Decision Support System On Computer Maintenance Management SystemUsing Association Rule and Fisher Exact Test One Side P-Value

Abstract

Decision-makinghas been growth rapidly and many methods can used. Thus, how to apply that methode not only fast but also right. One of implementationdecision making is decision support system in Computer Maintenance Management System (CMMS). This research using data test with Association Rule and Fisher Exact One Side P-Value from same problems in Computer Maintenance Management System (CMMS). Object from this research to get pattern of association between symptom and root cause, to prove relation those variable. Previous research prove any relation between that two variable when use Association Rule and Pearson Chi-Square but many rule must eliminated because not ineligable fromPearson Chi-Square. The meaning of this research to confirm proof of test relation between symptom and root cause in CMMS. And hope with this result of test can make strength hypothesis from previous research.

Spesifikasi Kebutuhan Perangkat Lunak Aplikasi Rekomendasi Ijin Penelitian

“…Use case adalah abstraksi dari interaksi antara system dan actor. Use case bekerja dengan mendeskripsikan tipe interaksi antara user sebuah system dengan sistemnya sendiri melalui sebuah cerita bagaimana sebuah system dipakai [8] Tabel III menggambarkan perbedaan pelaksanaan proses bisnis penerbitan rekomendasi ijin antara proses manual dan menggunakan aplikasi. [1].…”

Optimasi Artificial Neural Network Dengan Genetic Algorithm Pada Prediksi Debit Inflow Waduk Sengguruh

“…Hasil dari fungsi perambatan ini selanjutnya akan dibandingkan dengan suatu nilai ambang batas (threshold) melalui fungsi aktivasi. Jika hasil dapat melewati ambang batas tersebut maka neuron tersebut akan diaktifkan dan dapat mengirimkan output melalui bobot-bobot outputnya ke semua neuron yang berhubungan dengannya [11] [12].…”

Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

“…In previous studies [11] [12] have proven the relationship between symptoms and root causes using apriori algorithm as association rule methode. And conducted by [11] [12], there was an influence between the symptoms and root cause to get the solution using the Association Rule and correlation process with Chi-Square or Fisher Exact Test.…”

“…In previous studies [11] [12] have proven the relationship between symptoms and root causes using apriori algorithm as association rule methode. And conducted by [11] [12], there was an influence between the symptoms and root cause to get the solution using the Association Rule and correlation process with Chi-Square or Fisher Exact Test. [13] [14] [15] Assoction rule is one of the best method to get correlation of cause-effect data like in work order and [16] already proved in problem solving data in manufacturing.…”

“…And this is in accordance with the research that will be carried out because the data owned amounts to thousands. And research [11] [12] show that the execution process is longer when the data used is getting bigger, so researchers intend to use other methods. And the focus of this research is not on execution time but more on the pattern generated in the eclat algorithm with data transactions contained in work orders within the CMMS.…”