BIMA MUHAMMAD SITUMORANG, NPM 2209100027 (2026) ANALISIS SENTIMEN UNTUK EVALUASI KUALITAS PELAYANAN KEDAI KOPI SAA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Perkembangan industri kedai kopi yang pesat menyebabkan tingkat persaingan semakin ketat, sehingga kualitas pelayanan menjadi faktor kunci dalam menjaga keberlangsungan usaha. Kedai Kopi SAA menerima berbagai umpan balik dan ulasan dari pelanggan melalui platform digital, namun pengolahan data ulasan yang bersifat kualitatif dan tidak terstruktur secara manual dinilai kurang efektif serta subjektif. Penelitian ini bertujuan untuk mengimplementasikan analisis sentimen dalam mengklasifikasikan ulasan pelanggan Kedai Kopi SAA menggunakan algoritma Support Vector Machine (SVM) serta mengevaluasi kualitas pelayanan secara objektif berbasis metodologi Knowledge Discovery in Database (KDD). Data yang digunakan berjumlah 50 ulasan pelanggan yang diperoleh dari kuesioner dan media digital, yang kemudian dibagi menjadi data latih (80%) dan data uji (20%). Tahapan pengolahan data meliputi data selection, text preprocessing (data cleaning, case folding, tokenizing, stopword removal, dan stemming), ekstraksi fitur menggunakan pembobotan Term Frequency Inverse Document Frequency (TF-IDF), serta klasifikasi sentimen ke dalam kategori positif, netral, dan negatif yang dipetakan pada indikator kualitas pelayanan (Baik dan Kurang Baik). Hasil pengujian menunjukkan bahwa algoritma SVM mampu mengklasifikasikan sentimen pelanggan dan mengevaluasi kualitas pelayanan Kedai Kopi SAA dengan sangat baik. Evaluasi model menggunakan confusion matrix menghasilkan tingkat Accuracy, Precision, Recall, dan F1-Score sebesar 100% pada data pengujian. Hasil analisis sentimen ini memberikan gambaran yang objektif bagi pihak manajemen Kedai Kopi SAA mengenai aspek-aspek pelayanan yang perlu dipertahankan dan ditingkatkan. Kata Kunci: Analisis Sentimen, Kualitas Pelayanan, Kedai Kopi SAA, Support Vector Machine (SVM), TF-IDF, KDD ================================================================================== The rapid growth of the coffee shop industry has led to increasingly intense competition, making service quality a crucial factor in maintaining business sustainability. SAA Coffee Shop receives various feedback and customer reviews through digital platforms. However, processing qualitative and unstructured review data manually is considered ineffective and subjective. This study aims to implement sentiment analysis to classify customer reviews of SAA Coffee Shop using the Support Vector Machine (SVM) algorithm and objectively evaluate service quality based on the Knowledge Discovery in Database (KDD) methodology. The dataset consists of 50 customer reviews collected from questionnaires and digital platforms, divided into training data (80%) and testing data (20%). The data processing stages include data selection, text preprocessing (data cleaning, case folding, tokenizing, stopword removal, and stemming), feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF) weighting, and sentiment classification into positive, neutral, and negative categories mapped to service quality indicators (Good and Poor). The testing results demonstrate that the SVM algorithm successfully classifies customer sentiment and evaluates the service quality of SAA Coffee Shop with high performance. Model evaluation using a confusion matrix achieved an Accuracy, Precision, Recall, and F1-Score of 100% on the test data. The findings of this sentiment analysis provide objective insights for the management of SAA Coffee Shop regarding service aspects that need to be maintained and improved. Keywords: Sentiment Analysis, Service Quality, SAA Coffee Shop, Support Vector Machine (SVM), TF-IDF, KDD
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Analisis Sentimen, Kualitas Pelayanan, Kedai Kopi SAA, Support Vector Machine (SVM), TF-IDF, KDD ===============Sentiment Analysis, Service Quality, SAA Coffee Shop, Support Vector Machine (SVM), TF-IDF, KDD |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 21 Oct 2026 04:48 |
| Last Modified: | 21 Oct 2026 04:48 |
| URI: | http://repository.ulb.ac.id/id/eprint/2941 |
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