Hyperparamaters Fine Tuning for Bidirectional Long Short Term Memory on Food Delivery
Keywords:
Long Short-Term Memory, Fine-tuning, Food Delivery, Hyperparameters, Sentiment Analysis
Abstract
Food delivery is growing rapidly in Indonesia. Every food delivery order holds big promotions to attract users’ attention, so it has advantages and disadvantages. However, users only focus on evaluating drivers and restaurants, so the company does not get feedback on its services. This research aimed to understand user sentiment and maximize model accuracy with hyperparameters and fine-tuning. Sentiment analysis can be used to determine user sentiment based on reviews, and the results of this analysis can provide suggestions for companies. The bidirectional long short-term memory method was used for sentiment analysis to understand a word’s meaning better. The Bidirectional Short-Term Memory model andWord2Vec extraction features were proven to be better than several other extraction models
and features. The dataset was balanced, and the hyperparameters in the model and optimization could also improve accuracy. So, the Gofood and Shopeefood research results had an accuracy of 98.1%, and Grabfood’s was 97.4%.
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Hyperparamaters
Kamsia Boba Mataram,” Bakti Sekawan : Jurnal Pengabdian Masyarakat, vol. 2, no. 1, pp. 42–47, 2022.
[2] A. Rahman, I. #1, H. Sulistiani, B. Miftaq, H. #3, A. Nurkholis, and S. #5, “Analisis Perbandingan Algoritma LSTM dan Naive
Bayes untuk Analisis Sentimen,” JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 8, no. 2, pp. 299–303, 2022.
[3] K. S. Nugroho, I. Akbar, A. N. Suksmawati, and I. Istiadi, “Deteksi Depresi dan Kecemasan Pengguna Twitter,” The 4th
Conference on Innovation and Application of Science and Technology (CIASTECH 2021), no. Ciastech, pp. 287–296, 2021.
[4] M. Afif, A. Fawwaz, K. N. Ramadhani, and F. Sthevanie, “Klasifikasi Ras pada Kucing menggunakan Algoritma Convolutional
Neural Network(CNN),” Jurnal Tugas Akhir Fakultas Informatika, vol. 8, no. 1, pp. 715–730, 2020.
[5] D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan
Grab Indonesia,” Jurnal Manajemen Informatika (JAMIKA), vol. 12, no. 2, pp. 89–99, 2022.
[6] G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, “Sentiment analysis of comment texts based on BiLSTM,” IEEE Access, vol. 7, pp.
51 522–51 532, 2019.
[7] S. Wang, Y. Chen, H. Ming, H. Huang, L. Mi, and Z. Shi, “Improved Danmaku Emotion Analysis and Its Application Based on
Bi-LSTM Model,” IEEE Access, vol. 8, pp. 114 123–114 134, 2020.
[8] H. Elfaik and E. H. Nfaoui, “Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text,” Journal
of Intelligent Systems, vol. 30, no. 1, pp. 395–412, 2021.
[9] F. Shannaq, B. Hammo, H. Faris, and P. A. Castillo-Valdivieso, “Offensive Language Detection in Arabic Social Networks
Using Evolutionary-Based Classifiers Learned From Fine-Tuned Embeddings,” IEEE Access, vol. 10, no. June, pp. 75 018–
75 039, 2022.
[10] Y. Pei, S. Chen, Z. Ke, W. Silamu, and Q. Guo, “AB-LaBSE: Uyghur Sentiment Analysis via the Pre-Training Model with
BiLSTM,” Applied Sciences (Switzerland), vol. 12, no. 3, 2022.
[11] Y. Huang, Y. Jiang, T. Hasan, Q. Jiang, and C. Li, “Topic BiLSTM model for sentiment classification,” ACM International
Conference Proceeding Series, vol. Part F1376, pp. 143–147, 2018.
[12] C. Kaope and Y. Pristyanto, “The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance,”
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 2, pp. 227–238, 2023.
[13] A. Nurdin, B. Anggo Seno Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan
Fasttext Pada Klasifikasi Teks,” Jurnal Tekno Kompak, vol. 14, no. 2, p. 74, 2020.
[14] Y. Karyadi, “Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU,” JATISI (Jurnal Teknik Informatika
dan Sistem Informasi), vol. 9, no. 1, pp. 671–684, 2022.
[15] F. Hidayat, “Implementasi Klasifikasi Gambar Untuk Industri Pakaian Menggunakan Image Search Engine Berbasis Website,”
vol. 10, no. 1, pp. 356–362, 2023.
[16] A. S. Talita and A.Wiguna, “Implementasi Algoritma Long Short-Term Memory (LSTM) Untuk Mendeteksi Ujaran Kebencian
(Hate Speech) Pada Kasus Pilpres 2019,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19,
no. 1, pp. 37–44, 2019.
[17] D. Aisyah, T. W. Purboyo, and M. Kallista, “Prediksi Penderita Tuberkulosis Dengan Algoritma Long Short-Term Memory
Prediction of Tuberculosis Using Long Short-Term Memory (Lstm) Algorithm,” vol. 10, no. 1, pp. 742–749, 2023.
[18] D. Junggu and M. Pasaribu, “PENINGKATAN AKURASI KLASIFIKASI SENTIMEN ULASAN MAKANAN AMAZON
DENGAN BIDIRECTIONAL LSTM DAN,” pp. 9–20.
[19] F. Hafifah, S. Rahman, and S. Asih, “Klasifikasi Jenis Kendaraan Pada Jalan Raya Menggunakan Metode Convolutional Neural
Networks (CNN),” TIN: Terapan Informatika Nusantara, vol. 2, no. 5, pp. 292–301, 2021.
[20] E. I. Haksoro and A. Setiawan, “Pengenalan Jamur Yang Dapat Dikonsumsi Menggunakan Metode Transfer Learning Pada
Convolutional Neural Network,” Jurnal ELTIKOM, vol. 5, no. 2, pp. 81–91, 2021.
[21] M. I. Gunawan, D. Sugiarto, and I. Mardianto, “Peningkatan Kinerja Akurasi Prediksi Penyakit Diabetes Mellitus Menggunakan
Metode Grid Seacrh pada Algoritma Logistic Regression,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 6, no. 3, p.
280, 2020.
[22] I. G. T. Isa and B. Junedi, “Hyperparameter Tuning Epoch dalam Meningkatkan Akurasi Data Latih dan Data Validasi pada
Citra Pengendara,” Prosiding Sains Nasional dan Teknologi, vol. 12, no. 1, p. 231, 2022.
Hyperparamaters
Published
2023-11-08
How to Cite
Rahman, R., Hermanto, T., & Defriani, M. (2023). Hyperparamaters Fine Tuning for Bidirectional Long Short Term Memory on Food Delivery. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(1), 53-66. https://doi.org/https://doi.org/10.30812/matrik.v23i1.3084
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