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Data mining has two main concepts of data distribution, namely supervised learning and unsupervised learning. The most easily recognizable concepts from data distribution is related to the dataset, with and without target class. Analytic Hierarchy Process (AHP) technique that carries the concept of pairwise comparison able to answer the problem related to the dataset, which is to change unsupervised to be supervised by determining eigenvalue value of each attribute and sub attribute in AHP method. The case study conducted in this issue is related to determining the target classes used to predict the success of a student learning in UIN Suska Riau. The three main attributes are Procrastination, Total Credits (SKS) and Number of Repeated Courses, each having eigenvalues of 0.319; 0.189 and 0.171 which become the feedback in the determination of the Target Timely Graduation (TG) or Possibility of Timely Graduation (PTG). The biggest consistency ratio generated in the AHP case is 9.4% in the GPA attribute. This research recommends that further research should use datasets that have been arranged based on experimental combinations of the three main attributes above, then applied to the classification or prediction algorithm. So that it would obtain a decision of accuracy from data used against the real result on the field.<div style="mso-element: comment-list;"><div style="mso-element: comment;"><div id="_com_2" class="msocomtxt"><!--[if !supportAnnotations]--></div><!--[endif]--></div></div>

<em>XYZ Group in running business have been several problems that</em> <em>is in the  marketing of products not yet doing activity promotions, weakening the number of requests, well as the quality of a product produced less good. This research aims to obtain the weighting of every factor which are under consideration, to know the position of the company, and provide suggestions for marketing strategies that can did be company. The see problems happen, we need marketing strategies by using the method Analytical Hie-rarchy Proses (AHP) and Strength, Weakness, Opportunity, Threat (SWOT). Based on weighting 7P using method AHP obtained product weight  as much as 12.44%, promotion of 17.90%, price of 9.93%, place of 15.82%, people of 15.63%, process of 13.45%, and physical evidence of 14, 83%. In matrix SWOT known kuadran-III the company are in a meeting between IFAS at the point -0,05 and EFAS at the point 0,49. It means , the company had a great opportunity that the market high, but still have obstacles where which is still using a simple production equipment. Alternative strategy that can be carried out is to ensure the quality of the product, increase production facilities, improve the distribution of the product, and make use of the printed media and sosial media for promotion activity</em>

Penelitian ini dilakukan di PT. Riau Crumb Rubber Factory adalah perusahaan yang bergerak dalam pengolahan karet mentah menjadi barang setengah jadi (work in process) yang kemudian diekspor ke luar negeri. Jenis produk yang dihasilkan yaitu crumb rubber SIR-10 dan SIR-20 (Standart Indonesia Rubber). Salah satu potensi terjadinya human error yang teridentifikasi tersebut identifikasi jenis dan kejadian kesalahan kerja operator di stasiun proses kerja blower, press, metal detector dan packing. Metode yang digunakan adalah Systematic Human Error Reduction and Prediction Approach (SHERPA) Berdasarkan hasil identifikasi tersebut selanjutnya ditelusuri penyebab terjadinya kesalahan untuk ditentukan pendekatan guna mengurangi kejadian kesalahan kerja operator. Dari hasil pengolahan data potensi terjadinya human error diakibatkan karena operator menjatuhkan balok karet, operator lupa memeriksa dan operator tidak memperhatikan set-up mesin. Terdapat 11 deskripsi error dari 27 task, prediksi error yang mungkin terjadi sesuai dengan HTA dari hasil SHERPA berupa strategi perbaikan untuk meminimasi potensi terjadinya error agar dapat mengurangi resiko kesalahan. Terdapat dua macam usulan perbaikan yaitu dengan menggunakan form checklist dan SOP penggunaan mesin.