Proposed improvement of product support packaging material defects using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach
DOI:
https://doi.org/10.31315/opsi.v18i1.11803Keywords:
Cross-industry standard process for data mining (CRISP-DM), Decision tree, Power business intelligence, Statistical process control, Fault tree analysisAbstract
This research was conducted because the defect rate of packaging materials supporting lithos M products exceeded the Company's tolerance standard of 2%. This research aims to identify the causes and provide suggestions to improve the Quality of product support packaging materials. The methods used in data mining with the CRISP-DM (Cross-Industry Standard Process For Data Mining) approach. The Business Understanding stage determines the problem and research objectives, Power Business Intelligence, SIPOC (Supplier, Input, Process, Output, Customer) Diagrams, Operation Process Chart, QC Action, and CTQ (Critical to Quality). The Data Understanding stage creates a Control P Chart, calculates DPMO and the sigma level obtained by the unscramble machine dented bottle value 762.31 with a Sigma level of 4.66, Sticker 2nd defect Internal 187.47 with a sigma level of 5.06, Cap 2nd defect internal 67.18 with a sigma level of 5.32, and uses Fault Tree Analysis. The Data Preparation stage performs data cleaning, integration, transformation, and preprocessing. The Modelling stage makes classification with C4.5 and the Cart decision tree algorithm. The evaluation stage uses a Confusion Matrix accuracy of 78.8 percent and 89.4 percent, respectively. The Deployment stage produces improvement proposals by creating a Dashboard, Standard Operating Procedure, and Check Sheet.
References
Z. Liao, J. Chen, X. Chen, M. Song, “Digital platform capability, environmental innovation quality, and firms’ competitive advantage: The moderating role of environmental uncertainty”, Int. J. of Production Economics, vol. 268, pp. 2024, doi: 10.1016/j.ijpe.2023.109124
[2] R. D. Safitri and A. N. Khairi, “Quality control of dry noodle product processing in Semarang Central Java,” J. Agri-Food Sci. Technol., vol. 2, no. 1, pp. 88–95, 2022, doi: 10.12928/jafost.v2i1.4775.
[3] S. S. Salvi and S. S. Kerkar, “Quality assurance and quality control for project effectiveness in construction and management,” Int. J. Eng. Res., vol. V9, no. 02, pp. 26–29, 2020, doi: 10.17577/ijertv9is020028.
[4] R. Elyas and W. Handayani, “Statistical Process Control (SPC) untuk pengendalian kualitas produk mebel di ud. ihtiar jaya,” Bisma J. Manaj., vol. 6, no. 1, p. 50, 2020, doi: 10.23887/bjm.v6i1.24415.
[5] M. A. Abdillah, A. Setyanto, and S. Sudarmawan, “Implementasi decision tree algoritma c4.5 untuk memprediksi kesuksesan pendidikan karakter,” Respati, vol. 15, no. 2, p. 59, 2020, doi: 10.35842/jtir.v15i2.349.
[6] A. R. M. T. Islam, M. A. A. Mamun, M. Hasan, M. N. Aktar, M. N. Uddin, M. A. B. Siddique, M. H.Chowdhury, M. S. Islam, A. B. M. M. Bari, A.M. Idris, V. Senapathi, "Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis". Journal of Contaminant Hydrology, Vol. 269, 2025.
[7] L. Shangguan, Z. Sun. "Enhancing data quality and real-time sharing performance in water informatics through decision tree mining algorithm", Measurement: Sensors, Volume 31, 2024.
[8] R. A. Casonatto, T. D. P. G. Souza, A. M. Mariano, "Quality and risk management in data mining: A CRISP-DM perspective", Procedia Computer Science. Vol. 242, 2024, pp. 161-168
[9] A. Fitzner, A. Kassoumeh, M Krause, J. Wonneberger, L. Grabinski, "Data mining approach using cluster analysis and decision trees for optimizing electrode paste quality in lithium-ion battery production", Procedia CIRP, Vol. 130, pp. 449-455, 2024.
