INTEGRATING MULTIVARIATE METHOD AND QUALITY FUNCTION DEPLOYMENT TO ANALYZE IN-PATIENT SATISFACTION

Background : The increasing competition in healthcareindustry has caused the delivery of service quality to patientsbecome essential. Every hospital competes to deliver the bestservice to its patients. As a result, it is necessary to analyzehospitalized patient satisfaction. This study discusses servicequality improvement in healthcare industry by analyzing inpatientsatisfaction using Multivariate Analysis and QualityFunction Deployment (QFD).Objectives: The objectives of this study are to identify patients’characteristics which are significantly affect their satisfactionlevel, to identify service attributes and dimensions which arecritical to patients, and subsequently improve those attributes.Method: The identification of characteristics and servicedimensions which are significantly affect patients’ satisfactionlevel is accomplished using Multivariate Analysis. While thecritical service attributes identification is completed usingImportance-Performance Analysis. Afterward, using Houseof Quality (HOQ), as the basis of QFD, those critical serviceattributes are developed into service elements.Result: Using Discriminant Analysis, the result of this studyshows that patients’ characteristics which significantly affecttheir satisfaction level are sex and occupation. The male andunemployed patients are more satisfied than the female andemployed patients. Afterward, Factor Analysis brings aboutfive new factors (service dimensions), which are the linearcombinations of the original 42 service attributes. Based onthe Importance-Performance Analysis, there are four serviceattributes which are critical to be improved which have highimportance level, but low performance level. Then, using theQuality Function Deployment (QFD), the four critical serviceattributes are developed into service elements. The serviceelements with high priorities are training program, recruitmentof experts, standard of information flow, online administrationsystem, and computer as provider of information.Conclusion: Service quality improvement in healthcareindustry can be analyzed more comprehensive by integratingMultivariate Method and Quality Function Deployment (QFD).The result of this study may provide contributions to hospitalsin general in enhancing its service performance to achieve itspatients’ satisfaction.Keywords: customer satisfaction, healthcare industry,multivariate analysis, quality function deploymen

Integrating Multivariate Method and Quality Function Deployment to Analyze In-patient Satisfaction

Background : The increasing competition in healthcareindustry has caused the delivery of service quality to patientsbecome essential. Every hospital competes to deliver the bestservice to its patients. As a result, it is necessary to analyzehospitalized patient satisfaction. This study discusses servicequality improvement in healthcare industry by analyzing inpatientsatisfaction using Multivariate Analysis and QualityFunction Deployment (QFD).Objectives: The objectives of this study are to identify patients'characteristics which are significantly affect their satisfactionlevel, to identify service attributes and dimensions which arecritical to patients, and subsequently improve those attributes.Method: The identification of characteristics and servicedimensions which are significantly affect patients' satisfactionlevel is accomplished using Multivariate Analysis. While thecritical service attributes identification is completed usingImportance-Performance Analysis. Afterward, using Houseof Quality (HOQ), as the basis of QFD, those critical serviceattributes are developed into service elements.Result: Using Discriminant Analysis, the result of this studyshows that patients' characteristics which significantly affecttheir satisfaction level are sex and occupation. The male andunemployed patients are more satisfied than the female andemployed patients. Afterward, Factor Analysis brings aboutfive new factors (service dimensions), which are the linearcombinations of the original 42 service attributes. Based onthe Importance-Performance Analysis, there are four serviceattributes which are critical to be improved which have highimportance level, but low performance level. Then, using theQuality Function Deployment (QFD), the four critical serviceattributes are developed into service elements. The serviceelements with high priorities are training program, recruitmentof experts, standard of information flow, online administrationsystem, and computer as provider of information.Conclusion: Service quality improvement in healthcareindustry can be analyzed more comprehensive by integratingMultivariate Method and Quality Function Deployment (QFD).The result of this study may provide contributions to hospitalsin general in enhancing its service performance to achieve itspatients' satisfaction

