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All Journal Bulletin of Information Technology (BIT) Jurnal Armada Informatika The Journal of Information Technology, Computer Science, and Electrical Engineering
Rahma Yuni Simanullang
universitas Pembangunan Panca Budi
Computer Science & IT Control & Systems Engineering

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Implementation of the K-Means Algorithm on Smart Systems in Grouping Gadget Accessory Purchase Patterns Rahma Yuni Simanullang; Maha Valne Datin Mahfujah Tambunan; Puspita Wanny; Utari; Khairunnisa'; Siska Mayasari Rambe
Journal of Information Technology, computer science and Electrical Engineering Vol. 2 No. 3 (2025): October 2025 - January 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v2i3.232

Abstract

The development of the gadget accessories retail industry demands adaptive marketing strategies to understand consumer behavior patterns more effectively. The main problem faced is the difficulty in grouping customers based on the characteristics of diverse purchasing behaviors. To overcome this, this study aims to implement the K-Means Clustering algorithm in intelligent systems to group customer data of gadget accessories into several groups that have similar purchasing patterns. The K-Means algorithm is used because of its ability to detect patterns and segment based on the similarity of customer attributes through an iterative process until convergence is achieved. The results of the study show that this method has succeeded in forming three main clusters, namely high-value customers with a high purchase frequency and dominance of premium products (C1), customers with low activity who require a special promotional approach (C2), and potential customers with medium activity who have the potential to increase their loyalty (C3). The results of this segmentation prove that the K-Means algorithm is effective in analyzing consumer behavior and can be the basis for data-driven decision-making for a more efficient marketing strategy and product recommendation system in the gadget accessories retail sector.
Optimization of Tomato Production Prediction Using XGBoost and CatBoost Based on Lag Features in Aceh Province Eva Mufida Padilla; Utari; Rahma Yuni Simanullang
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.305

Abstract

Tomato production in each regency/municipality changes from year to year, necessitating a predictive method that can support production planning, distribution, and agricultural policy decision-making. This study aims to develop and compare machine learning models for predicting tomato production based on historical data from regencies/municipalities in Aceh Province. The dataset consists of 230 observations from 23 regencies/municipalities covering the period from 2015 to 2024, with attributes including region, year, and tomato production measured in quintals. The research stages include data cleaning, handling missing values, data transformation, and the construction of historical features, including production in the previous year, the three-year average production, and the production growth rate. The algorithms evaluated include Linear Regression, Random Forest Regressor, XGBoost Regressor, and CatBoost Regressor. The data are divided chronologically to prevent data leakage, while model performance is evaluated using Mean Absolute Error, Root Mean Squared Error, and the coefficient of determination. Preliminary analysis indicates that the production data have a very wide range and an uneven distribution pattern, requiring logarithmic transformation. This study is expected to identify a predictive model with the lowest error rate and provide supporting information for local governments and stakeholders in planning tomato commodity development in a more measurable and systematic manner.
Analysis of Inpatient Data Using Cluster Analysis on Simulation Dataset Andysah Putera Utama Siahaan; Nur Azizah Harahap; Rahma Yuni Simanullang; Khairunnisa; Puspita Wanny; 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.
Co-Authors Andysah Putera Utama Siahaan Eva Mufida Padilla Khairunnisa Khairunnisa' Maha Valne Datin Mahfujah Tambunan Nur Azizah Harahap Puspita Wanny Siska Mayasari Rambe Utari