Risk-based thinking (RBT) is one of the distinct new features of the International Organization for Standardization 9001:2015. Interestingly, the standard does not prescribe any tools. Hence, organizations are puzzled as to the extent of conformance. Some organizations have adopted formal tools. However, these tools seem insufficient in linking the standard into an evidence-based decision support system. To resolve gaps in RBT implementation, this paper proposes a framework based on fuzzy inference system (FIS) and support vector machine (SVM) to automate risk analysis and evaluation, proposal and verification of action plans, and prediction of the feasibility of risks and opportunities according to text patterns. Modeling results indicate that the framework has no significant difference in terms of accuracy compared with the conventional method. Both FIS-1 and FIS-2 models, however, are statistically significantly faster at 3.26 and 1.15 s, respectively. Meanwhile, the SVM model, whose text classification features are not evident in the conventional method, has a 97.16% classification accuracy and 2.6% confusion error during training, and 95% classification accuracy during testing. Results affirm that FIS and SVM are efficient tools in feasibly conforming with the RBT requirements of the ISO 9001:2015 international standard.

Bahasa Abstract

Pemikiran Berbasis Risiko ISO 9001:2015: Suatu kerangka Kerja yang Menggunakan Mesin Vektor Pendukung yang Kabur. Pemikiran berbasis risiko (Risk-based thinking (RBT)) merupakan salah satu dari fitur-fitur baru yang berbeda dari Organisasi Internasional untuk Standarisasi 9001:2015 (International Organization for Standardization 9001:2015). Yang menarik, standar tidak menentukan perkakas apapun. Oleh karenanya, berbagai organisasi dibingungkan dengan tingkat kesesuaian. Sebagian organisasi telah mengadopsi perkakas formal. Namun demikian, perkakas ini nampaknya tidak mencukupi dalam menghubungkan standar ke dalam suatu sistem pendukung keputusan berbasis kejadian. Untuk menyelesaikan adanya celah-celah di dalam implementasi RBT, naskah ini mengusulkan suatu kerangka kerja berdasarkan pada sistem dugaan yang kabur (fuzzy inference system (FIS)) dan mesin vektor pendukung (support vector machine (SVM)) untuk mengotomatisasi analisis risiko dan evaluasi, usulan dan verifikasi rencara aksi, dan prediksi kelayakan risiko serta peluang yang sesuai dengan pola-pola naskah. Hasil-hasil pemodelan menunjukkan bahwa kerangka kerja tersebut tidak memiliki perbedaan yang signifikan dalam hal akurasi dibandingkan dengan metode konvensional. Namun demikian, baik model FIS-1 maupun FIS-2, secara statistik jauh lebih cepat pada masing-masing 3,26 dan 1,15 detik. Sementara, model SVM, yang fitur-fitur klasifikasi naskahnya bukan kejadian di dalam metode konvensional, memiliki akurasi klasifikasi 97,16% dan kesalahan yang membingungkan 2,6% selama pelatihan, dan akurasi klasifikasi 95% selama pengujian. Hasil-hasilnya menegaskan bahwa FIS dan SVM merupakan perkakas yang efisien dengan penyesuaian yang layak dengan persyaratan RBT dari standar internasional ISO 9001:2015.


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