Chatbot Pemilihan Makanan dan Minuman Berdasarkan Kalori menggunakan Natural Language Processing

Authors

  • Nur cholis Anggoro Universitas Mercu Buana Yogyakarta Author
  • Mutaqin Akbar Universitas Mercu Buana Yogyakarta Author

Keywords:

Artificial neural network, Bag of words, Healthy food, Natural language processing, TF-IDF

Abstract

Chatbot is a computer program based on Artificial Intelligence (AI), or often called virtual robots that can simulate conversations like humans. This technology is also known as a digital assistant that can understand and process user requests, provide interactions, and provide relevant answers. Chatbot has many benefits, one of which can help determine food selection. In order for the chatbot to be able to understand the meaning of an input, it is necessary to have a training model as a reference. Models such as the Bag of words and the term frequency-inverse document frequency (TF-IDF) are examples of models that are commonly used. This research aims to implementing natural language processing on chatbots, determine the differences in the Bag of Words model and TF-IDF in terms of accuracy, time needed to train the model, and loss, and determine the calculation of calories using the basal metabolic rate method. In this research, a comparison will be made between the Bag of words and TF-IDF models for chatbots that will recommend food. Comparisons are made by looking at the model's performance when differentiating and how the model understands the meaning of a sentence. The results showed that the TF-IDF model has better performance than Bag of word with training loss of 0.00966 in 200 epochs and an average testing accuracy of 96.6%.

References

E. V. Osilla, A. O. Safadi, and S. Sharma, “Calories,” in StatPearls, Treasure Island (FL): StatPearls Publishing, 2023. Accessed: Sep. 17, 2023. [Online]. Available: http://www.ncbi.nlm.nih.gov/books/NBK499909/

P. Banerjee, V. V. R. Mendu, D. Korrapati, and S. M. Gavaravarapu, “Calorie counting smart phone apps: Effectiveness in nutritional awareness, lifestyle modification and weight management among young Indian adults,” Health Informatics J., vol. 26, no. 2, pp. 816–828, Jun. 2020, doi: 10.1177/1460458219852531.

N. Haristiani, “Artificial Intelligence (AI) Chatbot as Language Learning Medium: An inquiry,” J. Phys. Conf. Ser., vol. 1387, n

T. Lalwani, S. Bhalotia, A. Pal, V. Rathod, and S. Bisen, “Implementation of a Chatbot System using AI and NLP,” SSRN Electron. J., 2018, doi: 10.2139/ssrn.3531782.

R. Khoirunisa, E. Apriliyanto, A. S. Sandi, and K. Kusrini, “Penggunaan Natural Language Processing Pada Chatbot Untuk Media Informasi Pertanian,” Indones. J. Appl. Inform., vol. 4, no. 2, p. 55, Aug. 2020, doi: 10.20961/ijai.v4i2.38688.

M. Adam, M. Wessel, and A. Benlian, “AI-based chatbots in customer service and their effects on user compliance,” Electron. Mark., vol. 31, no. 2, pp. 427–445, Jun. 2021, doi: 10.1007/s12525-020-00414-7.

University of Goettingen et al., “Chatbots at Digital Workplaces –A Grounded-Theory Approach for Surveying Application Areas and Objectives,” Pac. Asia J. Assoc. Inf. Syst., vol. 12, pp. 63–103, Jun. 2020, doi: 10.17705/1pais.12203.

M. Jain, P. Kumar, R. Kota, and S. N. Patel, “Evaluating and Informing the Design of Chatbots,” in Proceedings of the 2018 Designing Interactive Systems Conference, Hong Kong China: ACM, Jun. 2018, pp. 895–906. doi: 10.1145/3196709.3196735.

A. B. Kocaballi, L. Laranjo, and E. Coiera, “Understanding and Measuring User Experience in Conversational Interfaces,” Interact. Comput., vol. 31, no. 2, pp. 192–207, Mar. 2019, doi: 10.1093/iwc/iwz015.

J. Seering, M. Luria, C. Ye, G. Kaufman, and J. Hammer, “It Takes a Village: Integrating an Adaptive Chatbot into an Online Gaming Community,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu HI USA: ACM, Apr. 2020, pp. 1–13. doi: 10.1145/3313831.3376708.

J. Casas, E. Mugellini, and O. A. Khaled, “Food Diary Coaching Chatbot,” in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore Singapore: ACM, Oct. 2018, pp. 1676–1680. doi: 10.1145/3267305.3274191.

