Pembentukan Model Ruangan Kegagalan Cerun Bagi Sub-Lembangan Hulu Sungai Langat

Penambahan tekanan bagi tujuan pembangunan di Malaysia dalam tahun kebelakangan ini disebabkan oleh pertambahan populasi Increasing pressure for development in Malaysia in recent years due to rapid populatio

Gis in studying slope failure in Penang: challenges and potential

Geographic Information System (GIS) is an information system that is used to store, display, analyse and manipulate spatial data. Geographic information system (GIS) can help users to visualise, question, analyse, and interpret data to understand patterns, trends, and relationships. GISbased maps and visualizations have greatly assisted understanding of situations. In recent years, slope failure hazard assessment has played an important role in developing land utilisation planning aimed at minimizing the loss of lives and damages to property. There are various GIS-based slope failure studies that involve many approaches. These approaches can be classified into qualitative factor overlay, geotechnical process models, and statistical models. At present, not many studies have satisfactorily studied the integration of these models with GIS to map slope failure. This paper deals with several aspects of landslide by presenting a focused review of GIS-based slope failure hazard zone. The paper starts with a framework for GIS-based study of slope failure, followed by a critical review of the state-of-the-art applications of GIS and digital elevation models (DEM) for mapping and modelling landslide hazards. The paper ends with a description of an integrated system for effective landslide hazard zonation. The adoption of a GIS-based framework for knowledge discovery allows designers to identify the suitability of development within certain areas. The usage of GIS can be beneficial in various fields, including the issue of slope failure. Moreover, GIS is also beneficial to organisations of all sizes and in virtually every industry. GIS is important in understanding what is happening and forecasting future trend in a geographic space

Optimizing Tuberculosis Treatment Predictions: A Comparative Study of XGBoost with Hyperparameter in Penang, Malaysia (Mengoptimumkan Peramalan Rawatan Tuberkulosis: Suatu Kajian Perbandingan XGBoost dengan Hiperparameter di Penang, Malaysia)

The bacterium Mycobacterium tuberculosis causes a viral infection affecting the lungs and liver. Tuberculosis (TB) is a significant public health concern in developing countries, where it is often associated with poverty, poor living conditions, and limited access to healthcare services. According to the World Health Organization (2023), Tuberculosis continues to pose a substantial risk to public health on a global scale, with millions of people affected each year and around 1.5 million deaths in 2020. Healthcare providers often encounter significant challenges in addressing TB, leading to uncertain treatment outcomes. This study introduces a novel method for enhancing TB treatment using sophisticated machine learning techniques, particularly emphasizing the application of XGBoost and various predictive models in Penang State, Malaysia, to predict individual treatment outcomes based on clinical data. The models were trained using 2017 Penang data. Comparing predicted accuracy helps establish the optimum method. Clinical data was anonymized and analyzed. Decision tree accuracy is 63.7% using 2017 data. Logistic Regression is 63.3% accurate, while XGBoost is 66.3%. Hyperparameter-tuned XGBoost performs best at 68.1%. Comparing observed and expected results determines accuracy. TB result predictions are accurate using supervised learning. Calibrated ensemble models like XGBoost makes reliable predictions. Additional clinical characteristics may improve forecasts. The primary objective was to develop a reliable, clinically validated instrument that enhances TB treatments while optimizing resource efficiency across diverse healthcare environments

Modelling landslide using GIS and RS: a case study of upper stream of Langat river basin, Malaysia

Increasing pressure for development in Malaysia in recent years due to rapid population growth and urbanization has caused numerous environmental related problems such as landslide and soil erosion. Increasing landslide event in Malaysia has caused degradation to properties, life and environment. This paper describes a study to develop landslide model by using logistic regression approach, partly to measure the significance of each causative factor that contributes to landslide. In this study causative factors are divided into physical, human activities and location. This model is based upon the model developed by a number of researchers. Landslide events in the Hulu Sungai Langat sub-basin, which is the upper stream of Langat Basin, Selangor, Malaysia were used to develop the mode

Breast, Cervical And Colorectal Cancers: A Geographical Analysis of Case Distribution and Their Relationship with Service Facilities

Pada masa kini, kanser merupakan masalah kesihatan yang utama, dan kematian yang melibatkan kanser dianggarkan akan mencecah 12 juta pada tahun 2030. Di Malaysia, Makna melaporkan lebih kurang 20,000 - 40,000 kes kanser setahun. Dalam tahun 2007, sebanyak 18219 kes baru didaftarkan melibatkan 8,123 (44.6%) lelaki dan 10,096 (55.4%) perempuan. Cancer is now a major health problem, and deaths from cancer worldwide are estimated to reach 12 million deaths in 2030. In Malaysia, the National Cancer Registry reports approximately 20,000 - 40,000 cancer cases per year. In 2007, 18,219 new cancer cases were registered involving 8,123 (44.6%) male and 10,096 (55.4%) female cancer cases respectively

Assessment of three GPM IMERG products for GIS-based tropical flood hazard mapping using analytical hierarchy process

The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1“2 found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of very high" and "high" hazard levels that require particular attention from local stakeholders