Konsep Sistem Pendukung Keputusan (Decision Support System) merupakan salah satu cabang kecerdasan buatan (artificuial intelligence) yang banyak digunakan. Terdapat banyak metode yang dapat digunakan oleh pengambil keputusan untuk membantu menemukan solusi atau alternatif yang optimum untuk sebuah masalah. Salah satu metode tersebut adalah Fuzzy Multi-Criteria Decision Making (FMCDM). Metode ini akan membantu pengambil keputusan pada situasi dimana terdapat banyak alternatif keputusan dengan beberapa kriteria. Tulisan ini akan mengaplikasi metode FMCDM untuk menentukan lokasi pemancar televisi di Yogyakarta pada tiga alternatif lokasi dan lima kriteria.
The increasing number of businesses makes everyone compete to be the best to get costumers. When a business owner can't take the advantage of information technology, then there is a lot of information will be wasted. The sales report which still made manually will bring difficulty to the owner seeing the development of business, consequently there are possibility that the owner will make a wrong decisions, especially about addition or reduction of inventory. The purpose of this research was to utilize any information as much as possible by using management information system. This management information system would convert the data to be easily to understood such as by using graphics. Management information system was come with forecasting to help users, especially for managers in decision making. The method that was used to forecast the management information system is the moving average, one of the time series methods in forecasting. The use of forecasting with the moving average method was to predict the number of sales that would occur in the months to come. The result of the research that has been done was a management information system that can be used by Zaky's Hijab. The system that was created also could forecast the next number of sales based on the existing category. Not all data can be utilized as forecasting calculation data using moving averages.
<p>Depresi, kecemasan dan stress merupakan tiga gangguan yang sering dijumpai di masyarakat. Ketiga gangguan tersebut memiliki gejala yang hampir mirip. <em>Depression, Anxiety and Stress Scales</em> (DASS) merupakan salah satu alat ukur yang dapat digunakan untuk mengukur tingkat keparahan ketiga gangguan tersebut. DASS dengan jumlah item/gejala sebanyak 42 item dikenal dengan nama DASS-42. Alat ukut ini membedakan dengan jelas item/gejala dari setiap gangguan. Setiap gangguan memiliki item yang mempengaruhi sebanyak 14 item. Pada penelitian ini dibangun model Sistem Pendukung Keputusan Kelompok (SPKK) yang memungkinkan para psikolog untuk berkolaborasi memberikan preferensi terkait prioritas gangguan yang akan terjadi apabila diketahui item/gejala tertentu menurut DASS-42. Preferensi diberikan dengan format <em>ordered vectors</em>. Untuk memudahkan proses agregasi/komposisi, selanjutnya dilakukan transformasi preferensi ke relasi preferensi fuzzy. Operator <em>Ordered Weighted Averaging</em> (OWA) digunakan untuk melakukan agregasi peferensi menjadi satu matriks. Proses seleksi alternatif terbaik dilakukan dengan menggunakan <em>Quantifier Guided Dominance Degree</em> (QGDD). Hasil pengujian menunjukkan bahwa ketepatan hasil SPKK terhadap DASS-42 adalah sebesar 71,43% (30 dari 42 item/gejala). Item/gejala yang beririsan secara signifikan antara gangguan kecemasan dan stress sebesar 16,67%. (7 dari 42), antara depresi dan kecemasan sebesar 9,52% (4 dari 42). Secara umum SPKK ini mampu mengakomodasi preferensi para pengambil keputusan dalam memberikan bobot pengaruh. Gangguan kecemasan dan gangguan stress memiliki gejala yang sangat mirip sehingga untuk beberapa item.gejala pada DASS-42 ada perbedaan yang cukup signifikan.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Depression, anxiety and stress are three disorders that are often found in the community. These three disorders have almost identical symptoms. Depression, Anxiety and Stress Scales (DASS) is an psychological instrument that can be used to measure the severity of these disorders. DASS with a total of 42 items known as DASS-42. This instrument distinguishes clearly the symptoms of each disorder. Each disorder has 14 items affect. The three disorders have a number of symptoms that are similar, even a symptom may affect two or three disorders with different levels of influence. In this study, a Group Decision Support System (GDSS) model was developed so that psychologists can