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All Journal INTECOMS: Journal of Information Technology and Computer Science VISA: Journal of Vision and Ideas
Satria, Riyan
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Computer Science & IT Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences

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Analisis Performa Algoritma K-Means Clustering untuk Segmentasi Pasar di UMKM Afrizal, Muhammad; Saputra, Ilham; Satria, Riyan; Rahmaddeni, Rahmaddeni
VISA: Journal of Vision and Ideas Vol. 5 No. 2 (2025): Journal of Vision and Ideas (VISA)
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/visa.v5i2.6942

Abstract

This study aims to analyze the performance of the K-Means Clustering algorithm in market segmentation for Micro, Small, and Medium Enterprises (MSMEs). Using a quantitative approach, the data collected includes demographic information, purchasing behavior, and product preferences from respondents. The analysis process begins with data preprocessing, including normalization and outlier removal, before applying the K-Means algorithm to group customers into several segments. The performance evaluation of the algorithm is conducted using the Silhouette Score and Davies-Bouldin Index metrics. The analysis results indicate that the K-Means algorithm successfully identifies four distinct customer clusters, each with unique characteristics. The average Silhouette Score of 0.72 and a Davies-Bouldin Index of 0.45 suggest that the resulting clusters are well-defined and clearly separated. These findings provide valuable insights for MSMEs in formulating more effective and targeted marketing strategies.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI MYPERTAMINA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) Saputra, Ilham; Rahmaddeni, Rahmaddeni; Rahmi, Rahmi; Satria, Riyan; Anderson, Ranap
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 5 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/rq7bjv10

Abstract

Aplikasi MyPertamina menghasilkan volume ulasan pengguna yang besar di Google Play Store, menjadikannya sumber wawasan yang krusial namun tidak efisien jika dianalisis secara manual. Penelitian ini membangun sebuah model analisis sentimen otomatis menggunakan metode deep learning dengan arsitektur Long Short-Term Memory (LSTM). Data ulasan yang dikumpulkan menggunakan teknik scrapping data dilakukan tahap preprocessing untuk memberikan data yang bersih, lalu model diuji dan menunjukkan performa yang sangat baik dengan akurasi mencapai 80%. Hasil analisis mengidentifikasi sentimen negatif didominasi oleh keluhan teknis seperti 'error' dan 'susah login', sedangkan sentimen positif menyoroti manfaat aplikasi yang 'membantu' dan 'praktis'. Penelitian ini membuktikan bahwa model yang dibangun efektif dalam menyediakan masukan strategis yang dapat ditindaklanjuti oleh PT Pertamina untuk meningkatkan kualitas layanan dan kepuasan pengguna
Co-Authors Anderson, Ranap Muhammad afrizal Rahmaddeni Rahmaddeni Rahmi Rahmi Saputra, Ilham