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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURTEKSI JSR : Jaringan Sistem Informasi Robotik
Andre Mariza Putra
Politeknik Negeri Sriwijaya
Computer Science & IT Engineering

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SIMPLE ADDITIVE WEIGHTING (SAW) PADA SISTEM PENDUKUNG KEPUTUSAN UNTUK PENGANGKATAN KARYAWAN TETAP Febie Elfaladonna; Andre Mariza Putra; Ria Rahmawati
JSR : Jaringan Sistem Informasi Robotik Vol 6, No 1 (2022): JSR : Jaringan Sistem Informasi Robotik
Publisher : AMIK Mitra Gama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58486/jsr.v6i1.142

Abstract

Decision support system as an interactive computer-based system that can assist decision makers in using data and models to solve unstructured problems. The management of human resources of a company greatly influences many aspects of determining the success of the work of the company. From previous research, decision support systems have been widely applied in various industries to facilitate management in making decisions. The obstacle faced by human resource management in a manufacturing industry is the criteria for hiring employees that have not been measured, so the decisions taken are still subjective. The author describes the process of determining the status of contract employees to permanent employees using the Simple Additive Weighting (SAW) method. The result of this research is a decision support system that can facilitate the management and determination of contract employees to become permanent employees based on the calculation method.
SUPPORT VECTOR MACHINE ANALYSIS FOR INTEREST AND TALENT CLASSIFICATION WITH PYTHON LIBRARY Devi Sartika; Febie Elfaladonna; Andre Mariza Putra
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 3 (2024): Juni 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i3.3067

Abstract

Abstract: Recognizing one's interests and talents early on is crucial in guiding an individual toward a prosperous future. While distinct, interests and talents share a close relationship. Interest denotes a genuine attraction to something without external pressure, and when consistently nurtured, it evolves into a skill or talent. Machine learning, specifically utilizing the SVM algorithm with the RBF kernel, can be applied to categorize interests and talents. Prior to SVM modeling, conducting Exploratory Data Analysis (EDA) is imperative for scrutinizing interests and talents. This analysis facilitates the identification of variables, enabling the elimination of missing values and ensuring the selection of appropriate interest and talent variables. The primary objective is to achieve optimal accuracy in modeling the classification of interests and talents. The insights gained from this research contribute to the creation of an application designed for categorizing interests and talents within SDN XYZ school. This application is designed for student use, assisting them in making informed decisions about their future education and career paths            Keywords: exploratory data analysis; interests and talents; machine learning; SVM Algorithm  Abstrak: Mengenali minat dan bakat seseorang sejak dini sangat penting dalam membimbing individu menuju masa depan yang sukses. Meskipun berbeda, minat dan bakat memiliki hubungan yang erat. Minat mengindikasikan ketertarikan yang tulus terhadap sesuatu tanpa tekanan eksternal, dan ketika terus-menerus dibina, berkembang menjadi keterampilan atau bakat. Pembelajaran mesin, khususnya dengan menggunakan algoritma SVM dan kernel RBF, dapat digunakan untuk mengelompokkan minat dan bakat. Sebelum pemodelan SVM, melakukan Analisis Data Eksploratif (EDA) sangat penting untuk mengkaji minat dan bakat. Analisis ini memfasilitasi identifikasi variabel, memungkinkan penghilangan nilai yang hilang, dan memastikan pemilihan variabel minat dan bakat yang tepat. Tujuan utamanya adalah mencapai akurasi optimal dalam pemodelan klasifikasi minat dan bakat. Temuan dari penelitian ini berkontribusi pada pengembangan aplikasi yang ditujukan untuk mengkategorikan minat dan bakat di sekolah SDN XYZ. Aplikasi ini dirancang untuk digunakan oleh siswa, membantu mereka membuat keputusan yang terinformasi mengenai pendidikan dan karier masa depan mereka. Kata kunci: Algoritma SVM; exploratory data analysis; machine learning; minat dan bakat 
PENGEMBANGAN SISTEM INFORMASI P3M TERINTEGRASI MELALUI REFACTORING DAN PENAMBAHAN FITUR DENGAN METODE R&D Zulkarnaini; Muhammad Noval; Andre Mariza Putra; Ayu Octarina
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7268

Abstract

This study aims to develop an integrated Information System for the Center for Research and Community Service (P3M) at Politeknik Negeri Sriwijaya through the addition of new features and refactoring of the existing system. The previous system suffered from several limitations, including a monolithic architecture, data duplication across modules, and limited flexibility for further development, which negatively affected the efficiency of research and community service management. This study employed the Research and Development (R&D) method, consisting of requirement analysis, system design, prototype development, testing, evaluation, and refinement stages. The results show that the developed system successfully integrates the management of research and community service proposals, reviewer assessment processes, real-time activity monitoring, and automated report generation. Code refactoring improves readability, modularity, and system sustainability. User testing involving administrators, lecturers, and reviewers indicates improvements in administrative efficiency, data accuracy, and user satisfaction. This study contributes to the development of research management information systems in vocational higher education by offering a fully integrated system and a systematic refactoring approach to legacy applications.
Co-Authors Ayu Octarina Devi Sartika Febie Elfaladonna Muhammad Noval Ria Rahmawati Zulkarnaini