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All Journal JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi
Asparizal
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Computer Science & IT

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The Optimizing Sales Strategies to Address Excessive Stock Accumulation: A Data Mining Approach Susandri; Muhammad Arief Solihin; Hamdani; Asparizal
JAIA - Journal of Artificial Intelligence and Applications Vol. 4 No. 1 (2024): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v4i1.1110

Abstract

The Two Pelita Weaving Business has recorded significant sales in the weaving industry, despite facing challenges in managing product stock due to the accumulation of excess stock caused by a lack of customer interest. This study employs data mining techniques, specifically the Association Rule and Apriori algorithms, to analyze sales patterns. The analysis results using Python and Orange Data Mining showed consistency in the relationship between Siku Keluang Weaving and Pucuk Rebung Weaving products, with high occurrence rates of purchase patterns (11.74% and 10%, respectively). High confidence levels with Python at 96.36% and Orange Data Mining at 99.1% indicate that customers who purchase Siku Keluang Weaving are also likely to purchase Pucuk Rebung Weaving products.
Sentimen Pengguna Aplikasi BRImo: Kinerja Algoritma Support Vector Machine, Naive Bayes, dan Adaboost Susandri; Yurnalis; Edwar Ali; Susanti; Asparizal
SATIN - Sains dan Teknologi Informasi Vol 9 No 2 (2023): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v9i2.1057

Abstract

Dalam konteks perkembangan industri perbankan yang semakin maju, pemanfaatan teknologi modern menjadi faktor kunci untuk meningkatkan kualitas layanan dan memenangkan persaingan di era digital. Bank Rakyat Indonesia (BRI) memikat perhatian masyarakat melalui peluncuran aplikasi perbankan seluler, BRImo. Namun Bank ini perlu meraih pandangan dan pengalaman nasabah terhadap aplikasi mobile banking untuk meningkatkan kualitas pelayanan. Penelitian ini memiliki tujuan untuk menganalisis ulasan pengguna BRImo sebagai objek penelitian. Komparasi dilakukan antara algoritma Support Vector Machine (SVM), Naive Bayes (NB), dan Adaboost dalam mengolah data teks. Evaluasi dilakukan berdasarkan tingkat akurasi, presisi, recall, dan nilai F1-score. Hasil penelitian menunjukkan bahwa algoritma SVM memberikan kinerja terbaik dalam mengklasifikasikan tanggapan masyarakat terhadap aplikasi BRImo, dengan tingkat akurasi sebesar 90,4%, presisi 90,8%, recall 90%, dan nilai F1-score 90,3%. Sebagai perbandingan, algoritma Adaboost memberikan nilai terendah dengan tingkat akurasi sebesar 87%, presisi 87,2%, recall 86,8%, dan nilai F1-score 86,9%.
Co-Authors Edwar Ali Hamdani Muhammad Arief Solihin Susandri SUSANTI Yurnalis