IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v59y2025i1d10.1007_s11135-024-02006-5.html
   My bibliography  Save this article

Efficiency analysis of halal certification bodies in Indonesia: a hybrid data envelopment analysis and machine learning approach

Author

Listed:
  • Isti Surjandari

    (Universitas Indonesia)

  • Nadhira Riska Maulina

    (Universitas Indonesia)

  • Chairul Bahri

    (Kennesaw State University)

Abstract

This study aims to analyze Halal Certification Body (Lembaga Pemeriksa Halal (LPH)) efficiency in Indonesia by using the Data Envelopment Analysis (DEA) and Machine Learning (ML) methods. As the largest Muslim country in the world, with more than 200 million Muslims or approximately 12% of the world’s total Muslim population, Indonesia aims to become a global halal hub by 2024. Therefore, LPH efficiency measurement plays an important role in supporting this mission. The efficiency analysis is based on input and output factors, which are selected based on the characteristics of an LPH. An artificial neural network (ANN) and logistic regression (Logit) are used to classify LPHs into efficient and inefficient groups based on their DEA scores, as these two methods can complement DEA. The data used in this study are collected from LPHs and the Ministry of Religious Affairs in Indonesia. The results show that the DEA and ML methods can be used to identify efficient and inefficient LPHs and the factors affecting their efficiency. The DEA-ANN predictive model has an accuracy of 85.06%, which is higher than that of the DEA-logit model (79%). This study contributes to a more efficient and effective halal certification system in Indonesia, supporting the growth of the halal industry in this country.

Suggested Citation

  • Isti Surjandari & Nadhira Riska Maulina & Chairul Bahri, 2025. "Efficiency analysis of halal certification bodies in Indonesia: a hybrid data envelopment analysis and machine learning approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 973-987, February.
  • Handle: RePEc:spr:qualqt:v:59:y:2025:i:1:d:10.1007_s11135-024-02006-5
    DOI: 10.1007/s11135-024-02006-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-024-02006-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11135-024-02006-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Amado, Carla A.F. & Santos, Sérgio P. & Marques, Pedro M., 2012. "Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment," Omega, Elsevier, vol. 40(3), pages 390-403.
    2. Imad Ramadan, 2016. "Data Envelopment Analysis (DEA) Approach for the Jordanian Banking Sector's Performance," Modern Applied Science, Canadian Center of Science and Education, vol. 10(5), pages 170-170, May.
    3. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    4. Nadia M. Guerrero & Juan Aparicio & Daniel Valero-Carreras, 2022. "Combining Data Envelopment Analysis and Machine Learning," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    5. Chich-Jen Shieh & Jyh-Rong Chou & Zehra Nur Ersozlu, 2018. "Performance evaluation of special education in China based on Data Envelopment Analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 1319-1327, December.
    6. Zhensheng Chen & Xueli Chen & Xiaoqing Gan & Kaixuan Bai & Tomas Baležentis & Lixin Cui, 2020. "Technical Efficiency of Regional Public Hospitals in China Based on the Three-Stage DEA," IJERPH, MDPI, vol. 17(24), pages 1-17, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pengyu Ren & Zhaoxia Liu, 2021. "Efficiency Evaluation of China’s Public Sports Services: A Three-Stage DEA Model," IJERPH, MDPI, vol. 18(20), pages 1-12, October.
    2. Guillen, Maria D. & Charles, Vincent & Aparicio, Juan, 2025. "Enhanced efficiency assessment in manufacturing: Leveraging machine learning for improved performance analysis," Omega, Elsevier, vol. 134(C).
    3. Jianfei Shen & Fengyun Li & Di Shi & Hongze Li & Xinhua Yu, 2018. "Factors Affecting the Economics of Distributed Natural Gas-Combined Cooling, Heating and Power Systems in China: A Systematic Analysis Based on the Integrated Decision Making Trial and Evaluation Labo," Energies, MDPI, vol. 11(9), pages 1-28, September.
    4. Zervopoulos, Panagiotis D. & Brisimi, Theodora S. & Emrouznejad, Ali & Cheng, Gang, 2016. "Performance measurement with multiple interrelated variables and threshold target levels: Evidence from retail firms in the US," European Journal of Operational Research, Elsevier, vol. 250(1), pages 262-272.
    5. Athanasia Mavrommati & Alexandra Pliakoura, 2025. "Performance dynamics in Greek wine sector: a study of technical efficiency and strategic implications," Operational Research, Springer, vol. 25(1), pages 1-22, March.
    6. Pejman Peykani & Mostafa Sargolzaei & Negin Sanadgol & Amir Takaloo & Hamidreza Kamyabfar, 2023. "The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-24, November.
    7. Reza Sanei & Farhad Hosseinzadeh lotfi & Mohammad Fallah & Farzad Movahedi Sobhani, 2022. "An Estimation of an Acceptable Efficiency Frontier Having an Optimum Resource Management Approach, with a Combination of the DEA-ANN-GA Technique (A Case Study of Branches of an Insurance Company)," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    8. Manuel Xavier, José & Ferreira Moutinho, Victor & Carrizo Moreira, António, 2015. "An empirical examination of performance in the clothing retailing industry: A case study," Journal of Retailing and Consumer Services, Elsevier, vol. 25(C), pages 96-105.
    9. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, June.
    10. Sahoo, Biresh K. & Tone, Kaoru, 2013. "Non-parametric measurement of economies of scale and scope in non-competitive environment with price uncertainty," Omega, Elsevier, vol. 41(1), pages 97-111.
    11. Lin, Tzu-Yu & Chiu, Sheng-Hsiung, 2013. "Using independent component analysis and network DEA to improve bank performance evaluation," Economic Modelling, Elsevier, vol. 32(C), pages 608-616.
    12. Manel Frifita & Zouhair Hadhek, 2025. "Do Country Risks Matter for Tourism efficiency? Evidence from Mediterranean countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 5093-5142, March.
    13. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    14. Fernando A. F. Ferreira & Marjan S. Jalali & Paulo Bento & Carla S. E. Marques & João J. M. Ferreira, 2017. "Enhancing individual entrepreneurial orientation measurement using a metacognitive decision making-based framework," International Entrepreneurship and Management Journal, Springer, vol. 13(2), pages 327-346, June.
    15. Wei, Ming & Zhang, Shaopeng & Liu, Tao & Sun, Bo, 2023. "The adjusted passenger transportation efficiency of nine airports in China with consideration of the impact of high-speed rail network development: A two-step DEA-OLS method," Journal of Air Transport Management, Elsevier, vol. 109(C).
    16. Maria Cristina Gramani, 2014. "Inter-Regional Performance of the Public Health System in a High-Inequality Country," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-8, January.
    17. Santos, Sérgio P. & Belton, Valerie & Howick, Susan & Pilkington, Martin, 2018. "Measuring organisational performance using a mix of OR methods," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 18-30.
    18. Madjid Tavana & Kaveh Khalili-Damghani & Rahman Rahmatian, 2015. "A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies," Annals of Operations Research, Springer, vol. 226(1), pages 589-621, March.
    19. Varmaz, Armin & Varwig, Andreas & Poddig, Thorsten, 2013. "Centralized resource planning and Yardstick competition," Omega, Elsevier, vol. 41(1), pages 112-118.
    20. Huayong Niu & Zhishuo Zhang & Manting Luo, 2022. "Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning," IJERPH, MDPI, vol. 19(19), pages 1-28, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:59:y:2025:i:1:d:10.1007_s11135-024-02006-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.