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Abstract—This paper proposes improvements to pairwise group decision makers may express their preference relations in linguistic labels which are more practically implementable for solving group decision making problems. Second, it modifies the computational procedures by using fuzzy sets representation and computation, and by avoiding the use of strict representation, preserves the preference accuracy, and produce more intuitively meaningful solutions. Finally, it considers fuzzy criteria of the alternative explicitly. Solution are first derived based on each criterion, and then by using neat ordered weighted average (OWA) operator the final solution which accommodate all criteria are determined. The proposed method is verified for solving fuzzy group decision making problems, i.e.,advertising media selection cases.

Marimin, Motohide Umano, Itsuo H, and Hiroyuki Tamura. 1998. Linguistic Labels for Expressing Fuzzy Preference Relations in Fuzzy Group Decision Making. IEEE Transaction on System, Man and Cybernetics. Part B: Cybernetic. ISSN 1083-4419, Volume 28.

In this article, we propose a non-numeric method for pairwise group-decision analysis based on fuzzy preference relations. The proposed method expresses and processes the preference relations non-numerically. The computation processes are relatively simple and fast we apply our method for selecting the most prospective new agro-industries to be implemented by multi-corporate company. Moreover, in the application we develop a procedure consisting of 4 stages: (1) a barin-storming process for alternatives and criteria identifications, (2) a pre-selection of prospective alternatives using modification of fuzzy Delphi method, (3) a pre-selection of the significant criteria by using a non-numeric pairwise fuzzy group-decision analysis. The final results are compared with results obtained by a semi-numeric method. The proposed method is suitable for group-decision making cases in which a full consensus is the most important role in selecting the alternatives.

Marimin, Motohide Umano, Itsuo H, and Hiroyuki Tamura. 1997. Non-Numeric Method for Pairwise Fuzzy Group-Decision Analysis. Journal of Intelligent and Fuzzy System, Volume 5, Nomor 3.

Marimin1, Winnie Septiani2, Sukardi1 and Tatit K. Bunasor1

1Department of Agroindustrial Technology, Faculty of Agricultural Technology

Bogor Agricultural University (IPB), Bogor, Indonesia

Email : marimin@indo.net.id

2Department of Industry Engineering, Trisaksi University, Jakarta, Indonesia

Email : win-nie@lycos.com

ABSTRACT

The aim of this research was to develop an intelligent system for pasteurised milk quality assessment and prediction that could help the quality decision makers to assess and predict the pasteurised milk quality. Utilizing Expert System and Artificial Neural Network (ANN), which called SINKUAL-SP, did these analyses. The reasoning strategy used was “Forward Chaining” and the tracing method used was “Best First Search”. Certainty Factor (CF) was used for handling uncertainty.

Multi-layer neural network architecture was used. The suited activation function was Sigmoid Bipolar, which gave the best performance network with learning rate 0.005 and momentum 0.9 together with RMSE, MSE and SSE as an error criterion. The validation for neural network indicated the conformity between the output of neural network and the goal output with RMSE value of 0.0099.

The system was verified and validated by using real data collected from pasteurised milk and milk Products Company at West Java, Indonesia. In this company, the quality of fresh milk was at grade B (good), the quality of process was at grade A and the quality of packaging and storage was at grade B. This system suggested the user to always improve the quality of pasteurised milk to achieve grade A quality.

Based on the system output, quality system reconstruction was the highest priority strategy. The quality improvement system seemed to be a way to improve the process quality of pasteurised milk.

Keywords: Neural Network, expert system, pasteurised milk, quality system, soft system.

Published at: International System Science and Studies Annual Conference, Tokyo, August 2007.

 

Mengambil keputusan apalagi keputusan yang tepat tidak semudah membalik telapak tangan, untuk itu, diperlukan pertimbangan, bahkan perhitungan matematis dengan berbagai kriteria. Tidak jarang, untuk sampai pada sebuah keputusan, harus melalui proses panjang dan berbelit.

Seiring dengan kemajuan ilmu pengetahuan, kini sistem pengambilan keputusan semakin dapat dikuantifikasikan sehingga hasilnya bisa dipertanggung jawabkan. Siapapun Anda, baik individu maupun lembaga, pasti memerlukan buku ini karena membahas :

Sistem dan teori keputusan;

Pengambilan keputusan berbasis indeks kinerja;

Alat dan metoda untuk pelaksanaan manajemen kualitas total;

Pengambilan keputusan dengan pemungutan suara (voting);

Proses hierarki analitik;

Teknik pemodelan interpretasi struktural;

Sistem penunjang keputusan.

 

Marimin, 2004. Teknik dan Aplikasi Pengambilan Keputusan Kriteria Majemuk, Grassindo, Jakarta.

Integrasi Teknologi Informasi dan Komunikasi dan Sistem Informasi Manajemen dalam Pengembangan Sumber Daya Manusia mampu mengakomodasikan konsep dan praktik bisnis yang inovatif, holistik dan sinergisistik untuk menghasilkan produk barang atau jasa yang berdaya saing tinggi dengan meletakkan Sumber Daya Manusia sebagai faktor pengerak utamanya. Kini penerapan Sistem Informasi Manajemen Sumber Daya Manusia telah menjadi keperluan mendesak bagi institusi bisnis pemerintahan dan swasta untuk memacu produktivitas operasi dan meningkatkan efektivitas pengambilan keputusan strategis.

Sebagai manajer, praktisi bisnis dan pecinta teknologi informasi dan komunikasi, sistem informasi manajemen dan pengembangan Sumber Daya Manusia, maka Anda perlu membaca buku ini, karena membahas :

  • Sistem dan perkembangan teknologi informasi dan komunikasi;
  • Konsep dan peran Sistem Informasi Manajemen;
  • Pengelolaan dan pengembangan Sistem Informasi Manajemen;
  • Pengembangan Sistem Informasi Manajemen Sumber Daya Manusia secara iteratif dan efektif;

Contoh kasus Sistem Informasi Manajemen Sumber Daya Manusia: Sistem Informasi Personalia Daerah.

Marimin, Henry Tanjung, dan Haryo Prabowo, 2005, Sistem Informasi Manajemen Pengembangan Sumberdaya Manusia, Grassindo, Jakarta (Akan terbit akhir 2005).

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