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Tursina Tursina
Rina Septiriana


The diagnosis of respiratory problems was usually made through direct consultation with a pediatric respiratory specialist or by studying several previous respiratory disorders cases. These cases were gleaned from prior experiences or the knowledge of subject-matter experts. Case-Based Reasoning (CBR) is the processing of diagnosing a patient based on past cases or expertise. Retrieve, reuse, revise, and retain are some of the steps of case-based reasoning. The retrieval stage of CBR was where the classification method searches for similarity values. Numerous algorithms exist for classification techniques, such as C4.5 and the Nearest Neighbour algorithm. This study compares the similarities between the C4.5 and Nearest Neighbor algorithms. The Nearest Neighbour approach was used to search for similarity, and the results show that 99.33% of the items classified based on learning data were nearest to the object. By contrast, the accuracy value for the C4.5 approach was 100%.


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Tursina, T. and Septiriana, R. (2024) “Comparison of the nearest neighbor algorithm and C4.5 for the retrieval on case-based reasoning process (case study: children respiratory disorders)”, Jurnal Mantik, 8(1), pp. 174-185. doi: 10.35335/mantik.v8i1.5018.
Altuhaifa, F. A., Win, K. T., & Su, G. (2023). Predicting lung cancer survival based on clinical data using machine learning: A review. Computers in Biology and Medicine, 165, 107338.
Amer, A. A., & Abdalla, H. I. (2020). A set theory based similarity measure for text clustering and classification. Journal of Big Data, 7(1), 74.
Arunadevi, J., Ramya, S., & Raja, M. R. (2018). A study of classification algorithms using Rapidminer.
Astuti, P. (2016). Komparasi Algoritma C 4.5 , KNN, dan Neural Network dalam proses kelayakan pnenerimaan kridit Kendaraan Bermotor. Faktor Exacta.
Brownlee, J. (2020). Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery.
Fatoni, C. S., & Noviandha, F. D. (2018). Case Based Reasoning Diagnosis Penyakit Difteri dengan Algoritma K-Nearest Neighbor. Creative Information Technology Journal, 4(3), 220.
Goel, P., & Thareja, R. (2017). Analysis of Various Data Mining Techniques using Novel Ratings Prediction. IARS International Research Journal, 7(2).
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for Multi-Class Classification: an Overview.
Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques (4th ed.). Morgan Kaufmann.
Homem, T. P. D., Santos, P. E., Reali Costa, A. H., da Costa Bianchi, R. A., & Lopez de Mantaras, R. (2020). Qualitative case-based reasoning and learning. Artificial Intelligence, 283, 103258.
Kurniawan, D., Anggrawan, A., & Hairani, H. (2020). Graduation Prediction System On Students Using C4.5 Algorithm. MATRIK?: Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 19(2), 358–365.
Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., & Séroussi, B. (2019). Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach. Artificial Intelligence in Medicine, 94, 42–53.
Lubis, A. R., Lubis, M., & Khowarizmi, A.-. (2020). Optimization of distance formula in K-Nearest Neighbor method. Bulletin of Electrical Engineering and Informatics, 9(1), 326–338.
Ngastiyah. (2004). Asuhan Keperawatan Penyakit Dalam Edisi I. EGC.
Ramadhani, R., Helilintar, R., & Rochana, S. (2017). Data Mining K-Nearest Neigbor. akultas Teknik Universitas Nusantara PGRI Kediri.
Sembiring, M. A. A. S., Sibuea, M. F. L., & Sapta, A. (2018). Analisa Kinerja Algoritma C.45 Dalam Memprediksi Hasil Belajar. Journal Of Science And Social Research, 1(1).
Shadman Roodposhti, M., Aryal, J., Lucieer, A., & Bryan, B. (2019). Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest. Entropy, 21(1), 78.
Tchomté, N., Asghar, S., Javaid, N., Dayang, P., Danga, D., & Oyono, D. (2020). A Case Based Reasoning Coupling Multi-Criteria Decision Making with Learning and Optimization Intelligences: Application to Energy Consumption. EAI Endorsed Transactions on Smart Cities, 4(9), 162292.
Utomo, D. P., & Nasution, S. D. (2016). Sistem Pakar Mendeteksi Kerusakan Toner dengan Menggunakan Metode Cased Based- Reasoning. Jurnal Riset Komputer (JURIKOM), 3(5).
Widyastuti, M., Fepdiani Simanjuntak, A. G., Hartama, D., Windarto, A. P., & Wanto, A. (2019). Classification Model C.45 on Determining the Quality of Custumer Service in Bank BTN Pematangsiantar Branch. Journal of Physics: Conference Series, 1255(1), 012002.
Yuliansyah, H., Imaniati, R. A. P., Wirasto, A., & Wibowo, M. (2021). Predicting Students Graduate on Time Using C4.5 Algorithm. Journal of Information Systems Engineering and Business Intelligence, 7(1), 67.
Zeng, G. (2020). On the confusion matrix in credit scoring and its analytical properties. Communications in Statistics - Theory and Methods, 49(9), 2080–2093.
Zhang, Z., Yang, W., & Wushour, S. (2020). Traffic Accident Prediction Based on LSTM-GBRT Model. Journal of Control Science and Engineering, 2020, 1–10.