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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Eksplora Informatika JURNAL PENDIDIKAN TAMBUSAI JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jusikom: Jurnal Sistem Informasi Ilmu Komputer CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi PLAKAT : Jurnal Pelayanan Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknologi Informasi dan Robotika International Journal Of Computer, Network Security and Information System (IJCONSIST) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Innovative and Creativity International Journal of Information Engineering and Science
Ani Dijah Rahajoe
Bhayangkara University, Indonesia
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Customer Data Management Analysis for Customer Segmentation Using K-Means Clustering Method Andre Leto; Reza Aminullah; Ani Dijah Rahajoe
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.345

Abstract

This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.
PERAN BIG DATA DALAM MANAJEMEN DATA DAN INFORMASI SEBAGAI SISTEM PENDUKUNG KEPUTUSAN (SYSTEMATIC LITERATURE REVIEW) Suryantari, Putu Anggi; Muttaqin, Faisal; Rahajoe, Ani Dijah
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8899

Abstract

Big Data telah menjadi komponen penting dalam manajemen data dan informasi seiring dengan meningkatnya volume dan kompleksitas data yang dihasilkan oleh organisasi. Pemanfaatan Big Data yang tepat memungkinkan organisasi untuk mengelola data secara lebih terstruktur dan menghasilkan informasi yang bernilai bagi pengambilan keputusan manajemen. Penelitian ini bertujuan untuk menganalisis peran Big Data dalam manajemen data dan informasi sebagai sistem pendukung keputusan melalui pendekatan Systematic Literature Review. Metode penelitian dilakukan dengan mengkaji 15 artikel ilmiah yang relevan berdasarkan proses pencarian dan penilaian kualitas literatur. Hasil penelitian menunjukkan bahwa pengelolaan Big Data yang baik mempertimbangkan karakteristik utama Big Data yang meliputi volume, kecepatan, variasi, keandalan, dan nilai data. Selain itu, pemanfaatan Big Data berperan dalam meningkatkan kualitas informasi, mempercepat proses pengambilan keputusan, serta mendukung keputusan manajemen yang lebih akurat dan berbasis data. Dengan demikian, Big Data memberikan kontribusi positif dalam mendukung sistem pendukung keputusan pada organisasi.
Penerapan Algoritma ECLAT untuk Market Basket Analysis pada Data Transaksi Ritel Rahajoe, Ani Dijah; Megantara, Sofia Ramadhani; Fransiska, Amelia; Feriza, Reyana Dinda Maulan; Indartono, Taqiyya; Perkasa, Laurensius Gading Surya
Jurnal Pendidikan Tambusai Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v10i1.36325

Abstract

Dalam perkembangan teknologi di sektor ritel modern telah menghasilkan data transaksi dalam jumlah besar yang menyimpan informasi penting dari hasil transaksi konsumen yang merujuk pada pola perilaku belanja konsumen. Dalam hal ini produsen bisa menganalisis hubungan antar produk menggunakan Market Basket Analysis (MBA) yang merupakan teknik data mining yang digunakan untuk mengidentifikasi hubungan antar produk yang dibeli secara bersamaan. Penelitian bertujuan untuk mengungkap pola hubungan antar produk pada data transaksi ritel menggunakan algoritma ECLAT. Dataset yang digunakan berasal dari platform kaggle dengan total data 7.501 transaksi dari 120 jenis produk. Algoritma ECLAT diterapkan dengan batas minimum support sebesar 3% setelah melalui tahap proses data. Hasil penelitian ini menunjukkan terdapat sejumlah kombinasi produk yang sering dibeli konsumen dengan nilai support yang tinggi. Temuan ini dapat digunakan oleh pemilik ritel untuk menata letak produk, promosi bundling, dan persediaan yang dapat dioptimalkan.
Optimasi Hyperparameter CatBoost dengan Particle Swarm Optimization untuk Klasifikasi Hipertensi Al Afgany, Muhammad Iqbal; Ani Dijah Rahajoe; Wahanani, Henni Endah
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16292

Abstract

Hypertension is a cardiovascular disease affecting 11,952,694 residents aged ≥15 years in East Java in 2019, yet only 40.1% received healthcare services. This study aims to analyze the effect of Particle Swarm Optimization (PSO) on CatBoost algorithm performance in hypertension level classification. The research dataset combined data from Puskesmas Kepatihan Gresik (191 data) and Kaggle (12,500 data) divided with an 80:10:10 ratio. PSO was used for CatBoost hyperparameter optimization including iterations, depth, learning_rate, and l2_leaf_reg. Model evaluation utilized accuracy, precision, recall, and F1-score metrics. Results show that CatBoost with PSO optimization achieved 96% accuracy with optimal configuration of iterations=100, depth=3, learning_rate=0.055, and l2_leaf_reg=3, 2% higher than without optimization (94%). This study proves the effectiveness of PSO in optimizing CatBoost hyperparameters for more accurate early hypertension detection
Co-Authors Agung Subekti, Mohamad Rafli Ainur Rahim Al Afgany, Muhammad Iqbal Andre Leto Anna Fauziah arif arizal Arif Setyo Wibowo Chaurina, Agfanadita Rezkia Daniar, Aninditya Dian Agus Prawinata Diyasa, I Gede Susrama Mas Efendi, Fairus Irhab Adinata Eka Prakarsa Mandyartha Feriza, Reyana Dinda Maulan Firdaus, Muhammad Rizky Fransiska, Amelia Henni Endah Wahanani I Gede Susrama Mas Diyasa Ida Retno Moeljani Indartono, Taqiyya Jr, Angelo A. Beltran Lapon, Piter Rudi Irson Rivaldo M Mahaputra M. Mahaputra Made Hanindia Prami Swari Mahardika, Azmi Maulana Maulana, Achmad Ario Dwi Megantara, Sofia Ramadhani Muhammad Farhan Maulana Muttaqin, Faisal N, Pangesti Nurlaili, Afina Lina P. Eko Prasetyo Perkasa, Laurensius Gading Surya Pramnesti, Adisty Regina Retno Mumpuni Reza Aminullah Safeyah, Muchlisiniyati Sanjaya, Yushinta Aristina Satrio Budi Wahyuono Soffiana Agustin, Soffiana Subekti, Mohamad Rafli Agung Suriansyah, Muhammad Suryantari, Putu Anggi Syariful Alim Trenggono, Brahmantio Widyo Waskito, Muhammad Rizal Winarko, Edi Zakaria, Rino