light Gradient Boosting Machine, Extreme Gradient Boosting, and others, were tested. The study engaged a sample of 38,326 Islamic Elementary school students, 29,350 Islamic Junior High school students, and 13,474 Islamic High school students across Indonesia. The findings revealed that the Light Gradient Boosting Machine was the most effective model for Islamic Elementary and High school data, while the Cat Boost Classifier excelled for Islamic Junior High school data. These results highlight the extent of learning loss in IRE and offer invaluable perspectives for education stakeholders. Future studies are encouraged to further explore the root causes of this learning loss and devise specific interventions to tackle these issues effectively.


Education; Gradient Boosting Classifier; Islamic Religious Education; Learning loss; Values.

Full Text:



I. Muhtarom, “Apa Itu Learning Loss yang Ditakutkan Nadiem Makarim?,” Tempo.co, 2021.

R. D. Adiputri, “”Learning Loss” di Masa Pandemi,” kompas.id, 2022.

W. Durongkaveroj, “Learning loss due to university closures during the COVID-19 pandemic: Evidence from Thailand’s largest public university,” Thail. World Econ., vol. 41, no. 2, 2023.

J. P. Azevedo, A. Hasan, D. Goldemberg, K. Geven, and S. A. Iqbal, “Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes: A set of global estimates,” World Bank Res. Obs., vol. 36, no. 1, pp. 1–40, 2021.

S. Budi, I. S. Utami, R. N. Jannah, N. L. Wulandari, N. A. Ani, and W. Saputri, “Deteksi Potensi Learning Loss pada Siswa Berkebutuhan Khusus Selama Pembelajaran Daring Masa Pandemi Covid-19 di Sekolah Inklusif,” J. Basicedu, vol. 5, no. 5, pp. 3607–3613, 2021.

J. Schult, N. Mahler, B. Fauth, and M. A. Lindner, “Did students learn less during the COVID-19 pandemic? Reading and mathematics competencies before and after the first pandemic wave,” Sch. Eff. Sch. Improv., vol. 33, no. 4, pp. 544–563, 2022.

M. A. Maulyda, M. Erfan, and V. R. Hidayati, “Analisis situasi pembelajaran selama pandemi covid-19 di sdn senurus: kemungkinan terjadinya learning loss,” COLLASE (Creative Learn. Students Elem. Educ., vol. 4, no. 3, pp. 328–336, 2021.

J. Hoofman and E. Secord, “The effect of COVID-19 on education,” Pediatr. Clin., vol. 68, no. 5, pp. 1071–1079, 2021.

M. A. Callejon-Leblic et al., “Loss of smell and taste can accurately predict COVID-19 infection: a machine-learning approach,” J. Clin. Med., vol. 10, no. 4, p. 570, 2021.

C. Ardington, G. Wills, and J. Kotze, “COVID-19 learning losses: Early grade reading in South Africa,” Int. J. Educ. Dev., vol. 86, p. 102480, 2021.

INOVASI & Pusat Standar dan Kebijakan Pendidikan, I.N.O.V.A.S.I., and P. S. Kebijakan Pendidikan, Studi Analisa Situasi Pembelajaran dalam Masa Pandemi. Jakarta, 2022.

A. Widodo, P. D. Angga, M. Syazali, and U. Umar, “Mainstreaming Parental Involvement in Post-Pandemic: Resolving Learning Loss with the Partnership Model in Elementary Schools,” J. Kependidikan J. Has. Penelit. dan Kaji. Kepustakaan di Bid. Pendidikan, Pengajaran dan Pembelajaran, vol. 9, no. 2, p. 365, 2023.

A. Banawi, A. Latuconsina, and S. Latuconsina, “Exploring the Students’ Reading, Writing, and Numeracy Skills in Southeast Maluku Regency Coastal Elementary Schools,” Al Ibtida J. Pendidik. Guru MI, vol. 9, no. 2, pp. 252–264, 2022.

F. Firdaus and A. Septiady, “Peningkatan Kemampuan Literasi Dan Numerasi Di Sekolah 3t (Tertinggal, Terluar, Terdepan) Di Masa Pandemi Covid-19 Melalui Program Kampus Mengajar,” SKYLANDSEA Prof. J. Ekon. Bisnis dan Teknol., vol. 1, no. 2, pp. 213–220, 2021.

