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All Journal Jurnal Ilmiah Informatika Komputer KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal ICT : Information Communication & Technology Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Jurnal Informatika Terpadu International Journal of Social Science Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Dimamu Jurnal Sistem Informasi dan Teknologi (SINTEK) AMMA : Jurnal Pengabdian Masyarakat Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Kecerdasan Buatan dan Teknologi Informasi Sintek Kuwera
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DATA-DRIVEN CONSUMER SEGMENTATION APPROACH FOR JEANS RETAIL SALES USING FUZZY C-MEANS CLUSTERING Nana Suarna; Nining Rahaningsih; Annisa Annastia Suarna
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.479

Abstract

The fashion retail industry generates large volumes of sales transaction data containing valuable information regarding consumer purchasing behavior and preferences. However, extracting meaningful insights from heterogeneous retail data remains challenging when using conventional analytical approaches. This study aims to analyze jeans sales transaction data and identify consumer purchasing patterns using the Fuzzy C-Means (FCM) clustering algorithm. The proposed approach adopts the Knowledge Discovery in Databases (KDD) framework, consisting of data selection, preprocessing, transformation, data mining, and evaluation stages to ensure systematic analysis. The dataset used in this study consists of 799 jeans sales transaction records collected in 2024 from Shakila Collection, involving four attributes: product name, payment method, price, and purchase quantity. To improve clustering effectiveness, only price and purchase quantity were selected as the primary variables due to their relevance in representing consumer purchasing behavior. Clustering performance was evaluated using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. Experimental results show that the best clustering configuration was achieved at , producing three consumer segments consisting of 175 items in Cluster 0, 590 items in Cluster 1, and 34 items in Cluster 2. The findings indicate that medium-priced products tend to have higher purchasing intensity and more flexible purchase quantities, whereas premium-priced products exhibit relatively lower demand. The novelty of this study lies in integrating Fuzzy C-Means clustering with consumer preference analysis to generate practical business insights for pricing strategies, inventory optimization, and targeted marketing, thereby supporting more effective data-driven decision-making in fashion retail businesses.
Analisa Penggunaan Metode Lexicon Based Dan Algoritma Naive Bayes Pada Sentimen Ulasan Aplikasi Duolingo Muhammad Abib Allesdio; Ade Irma Purnamasari; Irfan Ali; Nana Suarna; Agus Bahtiar
Jurnal Sistem Informasi dan Teknologi Vol 6 No 2 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i2.261

Abstract

Peningkatan jumlah ulasan pengguna pada aplikasi mobile membuka peluang untuk memahami persepsi dan pengalaman pengguna melalui analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Duolingo yang diambil dari Google Play Store menggunakan dua pendekatan, yaitu metode lexicon-based dan algoritma Naive Bayes berbasis Python. Metode lexicon-based digunakan untuk memberikan skor polaritas berdasarkan leksikon sentimen, sedangkan Naïve Bayes diterapkan sebagai model klasifikasi dengan dukungan fitur TF-IDF. Proses penelitian meliputi tahapan pengumpulan data, preprocessing teks (cleaning, case folding, tokenisasi, stopword removal, dan stemming), pembobotan sentimen, pelatihan model, serta evaluasi performa menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode lexicon-based mampu memberikan gambaran umum polaritas ulasan, namun performanya sangat dipengaruhi oleh kelengkapan leksikon dan variasi bahasa informal pengguna. Sementara itu, algoritma Naive Bayes menunjukkan performa yang lebih stabil dan akurasi lebih tinggi dalam mengklasifikasikan sentimen dibandingkan pendekatan leksikon. Perbandingan kedua metode memperlihatkan bahwa Naive Bayes lebih efektif dalam menangani data teks pendek, tidak terstruktur, serta mengakomodasi variasi kata dan ejaan. Temuan penelitian ini memberikan pemahaman yang lebih dalam mengenai persepsi pengguna terhadap Duolingo serta menjadi referensi metodologis bagi penelitian sentiment analysis selanjutnya, khususnya yang melibatkan kombinasi metode leksikon dan klasifikasi probabilistik.
Clusterization of Family Planning Participants Based on Pregnancy Risk Using K-Means Algorithm in Ciherang Village Melva Regina Arpratika; Nana Suarna; Agus Bahtiar; Martanto; Odi Nurdiawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2248

Abstract

This study aims to group family planning (KB) participants in Ciherang Village based on pregnancy risk levels using the K-Means clustering algorithm. The identification of pregnancy risk is still performed manually, resulting in less effective analysis. Therefore, a data mining approach is applied to improve decision-making accuracy. The data used in this study were obtained from KB cadres, including variables such as age, number of children, education, occupation, and contraceptive methods. The research method follows the Knowledge Discovery in Database (KDD) stages: data selection, preprocessing, transformation, data mining, and evaluation. The K-Means algorithm is used for clustering, while the Davies–Bouldin Index (DBI) is applied to evaluate clustering quality. The results show that the optimal number of clusters is K = 2 with a DBI value of 0.721. The first cluster represents low pregnancy risk participants, while the second cluster represents high pregnancy risk participants. Age and number of children are identified as the most influential factors. This study provides useful insights for healthcare providers in developing targeted strategies for family planning programs. Keywords: Data Mining; Davies–Bouldin Index; K-Means Clustering; Pregnancy Risk; Family Planning
Co-Authors Abdul Ajiz Abdul Rasyid Faiq Hadinata Achmad Baihaqi Achmad Fikri Ulumudin Achmad Luthfi Ade Awaludin Ade Irma Purnamasari Adinda Aulia Putri Akhyanto Akhyanto Annisa Annastia Suarna Arye Fandia Kusuma Basysyar, Fadhil Muhammad Cep Lukman Rohmat Danya Rizki Chaerunisa Dion Nugraha Dita Amalia Rizki Dita Rizki Amalia Dodi Solahudin Dodi Solihudin Dwi Atmoko Elisa Sriyulia Ervina Mahara Euis Fadilah Fadhil Muhammad Basysyar Fadrin Helmi Farah Nur Farida Fathurrohman Febriyana Lorenza Gildan Jaya Muhammad Ramadhan Heliyanti Susana Herman Iin Irfan Ali Kuju Susilawati Lin Lin Martanto . Melisa Hikari Melva Regina Arpratika Moh Nurdayat Dayat Mohamad Ibnu Abas Muhammad Abib Allesdio Muhammad Alfian Nur Rahmat Mulyawan Mulyawan Mulyawan Nana Mulyanasari Nanda Septian Nining Rahaning Nining Rahaningsih Nining Rahaningsih Nisa Dieanwati Nuris Nisa Dienwati Nur Kholidin Nursyifa Puspa Ar-rahmi Slamet Nurul Ibnu Al Muharom Odi Nurdiawan Opiana . Raditya Danar Dana Rini Astuti Rini Astuti Ryan Hamonangan Saeful Anwar Sayudin Sayudin Shandy Alfian Sri Muflikah Kurniarti Sulaeman Syaeful Annas Usup Supendi Willy Prihartono Yudhistira Arie Wijaya