Pengembangan Aplikasi Sido Chatbot sebagai Aplikasi Pengenalan Objek Wisata Kediri Menggunakan Rule-Based Pattern Matching
Bibliographic record
Abstract
Pariwisata merupakan salah satu pendorong utama pertumbuhan ekonomi di Indonesia, dengan kontribusi yang signifikan terhadap lapangan kerja dan ekonomi kreatif. Provinsi Jawa Timur, khususnya Kabupaten Kediri, memiliki potensi besar sebagai destinasi wisata yang berdaya saing. Upaya transformasi digital dalam sektor pariwisata, termasuk pengembangan infrastruktur teknologi dan analisis data, menjadi langkah penting untuk meningkatkan daya tarik wisata. Salah satu solusi efektif dalam memberikan informasi wisata adalah melalui penerapan chatbot. Metodologi kuantitatif deskriptif diterapkan dengan menggunakan pendekatan rule-based pattern matching untuk pengembangan chatbot, serta pengujian keberhasilan mencapai persentase 83,33%, yang mengindikasikan sebagian besar fungsionalitas aplikasi berjalan dengan baik. Namun, masih terdapat area yang memerlukan perbaikan. Untuk meningkatkan performa, perlu diperluas cakupan data latih yang mencakup berbagai jenis percakapan, serta pengintegrasian kecerdasan buatan (AI) guna memperkaya pengetahuan chatbot. Evaluasi dan penyesuaian model chatbot secara berkala juga penting untuk meningkatkan kualitas dan responsivitasnya dalam memberikan informasi yang akurat kepada pengunjung wisata di Kediri.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".