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All Journal Applied Information System and Management Voices of English Language Education Society Journal of Innovation and Future Technology (IFTECH) Ilmu Komputer untuk Masyarakat Biner : Jurnal Ilmiah Informatika dan Komputer Journal of Digital Technology and Computer Science
Feby Charlos
Universitas Pamulang
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : journal of digital technology and computer science

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Implementation of K-Means Algorithm in Data Mining for Drug Market Segmentation Joko Prasetiana; Feby Charlos
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.582

Abstract

Purpose – This study aims to implement the K-Means clustering algorithm in data mining to segment pharmaceutical products based on stock and sales patterns. The study addresses the need for data-driven product classification to support more effective inventory management and marketing decision-making in pharmaceutical businesses. Methods – This research applied a quantitative data mining approach using secondary sales transaction data from a pharmaceutical distributor covering the period from January 2022 to December 2023. The dataset consisted of 1,248 transaction records, which were aggregated into 12 pharmaceutical products based on stock quantity and sold quantity variables. Data preprocessing included cleaning, transformation, aggregation, and scale checking through Min-Max normalization. The reported K-Means calculation was presented using original-scale stock and sold quantity values for interpretability, while the optimal number of clusters was determined using the Elbow Method and validated with the Silhouette Score. Findings – The Elbow Method indicated that three clusters were optimal, supported by a Silhouette Score of 0.71. The clustering results classified products into high-demand, moderate-demand, and low-demand segments. High-demand products require prioritized stock replenishment and distribution, while low-demand products need tighter inventory control and targeted promotional strategies. Research implications – The findings provide practical insights for improving procurement planning, inventory optimization, and promotional decision-making. However, the analysis is limited to 12 aggregated products and two variables. Originality – This study contributes by applying K-Means clustering specifically to pharmaceutical product-level market segmentation using stock and sales data.
Decision Support System for Laptop Selection Using the TOPSIS Method on Web-Scraped iPrice Data Feby Charlos; Riska Septiani
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.666

Abstract

Purpose – This study develops and revises a web-based decision support system for laptop selection by integrating web-scraped iPrice Indonesia product data, reproducible preprocessing, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The study responds to the difficulty consumers face when comparing many laptop alternatives with heterogeneous specifications, prices, and budget constraints. Methods – The prototype used a verified static CSV dataset derived from public product listings. Five complete laptop alternatives were evaluated with 12 criteria: brand, screen size, screen resolution, processor, storage type, storage capacity, graphics card, laptop weight, battery durability, new-price distance, preloved-price distance, and RAM. Categorical attributes were transformed into ordinal scores. TOPSIS was implemented in Python and Streamlit. Simple Additive Weighting (SAW), sensitivity analysis, and functional black-box testing were used as comparative and verification procedures. Findings – Under equal criterion weights of 1.5, a new-laptop budget of IDR 7,500,000, and a preloved-laptop budget of IDR 5,000,000, HP Envy x360 13-inch obtained the highest TOPSIS closeness coefficient of 0.746618238. SAW selected the same top alternative, although the complete ranking differed and produced a moderate Spearman correlation of 0.400. Research implications – The results show that transparent criterion transformation, budget-distance modeling, and interface-based preference adjustment can support practical laptop selection. The system does not replace consumer judgment because the ranking depends on the dataset, weights, scoring rules, and budget assumptions. Originality – This study contributes a reproducible DSS prototype that combines scraped price-comparison data, TOPSIS ranking, SAW benchmarking, sensitivity checking, and an Indonesian-language Streamlit interface for practical laptop recommendation.
Co-Authors Ahmad Lazuardi Adam Angga Pramadjaya Bagus Setya Faizul Hadi Fatur Ramdan Hidayat Fauzan Dika Ilham Adi Wicaksono Ita Hardianti Kanim Kanim Lia Hapsari Tri Rejeki Lisdianto Dwi Kesumahadi Prasetiana, Joko Ratih Purwasih Reni Haerani Riska Septiani Risna Nurul Octavani Suryadi Syifa Ayunda Uut Uliyati Dewi Yuda Pratama Wibawa Ziehan Aprilia Salsadila