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All Journal JITEK (Jurnal Ilmiah Teknosains) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal SOLMA Jusikom : Jurnal Sistem Komputer Musirawas Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Sisfokom (Sistem Informasi dan Komputer) JUSIM (Jurnal Sistem Informasi Musirawas) JURNAL MAHAJANA INFORMASI Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Sains, Aplikasi, Komputasi dan Teknologi Informasi Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Dharma Wacana Jurnal Abdi Masyarakat Indonesia Ngabdimas : Pengabdian Pada Masyarakat Informatics and Enginering Dedication Jurnal Pengabdian Masyarakat Tapis Berseri Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Jurnal Teknologi Informasi Mura
Desi Puspita
Institut Teknologi Pagar Alam
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Veterinary Other

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Prediction System for Credit Eligibility Using C4.5 Algorithm Desi Puspita; Siti Aminah; Alfis Arif
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7311

Abstract

Independent Multipurpose Cooperative, Tasti, Pagar Alam City, currently granting credit, the data collection process for the selection of prospective members and prospective credit recipients is still done manually, so they must come to the cooperative to find out whether it is still eligible to receive credit or not, officers must evaluate the data. is to form a decision tree classification model for creditworthiness in cooperatives. The analytical method with the C4.5 Algorithm, the C4.5 Algorithm is one of the algorithms for performing data mining using data classification techniques. The C4.5 algorithm is used to search for knowledge in the data, by finding the relationship between input variables or criteria attributes with target variables or decision attributes. The resulting knowledge is converted into the form of a decision tree so that it is easy to understand. The C4.5 algorithm was chosen because it can make a decision to be able to predict whether a customer is eligible or not to be given credit. The attribute used is income with specified criteria. The results obtained from this study are predictive information on the feasibility of providing credit to cooperatives using the C4.5 algorithm by getting an accuracy value of 93% with an AUC value of 0.898 where this classification is classified as Good Classification or good.
Application Of The Backpropagation Method For Digital Image Feature Extraction On Coffee Fruit Classification Riduan Syahri; Desi Puspita
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8374

Abstract

The background of this research is that the process of classifying the types of coffee beans is still carried out conventionally and has not been computerized, that is, the classification of coffee beans is still based on experience, color and shape of the coffee beans. Of course, this takes a long time and errors often occur, so this research can help classify coffee cherries using the Backpropagation method quickly. The purpose of this study was to produce a system for applying the backpropagation method for extracting digital image features in the classification of coffee cherries, a system built using MATLAB software. In the results of the coffee fruit classification program with the Backpropagation algorithm that is matched in excel, results are obtained for an accuracy of 72% for 100 training data and results for an accuracy of 85% for 10 test data for manual calculations, results are obtained from the Performance Model Using the Confusion Matrix in training data obtained an accuracy of 72% and testing 85% of each of the 2 different types of coffee cherries.
Classification Of Outstanding Students Using Support Vector Machine (SVM) Based on Data Mining Riduan Syahri; Desi Puspita
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.13191

Abstract

This research aims to classify outstanding students at the Pagar Alam Institute of Technology using the Support Vector Machine (SVM) algorithm based on data mining. Early identification of outstanding students is crucial for supporting potential development and institutional decision-making. Historical data from 245 students from the 2016 to 2018 cohorts were utilized, encompassing course grades and Cumulative Grade Point Average (CGPA). The research process included data preprocessing such as normalization and splitting the data into 80% training data and 20% testing data. The SVM model was implemented with a Radial Basis Function (RBF) kernel and parameters C=1.0 and gamma=0.1. Evaluation results show that the model achieved an overall accuracy of 89.80% on the testing data. The model's performance was further validated through a confusion matrix (9 True Positives, 1 False Negative) and a classification report indicating good precision and recall for both classes. Furthermore, an Area Under the Curve (AUC) value of 0.93 signifies the model's excellent discriminative ability. This study contributes by providing an effective classification tool for identifying outstanding students, which can serve as a basis for the institution to design more targeted development and recognition programs.
Co-Authors Alfis Arif Arif, Alfis ariska, sendy Barrorotul Azizah Debi Guzmaliza Dewi, Nadiya Citra Ferry Putrawansyah Ferry Putrawansyah Fitria Rahmadayanti Gusmaliza, Debi Ichsan, Onne Akbar Nur Inda Anggraini Lesi Anggraini Melani Anggita Nadiya Citra Dewi Rony, Zahara Tussoleha Sasmita Sasmita Siti Aminah Syahri, Riduan Tri Susanti Yogi Isro Mukti, Yogi Isro