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All Journal JURNAL PENDIDIKAN TAMBUSAI Jurnal Teknologi Informasi dan Pendidikan Community Empowerment Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Tsaqofah: Jurnal Penelitian Guru Indonesia Journal of Artificial Intelligence and Digital Business JPNM : Jurnal Pustaka Nusantara Multidisiplin Jurnal Teknik Komputer dan Informatika (JTeKI) Journal of Hypermedia & Technology-Enhanced Learning Abdi Masyarakat UIKA Journal of Authentic Research Cermat : Jurnal Cendekiawan dan Riset Multidisiplin Akademik Terintegrasi Educational Journal
Asmara, Delvi
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Journal of Authentic Research

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Rancang Bangun Aplikasi Reservasi Wisata Air Terjun Lubuk Hitam Bungus dengan Penerapan Algoritma Neural Networks dan Fitur Barcode Digital Mahendra, Jimmy; Hendriyani, Yeka; Asmara, Delvi; Fatmi, Yulia
Journal of Authentic Research Vol. 5 No. 2 (2026): May
Publisher : LITPAM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/2ap08p90

Abstract

Penelitian ini bertujuan untuk merancang dan membangun aplikasi reservasi wisata pada objek wisata Air Terjun Lubuk Hitam Bungus berbasis Android dengan memanfaatkan teknologi WebView sebagai solusi atas masih digunakannya sistem reservasi manual yang menyebabkan antrean panjang, kesalahan pencatatan data, dan keterbatasan pengelolaan informasi pengunjung. Aplikasi yang dikembangkan mengintegrasikan algoritma Neural Network sebagai modul analitik untuk memprediksi jumlah kunjungan wisatawan berdasarkan data historis serta fitur barcode digital sebagai media validasi tiket elektronik. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan model pengembangan Waterfall yang meliputi analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Data prediksi menggunakan 36 data historis kunjungan periode Januari 2022–Desember 2024 yang diproses melalui tahap pembersihan data, normalisasi, pelatihan model, dan pengujian. Hasil penelitian menunjukkan bahwa aplikasi berhasil menyediakan fitur registrasi pengguna, pemesanan tiket daring, pembayaran, penerbitan e-ticket berbasis QR Code, dashboard admin, serta modul prediksi kunjungan. Pengujian Black Box Testing pada 9 fitur utama menunjukkan seluruh fungsi berjalan sesuai skenario uji dengan tingkat keberhasilan 100%. Evaluasi model prediksi menghasilkan Mean Absolute Error (MAE) sebesar 12,4 pengunjung, Mean Squared Error (MSE) sebesar 245,6, dan akurasi prediksi sebesar 87,3%. Uji usability terhadap 10 responden memperoleh nilai rata-rata 4,4 dari skala 5 yang menunjukkan aplikasi mudah digunakan dan diterima dengan baik. Implementasi sistem ini mengindikasikan bahwa digitalisasi layanan wisata berpotensi meningkatkan efisiensi reservasi, mempercepat validasi tiket, serta mendukung pengelolaan data kunjungan secara lebih terstruktur. Dengan demikian, aplikasi yang dikembangkan dapat menjadi alternatif solusi dalam mendukung transformasi digital sektor pariwisata menuju konsep smart tourism yang lebih efektif, adaptif, dan berbasis data. This study aims to design and develop a tourism reservation application for the Lubuk Hitam Bungus Waterfall tourist attraction based on Android by utilizing WebView technology as a solution to the continued use of manual reservation systems that cause long queues, data recording errors, and limitations in managing visitor information. The developed application integrates a Neural Network algorithm as an analytical module to predict tourist visit numbers based on historical data, as well as a digital barcode feature as an electronic ticket validation medium. The research method used was Research and Development (R&D) with the Waterfall development model, which includes requirements analysis, system design, implementation, testing, and maintenance. Prediction data used 36 historical visitation records from January 2022 to December 2024, processed through data cleaning, normalization, model training, and testing stages. The results show that the application successfully provides user registration, online ticket booking, payment, QR Code-based e-ticket issuance, admin dashboard, and visit prediction modules. Black Box Testing on 9 main features showed that all functions operated according to test scenarios with a 100% success rate. Evaluation of the prediction model produced a Mean Absolute Error (MAE) of 12.4 visitors, a Mean Squared Error (MSE) of 245.6, and a prediction accuracy of 87.3%. Usability testing involving 10 respondents obtained an average score of 4.4 out of 5, indicating that the application is easy to use and well accepted by users. The implementation of this system indicates that the digitalization of tourism services has the potential to improve reservation efficiency, accelerate ticket validation, and support more structured visitor data management. Therefore, the developed application can serve as an alternative solution to support the digital transformation of the tourism sector toward a smarter, more adaptive, and data-driven smart tourism concept.
Perancangan Sistem Rekomendasi Pemilihan Mobil Bekas Kategori MPV dan City Car Menggunakan K-Nearest Neighbor (K-NN) dengan Hybrid Filtering Amin, Rudi Kurnia Al; Hendriyani, Yeka; Asmara, Delvi; Fatmi, Yulia
Journal of Authentic Research Vol. 5 No. 2 (2026): May
Publisher : LITPAM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/qrfc5h79

