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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Hasil Pengabdian Kepada Masyarakat Martabe : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Education and Mathematical Science Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Instal : Jurnal Komputer sudo Jurnal Teknik Informatika Blend Sains Jurnal Teknik Innovative: Journal Of Social Science Research Jurnal Pengabdian Barelang Hanif Journal of Information Systems
Halim Maulana
Universitas Muhammadiyah Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Instal : Jurnal Komputer

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Performance Analysis of the Entropy Waspas Method in Determining Official Travel for Regional Revenue Agency Employees of North Sumatra Province Nasution, Chairul Ichwan; Maulana, Halim
Bahasa Indonesia Vol 16 No 03 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i03.211

Abstract

This research aims to (1) find out how the process of official travel for employees of the Regional Revenue Agency of North Sumatra Province is, (2) design a decision support system for a website system in the process of implementing official travel for employees of the Regional Revenue Agency of North Sumatra Province. This research method uses quantitative research. Data analysis technique using the Entropy-Waspas Method. The sample used in the research consisted of 27 alternatives. Data collection was carried out by observation, interviews and literature study. Next, a simulation of the Waspas entropy calculation was obtained by carrying out (1) determining alternatives and (2) determining criteria. The researchers obtained the results that (1) Using the Entropy Waspas Method in the support system for determining BAPENDA employees entitled to travel on business can make it easier for BAPENDA to select employees who have the right to travel on business and (2) the system calculation results show the same results between system calculations and manual calculations seen from the highest ranking value.
Analysis of Dengue Fever Spread Prediction Using Ensemble Learning Approach with Xgboost and Random Maulana, Halim; Sari, Ayu Sekar
Bahasa Indonesia Vol 16 No 03 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i03.227

Abstract

Dengue hemorrhagic fever (DHF) is a significant infectious disease in tropical countries, with a major public health impact. This study aims to develop a predictive model to estimate the number of dengue cases in two cities, San Juan and Iquitos, using the Random Forest and XGBoost algorithms. The dataset used is DengAI: Predicting Disease Spread, which includes various environmental and weather features such as temperature, rainfall, humidity, and vegetation index, as well as the number of dengue cases reported. The research process begins with data pre-processing to ensure data quality and suitability. After that, the predictive model was built using Random Forest and XGBoost. The model performance evaluation was carried out using Mean Absolute Error (MAE). The results showed that the XGBoost model had a better performance in predicting the number of dengue cases than the Random Forest model, with a lower MAE for both cities. The resulting predictive model can assist health authorities in planning and implementing more effective preventive measures. This study confirms the potential use of machine learning techniques in infectious disease epidemiology and provides important insights into environmental factors that influence the spread of dengue.
Co-Authors Al Khowarizmi Al Khowarizmi Anitya Jauza Rachmad Edy Rahman Syahputra Efendi, Raimon Efendi, Syahril Ferdy Riza Gusrion, Deval Husni Lubis Irvan, Irvan Juliaz, Deal Alfi Lutfi Basit Marah Doly Nasution Mhd. Basri Mintahul Rizka Nasution Mulkan Azhari Mulkan Azhari Nasution, Chairul Ichwan Purwanto Purwanto Rahmad Wahyudi Raveah Nasution Ritonga, Muhammad Nauval Asyqar Ridwan Sari Hutagalung, Fatma Sari, Ayu Sekar Sefhia Astri Yulika Purba Siti Nur Hidayah, Siti Nur Suryana, Febriyanno Tanjung, M. Iqbal