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Abstract
 Malnutrition in toddlers is a serious problem faced by developing countries like Indonesia, and the resulting long-term effects can reduce the intelligence of toddlers. The classification of the nutritional status of children under five is still carried out conventionally in community health centers. The K-Nearest Neighbor algorithm is included in a machine learning algorithm that can be used to classify one of the nutritional status classification problems. K-NN is used as a class determination algorithm for new data to be input according to the format. This research begins with a literature study, then identifies needs, followed by data collection that is planned to be used in the system to be built as well as a reference for making the design and the final stage of system design. This research succeeded in creating a system design using the Unified Model Language (UML), one use case that contains four functional systems, including uploading dataset files, displaying datasets, testing the accuracy of datasets, predicting new data, and designing system interfaces that will make system development easier..
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References
- Abbad Ur Rehman, H., Lin, C. Y., & Mushtaq, Z. (2021). Effective K-Nearest Neighbor Algorithms Performance Analysis of Thyroid Disease. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A, 44(1), 77–87. https://doi.org/10.1080/02533839.2020.1831967
- Abdelmadjid, L., & Mimoun, M. (2021). Uncertain Decision-Making Requirements Formalizing with Complement Fuzzy UML Model. Procedia Computer Science, 198, 317–322. https://doi.org/10.1016/j.procs.2021.12.247
- Atallah, D. M., Badawy, M., El-Sayed, A., & Ghoneim, M. A. (2019). Predicting kidney transplantation outcome based on hybrid feature selection and KNN classifier. Multimedia Tools and Applications, 78(14), 20383–20407. https://doi.org/10.1007/s11042-019-7370-5
- Becker, F. G. (2015). Data Mining Concepts,Models and Techniques.
- Bellino, G. M., Schiaffino, L., Battisti, M., Guerrero, J., & Rosado-Muñoz, A. (2019). Optimization of the KNN supervised classification algorithm as a support tool for the implantation of deep brain stimulators in patients with Parkinson’S Disease. Entropy, 21(4). https://doi.org/10.3390/e21040346
- Hafizan, H., & Putri, A. N. (2020). Penerapan Metode Klasifikasi Decision Tree Pada Status Gizi Balita Di Kabupaten Simalungun. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), 1(2), 68–72.
- https://doi.org/10.30645/kesatria.v1i2.23
- Husna, L. N., & Izzah, N. (2021). Gambaran Status Gizi Pada Balita : Literature Review. Prosiding Seminar Nasional Kesehatan, 1, 385–392. https://doi.org/10.48144/prosiding.v1i.689
- Koncz, P., & Paralic, J. (2011). An approach to feature selection for sentiment analysis. INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings, 357–362. https://doi.org/10.1109/INES.2011.5954773
- Lonang, S., & Normawati, D. (2022). Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination. Jurnal Media Informatika Budidarma, 6(1), 49–56.
- Lowe, C., Kelly, M., Sarma, H., Richardson, A., Kurscheid, J. M., Laksono, B., … Gray, D. J. (2021). The double burden of malnutrition and dietary patterns in rural Central Java, Indonesia. The Lancet Regional Health - Western Pacific, 14. https://doi.org/10.1016/j.lanwpc.2021.100205
- Lubna Irkanisa, N., Cholissodin, I., & Abdurrachman Bachtiar, F. (2019). Klasifikasi Status Gizi pada Balita Menggunakan Metode Extreme Learning Machine dan Algoritme Genetika. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(4), 3640–3646.
- Melinda, V., & Zainil, M. (2020). Penerapan model project based learning untuk meningkatkan kemampuan komunikasi matematis siswa sekolah dasar (studi literatur). Jurnal Pendidikan Tambusai, 4(2), 1526–1539.
- Mushtaq, Z., Yaqub, A., Sani, S., & Khalid, A. (2020). Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A, 43(1), 80–92. https://doi.org/10.1080/02533839.2019.1676658
- Sanjaya, R., & Fitriyani, F. (2019). Prediksi Bedah Toraks Menggunakan Seleksi Fitur Forward Selection dan K-Nearest Neighbor. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(3), 316. https://doi.org/10.26418/jp.v5i3.35324
- Syarif, M., & Nugraha, W. (2020). Pemodelan Diagram UML Sistem Pembayaran Tunai Pada Transaksi E-Commerce. Jurnal Teknik Informatika Kaputama (JTIK), 4(1), 70 halaman. Retrieved from http://jurnal.kaputama.ac.id/index.php/JTIK/article/view/240
- Utami, H. N., & Mubasyiroh, R. (2019). Masalah Gizi Balita Dan Hubungannya Dengan Indeks Pembangunan Kesehatan Masyarakat (Nutritional Problems Among Underfive Children and It’S Relationship With Public Health Development Index). Jurnal Penelitian Gizi Dan Makanan, 42(1), 10.
