Measuring the Water Surface Speed of the Cikidang River as a Supporting Facility for the Development of Ecotourism Areas

Pangandaran Regency is famous for its natural beauty, especially the beauty of its beaches. In this district there are many famous beaches. Besides the beach, there are also rivers that offer beautiful views. However, not many tourists have made the rivers in Pangandaran as tourist destinations, especially the Cikidang River. Cikidang River has the potential to be used as ecotourism. In building an ecotourism area on the banks of a river, it is necessary to carry out an in-depth analysis of the damage that might occur due to erosion. One of the factors that can increase erosion is the speed of water on the surface. Therefore, in this paper, measurement of river surface velocity is carried out in a simple way and analyzed using the velocity formula. From the results of this measurement, a recommendation was made to direct all parties involved in measuring the surface velocity of the Cikidang River as a means of supporting the development of ecotourism areas

FORMATION OF AN OPTIMAL PORTFOLIO OF LQ45 SHARES USING MARKOWITZ METHOD

Currently, there are still many investors who do not realize that stock management strategies are important. Therefore, as a practitioner, through this research, it is hoped that authors can help overcome this problem so that it can calculate maximum profits in-stock selection. This research aims at understanding stock management strategies by forming a Markowitz portfolio with the help of the K-Means grouping method. The population in this research included stock companies listed on the Indonesian Stock Exchange. The sample selection method used was the targeted sampling method. The sample data used was daily stock returns from LQ45 stock companies. The research results showed that based on data processing, stock grouping using the k-Means method and the Markowitz method was proven to produce maximum profits and low risk. Therefore, the method used in this research can be useful in the world of finance, especially to help investors

The ensemble distance on model-based clustering for regions clustering based on rainfall: The case of rainfall in West Java Indonesia

Time series data clusters are being researched thoroughly. The distance metric drives the development of the clustering time series. The ARIMA model is one of the models that can be employed in model-based clustering, although differing model selection criteria can lead to uncertainty in the model. In this investigation, we created a technique for ensemble distance-based time series data clustering. To express the distance between two series, five distances based on the five model selection criteria are utilized. The average of the five distances reflects the distance of two time series data. According to the simulation results, the ensemble distance method could boost clustering accuracy by more than 11%. Based on the pattern of rainfall levels, we applied our methods to find clusters of locations in the Province of West Java (Indonesia). The findings indicate that the rainfall pattern in the same cluster is similar. The cluster model is effective and feasible for representing individual models in a cluster

POLA PENYEBARAN PENYAKIT MENULAR BERDASARKAN KABUPATEN/KOTA DI JAWA TIMUR MENGGUNAKAN ANALISIS KORESPONDENSI

Infectious diseases are a problem that is still a challenge and has not been resolved in Indonesia. The number of infectious disease cases continues to increase every year in Indonesia. Infectious diseases that are still a problem in Indonesia include tuberculosis (TB), pneumonia and leprosy (Leprosy). Identification of infectious disease endemic areas is an important issue in the health sector, the average rate of people with physical disabilities and deaths originating from infectious diseases. Indonesia, as a country consisting of 34 provinces, including East Java as one of the provinces that has a high rate of infectious disease cases. Therefore, research was conducted using correspondence analysis which aimed to determine the pattern of infectious disease trends and grouping districts / cities in East Java Province based on similarities between the spread of disease sufferers in each district / city. This type of research uses quantitative methods with secondary data obtained from the Central Bureau of Statistics of East Java Province. The results of correspondence analysis show that  the spread of pneumonia and tuberculosis in East Java  Province has a relative tendency to almost all regencies/cities in East Java, while the spread of leprosy is closer to Sampang Regency. From this research, it can be used to supervise and control infectious diseases in districts / cities in East Java Province, so that the government can adjust policy formulation and actions to prevent an increase in infectious disease cases in East Jav

