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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence Portal Riset dan Inovasi Sistem Perangkat Lunak
Theresya Simanjuntak
Universitas Katolik Santo Thomas Medan
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Student Grouping Based on Grades and Attendance Using K-Means Theresya Simanjuntak; Jelita Astrid Gulo; Sardo Pardingotan Sipayung
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 1 (2026): Maret 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i1.7283

Abstract

Student grouping based on academic performance is needed to support decision-making in more targeted academic guidance programs. This research implemented K-Means Clustering algorithm to group students based on academic scores and attendance rates. The dataset consisted of 50 student samples with score and attendance percentage attributes ranging from 0-100. Optimal cluster determination used Elbow Method and Silhouette Score with K values varying from 2 to 6. Experimental results showed K=3 produced optimal separation with highest Silhouette Score of 0.72 and WCSS 8,230. Three clusters formed represented high-achieving students (30%), average-performing students (40%), and students requiring special attention (30%). The algorithm converged in average of 8-12 iterations with 90% consistency on multiple runs. Correlation analysis showed very strong relationship between scores and attendance (r=0.89). Interactive visualization system was developed using React.js and Recharts to facilitate result interpretation. This research provided practical contribution in form of clustering framework for early warning identification of at-risk students and academic intervention program recommendations.
Analisis Sistem Pakar untuk Diagnosa Kerusakan Perangkat Komputer menggunakan Metode Forward Chaining Theresya Simanjuntak; Ronita Olive Angelie; Saut Parlindungan Manurung; Jelita Astrid Gulo; Jahanra Girsang
Portal Riset dan Inovasi Sistem Perangkat Lunak Vol. 4 No. 1 (2026): Artikel Penelitian
Publisher : SoraTekno Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59696/prinsip.v4i1.222

Abstract

Kerusakan perangkat komputer menjadi permasalahan yang sering dihadapi pengguna dengan keterbatasan akses ke teknisi profesional. Penelitian ini bertujuan menganalisis implementasi sistem pakar untuk diagnosa kerusakan perangkat komputer menggunakan metode Forward Chaining sebagai mesin inferensi. Metode penelitian menggunakan pendekatan kualitatif deskriptif dengan teknik akuisisi pengetahuan melalui wawancara teknisi dan studi literatur. Basis pengetahuan dirancang menggunakan representasi aturan IF-THEN yang menghubungkan gejala dengan jenis kerusakan. Hasil pengujian terhadap 50 kasus kerusakan menunjukkan sistem mampu memberikan diagnosa dengan tingkat akurasi 86 persen. Forward Chaining terbukti efektif melakukan penalaran sistematis dari fakta gejala menuju kesimpulan diagnosa. Sistem ini memberikan solusi alternatif bagi pengguna awam untuk mengidentifikasi kerusakan komputer secara mandiri tanpa bergantung sepenuhnya pada teknisi profesional. Penelitian ini memberikan kontribusi dalam pengembangan sistem pakar di bidang troubleshooting komputer.
Co-Authors Jahanra Girsang Jelita Astrid Gulo Ronita Olive Angelie Sardo Pardingotan Sipayung Saut Parlindungan Manurung