[10] D. Wang, K. Yin, H. Wang, "Intelligent classification of construction quality problems based on unbalanced short text data mining", Ain Shams Engineering Journal, vol. 15, no.10, 2024. doi: 10.1016/j.asej.2024.102983
[11] T. L. Palgunadi, R. Fitriana, A. N. Habyba, Y. C. Liang, "Optimizing demand forecasting method with support vector regression for improved inventory planning", J. Optimasi Sistem Industri, vol.3, no.2, pp. 149-166, 2024. doi: 10.25077/josi.v23.n2.p149-166.2024
[12] N.K. Auliyaanisa, R. Fitriana, E.F. Harahap, "The support vector regression method with the grid search algorithm in forecasting sales of milk product". In: Mohd Saffe, S.N., Ismail, S.Z., Rosyidi, C.N., Tokhi, M.O. (eds) Proceedings of the 7th Asia Pacific Conference on Manufacturing Systems and 6th International Manufacturing Engineering Conference—Volume 1. IMEC-APCOMS 2024. Lecture Notes in Mechanical Engineering. Springer, Singapore., 2025. doi: 10.1007/978-981-96-4353-0_2
[13] F. Graciela, T. S. Dewayana, A. N, Habyba, "Enhancing customer satisfaction towards the service quality of Soekarno-Hatta International Airport using servqual and binary logistic regression", Opsi, Vol. 17,No. 2, 2024. doi: 10.31315/opsi.v17i2.11930
[14] T. C. Tyas, I. Giyanti, "Quality control of garment product using dmaic six sigma", Opsi, Vol. 17, No. 1, 2024. doi: 0.31315/opsi.v17i1.7107
[15] A. Sofiana1, E. P. Safitri, "Quality control related to inventory loss of animal feed raw materials using i-mr control map (case study: PT Cargill Indonesia, Plant Semarang)", Opsi, vol 16, no. 1, 2023, doi: 10.31315/opsi.v16i1.8897
[16] N. Rahmasuci, J. Saragih, I. Mayusda, "Quality improvement of tin ingot product using six sigma method at PT Timah Tbk", J. OPSI, vol. 16, no. 1, 2023, doi: 10.31315/opsi.v16i1.7434
[17] L. Fitria, D.Tauhida, A. Sokhibi, "Quality control with six sigma method to minimize polyester fabric product defects at PT Sukuntex", Opsi, vol 16, no. 1, 2023, doi: 10.31315/opsi.v16i1.6786
[18] M. Harahap, N. Hidayati, S. Panjaitan, E. Tambunan, and J. Sihombing, “Teacher quality affects on graduation of study programming data approach there with CRISP-DM method,” Sinkron, vol. 8, no. 4, pp. 2275–2282, 2023, doi: 10.33395/sinkron.v8i4.12762.
[19] M. D. Anies and H. A. Primandari, “Implementasi CRISP-DM model menggunakan metode decision tree dengan algoritma cart untuk prediksi lila ibu hamil berpotensi gizi kurang,” J. Ilm. Pendidik. Mat., vol. 10, no. 2, pp. 367–380, 2022, doi: 10.31941/delta.v10i2.2172
[20] A. Khumaidi, “Data mining for predicting the amount of coffee production using CRISP-DM method,” J. Techno Nusa Mandiri, vol. 17, no. 1, pp. 1–8, 2020, doi: 10.33480/techno.v17i1.1240.
[21] C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” Procedia Comput. Sci., vol. 181, no. 2019, pp. 526–534, 2021, doi: 10.1016/j.procs.2021.01.199.