Visiting Time Prediction Using Machine Learning Regression Algorithm

Smart tourists cannot be separated with mobile technology. With the gadget, tourist can find information about the destination, or supporting information like transportation, hotel, weather and exchange rate. They need prediction of traveling and visiting time, to arrange their journey. If traveling time has predicted accurately by Google Map using the location feature, visiting time has another issue. Until today, Google detects the user’s position based on crowdsourcing data from customer visits to a specific location over the last several weeks. It cannot be denied that this method will give a valid information for the tourists. However, because it needs a lot of data, there are many destinations that have no information about visiting time. From the case study that we used, there are 626 destinations in East Java, Indonesia, and from that amount only 224 destinations or 35.78% has the visiting time. To complete the information and help tourists, this research developed the prediction model for visiting time. For the first data is tested statistically to make sure the model development was using the right method. Multiple linear regression become the common model, because there are six factors that influenced the visiting time, i.e. access, government, rating, number of reviews, number of pictures, and other information. Those factors become the independent variables to predict dependent variable or visiting time. From normality test as the linear regression requirement, the significant value was less than p that means the data cannot pass the statistic test, even though we transformed the data based on the skewness. Because of three of them are ordinal data and the others are interval data, we tried to exclude and include the ordinal by transform it to interval. We also used the Ordinal Logistic Regression by transform the interval data in dependent variable into ordinal data using Expectation Maximization, one of clustering algorithm in machine learning, but the model still did not fit even though we used 5 functions. Then we used the classification algorithm in machine learning by using 5 top algorithm which are Linear Regression, k-Nearest Neighbors, Decision Tree, Support Vector Machines, and Multi-Layer Perceptron. Based on maximum correlation coefficient and minimum root mean square error, Linear Regression with 6 independent variables has the best result with the correlation coefficient 20.41% and root mean square error 48.46%. We also compared with model using 3 independent variable, the best algorithm was still the same but with less performance. Then, the model was loaded to predict the visiting time for other 402 destinations

Tourism Mobile Recommender Systems: A Survey

​The growth in tourism industry shows a positive trend in Indonesia because of human needs, infrastructure, and the Internet to support it. To link these aspects, tourists need mobile recommender systems to make everything handle and control easily. From one system tourists can get access to the information, plan their itinerary, get the suggestion, and share the experience. The collaborative, content and hybrid method become the source to enjoy the journey. This paper will show the progress in a last ten years, and what future research that still open to explore

Adoption of Variable Rate Technology

Site Specific Management (SSM), which also variously referred to as Variable Rate Technology (VRT), is an emergingtechnology that enables producers to make more precise input application decisions based on soil and fieldcharacteristics. This study analyzes factors influencing the adoption of VRT for fertilizer application for cash grainproduction in Ohio. Results show that producer and field characteristics might influence the adoption decision onvarious SSM components differently. It also provides insight as to the sequence of adoption of SSM componenttechnologies and how this sequence might differ for producers of differing characteristics

Model Struktural Pengaruh Atribut Produk Terhadap Kepuasan dan Loyalitas Pelangan : Studi Kasus Pelanggan Telkomsel di Jabodetabek

Industri telekomunikasi seluler berkembang sangat pesat di Indonesia. Hal ini diikuti dengan tingkat persaingan yang tinggi yang mendorong tiap operator untuk dapat meningkatkan kepuasan dan loyalitas pelanggannya sebagai strategi untuk dapat bertahan. Hingga saat ini tarif masih menjadi hal yang sensitive bagi pelanggan dan juga merupakan salah satu factor utama yang mempengaruhi preferensi pelanggan dalam memilih jasa telekomunikasi seluler. Hal ini menjadi salah satu sebab tingginya tingkat perpindahan pelanggan dari satu operator ke operator lain (chun rate), dimana Indonesia merupakan salah satu Negara dengan tingkat churn yang lebih tinggi di Asia. Tingginya tingkat churn dan pesaingan di industri telekomunikasi seluler ini menyebabkan kepuasan dan loyalitas pelanggan menjadi hal yang harus diperhatikan oleh setiap operator. Penelitian ini bertujuan untuk menganalisis hubungan antara atribut produk terhadap tingkat kepuasan dan loyalitas pelanggan pada industry telekomunikasi seluler di Indonesia dengan menggunakan Structural Equation Modeling (SEM). Sebagai studi kasus dipilih pelanggan Telkomsel di wilayah Jabodetabek ( Jakarta, Bogor, Depok, Tangerang, Bekasi ). Hasil dari penelitian ini berupa model structural yang menggambarkan rangkaian pola perilaku pelanggan pada industri telekomunikasi seluler untuk melihat pengaruh variabel atribut produk (kualitas produk, kualtias layanan, keterjangkauan tariff, dan image perusahaan) terhadap tingkat kepuasan (satisfaction), kepercayaan (trust), komitmen (commitment), keluhan (complaint), dan loyalitas (loyality) pelanggan.Katakunci: kepuasan pelanggan, loyalitas pelanggan, structural equation modelin

Human Factors and Ergonomic Design for Drivers, Children and Special Needs People

In this special edition, we have selected 15 papers from the 4th SEANES International Conference on Human Factors and Ergonomics in South-East Asia 2016, which discussed the application of human factors and ergonomics to current local and global needs

ADOPTION OF VARIABLE RATE TECHNOLOGY

Site Specific Management (SSM), which also variously referred to as Variable Rate Technology (VRT), is an emergingtechnology that enables producers to make more precise input application decisions based on soil and fieldcharacteristics. This study analyzes factors influencing the adoption of VRT for fertilizer application for cash grainproduction in Ohio. Results show that producer and field characteristics might influence the adoption decision onvarious SSM components differently. It also provides insight as to the sequence of adoption of SSM componenttechnologies and how this sequence might differ for producers of differing characteristics.Keywords: grid soil sampling, variable rate technology, yield monito