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All Journal Journal of Information Systems and Technology (JISTech) TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang JROCK (Journal Research on Computing Knowledge)
Muh. Hajar Akbar
Universitas Sembilanbelas November Kolaka
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education

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Journal : JROCK (Journal Research on Computing Knowledge)

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Optimasi Pengelolaan Data Mahasiswa dalam Sistem Pendukung Keputusan Pemilihan Asisten Laboratorium Muh. Nurtanzis Sutoyo; Muh. Hajar Akbar; Alders Paliling; Mardianto Mardianto
Journal Research on Computing Knowledge Vol. 1 No. 1 (2024): November 2024
Publisher : Fakultas Teknologi Informasi

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Abstract

The selection of laboratory assistants is a crucial process that requires decision-making based on various criteria, such as academic performance, activeness, technical skills, and communication abilities. However, manual processes often face challenges such as subjectivity and inefficiency. This study aims to develop a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to optimize the selection of laboratory assistants. We utilize the SAW method because it can integrate criterion weights and normalize data to produce objective preference values. We conducted a simulation using dummy data from 10 students and four criteria, applying weights determined through discussions with laboratory managers. The results show that student A5 achieved the highest preference score (1.000), reflecting optimal performance across all criteria. The developed system also demonstrated its ability to enhance the transparency, efficiency, and accuracy of the selection process. The implementation of this system offers a practical solution for managing student data and making fairer decisions, with potential applications in other selection contexts, such as scholarships or academic awards
Optimasi Prediksi Hasil Belajar Algoritma dengan Metode kNN Syahraeni Rahmadi; Jimsan; Muh. Hajar Akbar
Journal Research on Computing Knowledge Vol. 1 No. 2 (2025): Maret 2025
Publisher : Fakultas Teknologi Informasi

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Abstract

Penentuan performa akademik mahasiswa merupakan aspek penting dalam evaluasi pendidikan, terutama dalam mata kuliah Algoritma yang membutuhkan pemahaman logis dan analitis yang kuat. Penelitian ini menerapkan metode k-Nearest Neighbors (kNN) untuk mengklasifikasikan hasil belajar mahasiswa berdasarkan parameter akademik, seperti nilai tugas, nilai UTS, nilai UAS, jumlah latihan koding, dan tingkat kehadiran. Dataset yang digunakan terdiri dari 53 mahasiswa, dengan proses klasifikasi dilakukan melalui perhitungan Euclidean Distance untuk menentukan jarak antara data uji dan data latih. Hasil penelitian menunjukkan bahwa metode kNN dapat mengklasifikasikan mahasiswa dengan akurasi yang baik, di mana mahasiswa dengan tingkat latihan koding yang tinggi dan kehadiran yang konsisten cenderung memiliki performa akademik lebih baik. Dengan penerapan yang tepat, metode ini dapat digunakan sebagai alat bantu dalam analisis akademik serta membantu pendidik dalam mengidentifikasi mahasiswa yang membutuhkan bimbingan lebih lanjut.
Co-Authors alders paliling Anjar Pradipta Irwan Jaya Jimsan Jimsan La Ode Hasnuddin S. Sagala Mardianto Mardianto Muh Nurtanzis Sutoyo Syahraeni Rahmadi