[22] D. Andriansyah and L. Nulhakim, “The application of power business intelligence in analyzing the availability of rental units,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012019
[23] M. Sitanggang, E. Simamora, and F. D. Mobo “Increasing accuracy of classification in C4. 5 algorithm by applying principle component analysis for diabetes diagnosis,”Jurnal Matematika Dan Pendidikan Matematika, vol. 6, no.2, pp. 175–186, 2022, doi: 10.25217/numerical.v6i2.2610
[24] I. Sudirman and I. D. Sudirman, “Machine learning approach to uncover customer plastic bag usage patterns in a grocery store,” Int. J. Data Netw. Sci., vol. 7, no. 3, pp. 1125–1130, 2023, doi: 10.5267/j.ijdns.2023.5.011.
[25] P. Mishra and R. K. Sharma, “A hybrid framework based on SIPOC and Six Sigma DMAIC for improving process dimensions in supply chain network,” Int. J. Qual. Reliab. Manag., vol. 31, no. 5, pp. 522–546, 2014, doi: 10.1108/IJQRM-06-2012-0089
[26] A. I. Pratiwi, A. Hakim, and R. Y. Santosa, “Development of operation process map and analysis of inventory control based on material requirement planning in assembly line,” J. Ind. Eng. Halal Ind., vol. 1, no. 1, pp. 30–38, 2020, doi: 10.14421/jiehis.1800.
[27] J. Odunayo Braimah, O. J. Braimah, and M. B. Abdulsalam, “On the use of P-charts in the quality control of students performance in tertiary educational system using MINITAB,” Am. J. Math. Stat., vol. 5, no. 5, pp. 259–264, 2015, doi: 10.5923/j.ajms.20150505.06.
[28] N. C. Weeraratne, “The effect of average sample size method on p control chart with a variable sample size,” Imp. J. Interdiscip. Res., Vol.2, no.8 , 2016.
[29] R. Fitriana, D. Kemala Sari, and A. Nur Habyba, Pengendalian dan penjaminan mutu. Wawasan Ilmu, 2021.
[30] F. O. Ehiagwina, O. O. Kehinde, A. Sidiq Nafiu, L. O. Afolabi, and I. Olatinwo, “Fault tree analysis and its modifications as tools for reliability and risk analysis of engineering systems-An overview” Int. J. Res. Publ. Rev., vol. 3, no. 1, pp. 383–396, 2022.
[31] P. R. Sonawane, S. Bhandari, R. B. Patil, “Reliability and criticality analysis of a large-scale solar photovoltaic system using fuzzy-fault tree analysis approach”, J. of King Saud University - Engineering Sciences, 2024. doi:10.1016/j.jksues.2024.09.001
[32] R. Rahim et al., “C4.5 classification data mining for inventory control,” Int. J. Eng. Technol., vol. 7, no. March, pp. 68–72, 2018, doi: 10.14419/ijet.v7i2.3.12618.
[33] S. Celik and O. Yilmaz, “Prediction of body weight of Turkish tazi dogs using data mining Techniques: Classification and Regression Tree (CART) and multivariate adaptive regression splines (MARS),” Pak. J. Zool., vol. 50, no. 2, pp. 575–583, 2018, doi: 10.17582/journal.pjz/2018.50.2.575.583.
[34] R. Fitriana, A. Nur Habyba, and E. Febriani, Data mining dan aplikasinya. Wawasan Ilmu, 2022.
[35] A. Jha, M. Dave, and S. Madan, “Performance evaluation of binary and multi-class dataset using ensemble classifiers,” Int. J. Eng. Res. Technol., vol. 11, no. 03, pp. 425–430, 2022.
[36] V. R. Sari, N. Hayatin, and Y. Azhar, “Classifying cyberbullying data on Indonesian social media feeds utilizing sentiment analysis technique with decision tree model,” AIP Conf. Proc., vol. 2453(1):030011, 2022, doi: 10.1063/5.0094675.
[37] R. Hadhoud and W. A. Salameh, “How business intelligence can help you to better understand your customers,” Int. J. Bus. Intell. Res., vol. 11, no. 1, pp. 50–58, 2020, doi: 10.4018/IJBIR.2020010104.
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