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All Journal JITEK (Jurnal Ilmiah Teknosains) Journal Of Artificial Intelligence And Software Engineering
Buhori Muslim
Institut Teknologi Pagar Alam
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Mechanical Engineering Veterinary

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PENERAPAN ALGORITMA LINEAR REGRESI PADA SMART APLIKASI E-RESORT MENGGUNAKAN FRAMEWORK CODEIGNITER Sasmita Sasmita; Buhori Muslim
JITEK (Jurnal Ilmiah Teknosains) Vol 8, No 2/Nov (2022): JiTek
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jitek.v8i2/Nov.13643

Abstract

The purpose of this study is to apply linear regression algorithm to smart e-resort applications using a codeigniter framework so that it can run effectively and efficiently. Hotel Pagar Alam is currently still using manual methods, such as distributing brochures, this of course hinders the activities of hotels which are usually crowded and crowded on holidays or other major holidays that require hotels as a means to stay overnight, systems that can speed up the work of the manager and make it easier visitors to access reservation information as well as hotel facilities. By using the system development method Software Defelopment Life Cycle (SDLC) method. This smart e-resort was created using the PHP programming language with the Codeigniter framework and MySQL as the database. Testing of this system uses Black Box Testing with a score of 3.5 and expert review with a score of 4.1 from the implementation of Betha recapitulation in the valid category. Thus the application of the linear regression algorithm in the smart e-resort application using a codeigniter framework can run validly and efficiently
Classification of Taste Levels in Gerga Oranges Using the K-Cluster Classification Tree (K-CT) Method Asep Sayaputra; Febriansyah Febriansyah; Buhori Muslim
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9416

Abstract

ABSTRACTTechnological advancements have accelerated the adoption of various machine learning methods to support decision-making processes in the agricultural and horticultural sectors. One of the persistent challenges in fruit quality assessment is the identification of taste levels, which is commonly performed subjectively based on visual observation and individual experience. Such an approach may lead to inconsistencies in product quality evaluation. Therefore, this study aims to apply the K-Cluster Classification Tree (K-CT) method to classify the taste levels of Gerga oranges based on their physical characteristics. The K-CT method is a hybrid approach that integrates the K-Means Clustering algorithm with a Classification Tree to enhance classification performance while maintaining computational efficiency. The research utilized primary data collected through direct observation of 700 Gerga orange samples obtained from farmers and fruit traders in Tanjung Sakti District, Lahat Regency. Each sample was represented by six physical attributes, namely peel color, pore size, thrips presence, fruit shape, peel texture, and fruit diameter, while taste level served as the target variable. The dataset was divided into 80% training data and 20% testing data. The experimental results demonstrated that the K-CT method achieved a classification accuracy of 94.57%, outperforming Classification Tree, Random Forest, and Gradient Boosting models. Furthermore, the proposed method exhibited competitive computational efficiency and successfully identified fruit diameter as the most influential attribute affecting the taste level of Gerga oranges. These findings indicate that the K-CT method has considerable potential to be implemented as an objective, accurate, and efficient decision support system for fruit quality classification.
Co-Authors Asep Sayaputra Febriansyah . Sasmita Sasmita