R. Oostdam, M. van Diepen, B. Zijlstra, and R. Fukkink, “Effects of the COVID-19 school lockdowns on language and math performance of students in elementary schools: implications for educational practice and reducing inequality,” Eur. J. Psychol. Educ., pp. 1–21, 2023.

M. Krywult-Albańska and Ł. Albański, “Gender and educational inequalities during the COVID-19 pandemic: Preliminary insights from Poland,” Sustainability, vol. 13, no. 22, p. 12403, 2021.

C. Boruchowicz, S. W. Parker, and L. Robbins, “Time use of youth during a pandemic: evidence from Mexico,” World Dev., vol. 149, p. 105687, 2022.

C. F. Drane, L. Vernon, and S. O’Shea, “Vulnerable learners in the age of COVID-19: A scoping review,” Aust. Educ. Res., vol. 48, no. 4, pp. 585–604, 2021.

Organisation for Economic Co-operation and Development (OECD), “The Future of Education and Skills (Education 2023),” 2018.

Z. Ali, Pendidikan Agama Islam. Jakarta: Bumi Aksara, 2007.

S. Arifin, Pendidikan Agama Islam. Deepublish, 2018.

A. R. Nurjaman, Pendidikan Agama Islam. Bumi Aksara, 2020.

Direktorat Jendral Pendidikan Islam Kementrian Agama RI, “Rekapitulasi Data Pokok Pendidikan Islam (Madrasah Semester Ganjil 2020/2021),” 2021.

F. Amalina et al., “Blending big data analytics: Review on challenges and a recent study,” Ieee Access, vol. 8, pp. 3629–3645, 2019.

J. Wang, Y. Zhao, P. Balamurugan, and P. Selvaraj, “Managerial decision support system using an integrated model of AI and big data analytics,” Ann. Oper. Res., pp. 1–18, 2022.

P. Ghavami, “Big data analytics methods,” in Big Data Analytics Methods, de Gruyter, 2019.

X. Shu and Y. Ye, “Knowledge Discovery: Methods from data mining and machine learning,” Soc. Sci. Res., vol. 110, p. 102817, 2023.

H. M. Shah, B. B. Gardas, V. S. Narwane, and H. S. Mehta, “The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: A comprehensive review,” Kybernetes, vol. 52, no. 5, pp. 1643–1697, 2023.

X. Zhu and A. B. Goldberg, “Introduction to semi-supervised learning,” Synth. Lect. Artif. Intell. Mach. Learn., vol. 3, no. 1, pp. 1–130, 2009.

J. E. Van Engelen, H. H. Hoos, J. E. Engelen, and H. H. Hoos, “A survey on semi-supervised learning,” Mach. Learn, vol. 109, no. 2, pp. 373–440, 2020.

M. F. A. Hady and F. Schwenker, “Semi-supervised learning,” Handb. Neural Inf. Process., pp. 215–239, 2013.

D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel, “Mixmatch: A holistic approach to semi-supervised learning,” Adv. Neural Inf. Process. Syst., vol. 32, 2019.

J. Ran, Y. Ji, and B. Tang, “A semi-supervised learning approach to ieee 802.11 network anomaly detection,” in 2019 IEEE 89th vehicular technology conference (VTC2019-Spring), 2019, pp. 1–5.

A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A survey on contrastive self-supervised learning,” Technologies, vol. 9, no. 1, p. 2, 2020.

H. Di, X. Ke, Z. Peng, and Z. Dongdong, “Surface defect classification of steels with a new semi-supervised learning method,” Opt. Lasers Eng., vol. 117, pp. 40–48, 2019.

C. Yin and Z. Chen, “Developing sustainable classification of diseases via deep learning and semi-supervised learning,” in Healthcare, 2020, vol. 8, no. 3, p. 291.

T. Jiang, J. L. Gradus, and A. J. Rosellini, “Supervised machine learning: a brief primer,” Behav. Ther., vol. 51, no. 5, pp. 675–687, 2020.

M. Binkhonain and L. Zhao, “A review of machine learning algorithms for identification and classification of non-functional requirements,” Expert Syst. with Appl. X, vol. 1, p. 100001, 2019.

P. C. Sen, M. Hajra, and M. Ghosh, “Supervised classification algorithms in machine learning: A survey and review,” in Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018, 2020, pp. 99–111.

R. Wang, T. Lei, R. Cui, B. Zhang, H. Meng, and A. K. Nandi, “Medical image segmentation using deep learning: A survey,” IET Image Process., vol. 16, no. 5, pp. 1243–1267, 2022.