UTOMO, Pradityo and Adi, Kusworo and Nurhayati, Oky Dwi (2024) PENGEMBANGAN MODEL PENGAMBILAN KEPUTUSAN KELOMPOK SECARA MULTI LEVEL DENGAN KRITERIA DINAMIS DAN METODE FUZZY LEARNING VECTOR QUANTIZATION. Doctoral thesis, UNIVERSITAS DIPONEGORO.

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Abstract

Kriteria dinamis merupakan masalah serius dalam forum pengambilan keputusan kelompok yang multi level. Semakin banyak jumlah pengambil keputusan, maka semakin banyak pula kriteria untuk mencapai kesepakatan. Beberapa model dalam penelitian yang ada tidak dapat menyelesaikan masalah kriteria dinamis dalam pengambilan keputusan kelompok yang multi level. Oleh karena itu, penelitian ini mengembangkan model pengambilan keputusan kelompok secara multi level dengan kriteria berbasis anggota forum. Pengembangan model melalui beberapa tahapan. Tahap pertama adalah pengelompokan data penilaian kriteria dengan metode K-Means. Hasil pengelompokan K-Means diklasifikasi dengan metode Fuzzy Learning Vector Quantization (FLVQ). Hasil klasifikasi FLVQ adalah tujuh kriteria berbasis anggota forum khususnya forum pemilihan rencana program kerja pemerintah, yaitu (1) urgensi, (2) keberlanjutan, (3) prioritas, (4) kebermanfaatan, (5) kesejahteraan, (6) kenyamanan, dan (7) keindahan. Kriteria tersebut digunakan untuk kriteria pengambilan keputusan. Pengambilan keputusan menggunakan metode Simple Additive Weighting Borda (SAW Borda). Perpaduan metode K-Means, FLVQ, dan SAW Borda disebut metode K-Means Fuzzy Learning Vector Quantization Simple Additive Weighting Borda (KMFLVQ-SAWB). Metode KMFLVQ-SAWB terbukti mampu untuk menyelesaikan permasalahan kriteria dinamis pada pengambilan keputusan kelompok. Hal ini dibuktikan dari akurasi metode tersebut pada pengambilan keputusan kelompok. Berdasarkan pengujian Confusion Matrix, metode KMFLVQ-SAWB mempunyai akurasi sebesar 100%. Kemudian, metode tersebut dikembangkan untuk model Multi Level K-Means Fuzzy Learning Vector Quantization Simple Additive Weighting Borda (ML-KMFLVQ-SAWB). Model tersebut juga diuji dengan metode Confusion Matrix. Berdasarkan pengujian, model ML-KMFLVQ SAWB mempunyai akurasi sebesar 92%. Dari akurasi tersebut, model ML-KMFLVQ-SAWB terbukti mampu untuk menyelesaikan masalah kriteria dinamis pada pengambilan keputusan kelompok secara multilevel.
Kata kunci : pengambilan keputusan, model, ML-MKMFLVQ-SAWB, kriteria dinamis, kriteria berbasis anggota forum

Dynamic criteria are a severe problem in multi-level group decision-making forums. The more decision-makers there are, the more criteria to reach an agreement. Some models in existing research cannot solve the problem of dynamic criteria in multi-level group decision-making. Therefore, this study develops a multi-level group decision-making model with forum member-based criteria. The model development goes through several stages. The first stage is grouping criteria assessment data using the K-Means method. The results of the K-Means grouping are classified using the Fuzzy Learning Vector Quantization (FLVQ) method. The results of the FLVQ classification are seven criteria based on forum members, especially the forum for selecting government work program plans, namely (1) urgency, (2) sustainability, (3) priority, (4) usability, (5) prosperty, (6) comfortability, and (7) artistic. These criteria are used for decision-making criteria. Decision-making uses the Simple Additive Weighting Borda (SAW Borda) method. The combination of the K-Means, FLVQ, and SAW Borda methods is called the K-Means Fuzzy Learning Vector Quantization Simple Additive Weighting Borda (KMFLVQ-SAWB) method. The KMFLVQ-SAWB method has been proven to solve dynamic criteria problems in group decision-making. This is demonstrated by the accuracy of the method in group decision-making. Based on the Confusion Matrix test, the KMFLVQ-SAWB method has an accuracy of 100%. Then, the method was developed for the Multi-Level K-Means Fuzzy Learning Vector Quantization Simple Additive Weighting Borda (ML-KMFLVQ-SAWB) model. The model was also tested using the Confusion Matrix method. Based on the test, the ML-KMFLVQ SAWB model has an accuracy of 92%. From this accuracy, the ML-KMFLVQ-SAWB model has been proven to solve dynamic criteria problems in multi-level group decision-making.
Keywords : decision-making, model, ML-MKMFLVQ-SAWB, dynamic criteria, forum member based criteria

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