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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology
Nsimba Malumba, Rodolphe
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Computer Science & IT

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PERFORMANCE COMPARISON OF CLASSICAL ALGORITHMS AND DEEP NEURAL NETWORKS FOR TUBERCULOSIS PREDICTION Mate Landry, Gilgen; Nsimba Malumba, Rodolphe; Balanganayi Kabutakapua, Fiston Chrisnovi; Boluma Mangata, Bopatriciat
Jurnal Techno Nusa Mandiri Vol. 21 No. 2 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i2.5609

Abstract

This study compares the performance of several classical machine learning algorithms and deep neural networks for the prediction of tuberculosis in the Democratic Republic of Congo (DRC), using a sample of 1000 cases including clinical and demographic data. The sample is divided into two sets: 80% for training and 20% for testing. The algorithms evaluated include Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest and Convolutional Neural Networks (CNN). The results show that the CNN has the best overall performance with an accuracy of 94%, an AUC of the ROC curve of 93%, an accuracy of 90%, an accuracy of 95%, a sensitivity of 88%, an F1-score of 91.3% and a Log Loss of 0.0386. The Random Forest follows closely behind with an accuracy of 92% and an AUC of 86%. The SVM and KNN models also performed strongly, but slightly less well. The Decision Tree obtained acceptable results, but inferior to the other algorithms evaluated. These results indicate that deep neural networks, and in particular the CNN, are superior for predicting tuberculosis compared with conventional machine learning algorithms. This superiority is particularly marked in terms of accuracy, sensitivity and reliability of predictions, as shown by the performance metrics obtained.
IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS Nsimba Malumba, Rodolphe; Longo Kayembe, Mardochee; Balanganayi Kabutakapua, Fiston Chrisnovic; Boluma Mangata, Bopatriciat; MAZAMBI KILONGO, Trésor; Tabiaki Tandele, Rufin; Ntanyungu Ndizieye, Emmanuel; Bukanga Christian, Parfum
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6380

Abstract

This article presents an analysis of agricultural yields in the Democratic Republic of Congo (DRC) using machine learning algorithms. The study is based on around 30,000 records covering several years of agricultural production. Each record includes variables such as seed type, climatic conditions (temperature, rainfall and humidity), soil characteristics (pH, nutrients), farming practices (fertilizer use, irrigation) and yields obtained. The data comes from a variety of sources, including METTELSAT, the World Meteorological Organization (WMO) and WorldClim for climate data, and the DRC Ministry of Agriculture and the FAO for soil and agricultural data. The algorithms evaluated include linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The performance of the algorithms is measured using metrics such as MSE, MAE, RMSE, R² Score and MAPE on three separate case studies (Farm A, Farm B and Farm C). The results show that artificial neural networks (ANNs) perform best, with MSE ranging from 600 to 850, MAE from 12 to 17, RMSE from 24.49 to 29.15, R² Score from 0.92 to 0.95, and MAPE from 8.5% to 10.7%. Next came GBM, random forest regression, SVM and finally linear regression. These results highlight the potential of machine learning algorithms to improve agricultural yield forecasts in the DRC.
Co-Authors Balanganayi Kabutakapua, Fiston Chrisnovi Balanganayi Kabutakapua, Fiston Chrisnovic BOLUMA MANGATA, Bopatriciat Bukanga Christian, Parfum Longo Kayembe, Mardochee Mate Landry, Gilgen MAZAMBI KILONGO, Trésor Ntanyungu Ndizieye, Emmanuel Tabiaki Tandele, Rufin