Accurately Determining Labor Test Results Using the Rough Set Method







Vol. 10 No. 4 (2024): April


Knowledge, Lab exams, Machine learning, Rough set method, Rules

Research Articles


How to Cite

Devita, R., & Defit, S. (2024). Accurately Determining Labor Test Results Using the Rough Set Method. Jurnal Penelitian Pendidikan IPA, 10(4), 1723–1730.


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An exam is something that must be done to test a person's ability or intelligence. The laboratory exam in the Computer Systems study program at Putra Indonesia University "YPTK" Padang consists of a digital systems exam, a fuzzy logic control exam, and a tool presentation. The Labor Exam must be passed by students who will take the comprehensive exam. In this study, laboratory exam data was taken for 20 students. So far, processing of student laboratory exam results has been done manually so it takes a long time to make decisions. To overcome this problem, a Rough Set method is used to determine laboratory test results. The Rough Set method is part of machine learning. This research produces 29 rules as knowledge, namely {Digital System} Or {A} = 3 rules, {Fuzzy Logic} Or {B} = 3 rules, {Tool Presentation} Or {C} = 3 rules, {Fuzzy Logic, Tool Percentage} Or {BC} = 6 rules, {Digital System, Fuzzy Logic} Or {AB} = 6 rules and {Digital System, Tool Percentage} Or {AC} = 8 rules. The Rough Set method can determine student laboratory exam results (pass or fail) accurately.


Abdulrahman, M. D., Faruk, N., Oloyede, A. A., Surajudeen-Bakinde, N. T., Olawoyin, L. A., Mejabi, O. V., Imam-Fulani, Y. O., Fahm, A. O., & Azeez, A. L. (2020). Multimedia Tools in the Teaching and Learning Processes: A Systematic Review. Heliyon, 6(11), e05312.


Ali, M. I., Davvaz, B., & Shabir, M. (2013). Some Properties of Generalized Rough Sets. Information Sciences, 224, 170–179.


Ali, W., Shaheen, T., Haq, I. U., Toor, H. G., Alballa, T., & Khalifa, H. A. E.-W. (2023). A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis. Mathematics, 11(19), 4153.

AL-Khafaji, M. A. K., & Hussan, M. S. M. (2018). General Type-2 Fuzzy Topological Spaces. Advances in Pure Mathematics, 08(09), 771–781.

Alkinani, H. H., Al-Hameedi, A. T. T., & Dunn-Norman, S. (2020). Data–Driven Decision–Making for Lost Circulation Treatments: A Machine Learning Approach. Energy and AI, 2, 100031.

Attaullah, A., Rehman, N., Khan, A., & Santos-García, G. (2023). Fermatean Hesitant Fuzzy Rough Aggregation Operators and Their Applications in Multiple Criteria Group Decision-Making. Scientific Reports, 13(1), 6676.


Ayub, S., Shabir, M., Riaz, M., Karaaslan, F., Marinkovic, D., & Vranjes, D. (2022). Linear Diophantine Fuzzy Rough Sets on Paired Universes with Multi Stage Decision Analysis. Axioms, 11(12), 686.


Bobillo, F., & Straccia, U. (2012). Generalized Fuzzy Rough Description Logics. Information Sciences, 189, 43–62.

Chen, X., Zhou, B., Štilić, A., Stević, Ž., & Puška, A. (2023). A Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production. Sustainability, 15(13), 10745.

Chinnaswamy, A., & Srinivasan, R. (2017). Hybrid Information Gain Based Fuzzy Roughset Feature Selection in Cancer Microarray Data. 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), 1–6.


Dagdia, Z. C., Zarges, C., Beck, G., & Lebbah, M. (2020). A Scalable and Effective Rough Set Theory-Based Approach for Big Data Pre-Processing. Knowledge and Information Systems, 62(8), 3321–3386.

Del Giudice, V., De Paola, P., & Cantisani, G. (2017). Rough Set Theory for Real Estate Appraisals: An Application to Directional District of Naples. Buildings, 7(4), 12.


Demin, A. V. (2020). Certainty and Uncertainty in Tax Law: Do Opposites Attract? Laws, 9(4), 30.

Gao, L., & Wu, W. (2020). Relevance Assignation Feature Selection Method Based on Mutual Information for Machine Learning. Knowledge-Based Systems, 209, 106439.


González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences, 11(12), 5467.


Halder, B., Mitra, S., & Mitra, M. (2019). Development of Cardiac Disease Classifier Using Rough Set Decision System. In Abraham, A., Dutta, P., Mandal, J. K., Bhattacharya, A., & Dutta, S. (Eds.), Emerging Technologies in Data Mining and Information Security, 813, 775–785. Springer Singapore.

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges. Journal of Big Data, 6(1), 44.

Herbert, J. P., & Yao, J. (2009). Criteria for Choosing A Rough Set Model. Computers & Mathematics with Applications, 57(6), 908–918.


