Control of control charts

Although the Shewhart chart is widely used in practice because of its simplicity, applying this control chart to monitor the mean of a process may lead to two types of problems. The first concerns the typically unknown parameters involved in the distribution, while the second concerns the validity of the assumption of normality itself. The objective of the research is to study and find solutions for these problems. More specifically, our goal is to determine the most suitable control chart to be used in practice. For this, subsequently, so called normal, parametric, nonparametric and combined approaches are considered, leading to corresponding control charts

25 years development of knowledge graph theory: the results and the challenge

The project on knowledge graph theory was begun in 1982. At the initial stage, the goal was to use graphs to represent knowledge in the form of an expert system. By the end of the 80's expert systems in medical and social science were developed successfully using knowledge graph theory. In the following stage, the goal of the project was broadened to represent natural language by knowledge graphs. Since then, this theory can be considered as one of the methods to deal with natural language processing. At the present time knowledge graph representation has been proven to be a method that is language independent. The theory can be applied to represent almost any characteristic feature in various languages.\ud The objective of the paper is to summarize the results of 25 years of development of knowledge graph theory and to point out some challenges to be dealt with in the next stage of the development of the theory. The paper will give some highlight on the difference between this theory and other theories like that of conceptual graphs which has been developed and presented by Sowa in 1984 and other theories like that of formal concept analysis by Wille or semantic networks

Utilizing Soft Computing for Determining Protein Deficiency

Abstract— In recent years, the occurrence of protein shortage of children under 5 years old in many poor area has dramatically increased. Since this situation can cause serious problem to children like a delay in their growth, delay in their development and also disfigurement, disability, dependency, the early diagnose of protein shortage is vital. Many applications have been developed in performing disease detection such as an expert system for diagnosing diabetics and artificial neural network (ANN) applications for diagnosing breast cancer, acidosis diseases, and lung cancer. This paper is mainly focusing on the development of protein shortage disease diagnosing application using Backpropagation Neural Network (BPNN) technique. It covers two classes of protein shortage that are Heavy Protein Deficiency. On top of this, a BPNN model is constructed based on result analysis of the training and testing from the developed application. The model has been successfully tested using new data set. It shows that the BPNN is able to early diagnose heavy protein deficiency accurately. Keywords— Artificial Neural Network, Backpropagation Neural Network, Protein Deficiency

STUDI LITERATUR: Analisis Distribusi Masalah Lokasi Fasilitas untuk Logistik Bantuan Kemanusiaan

Bencana adalah setiap peristiwa atau kejadian yang disebabkan oleh faktor alam dan/atau faktor non alam yang dapat mengakibatkan timbulnya kerusakan lingkungan, kerugian harta benda, gangguan ekologis, dan hilangnya jiwa manusia. Model pada masalah lokasi fasilitas yang terkait dengan model optimasi logistik merupakan pendekatan penting dalam manajemen bencana. Studi literatur ini bertujuan untuk menganalisis penerapan metode eksak dan metode heuristik tersebut dalam menentukan distribusi masalah lokasi fasilitas untuk logistik bantuan kemanusiaan. Metode yang dilakukan melalui penelusuran artikel pada situs Google Scholar, Science Direct, dan Informs Journal. Hasil penelurusan adalah mendapatkan 12 artikel yang memenuhi kriteria untuk dikaji. Penerapan untuk metode eksak dan metode heuristik dapat dilakukan secara terpisah maupun dikolaborasikan untuk mendapatkan solusi dari model yang sudah dibangun. Solusi yang diperoleh melalui metode eksak merupakan hasil optimal, namun untuk kasus dengan skala besar dan masalah yang rumit, metode heuristik dapat digunakan. Metode heuristik memungkinkan waktu penyelesaian solusi lebih cepat jika dibandingkan dengan metode eksak

Koreksi Bias Statistik Pada Data Prediksi Suhu Permukaan Air Laut Di Wilayah Indian Ocean Dipole Barat Dan Timur

The IOD can be measured using the Dipole Mode Index (DMI) which is calculated based on the sea surface temperature in the Indian Ocean. Therefore, DMI can be predicted using sea surface temperature forecasting data, such as data provided by the European Center for Medium-Range Weather Forecasts (ECMWF). However, the data still has a bias as compared to the actual data, so to get a more accurate prediction, corrected data is needed. Therefore, the aim of this study is to predict DMI based on sea surface temperature forecasting data that has been corrected for bias using the quantile mapping method, a method that connects the distribution of forecasting and actual data. The results showed that the DMI prediction using corrected data was more accurate than the DMI prediction using ECMWF data. DMI predictions using corrected data have high accuracy to predict IOD events in October-April