collaborate to give preference regarding priority of disorders that would occur if certain items / symptoms were identified by DASS-42. Preferences are given in ordered vectors format. The preferences given by each decision maker aggregated to get a single preference. These preferences will be transformed to the fuzzy preference relation format. Ordered Weighted Averaging (OWA) operator used to aggregation process for all decision maker preference. The OWA operator are used to aggregate into one matrix. The best alternative selected by using Quantifier Guided Dominance Degree (QGDD). The test results show that the accuracy of the GDSS results on DASS-42 is 71.43% (30 of 42 items / symptoms). Symptoms that overlap significantly between anxiety and stress disorders are 16.67%. (7 of 42), between depression and anxiety by 9.52% (4 of 42). The GDSS is able to accommodate the preferences of decision makers in giving influence weight. Anxiety and stress disorder have very similar symptoms so that for some symptoms in the DASS-42 there are significant differences.</em></p><p><em><strong><br /></strong></em></p>
A development of a Clinical Group Decision Support System (CGDSS) has been carried out for diagnosing both neurosis and personality disorders. The knowledge, stored in the knowledge base, were generated from the aggregated preferences given by decision makers. Two types of preferences used here, i.e. the preferences of a mental evidence by a mental condition; and the preferences of a mental disorder by mental condition. Ordered Weighted Averaging operator was adopted to aggregate those preferences. This aggregation process was carried out after transforming the selected subset to fuzzy preference relation format. Then the Bayesian theorem was adopted to compute the probability of evidence given a particular disorder. After developing the knowledge base, the next step is to develop an inference engine. The method used for developing an inference engine is Multi-Attribute Decision Making concept, this is because of the system was directed to choose the best disorder when a particular condition was given. Many methods have been developed to solve MADM problem, however only the SAW, WP, and TOPSIS were appropriate to solve problem here. In this knowledge base, the relation between each disorder and evidence were represented X matrix (m x n) that consist of probability value. Where the X ij was probability of jth mental evidence given i th mental disorder; i=1,2,...,m; and j=1,2,...,n. Sensitivity analysis process was to compute the sensitivity degree of each attribute to the ranking outcome in each method. The sensitivity analysis was aimed to determine the degree of sensitivity of each attribute to the ranking outcome of each method. This degree implies that there were a relevant between an attribute and a ranking outcome. This relevant attribute can be emitted by influence degree of attribute C j to ranking outcome f j . Then, relation between sensitivity degree and influence degree for each attribute, can be found by computing the Pearson's correlation coefficient. The biggest correlation coefficient shows as the best result. This research shows that TOPSIS method always has the highest correlation coefficient, and it is getting higher if the change of the ranking is increased. The experimental results shows that that TOPSIS is the appropriate method for the clinical group decision support system for the above purposes.
Until recently, Body Mass Index (BMI) has been used as a method for measuring the nutrient state of an individual. Two people having the same weight and height may have different nutrient states. Whenever this occurs, the use of BMI for measuring the nutrient state shall be deemed irrelevant. The anthropometry will be vital in measuring the nutrient state. On the contrary, as the development of IT progresses, so does the improvement of numerical computation. One of the computational algorithms that have been improving is probabilistic reasoning with Naive Bayesian Classification (NBC) as its method. This algorithm is intended to data classification. In this research, the NBC algorithm will be applied for measuring the human nutrient status by using anthropometry data as input system. The result of this research shows that NBC can solve this problem adequately. Research results shows total performance of this system as 93.2%.Keywords: classification, Naive Bayesian Classification (NBC), nutrition
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