P. K. Prasetyo, P. Achananuparp, and E.-P. Lim, “Foodbot: A Goal-Oriented Just-in-Time Healthy Eating Interventions Chatbot,” in Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare, May 2020, pp. 436–439. doi: 10.1145/3421937.3421960.

D. P. Utama, P. Sudarmaningtyas, and A. D. Churniawan, “RANCANG BANGUN APLIKASI PENJUALAN MAKANAN SEHAT BEDASARKAN PERHITUNGAN KALORI MENGGUNAKAN BMR PADA RUMAH SAKIT ISLAM JEMURSARI,” JSIKA, vol. 9, no. 3, pp. 1–9, 2019.

Zhixiang, Xu, M. Chen, K. Q. Weinberger, and F. Sha, “An alternative text representation to TF-IDF and Bag-of-Words.” arXiv, Jan. 28, 2013. Accessed: Mar. 29, 2023. [Online]. Available: http://arxiv.org/abs/1301.6770

S. Oetoro, E. Parengkuan, and J. Parengkuan, Smart Eating -1000 Jurus Makan Pintar dan Hidup Bugar. Gramedia Pustaka Utama, 2013.

E. Adamopoulou and L. Moussiades, “An Overview of Chatbot Technology,” in Artificial Intelligence Applications and Innovations, vol. 584, I. Maglogiannis, L. Iliadis, and E. Pimenidis, Eds., in IFIP Advances in Information and Communication Technology, vol. 584. , Cham: Springer International Publishing, 2020, pp. 373–383. doi: 10.1007/978-3-030-49186-4_31.

A. A. Heidari, H. Faris, S. Mirjalili, I. Aljarah, and M. Mafarja, “Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks,” in Nature-Inspired Optimizers, vol. 811, S. Mirjalili, J. Song Dong, and A. Lewis, Eds., in Studies in Computational Intelligence, vol. 811. , Cham: Springer International Publishing, 2020, pp. 23–46. doi: 10.1007/978-3-030-12127-3_3.

I. Firmansyah and B. H. Hayadi, “KOMPARASI FUNGSI AKTIVASI RELU DAN TANH PADA MULTILAYER PERCEPTRON,” J. Inform. Dan Komput., vol. 6, no. 2, pp. 200–206, Sep. 2022.

M. Akbar, “Traffic sign recognition using convolutional neural networks,” J. Teknol. Dan Sist. Komput., vol. 9, no. 2, pp. 120–125, Apr. 2021, doi: 10.14710/jtsiskom.2021.13959.

R. Umar, I. Riadi, and P. Purwono, “Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial dengan Metode Stochastic Gradient Descent,” JOINTECS J. Inf. Technol. Comput. Sci., vol. 5, no. 2, p. 55, May 2020, doi: 10.31328/jointecs.v5i2.1324.

X. Ying, “An Overview of Overfitting and its Solutions,” J. Phys. Conf. Ser., vol. 1168, p. 022022, Feb. 2019, doi: 10.1088/1742-6596/1168/2/022022.

A. Pinto, H. Gonçalo Oliveira, and A. Oliveira Alves, “Comparing the Performance of Different NLP Toolkits in Formal and Social Media Text,” p. 16 pages, 2016, doi: 10.4230/OASICS.SLATE.2016.3.

N. Indurkhya and F. J. Damerau, Handbook of natural language processing. Boca Raton, FL: Chapman & Hall/CRC, 2010.

W. T. H. Putri and P. Hendrowati, “PENGGALIAN TEKS DENGAN MODEL BAG OF WORDS TERHADAP DATA TWITTER,” J. Muara Sains Teknol. Kedokt. Dan Ilmu Kesehat., vol. 2, no. 1, pp. 129–138, Apr. 2018, doi: https://doi.org/10.24912/jmstkik.v2i1.1560.

St. Luke’s Medical Center, Quezon City, Philippines, S. C. Luy, and O. A. Dampil, “Comparison of the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry for Measurement of Basal Metabolic Rate among Adult Obese Filipino Patients with Prediabetes or Type 2 Diabetes Mellitus,” J. ASEAN Fed. Endocr. Soc., vol. 33, no. 2, pp. 152–159, Nov. 2018, doi: 10.15605/jafes.033.02.07

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Published

2023-11-01

How to Cite

Chatbot Pemilihan Makanan dan Minuman Berdasarkan Kalori menggunakan Natural Language Processing. (2023). Informatics and Artificial Intelligence Journal, 1(1), 29-38. https://jurnal.forai.or.id/index.php/forai/article/view/2