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Maulani , Emi
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

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Naive Bayes Classification Model Analysis of Livable Housing Model Based on Physical Characteristics Burhanuddin, Burhanuddin; Maulani , Emi; Asria Nanda , Syarifah; Fitri , Zahratul; Fadhliani, Fadhliani
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26974

Abstract

Analysis of Naive Bayes Classification Model of Livable Housing Model Based on Physical Characteristics is one of the crucial aspects in improving the quality of life and welfare of the community. This study aims to examine the application of the Naive Bayes classification method in determining the level of livability based on the physical characteristics of the building. The dataset used is house data that includes several variables, namely roof condition (good, damaged), wall type (wall, semi-permanent, wood), floor condition (ceramic, cement, soil), building area (<36 m², ≥36 m²), ventilation (adequate, inadequate), and sanitation access (adequate, inadequate). The target variable in this study is the housing category, namely livable and uninhabitable. The research stages include data collection, data preprocessing, dividing the dataset into training data and test data, and implementation of the Naive Bayes algorithm. The posterior probability calculation is carried out based on the probability distribution of each variable against the class with a maximum likelihood approach. Model performance evaluation is carried out using a confusion matrix with indicators of accuracy, precision, and recall. The results of the analysis show that the variables of floor condition and sanitation access have the highest probability value against the livable category, thus playing a dominant role in the classification process. Furthermore, these two variables were also shown to have the most significant influence in determining whether housing is habitable or uninhabitable.
Classification Analysis Model of Construction Materials to Support Decision-Making in Construction Projects Maulani , Emi; Asria Nanda, Syarifah; Burhanuddin, Burhanuddin; Faridhatul Ulva, Ananda; fitri, zahratul
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26978

Abstract

This study aims to analyze and compare the performance of Gaussian Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying the feasibility of construction materials to support decision-making in construction projects. A quantitative comparative study design was applied using 127 samples of structural building materials collected from 15 contractor companies in Lhokseumawe City, Indonesia. The dataset consists of five predictor variables: price, compressive strength, water absorption, delivery time, and supplier rating. Data preprocessing included missing value imputation, outlier handling using the interquartile range method, normalization using Min-Max scaling, and class balancing using Synthetic Minority Over-sampling Technique (SMOTE). Model evaluation was conducted using accuracy, precision, recall, F1-score, and AUC, while feature importance was analyzed using permutation importance. The results show that the KNN model (k = 5) outperforms Gaussian Naïve Bayes across all evaluation metrics, achieving an accuracy of 92.11% and an AUC of 0.934.
Co-Authors Ananda Faridhatul Ulva Asria Nanda , Syarifah Asria Nanda, Syarifah Burhanuddin Burhanuddin Fadhliani, Fadhliani Fitri , Zahratul Zahratul Fitri, Zahratul