EFEKTIVITAS IKLAN TOKO ISTANA SPREI MELALUI MEDIA SOSIAL

The Istana Sprei Shop has been selling products in the form of bed sheets and bed covers since 2011. Almost all of the bed sheet and bed cover products offered are self-produced and the Istana Bed Cover Shop serves orders according to customer wishes. The Istana Bed Sheet Store uses Instagram as a medium to communicate with consumers. From these promotional activities, consumers will go through the stages of Attention, Interest, Desire, and take action. The aim of this research is to find out and analyze the grouping of AIDA stages via Instagram social media among consumers of the Istana Sprei shop. The analysis technique used is non-hierarchical clustering or k-means clustering. From the calculations, 2 clusters were formed. The first cluster consisted of 81 members and was named Peduli AIDA. The second cluster consists of 19 members and is called Don't Care AIDA. Based on these results, it is recommended that Istana Sprei shop pay more attention to the promotions it carries out via Instagram social media so that consumers buy the products being promoted and it is hoped that the Istana Sprei shop will use social media such as Tiktok, Facebook, Telegram or the like so that the marketing reach at the Istana Sprei shop is greater. The area and decline in sales volume of the Istana Sprei store can now be overcome

Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

Learning multiple fault diagnosis

This paper describes two methods for integrating model-based diagnosis (MBD) and explanation-based learning. The first method (EBL) uses a generate-test-debug paradigm, generating diagnostic hypotheses using learned associational rules that summarize model-based diagnostic experiences. This strategy is a form of "learning while doing" model-based troubleshooting and could be called "online learning." The second diagnosis and learning method described here (EEL-STATIC) involves ''learning in advance." Learning begins in a training phase prior to performance or testing. Empirical results of computational experiments comparing the learning methods with MBD on two devices (the polybox and the binary full adder) are reported. For the same diagnostic performance, EBL-STATIC is several orders of magnitude faster than MBD while EBL can cause performance slow-down

Learning approximate diagnosis

Model-based diagnosis (MBD) provides several advantages over experiential rule-based systems. A principal shortcoming of MBD is that MBD learns nothing from any given example. An MBD system facing the same task a second time will incur the same computational effort as that incurred the first time. Our earlier work on incorporating explanation-based learning (EBL) in MBD [4] suggested a diagnostic architecture integrating EBL and MBD components. In this architecture, EBL was used to learn diagnostic rules. But the diagnoses proposed by the rules could be erroneous. So constraint suspension testing was used to check all proposed diagnoses. Insisting on perfect accuracy causes the performance of this scheme for "learning while doing" to deteriorate rapidly with the size of the device to be diagnosed. In this paper, we describe a method for trading off accuracy for efficiency. In this approach, most diagnosis problems are handled by the associational rules learned from previous problems. Model-based reasoning and learning are activated only when performance drops below a given threshold. We present empirical results on circuits of increasing number of components illustrating how this approach scales up

A qualitative logic of decision

An important aspect of intelligent behavior is the ability to reason, make decisions, and act in spite of uncertainty. This paper presents a qualitative logic of decision that supports decision-making under uncertainty. To be specific, the paper presents a knowledge representation language based upon subjective Bayesian decision theory that aims to capture some aspects of common-sense reasoning associated with making decisions about actions. The language addresses the problem of describing justifications of rational choices in situations where the alternatives involve trading off potential losses and gains. The logic and an associated qualitative arithmetic are implemented in an efficient PROLOG program. Examples illustrate their use in several concrete decision-making situations