Normal, parametric and nonparametric control charts, a data driven choice

Standard control charts are often seriously in error when the distributional form of the observations differs from normality. Recently, control charts have been developed for larger parametric families. A third possibility is to apply a suitable (modified version of a) nonparametric control chart. This paper deals with the question when to switch from the control chart based on normality to a parametric control chart, or even to a nonparametric one. This model selection problem is solved by using the estimated model error as yardstick. It is shown that the new combined control chart asymptotically behaves as each of the specific control charts in their own domain. Simulations exhibit that the combined control chart performs very well under a great variety of distributions and hence it is recommended as an omnibus control chart, nicely adapted to the distribution at hand. The combined control chart is illustrated by an application on real data. The new modified nonparametric control chart is an attractive alternative and can be recommended as well

Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra

Land and forest fires in Kalimantan and Sumatra, Indonesia occurred annually at different magnitude and duration. Climate and sea interaction, like El Niño, influences the severity of dry seasons preceding the fires. However, research on the influence of El Niño intensity to fire regime in Kalimantan and Sumatra is limited. Therefore, this study aims to analyze the spatial and temporal patterns of the effects of El Niño intensity on land and forest fires in fire-prone provinces in Indonesia. Here, we applied the empirical orthogonal function analysis based on singular value decomposition to determine the dominant patterns of hotspots and rainfall data that evolve spatially and temporally. For analysis, the study required the following data: fire hotspots, dry-spell, and rainfall for period 2001-2019. This study revealed that El Niño intensity had a different impacts for each province. Generally, El Niño will influence the severity of forest fire events in Indonesia. However, we found that the impact of El Niño intensity varied for Kalimantan, South Sumatra, and Riau Province. Kalimantan was the most sensitive province to the El Niño event. The duration and number of hotspots in Kalimantan increased significantly even in moderate El Niño event. This was different for South Sumatra, where the duration and number of hotspots only increased significantly when a strong El Niño event occurred.Land and forest fires in Kalimantan and Sumatra, Indonesia occurred annually at different magnitude and duration. Climate and sea interaction, like El Niño, influences the severity of dry seasons preceding the fires. However, research on the influence of El Niño intensity to fire regime in Kalimantan and Sumatra is limited. Therefore, this study aims to analyze the spatial and temporal patterns of the effects of El Niño intensity on land and forest fires in fire-prone provinces in Indonesia. Here, we applied the empirical orthogonal function analysis based on singular value decomposition to determine the dominant patterns of hotspots and rainfall data that evolve spatially and temporally. For analysis, the study required the following data: fire hotspots, dry-spell, and rainfall for period 2001-2019. This study revealed that El Niño intensity had a different impacts for each province. Generally, El Niño will influence the severity of forest fire events in Indonesia. However, we found that the impact of El Niño intensity varied for Kalimantan, South Sumatra, and Riau Province. Kalimantan was the most sensitive province to the El Niño event. The duration and number of hotspots in Kalimantan increased significantly even in moderate El Niño event. This was different for South Sumatra, where the duration and number of hotspots only increased significantly when a strong El Niño event occurred

Utilizing Soft Computing for Determining Protein Deficiency

Abstract— In recent years, the occurrence of protein shortage of children under 5 years old in many poor area has dramatically increased. Since this situation can cause serious problem to children like a delay in their growth, delay in their development and also disfigurement, disability, dependency, the early diagnose of protein shortage is vital. Many applications have been developed in performing disease detection such as an expert system for diagnosing diabetics and artificial neural network (ANN) applications for diagnosing breast cancer, acidosis diseases, and lung cancer. This paper is mainly focusing on the development of protein shortage disease diagnosing application using Backpropagation Neural Network (BPNN) technique. It covers two classes of protein shortage that are Heavy Protein Deficiency. On top of this, a BPNN model is constructed based on result analysis of the training and testing from the developed application. The model has been successfully tested using new data set. It shows that the BPNN is able to early diagnose heavy protein deficiency accurately.   Keywords— Artificial Neural Network, Backpropagation Neural Network, Protein Deficiency

Rarity of Joint Probability Between Interest and Inflation Rates in the 1998 Economic Crisis in Indonesia and Their Comparison Over Three Time Periods

After more than twenty years, there has been no economic crisis as severe as 1998 based on inflation and interest rates. It is interesting to compare the conditions before and after the 1998 crisis and the economic conditions in the last decade in Indonesia. Therefore, this study aims to analyze the relationship between inflation and interest rates using a copula-based joint distribution. The joint return period of the 1998 economic crisis is estimated from this joint distribution. The results showed that the Gumbel copula is the most suitable bivariate copula to construct a joint distribution between inflation and interest rates in 1990-2019, with an upper tail dependency of 0.6224. Moreover, the joint return period between inflation and interest rates more severe than 1998 is 389 years with a 95% confidence interval of [47, ∞] years. This result is uncertain because many factors affect inflation and interest rates. The inflation rate decreased after the 1998 crisis. Meanwhile, in the last decade, the inflation and interest rates were much lower than in the two previous periods