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Rima Liana Gema
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jcsitech@upiyptk.ac.id
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INDONESIA
Journal of Computer Scine and Information Technology
Published by Universitas Putra Indonesia YPTK Padang
ISSN : 25021486     EISSN : 25021486     DOI : https://doi.org/10.35134/jcsitech
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology
Journal of Computer Science and Information Technology is a threetly journal published by Universitas Putra Indonesia YPTK, Padang. It publishes scientific and technical papers describing original research work or novel product/process development. The objectives are to promote exchange of information and knowledge in research work, new inventions/developments of Computer Science and on the use of Information Technology towards the structuring of an information-rich society and to assist the academic staff from local and foreign universities, business and industrial sectors, government departments and academic institutions on publishing research results and studies in Computer Science and Information Technology through a scholarly publication. This journal is useful to researchers, engineers, scientists, teachers, managers and students who are interested in keeping a track of original research and development work being carried out in the broad area of computer science. Subjects covered by this journal are: Algorithms, Artificial intelligence, Computer graphics, Compiler programming and languages, Computer vision, Data mining, High performance computing, Information technology, Internet computing, Multimedia, Networks, Network Security, Operating systems, Quantum learning systems, Pattern Recognition, Sensor networks, Soft computing.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Volume 7 Issue 4 (2021): JCSITech" : 5 Documents clear
Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach Charity Ojochogwu Egbunu; Matthew Tunde Ogedengbe; Terungwa Simon Yange; Malik Adeiza Rufai; Habibat Iye Muhammed
Journal of Computer Scine and Information Technology Volume 7 Issue 4 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i4.15

Abstract

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented random forest algorithm using Python programming language to predict the aforementioned climate parameters. The data used was gotten from the Nigerian Meteorological (NiMet) Agency, Lokoja, Kogi State between 1988 and 2018. The result shows that random forest algorithm is effective in climate prediction as the accuracy from the model based on the climatic factors considered was 94.64%. With this, farmers would be able to plan ahead to prevent the impact of the fluctuations in these climatic factors; thus, the yield of crops would be increased. This would dwarf the negative impact of food insecurity to the populace.
A Mathematical Approach to Healthcare Insurance Data Analytics Terungwa Simon Yange; Ishaya Peni Gambo; Theresa Omodunbi; Hettie Abimbola Soriyan
Journal of Computer Scine and Information Technology Volume 7 Issue 4 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i4.18

Abstract

The emergence of big data analytics as a way of deriving insights from data has brought excitement to mathematicians, statisticians, computer scientists and other professionals. However, the near absence of a mathematical foundation for analytics has become a real challenge amidst the flock of big data marketing activities, especially in healthcare insurance. This paper developed a mathematical model for the analytics of healthcare insurance data using set theory. A prototype for the model was implemented using Java Programming Language, MapReduce Framework, Association Rule Mining and MongoDB. Also, it was tested for accuracy using data from the National Health Insurance Scheme in Nigeria with a view to reducing delays in the processes of the Scheme. The result showed that the accuracy level was 97.14% on average, which depicts a higher performance for the model. This result implies that delays affecting the processing of data submitted by the providers and enrollees to the HMOs reduced drastically leading to the improvement in the flow of resources.
Violence Detection in Ranches Using Computer Vision and Convolution Neural Network Terungwa Simon Yange; Charity Ojochogwu Egbunu; Oluoha Onyekware; Malik Adeiza Rufai; Comfort Godwin
Journal of Computer Scine and Information Technology Volume 7 Issue 4 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i4.22

Abstract

This study engaged the convolutional neural network in curbing losses in terms of resources that farmers spends in treating animals where injuries must have emancipated from violence among other animals and in worst case scenario could eventually lead to death of animals. Animals in a ranch was the target and the study proposed a method that detects and reports activities of violence to ranchers such that farmers are relieved of the stress of close supervision and monitoring to avoid violence among animals. The scope of the study is limited to violence detection in cattle, goat, horse and sheep. Different machine learning models were built for each animal. The models yielded good results; the horse violence detection model had an outstanding performance of 93% accuracy, 93% accuracy for the sheep model, 88% accuracy for the goat model and 84% accuracy for the cattle model.
Designing a Star Schema for Optimizing the Total Sales of Motorcycles Eka Praja Wiyata Mandala; Randy Permana; Dewi Eka Putri
Journal of Computer Scine and Information Technology Volume 7 Issue 4 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i4.23

Abstract

Motorcycle sales have increased significantly, motorcycle manufacturers are competing to produce the latest models which are then sold to consumers. As a result, motorcycle dealers are overwhelmed with more and more data, not knowing what to do with it. Motorcycle dealers also have difficulty calculating the total sales of motorcycles. We try to provide solutions to deal with data overflow. We propose designing a star schema as the basis for creating a data warehouse. To create a star schema, we propose a four-step sequence in creating an effective star schema, starting from requirements analysis and reporting, understanding business processes, connecting and matching business processes with suitable entities and determining the dimensions of the business processes. We get a star schema with 1 fact table, motorcycle_sales and 11 of dimension tables, such as brand, color, customer, customer_contract, distributor, district, motorcycle, repair_workshop, sell_location, type and time. The star schema is an optimized model that provides the best performance in presenting more complex information
Implementation of C4.5 Method and Artificial Neural Networks to Predict Sales Elsa Trisna Putri
Journal of Computer Scine and Information Technology Volume 7 Issue 4 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i4.24

Abstract

Technology and communication is currently growing along with the increasing needs of each individual in various fields such as: business, education, agriculture, health and technology. With the development of technology today, everyone can communicate and obtain and convey various information needed anytime and anywhere quickly, accurately and dynamically.Sales is an activity of buying and selling products or goods carried out between sellers and buyers, can interact within the same scope or online by using legal payment transactions. Building materials are materials used to design buildings such as houses, mosques, schools etc. For buildings, many natural materials are used such as: clay, sand, wood or bamboo, stone and others. In this study, the implementation of the c45 method and an artificial neural network to predict sales for the next year. The sale of building materials at the prayer shop, whose sales are not yet computerized by designing a computerized sales system. Toko Doa Mama is a building shop that sells various building materials such as: sand, paint, wood, or bamboo, saws, cement, roof tiles, gravel, nails, bricks, and others. But sales and marketing are still not computerized which results in frequent errors in calculating sales transactions, data collection of incoming goods and outgoing goods which are still in the form of archives, resulting in accumulation and lack of data security. Therefore, we need a computerized where the computer can help a job to be more effective and efficient

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