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Journal : International Journal Of Computer, Network Security and Information System (IJCONSIST)

Southeast Asia Happiness Report in 2020 Using Exploratory Data Analysis Riyantoko, Prismahardi Aji
IJCONSIST JOURNALS Vol 2 No 1 (2020): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.725 KB) | DOI: 10.33005/ijconsist.v2i1.31

Abstract

The happiness index to be one of part to presents that each country has indicator which affect each other. Many countries have a basic indicator to determine that happiness score, there are economy sector, social support, trust to the government, generosity, and measure life satisfactions. The indicator is presents in the dataset, it means need to explore, analysis, and visualization to give knowledge to the other people. Data science is one knowledge field to determine data. Exploratory data analysis (EDA) is part of data science process. In this works, we present happiness report in the Southeast Asia region using the dataset World Happiness Report 2020. The results, we describe and discuss the dataset using table with column and value or score, the other we using bar-plot, correlation bar-plot, bar-plot analysis, and map-plotting visualization. Output of EDA is only recommendation to next parts in the Data Science process, minimum has knowledge to reducing data, merge data, cleansing data, visualization data to be based of knowledge to build data modelling.
Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease Riyantoko, Prismahardi Aji; Fahrudin, Tresna Maulana; Hindrayani, Kartika Maulida; Idhom, Mohammad
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.79 KB) | DOI: 10.33005/ijconsist.v2i02.49

Abstract

This paper presents data stroke disease that combine exploratory data analysis and machine learning algorithms. Using exploratory data analysis we can found the patterns, anomaly, give assumptions using statistical and graphical method. Otherwise, machine learning algorithm can classify the dataset using model, and we can compare many model. EDA have showed the result if the age of patient was attacked stroke disease between 25 into 62 years old. Machine learning algorithm have showed the highest are Logistic Regression and Stochastic Gradient Descent around 94,61%. Overall, the model of machine learning can provide the best performed and accuracy.
Implementation of A* Algorithm and Contraction Hierarchies for Delivery Route Optimization (Case Study: CV. Almaed.id) Gunawan, Boy Erdyansyah; Idhom, Mohammad; Akbar , Fawwaz Ali; Riyantoko, Prismahardi Aji
IJCONSIST JOURNALS Vol 5 No 2 (2024): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i2.140

Abstract

In the digital era, manufacturing companies like CV. Almaed.id are required to have an efficient distribution system to compete in the furniture industry. This study proposes the application of the A* algorithm and Contraction Hierarchies (CH) to optimize product delivery routes. This system utilizes road network data from OpenStreetMap and calculates geographic distances using the Haversine method. Implementation results show that the combination of A*, CH, and Haversine can accelerate route calculation and reduce operational costs compared to manual methods.