Selection of suppliers is one of the most important things to meet the needs of the company consistently and at an acceptable cost, decision support system used to select suppliers by identifying suppliers with the highest potential. The object of this study is the drug suppliers. In this study we use four criterias to select suppliers of drugs: drug completeness, price, time delivery, payment or loan term. AHP method is used to calculate the weight of each criterion based on the pharmacist assessment. The weight criterias of the AHP process result is used for the calculation of the alternatives rank. PROMETHEE method used to alternatives calculation analysis that produce alternatives rankings.
<span lang="EN-GB">Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. If conducted incorrectly, determining the amount of K topics will result in limited word correlation with topics. Too large or too small number of K topics causes inaccuracies in grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process uses Indonesian news articles with the number of documents at 25, 50, 90, and 600; in each document, the numbers of words are 3898, 7760, 13005, and 4365. The results show that the maximum likelihood and MDL approach result in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times but the number of words does. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimisation model has resulted in many LDA topics as a classification model. This experiment shows that the highest average accuracy is 61% with alpha 0.1 and beta 0.001.</span>
The prediction of student academic performance with high accuracy is of paramount importance in improving educational outcomes and developing tailored learning methodologies. It also serves as a preventative measure against student dropouts. This study is centered on enhancing the precision of such predictions by optimizing hyperparameters in machine learning techniques. In pursuit of optimal performance, a range of machine learning techniques is compared, and the most accurate one selected for hyperparameter optimization. The adopted method for this optimization is the Grid Search (GS) technique. It is found that hyperparameter optimization in the Gradient Boosting Regression Tree (GBRT) using the GS method bolsters the accuracy of predictions pertaining to student academic performance. The results obtained in this study are validated using a five-fold cross-validation method. This rigorous validation ensures the robustness of our findings. Thus, the study presents a critical contribution to the effective prediction of student academic performance, potentially informing the development of more efficient and personalized educational strategies.
Selection of funds proposals was done to select an eligible applicant. The Selection involves multiple assessors (decision group) to take a decision. This study used Analytic Process Hirarcy Dampster Number or known as the AHP D Numbers or D-AHP. This method can be used for decision making process of individuals and groups. In addition D-AHP also solve the problem with complete information and incomplete information. D-AHP process by considering the selection criteria and alternative assessment. Assessment by doing a comparison between the criteria. Further comparison of the alternative criteria. The end result is a ranking of the options. .
AbstrakPenyeleksian proposal dana dilakukan untuk memilih pemohon yang berhak mendapatkan bantuan. Penyeleksian melibatkan beberapa penilai (keputusan berkelompok) untuk mengambil keputusan. Penelitian ini menggunakan metode Analytic Hirarcy Proses Dampster Number atau dikenal dengan AHP D Numbers atau D-AHP. Metode ini mengatasi masalah dengan informasi yang lengkap dan informasi yang tidak lengkap. D-AHP memproses penyeleksian dengan mempertimbangkan penilaian kriteria dan alternatif. Penilaian dengan melakukan perbandingan antar kriteria. Selanjutnya perbandingan kriteria terhadap alternatif. Hasil akhirnya berupa perangkingan terhadap pilihan.
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