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All Journal Journal of Computer System and Informatics (JoSYC) Bulletin of Information Technology (BIT) Journal of Artificial Intelligence and Digital Business
Wanny, Puspita
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Implementation of Apriori Algorithms to Analyze and Determine Consumer Purchase Patterns in Gadget Stores as Sales Increase Strategy Simanullang, Rahma Yuni; ', Khairunnisa; Wanny, Puspita; Utari, Utari; Novelan, Muhammad Syahputra
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7355

Abstract

This study aims to identify the pattern of product purchases that often occur simultaneously at a gadget store in order to develop a more effective sales strategy. The research problem focuses on how to find associations between products based on sales transaction data. The proposed solution is to apply data mining techniques, specifically a priori algorithms, to analyze transaction data and find significant association rules. The A priori algorithm is used through several stages, including the calculation of support for each item, the elimination of items with support below the minimum threshold, the formation of itemset combinations, and the calculation of confidence to generate association rules. The results showed two association rules that met the minimum confidence threshold (60%), namely: (1) If customers buy USB-C, they tend to buy Powerbank (confidence: 67%), and (2) If customers buy Smartwatches, they tend to buy Screen Protectors (confidence: 67%), and (3) If customers buy Screen Protectors, they tend to buy Smartwatches (confidence: 100%). These patterns can be used by the store for strategic product placement and bundling promotions.
Analysis of Inpatient Data Using Cluster Analysis on Simulation Dataset Putera Utama Siahaan , Andysah; Azizah Harahap, Nur; Yuni Simanullang, Rahma; Khairunnisa; Wanny, Puspita; Utari
Bulletin of Information Technology (BIT) Vol 6 No 1: Maret 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i1.1830

Abstract

This study aims to analyze inpatient data using the K-Means Clustering method on a simulated dataset. The dataset includes various patient-related attributes such as age, billing amount, length of stay, medical condition, and type of admission. Several preprocessing steps were applied, including date conversion, duration calculation, numerical normalization, and one-hot encoding for categorical attributes. The Elbow Method was used to determine the optimal number of clusters, and clustering quality was evaluated using both the Silhouette Score and Davies-Bouldin Index. The analysis results show that the patients can be segmented into three major clusters, each exhibiting distinct characteristics—for example, younger patients with short and low-cost stays, and elderly patients with prolonged and more expensive hospitalizations. The resulting Silhouette Score of 0.14 and Davies-Bouldin Index of 1.74 reflect a moderate clustering performance, yet the model remains informative and meaningful. These clusters provide actionable insights that hospitals can use to optimize their service strategies, improve resource allocation, and enhance operational efficiency. Moreover, the study illustrates the practical application of unsupervised learning techniques in healthcare settings, contributing to data-driven decision-making practices and offering a foundation for further research into patient segmentation.
Penerapan Teorema Bayes pada Sistem Pakar untuk Diagnosis Infeksi Human Metapneumovirus (HMPV) Khairunnisa, Khairunnisa; Tambunan, Maha Valne Datin Mahfujah; Rambe, Siska Mayasari; Simanullang, Rahma Yuni; Syahri, Rahma; Utari, Utari; Wanny, Puspita; Amin, Muhammad
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5519

Abstract

Human Metapneumovirus (HMPV) adalah virus pernapasan yang dapat menimbulkan infeksi pada saluran pernapasan atas maupun bawah, khususnya pada kelompok yang memiliki risiko tinggi seperti anak-anak, lansia, serta individu dengan sistem kekebalan tubuh yang lemah. Secara klinis, infeksi HMPV menunjukkan gejala yang serupa dengan penyakit pernapasan lain, seperti influenza dan Respiratory Syncytial Virus (RSV), sehingga sering menimbulkan kesulitan dalam proses diagnosis pada tahap awal. Permasalahan ini semakin diperburuk oleh keterbatasan fasilitas pemeriksaan laboratorium khusus, misalnya RT-PCR, yang belum tersedia secara merata, terutama di daerah dengan keterbatasan sumber daya kesehatan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pakar berbasis Teorema Bayes sebagai alat bantu dalam melakukan diagnosis dini infeksi HMPV berdasarkan gejala klinis yang dialami pasien. Pendekatan probabilistik melalui Teorema Bayes diterapkan untuk mengelola ketidakpastian data dengan menghitung tingkat kemungkinan terjadinya infeksi berdasarkan pembobotan gejala yang ditetapkan oleh pakar serta input yang diberikan oleh pengguna. Metode penelitian yang digunakan meliputi analisis permasalahan, pengumpulan data melalui studi literatur dan konsultasi pakar, serta pengembangan sistem pakar dengan mengintegrasikan Teorema Bayes. Sistem yang dikembangkan menganalisis dua belas gejala utama yang berkaitan dengan infeksi HMPV. Hasil pengujian menunjukkan bahwa sistem menghasilkan nilai probabilitas infeksi HMPV sebesar 80% yang termasuk dalam kategori hampir pasti. Temuan ini membuktikan bahwa penerapan Teorema Bayes dalam sistem pakar dapat berperan secara efektif dalam mendukung diagnosis dini infeksi HMPV, meningkatkan akurasi pengambilan keputusan medis, serta membantu tenaga kesehatan dalam memberikan penanganan awal yang lebih cepat dan tepat.
Co-Authors ', Khairunnisa Andysah Putera Utama Siahaan Azizah Harahap, Nur Khairunnisa Khairunnisa Khairunnisa Muhammad Amin Muhammad Syahputra Novelan Rambe, Siska Mayasari Simanullang, Rahma Yuni Syahri, Rahma Tambunan, Maha Valne Datin Mahfujah Utari Utari Utari Yuni Simanullang, Rahma