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All Journal JAIS (Journal of Applied Intelligent System) Progresif: Jurnal Ilmiah Komputer Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pengabdian Mandiri Proceeding International Pelita Bangsa
Edy Widodo
Universitas Pelita Bangsa
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Comparison of K-Means and K-Medoid Algorithms in Classifying Village Status (Case Study: Gorontalo Province) Aswan Supriyadi Sunge; Nanang Tedi Kurniadi; Edy Widodo
Proceeding International Pelita Bangsa Vol. 1 No. 01 (2023): September 2023
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/pipb.v1i01.2675

Abstract

In the national development process, the village occupies a very important position. This is because it is the smallest government structure and has direct contact with the community. Seeing the importance of its role in national development, one of which is Gorontalo Province, based on directions from the central government, is trying to implement the Village Fund Allocation (ADD) policy for all villages in Gorontalo Province. In distributing the allocation of funds, it is necessary to map the status of the Village to find out the amount that must be given. This test uses the average execution time and the Davies Bouldin Index (DBI). After testing it is known that the K-Medoid Algorithm has a better DBI value than the K-Means Algorithm with the DBI value of the K-Medoid Algorithm being 0.050. On the other hand, the K-Means Algorithm has a better average execution time than the K-Medoid Algorithm, where the average execution time is 1 second.
Analisis Sentimen Ulasan Hotel Zuri Express Lippo Cikarang Menggunakan Algoritma Naive Bayes Dion Marcelino; Edy Widodo; Dodit Ardiatma
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3671

Abstract

The hospitality industry relies heavily on customer satisfaction, which is often reflected through reviews on online travel applications. Hotel Zuri Express Lippo Cikarang requires an automated sentiment monitoring system to enhance its service quality. This study aims to implement the Naive Bayes Classifier algorithm to classify customer review sentiments into positive and negative categories. The research dataset consists of 1,637 reviews obtained via web scraping from the google maps. The research methodology includes text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, and N-Gram (Bigram) feature modeling. The experimental results demonstrate that the model achieves an accuracy rate of 91.16%, with a precision of 0.91 and a recall of 0.99 for the majority class. Keyword analysis using Word clouds identified cleanliness and staff service as the primary factors driving customer satisfaction. Despite challenges in detecting negative sentiments within an imbalanced dataset, the Naive Bayes algorithm proved to be reliable and efficient for automated sentiment analysis, providing a robust tool to support fast and accurate management decision-making.Keywords: Sentiment Analysis; Customer Reviews; Hotel Zuri Express; Naive Bayes; Text mining.AbstrakIndustri perhotelan sangat bergantung pada kepuasan pelanggan yang tercermin melalui ulasan aplikasi travel online. Hotel Zuri Express Lippo Cikarang memerlukan sistem otomatis untuk memantau sentimen guna meningkatkan kualitas layanan. Penelitian ini bertujuan mengimplementasikan algoritma Naive Bayes Classifier dalam mengklasifikasikan sentimen ulasan pelanggan ke kategori positif dan negatif. Data penelitian mencakup 1.637 ulasan hasil web scraping dari google maps. Tahapan penelitian meliputi preprocessing teks, pembobotan kata Term Frequency-Inverse Document Frequency (TF-IDF), serta pemodelan fitur N-Gram (Bigram). Hasil pengujian menunjukkan model menghasilkan tingkat akurasi sebesar 91,16%, dengan nilai presisi 0,91 dan recall 0,99 pada kelas mayoritas. Analisis kata kunci melalui Word cloud mengidentifikasi aspek kebersihan dan pelayanan staf sebagai faktor utama kepuasan pelanggan. Meskipun terdapat tantangan dalam mendeteksi sentimen negatif pada dataset tidak seimbang, algoritma Naive Bayes terbukti handal dan efisien dalam melakukan analisis sentimen otomatis untuk mendukung pengambilan keputusan manajemen hotel secara cepat dan akurat.Kata kunci: Analisis Sentimen; Ulasan Pelanggan; Hotel Zuri Express; Naive Bayes; Text mining.
Co-Authors Agus Sulistiawan Ahmad Jaelani Ahmad Turmudi Zy al fiyan Ananto Tri Sasongko Antika Zahrotul Kamalia Asep Arwan Sulaeman Aswan S Sunge Aswan Supriyadi Sunge Dion Marcelino Dodit Ardiatma Gatot Tri Pranoto Muhamad Fatchan Nanang Tedi Kurniadi Nawangsih, Ismasari Riani Riani Sifa Fauziah