Rute Wisata Lombok

Bagian 8

Daftar Pustaka

Dua bagian terpisah dengan sengaja — jangan digabung saat menyusun artikel.

A. Daftar Pustaka Proposal (39 sumber)

Persis seperti tercantum pada proposal yang diserahkan ke hibah. Nomornya tidak boleh diubah urutannya — laporan kemajuan/akhir mengacu ke nomor yang sama demi konsistensi dengan dokumen yang sudah disetujui reviewer.

  1. 1. Achmad F, Prambudia Y, Reumanti AA. Improving Tourism Industry Performance through Support System Facilities and Stakeholders: The Role of Environmental Dynamism. Sustainability. 2023;15(5):4103.
  2. 2. Purwono R, Esquivas MA, Sugiharti L, Rojas O. Tourism Destination Performance and Competitiveness: The Impact on Revenues, Jobs, the Economy, and Growth. J Tour Serv. 2024;15(28).
  3. 3. Badan Pusat Statistik. Jumlah Kunjungan Wisatawan Mancanegara per Bulan ke Indonesia Menurut Pintu Masuk, 2008 - sekarang (Kunjungan). Jakarta: BPS; 2025.
  4. 4. Wibawa GK, Lestari D, Koerniawaty FT. Social Media Content Calendar as a Tourism Marketing Strategy Design in Taro Tourism Village. Eduvest - J Univers Stud. 2025;5(11):13648-61.
  5. 5. Kementerian Pariwisata Republik Indonesia. Perkembangan Jumlah Devisa Sektor Pariwisata Triwulan III Tahun 2025. Jakarta: Kemenpar; 2025.
  6. 6. Dinas Pariwisata Provinsi Nusa Tenggara Barat. Jumlah Kunjungan Wisatawan Berdasarkan Jenis Wisatawan. NTB Satu Data; 2025.
  7. 7. Hammad R, Azwar M, Syarif MA. Optimizing Tourism Recommendations with a Hybrid Model: Bridging User Preferences and Behavioral Patterns. J RESTI (Rekayasa Sist dan Teknol Informasi). 2025;9(4).
  8. 8. Asosiasi Penyelenggara Jasa Internet Indonesia. Survei Penetrasi Internet dan Perilaku Penggunaan Internet 2025. Jakarta: APJII; 2025.
  9. 9. Khoshroo M, Soltani M. Digital transformation of tourism: towards a model of technology acceptance by tourists in the Industry 5.0. Eur J Innov Manag. 2025;28(5).
  10. 10. B CC, Somarajan P, Jadhav S, Kapoor A. Empowering Safety-Conscious Women Travelers: Examining the Benefits of Electronic Word of Mouth and Mobile Travel Assistant. Int J Interact Mob Technol. 2024;18(5).
  11. 11. Liu S, Becnkendorf P, Mair J. How Do Tourists Use Metaheuristics for Decision-Making Mediated by Smartphones in a Destination? J Travel Res. 2022;62(8).
  12. 12. Sia PYH, Saidin SS, Iskandar YHP. Systematic review of mobile travel apps and their smart features and challenges available to purchase. J Hosp Tour Insights. 2022;6(5).
  13. 13. Hu H, Li C. Smart tourism products and services design based on user experience under the background of big data. Soft Comput. 2023;27:12711-24.
  14. 14. Wang X, Mou N, Zhu S, Yang T, Zhang X, Zhang Y. How to perceive tourism destination image? A visual content analysis based on inbound tourists' photos. J Destin Mark Manag. 2024;33:100923.
  15. 15. Panarto FA, Phanghegar TM, Gunardi V, Cenggoro TW. Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks. Procedia Comput Sci. 2024;245:710-9.
  16. 16. Nguyen-Da T, Li YM, Peng CL, Cho MY, Nguyen-Thanh P. Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN-LSTM: Case Study Vietnam and Provinces. Sustainability. 2023;15(9):7179.
  17. 17. Wang C, Xue H, Wang S. High-Frequency Tourist Flow Forecasting with Gated Recurrent Units and Attention Mechanisms. SAGE Open. 2025;15(3).
  18. 18. Song N, Zhang Y. Exploring the relationship between tourism development and environmental pollution using an LSTM-based time series model. Front Environ Sci. 2025;13.
  19. 19. Yuan L. Application of Genetic Algorithm in Optimizing Path Selection in Tourism Route Planning. Int J Marit Eng. 2025;167(A2(S)).
  20. 20. Widayanti R, Chakim MHR, Lukita C, Rahardja U, Lutfiani N. Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering. J Appl Data Sci. 2023;4(3):289-302.
  21. 21. Aldayel M, Al-Nafjan A, Al-Nuwaiser WM, Alrehaili G, Alyahya G. Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations. Electronics. 2023;12(9):4047.
  22. 22. Parthasarathy G, Devi SS. Hybrid Recommendation System Based on Collaborative and Content-Based Filtering. Cybern Syst. 2023;54(4):432-53.
  23. 23. Geng R, Yi Z. A hybrid tourism recommendation framework based on optical image recognition and collaborative filtering. In: Second International Conference on Intelligent Transportation and Smart Cities (ICITSC 2025). Louyang: SPIE; 2025. p. 136821.
  24. 24. Hu YH, Tsai CF, Sun YC. A novel hotel recommender system incorporating review sentiment and contextual information. Int J Data Sci Anal. 2025;19:573-84.
  25. 25. Verma M, Parganiha V. Privacy-preserving context-aware recommendation system with federated neural collaborative filtering. Knowledge-Based Syst. 2026;333:114997.
  26. 26. Bolotbekova A, Hakli H, Beskirli A. Trip route optimization based on bus transit using genetic algorithm with different crossover techniques: a case study in Konya/Türkiye. Sci Rep. 2025;15:2491.
  27. 27. Solano-Barliza A, Arregocés-Julio I, Aarón-Gonzalvez M, Zamora-Musa R, De-La-Hoz-Franco E, Escorcia-Gutierrez J, et al. Recommender systems applied to the tourism industry: a literature review. Cogent Bus Manag. 2024;11(1).
  28. 28. Chen Z, Zhang P, Peng L. Application of a hybrid genetic algorithm based on the travelling salesman problem in rural tourism route planning. Int J Comput Sci Math. 2024;19(1):1-14.
  29. 29. Lee HJ, Kim YS, Lee WS, Choi IH, Lee CK. RNN-Based Sequence-Aware Recommenders for Tourist Attractions. CAAI Trans Intell Technol. 2025;10(4).
  30. 30. Awienoor BMG Al, Setiawan EB. Movie Recommendation System Based on Tweets Using Switching Hybrid Filtering with Recurrent Neural Network. Int J Intell Eng Syst. 2024;17(2).
  31. 31. Meena G, Mohbey KK, Indian A, Jangid K. Point of Interest Recommendation System Using Sentiment Analysis. J Inf Sci Theory Pract. 2024;12(2).
  32. 32. Jeribi F, Perumal U, Alhameed MH. Recommendation System for Sustainable Day and Night-Time Cultural Tourism Using the Mean Signed Error-Centric Recurrent Neural Network for Riyadh Historical Sites. Sustainability. 2024;16(13):5566.
  33. 33. Xiao X, Li C, Wang X, Zeng A. Personalized tourism recommendation model based on temporal multilayer sequential neural network. Sci Rep. 2025;15:382.
  34. 34. Zhang D. Automated Tourism Path Recommendation System Using Convolutional Neural Network based Bidirectional Long Short-Term Memory. In: 2024 Second International Conference on Data Science and Information System (ICDSIS). Hassan: IEEE; 2024.
  35. 35. Azhan FF, Setiawan EB. Tourism Destination Recommendation on Social Media X (Twitter) with Content-Based Filtering (CBF) and Gated Recurrent Unit (GRU) Approach. In: IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). Mataram: IEEE; 2024.
  36. 36. Karthik RV, Pandiyaraju V, Ganapathy S. A context and sequence-based recommendation framework using GRU networks. Artif Intell Rev. 2025;58:170.
  37. 37. Jiang Q, Han Y. Knowledge graph-driven personalized attractions recommendations with tourists' long- and short-term interest modeling. Expert Syst Appl. 2025;275:127094.
  38. 38. Hammad R, Irfan P, Mukti MTP. Geolocation data incorporation in Mapbox for comprehensive mapping of tourism areas on Lombok Island. Matrix J Manaj Teknol dan Inform. 2024;14(1):33-42.
  39. 39. Hammad R, Apriani, Irfan P. Konvergensi Personalisasi Pariwisata Berkelanjutan Melalui Kolaborasi Pendekatan Hybrid Filtering dengan Optimasi Rute Berbasis Algoritma Genetika: Studi Kasus Pulau Lombok. Sinta. 2025.

