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All Journal Journal of Information Systems and Informatics DEVICE
S, Muhammad Sabri
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Mechanical Engineering

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POTENTIAL ENTRY OF DHF DISEASE BASED ON ENVIRONMENTAL CONDITIONS USING ARTIFICIAL METHODS NEURAL NETWORK PERCEPTION S, Muhammad Sabri; Herlinawati, Noor; MZ, Reza Rafiq; Kusrini, Kusrini
Device Vol 14 No 2 (2024): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i2.7694

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus transmitted by the Aedes aegypti mosquito. The spread of DHF is greatly influenced by environmental conditions such as temperature, rainfall, humidity, and population density. In Indonesia, DHF has become a significant public health problem, especially in densely populated urban areas. Therefore, it is important to develop a predictive model that can forecast the potential occurrence of DHF based on environmental variables to reduce the impact and control the spread of this disease. The objective of this research is to develop a predictive model using the Artificial Neural Network Perception (ANN) method to predict the potential occurrence of DHF based on environmental variables, and to create an application for predicting the potential of DHF. This model is expected to help authorities make appropriate decisions to prevent and control DHF outbreaks. The research methodology includes the following stages: data collection, data preprocessing, ANN model development, model evaluation, and implementation and validation. The expected output of this research is an ANN model that can accurately predict the potential occurrence of DHF based on environmental conditions. Additionally, it is hoped that a predictive system will be available for authorities to take effective preventive and control measures against DHF. The research is expected to make a significant contribution to public health, particularly in the prevention and control of DHF. The results include an application for predicting the potential occurrence of DHF in a specific area, with features such as a Dashboard Interface, Temperature Interface, Dataset Interface, and Result Model Interface. The RMSE results obtained for this research were 0.01441372. From the research results, it can be concluded that ANN can be used to predict the potential for dengue fever to enter.
Traffic Violation Clustering Using K-Medoids and Word Cloud Visualization S, Muhammad Sabri; Utami, Ema
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1002

Abstract

Traffic is the space for people to move around, including both drivers and pedestrians. According to data from the Central Statistics Agency in 2020, the number of motor vehicles in Makassar City was recorded by type: 248,682 passenger cars, 17,501 buses, 85,968 trucks, and 1,338,306 motorcycles, with a tendency for an increase in the following year. The high number of vehicle users can certainly affect the rising traffic violation rates on the road. This study aims to classify traffic violation types in Makassar City by utilizing the K-Medoids algorithm and to visualize the clustering results using Word Cloud, which is expected to provide information related to patterns of traffic violation clusters. This study uses a case study from the Traffic Police Department of Makassar City in 2021, with a total of 5,893 traffic violation cases. The data used is ticket data consisting of article and vehicle type features. The clustering results show that motorcycles and minibuses are the most frequently involved in traffic violations. Motorcycles (R2) are not only dominated by violations related to the use of standard SNI helmets but also significantly involved in violations related to incomplete requirements and the possession of SIM/STNK (Driver's License/Vehicle Registration) and failing to meet roadworthiness standards such as mirrors, headlights, horns, etc. Passenger vehicles, especially minibuses and cars, also dominate traffic violations. The violations involve not only the use of seat belts for R4 vehicles but also violations such as not having complete STNK, not being able to show SIM, failing to display the Vehicle Registration Mark (TKB), and others. The results of this study demonstrate that the clustering obtained is very strong, as evidenced by the high Silhouette Score of 0.867 at k = 9.
POTENTIAL ENTRY OF DHF DISEASE BASED ON ENVIRONMENTAL CONDITIONS USING ARTIFICIAL METHODS NEURAL NETWORK PERCEPTION S, Muhammad Sabri; Herlinawati, Noor; MZ, Reza Rafiq; Kusrini, Kusrini
Device Vol 14 No 2 (2024): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i2.7694

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus transmitted by the Aedes aegypti mosquito. The spread of DHF is greatly influenced by environmental conditions such as temperature, rainfall, humidity, and population density. In Indonesia, DHF has become a significant public health problem, especially in densely populated urban areas. Therefore, it is important to develop a predictive model that can forecast the potential occurrence of DHF based on environmental variables to reduce the impact and control the spread of this disease. The objective of this research is to develop a predictive model using the Artificial Neural Network Perception (ANN) method to predict the potential occurrence of DHF based on environmental variables, and to create an application for predicting the potential of DHF. This model is expected to help authorities make appropriate decisions to prevent and control DHF outbreaks. The research methodology includes the following stages: data collection, data preprocessing, ANN model development, model evaluation, and implementation and validation. The expected output of this research is an ANN model that can accurately predict the potential occurrence of DHF based on environmental conditions. Additionally, it is hoped that a predictive system will be available for authorities to take effective preventive and control measures against DHF. The research is expected to make a significant contribution to public health, particularly in the prevention and control of DHF. The results include an application for predicting the potential occurrence of DHF in a specific area, with features such as a Dashboard Interface, Temperature Interface, Dataset Interface, and Result Model Interface. The RMSE results obtained for this research were 0.01441372. From the research results, it can be concluded that ANN can be used to predict the potential for dengue fever to enter.
Co-Authors Ema Utami Herlinawati, Noor Kusrini, Kusrini MZ, Reza Rafiq