ANALISIS INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TENGAH MENGGUNAKAN ANALISIS REGRESI

The Human Development Index is one approach to measuring the success rate of human development. Central Java Province is one of the provinces that experienced an increase in the human development index in 2022. Therefore, this study was conducted to determine the regression model used and the factors that affect the human development index in Central Java Province in 2022. Some of the factors used in this study are life expectancy, average years of schooling, expected years of schooling and adjusted per capita expenditure. The method used in this research is multiple linear analysis, parameter significance test, and classical assumption test. By using the human development index as the response variable (Y), life expectancy (X1), average years of schooling (X2), expected years of schooling (X3) and adjusted per capita expenditure (X4) as predictor variables. From the results of the analysis that has been done, the equation Y = 6.55 + 0.4626X₁ + 1.341X₂ + 0.8971X₃ + 0.0008329X₄ +e is obtained. This shows that there is a relationship between the human development index and life expectancy, average years of schooling, expected years of schooling and adjusted per capita expenditure. The classical assumption test, namely the normality test, multicollinearity test, autocorrelation test and heteroscedasticity test, shows that the regression model can be use

The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling

In the era of big data and ubiquitous internet connectivity, user feedback data plays a crucial role in product development and improvement. However, extracting valuable insights from the vast pool of unstructured text data found in user feedback presents significant challenges. In this paper, we propose an innovative approach to tackle this challenge by combining the Contextualized Topic Model (CTM) and the Masked and Permuted Pre-training for Language Understanding (MPNet) model. Our approach aims to create a more accurate and context-aware topic model that enhances the understanding of user experiences and opinions. To achieve this, we first search for the optimal number of topics, focusing on generating distinguishable, general, and unique topics. Next, we perform hyperparameter optimization to fine-tune the model and maximize coherence metrics. The result is an exceptionally effective model that outperforms established topic modeling methods, including LSI, NMF, LDA, HDP, NeuralLDA, ProdLDA, ETM, and the default CTM, achieving the highest coherence CV score of 0.7091. In this study, the combination of CTM and MPNet has proven highly effective in the context of user feedback topic modeling. This model excels in generating coherent, distinguishable, and highly relevant user feedback topics, capturing the nuanced nature of user feedback data. The topics generated from this model include ‘Music and Audio Streaming,’ ’Application Performance,’ ‘Banking, Financial Services, and Customer Support,’ ’User Experience,’ ‘Other Topics,’ ’Application Content,’ and ‘Application Features.’ Our contributions include a powerful tool for developers to gain deeper insights, prioritize actions, and enhance user satisfaction by incorporating feedback into future product iterations. Furthermore, we introduce a new dataset as an open-source resource for further exploration and validation of user feedback analysis techniques and general natural language processing applications. With our proposed approach, we strive to drive business success, improve user experiences, and inform data-driven decision-making processes, ultimately benefiting both developers and users alike

Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification

COVID-19 is the disease that has spread over the world since December 2019. This disease has a negative impact on individuals, governments, and even the global economy, which has caused the WHO to declare COVID-19 as a PHEIC (Public Health Emergency of International Concern). Until now, there has been no medicine that can completely cure COVID-19. Therefore, to prevent the spread and reduce the negative impact of COVID-19, an accurate and fast test is needed. The use of chest radiography imaging technology, such as CXR and CT-scan, plays a significant role in the diagnosis of COVID-19. In this study, CT-scan segmentation will be carried out using the 3D version of the most recommended segmentation algorithm for bio-medical images, namely 3D UNet, and three other architectures from the 3D UNet modifications, namely 3D ResUNet, 3D VGGUNet, and 3D DenseUNet. These four architectures will be used in two cases of segmentation: binary-class segmentation, where each architecture will segment the lung area from a CT scan; and multi-class segmentation, where each architecture will segment the lung and infection area from a CT scan. Before entering the model, the dataset is preprocessed first by applying a minmax scaler to scale the pixel value to a range of zero to one, and the CLAHE method is also applied to eliminate intensity in homogeneity and noise from the data. Of the four models tested in this study, surprisingly, the original 3D UNet produced the most satisfactory results compared to the other three architectures, although it requires more iterations to obtain the maximum results. For the binary-class segmentation case, 3D UNet produced IoU scores, Dice scores, and accuracy of 94.32%, 97.05%, and 99.37%, respectively. For the case of multi-class segmentation, 3D UNet produced IoU scores, Dice scores, and accuracy of 81.58%, 88.61%, and 98.78%, respectively. The use of 3D segmentation architecture will be very helpful for medical personnel because, apart from helping the process of diagnosing someone with COVID-19, they can also find out the severity of the disease through 3D infection projections