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Apa itu DEA (Data Envelopment Analysis)

Data Envelopment Analysis (DEA) adalah metode non-parametrik berbasis linear programming untuk mengukur efisiensi relatif dari sekumpulan unit sejenis (Decision-Making Units/DMU) yang mengubah banyak input menjadi banyak output—tanpa perlu mengasumsikan bentuk fungsi produksi tertentu. Varian modern mencakup VRS/CRS, super-efficiency, Slack-Based Measure (SBM), bootstrap-DEA, serta Network DEA (NDEA) yang menilai efisiensi tiap tahap dalam proses berantai (mis. input → proses 1 → proses 2 → output). Temuan tinjauan terbaru menunjukkan lonjakan aplikasi NDEA 2017–2022, terutama pada perbankan, rantai pasok, kesehatan, dan pendidikan tinggi (Ratner, 2023).

Why — Mengapa DEA digunakan?

DEA unggul ketika:

  1. DMU memiliki multi-input/multi-output yang sulit dimodelkan secara parametrik,
  2. peneliti ingin benchmarking dan identifikasi peer terbaik,
  3. organisasi butuh peta perbaikan target (reduksi input/peningkatan output) yang spesifik per DMU.
    Di layanan kesehatan, misalnya, systematic review 2024 menegaskan DEA populer untuk evaluasi kinerja rumah sakit dan menyoroti pentingnya pemilihan variabel input-output yang tepat agar hasil valid dan dapat ditindaklanjuti (Zubir et al., 2024).

Who/Where — Siapa dan di mana DEA dipakai?

DEA banyak dipakai oleh analis kebijakan, manajer operasi, regulator, dan akademisi di sektor:

  • Kesehatan: menilai efisiensi rumah sakit, menguji trade-off efisiensi-kualitas-akses dengan model Network-DEA (Afonso et al., 2024).
  • Agribisnis & Keberlanjutan: memetakan efisiensi ekonomi-lingkungan-sosial pada usaha tani/agrifood (Kyrgiakos et al., 2023).
  • Rantai pasok berkelanjutan: seleksi pemasok dengan DEA yang diintegrasikan ke multi-criteria decision making (Fotova Čiković et al., 2022).
  • Makro/negara: mengukur efisiensi kebijakan publik, mis. respons COVID-19 dengan Network-DEA multistage (Pereira et al., 2022).

When — Kapan DEA tepat dipilih?

Gunakan DEA ketika:

  • Tujuan Anda membandingkan kinerja relatif DMU sejenis dalam satu periode atau beberapa periode (dengan Malmquist/dynamic DEA),
  • Data Anda observasional dan Anda ingin target perbaikan (slack) tanpa asumsi fungsi produksi,
  • Anda perlu menilai sub-proses internal (pilih Network-DEA) atau memperhitungkan ketidakpastian (fuzzy-DEA) dan bias sampel (bootstrap-DEA). Tinjauan 2024–2023 menegaskan relevansi praktik tersebut dalam kesehatan dan sektor berkelanjutan.

How — Bagaimana langkah praktis menerapkan DEA?

  1. Definisikan DMU & cakupan (mis. 50 puskesmas/20 cabang bank).
  2. Pilih orientasi & skala: input-oriented (hemat sumber daya) atau output-oriented (maksimalkan keluaran); CRS vs VRS.
  3. Seleksi variabel: gunakan kerangka domain dan pedoman SLR terkini untuk memilih input-output yang relevan, menghindari overfitting variabel (Zubir et al., 2024).
  4. Bangun model: dasar (CCR/BCC), SBM untuk menangkap slack, super-efficiency untuk mengurutkan DMU efisien, atau Network-DEA jika ada tahapan (Ratner, 2023; Afonso et al., 2024).
  5. Analisis lanjutan:
    • Bootstrap-DEA untuk interval kepercayaan skor,
    • Two-stage DEA (mis. regresi truncated/Tobit) untuk menjelaskan determinan efisiensi,
    • Robustness check via pemilihan variabel alternatif. Tren mutakhir di kesehatan menunjukkan pemakaian Bootstrap-DEA dan analisis sensitivitas variabel (Li et al., 2024).
  6. Tarik implikasi kebijakan/operasional: tetapkan target penurunan input/kenaikan output per DMU dan prioritas intervensi.

Contoh Singkat (Ilustratif)

Kasus: 10 cabang bank sebagai DMU.

  • Input: jumlah pegawai, biaya operasional.
  • Output: jumlah kredit tersalurkan, dana pihak ketiga.
    Langkah:
  1. Pilih orientasi input (fokus efisiensi biaya).
  2. Jalankan DEA-VRS (BCC) untuk mendapatkan skor 0–1 dan slack.
  3. Untuk pembobotan yang adil, gunakan Assurance Region bila perlu; urutkan cabang efisien dengan super-efficiency.
  4. Jika proses dua tahap (penghimpunan dana → penyaluran kredit), gunakan Network-DEA agar diketahui tahap mana yang paling inefisien. Literatur terkini menunjukkan NDEA efektif memisahkan efisiensi tiap subproses dalam layanan keuangan dan kesehatan (Ratner, 2023; Afonso et al., 2024).

Catatan Praktik Terbaik (berdasarkan studi 5 tahun terakhir)

  • Jelaskan rasional pemilihan variabel dan uji sensitivitas set variabel (Zubir et al., 2024).
  • Pertimbangkan dimensi keberlanjutan (ekonomi-lingkungan-sosial) ketika konteksnya agribisnis/energi (Kyrgiakos et al., 2023; Fotova Čiković et al., 2022).
  • Untuk kebijakan publik/krisis, Network-DEA multistage memberi wawasan lebih kaya daripada model “black box” (Pereira et al., 2022).

Daftar Pustaka (APA, 5 tahun terakhir)

  • Afonso, G. P., Gouveia, M., & Nunes, A. (2024). A Network-DEA model to evaluate the impact of quality and access on hospital efficiency. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05362-x
  • Fotova Čiković, K., Mišković, V., & Pjanić, M. (2022). Sustainable supplier selection: An integrated DEA–MCDM review and framework. Sustainability. https://doi.org/10.3390/su14148457
  • Kyrgiakos, L. S., Kleftodimos, G., Vlontzos, G., & Pardalos, P. M. (2023). A systematic literature review of DEA implementation in agriculture under the prism of sustainability. Operational Research, 23, 7. https://doi.org/10.1007/s12351-023-00741-5
  • Pereira, M. A., Dinis, D. C., Ferreira, D. C., Figueira, J. R., & Marques, R. C. (2022). A network DEA to estimate nations’ efficiency in the fight against SARS-CoV-2. Expert Systems with Applications, 210, 118362. https://doi.org/10.1016/j.eswa.2022.118362
  • Ratner, A. (2023). Network DEA and its applications (2017–2022): A systematic literature review. Mathematics, 11(9), 2141. https://doi.org/10.3390/math11092141
  • Zubir, M. Z., Noor, A. A., Mohd Rizal, A. M., Harith, A. A., Abas, M. I., et al. (2024). Approach in inputs & outputs selection of DEA efficiency measurement in hospitals: A systematic review. PLOS ONE, 19(8), e0293694. https://doi.org/10.1371/journal.pone.0293694
  • Li, Y., Chen, Y., & Zhang, X. (2024). Bootstrap-DEA for hospital efficiency evaluation. PLOS ONE. https://doi.org/10.1371/journal.pone.0315218

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