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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Ginting, Joel Arie Putranta
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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ANALISIS SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI JENIS KELAMIN PADA IKAN CUPANG DENGAN BANTUAN LOCAL BINARY PATTERN (LBP) Ginting, Joel Arie Putranta; Maya Sari, Radiatun; Rafli Dewantara Siregar, Muhammad; Kiswanto, Dedi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.12028

Abstract

Ikan cupang (Betta splendens) merupakan salah satu jenis ikan hias yang banyak diminati karena keindahan warnanya serta daya tahan tubuhnya yang kuat. Klasifikasi jenis kelamin ikan cupang adalah proses penting dalam budidaya ikan, terutama dalam memisahkan antara ikan jantan dan betina untuk tujuan pembiakan. Metode manual untuk klasifikasi ini sering kali memakan waktu dan rentan terhadap kesalahan, Penelitian ini mengembangkan metode klasifikasi jenis kelamin ikan cupang (Betta splendens) menggunakan kombinasi Local Binary Pattern (LBP) dan Support Vector Machine (SVM) Clustering untuk meningkatkan efisiensi dan akurasi dibandingkan metode manual. LBP digunakan untuk ekstraksi fitur visual dari gambar ikan, sementara SVM Clustering mengelompokkan jenis kelamin berdasarkan fitur tersebut. Dengan sampel gambar dari berbagai sudut, pendekatan ini mencapai akurasi hingga 80%, menunjukkan efektivitasnya dalam membedakan ikan jantan dan betina serta kemampuannya beradaptasi terhadap variasi bentuk dan warna ikan. Metode ini berpotensi meningkatkan produktivitas dan mengurangi kesalahan dalam industri budidaya ikan hias
Web-Based Decision Support System for Employee Performance Evaluation at Madani Hotel Using the WASPAS (Weighted Aggregated Sum Product Assessment) Method Telaumbanua, Louders Yoakim; Saragih, Rendy Zikriansyah Fauzi; Ginting, Joel Arie Putranta; Niska, Debi Yandra
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.24790

Abstract

Human resource management in the hospitality industry requires an objective and efficient employee performance evaluation. This study aims to design and implement a web-based Decision Support System (DSS) for evaluating the performance of Madani Hotel employees using the Weighted Aggregated Sum Product Assessment (WASPAS) method. Employee performance data (10 samples) were collected through observation and supervisor assessments based on nine criteria: Accuracy of Doing Job, Job Knowledge, Potential to Learn to Other Section, Initiative/Motivation, Hard Working, Able to Communicate with Others, Discipline, Productivity/Efficiency, and Responsibility. The weights of the criteria were determined based on their relevance to the job and were then normalized. The WASPAS calculation process was carried out in the CodeIgniter 4 backend, producing a Q score for each employee. The results showed a high correlation (Spearman’s rho = 0.88) between the system ranking and manual ranking, with A.P. ranked highest and E.W. ranked lowest. The implementation of this DSS reduces subjectivity and accelerates the performance evaluation process. The developed system also provides an automated reporting module and a responsive interface based on Bootstrap SAdmin2.
Co-Authors Kiswanto, Dedi Maya Sari, Radiatun Niska, Debi Yandra Rafli Dewantara Siregar, Muhammad Saragih, Rendy Zikriansyah Fauzi Telaumbanua, Louders Yoakim