Abstract

Perkembangan kebutuhan transportasi pribadi di Indonesia menunjukkan tren yang terus meningkat, terutama di wilayah perkotaan. Di Kota Padang, meningkatnya kebutuhan tersebut diikuti oleh semakin banyaknya pilihan mobil bekas kategori MPV dan City Car, namun banyaknya alternatif pilihan ini justru menimbulkan information overload bagi calon pembeli. Penelitian ini bertujuan merancang dan mengimplementasikan sistem rekomendasi berbasis website menggunakan metode K-Nearest Neighbor (K-NN) dengan pendekatan Hybrid Filtering. Metode pengembangan sistem yang diterapkan adalah model Waterfall. Pendekatan Hybrid Filtering menggabungkan Content-Based Filtering dan Collaborative Filtering, sementara algoritma K-NN digunakan untuk menghitung tingkat kemiripan antar data kendaraan berdasarkan atribut seperti harga, tahun produksi, dan spesifikasi teknis menggunakan Euclidean Distance. Sistem juga menerapkan mekanisme pre-filtering berdasarkan kategori kendaraan untuk mengurangi kompleksitas perhitungan KNN agar sistem tetap responsif. Hasil pengujian fungsional menggunakan Black Box Testing menunjukkan seluruh fitur sistem berjalan dengan baik. Pengujian akurasi menghasilkan nilai Precision rata-rata sebesar 0,78, Recall 0,83, dan Mean Absolute Error (MAE) sebesar 0,15. Simpulannya, sistem ini mampu memberikan rekomendasi yang akurat, relevan, serta membantu mempercepat proses pengambilan keputusan calon pembeli mobil bekas. The development of private transportation needs in Indonesia shows a continuously increasing trend, especially in urban areas. In Padang City, this growth is followed by various used car options in the MPV and City Car categories, yet these alternatives cause information overload for potential buyers. This research aims to design and implement a web-based recommendation system using the K-Nearest Neighbor (K-NN) method with a Hybrid Filtering approach. The system development method applied is the Waterfall model. The Hybrid Filtering approach combines Content-Based Filtering and Collaborative Filtering, while the K-NN algorithm calculates similarity levels between vehicle data based on attributes such as price, production year, and technical specifications using Euclidean Distance. The system also applies a pre-filtering mechanism based on vehicle categories to reduce K-NN computational complexity and maintain responsiveness. Functional testing results using Black Box Testing indicate that all system features operate correctly. Accuracy testing produced an average Precision value of 0.78, a Recall of 0.83, and a Mean Absolute Error (MAE) of 0.15. In conclusion, this system provides accurate and relevant recommendations, helping potential used car buyers accelerate their decision-making process.
Co-Authors Adam, Al Agariadne Dwinggo Samala Ahmaddul Hadi, Ahmaddul Amin, Rudi Kurnia Al Andini, Vina Fitri Arizqa, Tifani Putri Budayawan, Khairi Chintia Ningsih, Nur Dalifa, Mikola Dedy Irfan Dony Novaliendry Edidas , Edidas Elfi Tasrif Erdisna, Erdisna Fatmi, Yulia Geovanne Farell Gusra, Laura Febriani Hasibuan, Kholid Huda, Yasdinul Ika Parma Dewi Irma Delianti, Vera Irnanda, Muhammad Fakhri Kurniadi, Denny Mahendra, Jimmy Mahesi Agni Zaus Marta, Rizkayeni Mhlanga, David Miftahul Jannah Ningsih, Tiara Heliyan Nurainah, Titik Aipah Nurlatipah, Nurlatipah Nuzul Hidayat, Nuzul Putra, Rido Rahmayami, Maurah Tria Ramadhani, Wahyuni Resmi Darni Resmidarni, Resmidarni Saputra, Noris Dwi Saputri, Sindi Sefitra, Arif Bahy Sovia, Sovia Sulistia, Bima Syafitra, Kurniawan Syukhri, Syukhri Syukri, Raihan Tsoy, Dana Vera Irma Delianti Wahyuni, Titi Sri Yeka Hendriyani