Accurately Determining Labor Test Results Using the Rough Set Method

Authors

Retno Devita , Sarjon Defit

DOI:

10.29303/jppipa.v10i4.7069

Published:

2024-04-25

Issue:

Vol. 10 No. 4 (2024): April

Keywords:

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

Research Articles

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Devita, R., & Defit, S. (2024). Accurately Determining Labor Test Results Using the Rough Set Method. Jurnal Penelitian Pendidikan IPA, 10(4), 1723–1730. https://doi.org/10.29303/jppipa.v10i4.7069
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Abstract

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.

References

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. https://doi.org/10.

/j.heliyon.2020.e05312

Ali, M. I., Davvaz, B., & Shabir, M. (2013). Some Properties of Generalized Rough Sets. Information Sciences, 224, 170–179. https://doi.org/10.1016/

j.ins.2012.10.026

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. https://doi.org/10.3390/math11194153

AL-Khafaji, M. A. K., & Hussan, M. S. M. (2018). General Type-2 Fuzzy Topological Spaces. Advances in Pure Mathematics, 08(09), 771–781. https://doi.org/10.4236/apm.2018.89047

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. https://doi.org/10.1016/j.egyai.2020.100031

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. https://doi.org/10.

/s41598-023-28722-w

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. https://doi.org/10.3390/

axioms11120686

Bobillo, F., & Straccia, U. (2012). Generalized Fuzzy Rough Description Logics. Information Sciences, 189, 43–62. https://doi.org/10.1016/j.ins.2011.10.

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. https://doi.org/10.3390/su151310745

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. https://doi.org/10.

/IPACT.2017.8244875

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. https://doi.org/10.1007/s10115-020-01467-y

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. https://doi.org/10.3390/

buildings7010012

Demin, A. V. (2020). Certainty and Uncertainty in Tax Law: Do Opposites Attract? Laws, 9(4), 30. https://doi.org/10.3390/laws9040030

Gao, L., & Wu, W. (2020). Relevance Assignation Feature Selection Method Based on Mutual Information for Machine Learning. Knowledge-Based Systems, 209, 106439. https://doi.org/10.

/j.knosys.2020.106439

González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/

app11125467

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. https://doi.org/10.1007/978-981-13-1498-8_68

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. https://doi.org/10.1186/s40537-019-0206-3

Herbert, J. P., & Yao, J. (2009). Criteria for Choosing A Rough Set Model. Computers & Mathematics with Applications, 57(6), 908–918. https://doi.org/10.

/j.camwa.2008.10.043

Khairunnessa, F., Vazquez-Brust, D. A., & Yakovleva, N. (2021). A Review of the Recent Developments of Green Banking in Bangladesh. Sustainability, 13(4), 1904. https://doi.org/10.3390/su13041904

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. https://doi.org/10.3390/su13147681

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. https://doi.org/10.1088/1742-6596/1933/1/012031

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. https://doi.org/10.

/CITSM.2018.8674280

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. https://doi.org/10.3390/math10081

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. https://doi.org/10.1088/1742-6596/1114/1/012072

Nurhidayat, N., Defit, S., & Sumijan, S. (2020). Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras. Jurnal Informasi dan Teknologi, 83–88. https://doi.org/10.

/jidt.v2i3.67

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. https://doi.org/10.29074/ascls.30.

240

Pendrill, L. R. (2014). Using Measurement Uncertainty in Decision-Making and Conformity Assessment. Metrologia, 51(4), S206–S218. https://doi.org/10.

/0026-1394/51/4/S206

Pięta, P., & Szmuc, T. (2021). Applications of Rough Sets in Big Data Analysis: An Overview. https://doi.org/10.34768/AMCS-2021-0046

Pięta, P., Szmuc, T., & Kluza, K. (2019). Comparative Overview of Rough Set Toolkit Systems for Data Analysis. MATEC Web of Conferences, 252, 03019. https://doi.org/10.1051/matecconf/201925203019

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. https://doi.org/10.3390/axioms12080746

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. https://doi.org/10.3390/math8081308

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. https://doi.org/

30865/mib.v5i1.2745

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

1007/s42979-021-00592-x

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. https://doi.org/10.33395/

sinkron.v5i2.10863

Slim, H., & Nadeau, S. (2020). A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM. Sustainability, 12(5), 1918. https://doi.org/10.3390

/su12051918

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. https://doi.org/10.

/j.caeai.2022.100075

Swiniarski, R. W., & Skowron, A. (2003). Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters, 24(6), 833–849. https://doi.org/10.1016/S0167-8655(02)00196-4

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.

org/10.1016/j.apm.2022.09.002

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. https://doi.org/

1016/j.ins.2020.01.003

Zuhdi, I. (2022). Data Mining Menggunakan Metode Rough Set dalam Memprediksi Tingkat Penjualan Peralatan Komputer. Jurnal Informatika Ekonomi Bisnis, 142–147. https://doi.org/10.37034/infeb.

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Author Biographies

Retno Devita, Universitas Putra Indonesia YPTK Padang

Sarjon Defit, Universitas Putra Indonesia YPTK Padang

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