Khairunnessa, F., Vazquez-Brust, D. A., & Yakovleva, N. (2021). A Review of the Recent Developments of Green Banking in Bangladesh. Sustainability, 13(4), 1904.

Kocornik-Mina, A., Bastida-Vialcanet, R., & Eguiguren Huerta, M. (2021). Social Impact of Value-Based Banking: Best Practises and a Continuity Framework. Sustainability, 13(14), 7681.

Kristanto, S. P., Bahtiar, R. S., Sembiring, M., Himawan, H., Samboteng, L., Hariyadi, H., & Suparya, I. K. (2021). Implementation of ML Rough Set in Determining Cases of Timely Graduation of Students. Journal of Physics: Conference Series, 1933(1), 012031.

Kurniawan, H., Agustin, F., Yusfrizal, Y., & Ummi, K. (2018). Implementation Data Mining in Prediction of Sales Chips with Rough Set Method. 2018 6th International Conference on Cyber and IT Service Management (CITSM), 1–7.


Liu, L., Dou, Y., & Qiao, J. (2022). Evaluation Method of Highway Plant Slope Based on Rough Set Theory and Analytic Hierarchy Process: A Case Study in Taihang Mountain, Hebei, China. Mathematics, 10(8), 1264.

Manurung, H., Ongko, E., Harahap, A. J., Hartono, H., Abdullah, D., Erliana, C. I., Sriadhi, S., Putra, A. H. P. K., Muslim, A. H., Nanuru, R. F., Saleh, A. A., Indahingwati, A., Kurniawan, C., Iswara, I. B. A., Hasibuan, A., Wuryani, E., Hadikurniawati, W., & Winarno, E. (2018). Designing Data Mining Applications with Rough Set Algorithm for Provision of Recommendations in the Selection of Training Topics on Online Learning. Journal of Physics: Conference Series, 1114, 012072.

Nurhidayat, N., Defit, S., & Sumijan, S. (2020). Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras. Jurnal Informasi dan Teknologi, 83–88.


Pelton, S. B. (2017). Correlation of University Comprehensive and National Certification Exam Scores for Medical Laboratory Science Students. American Society for Clinical Laboratory Science, 30(4), 240–246.


Pendrill, L. R. (2014). Using Measurement Uncertainty in Decision-Making and Conformity Assessment. Metrologia, 51(4), S206–S218.


Pięta, P., & Szmuc, T. (2021). Applications of Rough Sets in Big Data Analysis: An Overview.

Pięta, P., Szmuc, T., & Kluza, K. (2019). Comparative Overview of Rough Set Toolkit Systems for Data Analysis. MATEC Web of Conferences, 252, 03019.

Puška, A., Štilić, A., Nedeljković, M., Božanić, D., & Biswas, S. (2023). Integrating Fuzzy Rough Sets with LMAW and MABAC for Green Supplier Selection in Agribusiness. Axioms, 12(8), 746.

Qu, J., Bai, X., Gu, J., Taghizadeh-Hesary, F., & Lin, J. (2020). Assessment of Rough Set Theory in Relation to Risks Regarding Hydraulic Engineering Investment Decisions. Mathematics, 8(8), 1308.

Raharjo, M. R., & Windarto, A. P. (2021). Penerapan Machine Learning dengan Konsep Data Mining Rough Set (Prediksi Tingkat Pemahaman Mahasiswa terhadap Matakuliah). Jurnal Media Informatika Budidarma, 5(1), 317.


Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160.


Sianturi, F. A., Sijabat, P. I., & Sitohang, A. (2021). Application of the Rough Set Method to the Level of Customer Satisfaction on Service Quality. SinkrOn, 5(2), 251–259.


Slim, H., & Nadeau, S. (2020). A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM. Sustainability, 12(5), 1918.


Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022). Assessment in the Age of Artificial Intelligence. Computers and Education: Artificial Intelligence, 3, 100075.


Swiniarski, R. W., & Skowron, A. (2003). Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters, 24(6), 833–849.

Wang, C., Fan, H., & Wu, T. (2023). Novel Rough Set Theory-Based Method for Epistemic Uncertainty Modeling, Analysis and Applications. Applied Mathematical Modelling, 113, 456–474. https://doi.


Zhang, L., Zhan, J., & Yao, Y. (2020). Intuitionistic Fuzzy TOPSIS Method Based on CVPIFRS Models: An Application to Biomedical Problems. Information Sciences, 517, 315–339.


Zuhdi, I. (2022). Data Mining Menggunakan Metode Rough Set dalam Memprediksi Tingkat Penjualan Peralatan Komputer. Jurnal Informatika Ekonomi Bisnis, 142–147.


Author Biographies

Retno Devita, Universitas Putra Indonesia YPTK Padang

Sarjon Defit, Universitas Putra Indonesia YPTK Padang


Copyright (c) 2024 Retno Devita, Sarjon Devit

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