B. Referensi Teknis Tambahan (12 sumber)

Ditambahkan tim pengembang selama implementasi untuk algoritma yang benar-benar dipakai (NeuMF, ResNet, EfficientNet, LSTM, GRU, dst) tetapi tidak eksplisit disebut pada 39 sumber proposal. Bukan bagian resmi Daftar Pustaka proposal — verifikasi ulang setiap entri sebelum dipakai di draft artikel IJIES.

  1. T1 He X, Liao L, Zhang H, Nie L, Hu X, Chua TS. Neural Collaborative Filtering. Proceedings of the 26th International Conference on World Wide Web (WWW). 2017:173-82.
  2. T2 He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:770-8.
  3. T3 Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML). 2019:6105-14.
  4. T4 Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A Large-Scale Hierarchical Image Database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2009:248-55.
  5. T5 Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation. 1997;9(8):1735-80.
  6. T6 Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP. 2014:1724-34.
  7. T7 Holland JH. Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press; 1975.
  8. T8 Goldberg DE. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley; 1989.
  9. T9 Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information Processing & Management. 1988;24(5):513-23.
  10. T10 Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems. 2002;20(4):422-46.
  11. T11 Herlocker JL, Konstan JA, Terveen LG, Riedl JT. Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems. 2004;22(1):5-53.
  12. T12 Pearson K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine. 1901;2(11):559-72.