- Widyatmoko, W., & Pamungkas, N. (2022). Pemodelan Unified Modeling Language pada Sistem Aplikasi Pariwisata (SiAP). Jurnal Bumigora Information Technology (BITe), 4(1), 73–84. https://doi.org/10.30812/bite.v4i1.1871
- Yudhana, A., Muslim, A., Wati, D. E., Puspitasari, I., Azhari, A., & Mardhia, M. M. (2020). Human emotion recognition based on EEG signal using fast fourier transform and K-Nearest neighbor. Advances in Science, Technology and Engineering Systems, 5(6), 1082–1088. https://doi.org/10.25046/aj0506131
- Yudhana, A., Sunardi, S., & Hartanta, A. J. S. (2020). Algoritma K-Nn Dengan Euclidean Distance Untuk Prediksi Hasil Penggergajian Kayu Sengon. Transmisi, 22(4), 123–129. https://doi.org/10.14710/transmisi.22.4.123-129
References
Abbad Ur Rehman, H., Lin, C. Y., & Mushtaq, Z. (2021). Effective K-Nearest Neighbor Algorithms Performance Analysis of Thyroid Disease. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A, 44(1), 77–87. https://doi.org/10.1080/02533839.2020.1831967
Abdelmadjid, L., & Mimoun, M. (2021). Uncertain Decision-Making Requirements Formalizing with Complement Fuzzy UML Model. Procedia Computer Science, 198, 317–322. https://doi.org/10.1016/j.procs.2021.12.247
Atallah, D. M., Badawy, M., El-Sayed, A., & Ghoneim, M. A. (2019). Predicting kidney transplantation outcome based on hybrid feature selection and KNN classifier. Multimedia Tools and Applications, 78(14), 20383–20407. https://doi.org/10.1007/s11042-019-7370-5
Becker, F. G. (2015). Data Mining Concepts,Models and Techniques.
Bellino, G. M., Schiaffino, L., Battisti, M., Guerrero, J., & Rosado-Muñoz, A. (2019). Optimization of the KNN supervised classification algorithm as a support tool for the implantation of deep brain stimulators in patients with Parkinson’S Disease. Entropy, 21(4). https://doi.org/10.3390/e21040346
Hafizan, H., & Putri, A. N. (2020). Penerapan Metode Klasifikasi Decision Tree Pada Status Gizi Balita Di Kabupaten Simalungun. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), 1(2), 68–72.
https://doi.org/10.30645/kesatria.v1i2.23
Husna, L. N., & Izzah, N. (2021). Gambaran Status Gizi Pada Balita : Literature Review. Prosiding Seminar Nasional Kesehatan, 1, 385–392. https://doi.org/10.48144/prosiding.v1i.689
Koncz, P., & Paralic, J. (2011). An approach to feature selection for sentiment analysis. INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings, 357–362. https://doi.org/10.1109/INES.2011.5954773
Lonang, S., & Normawati, D. (2022). Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination. Jurnal Media Informatika Budidarma, 6(1), 49–56.
Lowe, C., Kelly, M., Sarma, H., Richardson, A., Kurscheid, J. M., Laksono, B., … Gray, D. J. (2021). The double burden of malnutrition and dietary patterns in rural Central Java, Indonesia. The Lancet Regional Health - Western Pacific, 14. https://doi.org/10.1016/j.lanwpc.2021.100205
Lubna Irkanisa, N., Cholissodin, I., & Abdurrachman Bachtiar, F. (2019). Klasifikasi Status Gizi pada Balita Menggunakan Metode Extreme Learning Machine dan Algoritme Genetika. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(4), 3640–3646.
Melinda, V., & Zainil, M. (2020). Penerapan model project based learning untuk meningkatkan kemampuan komunikasi matematis siswa sekolah dasar (studi literatur). Jurnal Pendidikan Tambusai, 4(2), 1526–1539.
Mushtaq, Z., Yaqub, A., Sani, S., & Khalid, A. (2020). Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A, 43(1), 80–92. https://doi.org/10.1080/02533839.2019.1676658
Sanjaya, R., & Fitriyani, F. (2019). Prediksi Bedah Toraks Menggunakan Seleksi Fitur Forward Selection dan K-Nearest Neighbor. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(3), 316. https://doi.org/10.26418/jp.v5i3.35324
Syarif, M., & Nugraha, W. (2020). Pemodelan Diagram UML Sistem Pembayaran Tunai Pada Transaksi E-Commerce. Jurnal Teknik Informatika Kaputama (JTIK), 4(1), 70 halaman. Retrieved from http://jurnal.kaputama.ac.id/index.php/JTIK/article/view/240
Utami, H. N., & Mubasyiroh, R. (2019). Masalah Gizi Balita Dan Hubungannya Dengan Indeks Pembangunan Kesehatan Masyarakat (Nutritional Problems Among Underfive Children and It’S Relationship With Public Health Development Index). Jurnal Penelitian Gizi Dan Makanan, 42(1), 10.
Widyatmoko, W., & Pamungkas, N. (2022). Pemodelan Unified Modeling Language pada Sistem Aplikasi Pariwisata (SiAP). Jurnal Bumigora Information Technology (BITe), 4(1), 73–84. https://doi.org/10.30812/bite.v4i1.1871
Yudhana, A., Muslim, A., Wati, D. E., Puspitasari, I., Azhari, A., & Mardhia, M. M. (2020). Human emotion recognition based on EEG signal using fast fourier transform and K-Nearest neighbor. Advances in Science, Technology and Engineering Systems, 5(6), 1082–1088. https://doi.org/10.25046/aj0506131
Yudhana, A., Sunardi, S., & Hartanta, A. J. S. (2020). Algoritma K-Nn Dengan Euclidean Distance Untuk Prediksi Hasil Penggergajian Kayu Sengon. Transmisi, 22(4), 123–129. https://doi.org/10.14710/transmisi.22.4.123-129