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All Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi)
Lukman, Lukman
Universitas Indraprasta PGRI
Computer Science & IT Mathematics

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Prediksi Kelulusan Siswa dengan Metode Support Vector Machine (SVM) di SMK Adiluhur Lukman, Lukman; Herlinda, Herlinda
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 9, No 1 (2024)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v9i1.23355

Abstract

Student graduation is an indicator of the success of the educational process which is influenced by several factors, such as grades, extracurricular activities, interpersonal, non-academic, parental support, cognitive abilities. Predictions of student graduation can provide valuable information for schools to identify students at risk of not graduating and provide appropriate intervention. This research aims to develop a prediction model for student graduation using the Support Vector Machine (SVM) method. The data used includes academic grades, extracurricular activities, socio-economic conditions, and other relevant factors. The SVM method was chosen because of its ability to find the optimal hyperplane that maximally separates data classes. The modeling process includes data cleaning, feature selection, SVM parameter optimization, and performance evaluation using metrics such as accuracy, precision and recall. The research results show that the SVM model developed is able to predict student graduation with an accuracy of 95.06%. Model analysis reveals the main factors that influence student graduation, in an effort to increase student graduation rates. 
Rancang Bangun Aplikasi Stock Opname Berbasis Java Sandy, Agung Ferdinan; Anshary, Nico Bustanul; Lukman, Lukman
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 10, No 1 (2025)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v10i1.24988

Abstract

Stock-taking is an important process in stock management to ensure a match between the mechanism data and the physical amount in the warehouse, but many companies still use manual methods that are prone to errors and inefficiencies. This research aims to create and build a Java-based stock-taking program to improve the accuracy and efficiency of stock data collection. The methods used include mechanism requirements analysis, design with Entity-Relationship Diagram (ERD) and Unified Modeling Language (UML), and implication using Java and MySQL database, with testing through Black Box Testing method. The results show that this program is able to assist users in recording, managing, and verifying stock more accurately and efficiently than manual methods, with key features such as automatic recording, fast stock search, and real-time report generation that support the effectiveness of the stock-taking process in the company.
Co-Authors Anshary, Nico Bustanul Herlinda, Herlinda Sandy, Agung Ferdinan