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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Margaretha Yohanna
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Computer Science & IT Economics, Econometrics & Finance

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PREDIKSI KEHADIRAN PESERTA RAKORNAS APTIKOM MENGGUNAKAN METODE LEAST SQUARE Silaban, Cristina Adelia Putri; Manalu, Darwis Robinson; Margaretha Yohanna
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4642

Abstract

APTIKOM (Asosiasi Pendidikan Tinggi Informatika dan Komputer) merupakan asosiasi yang mewadahi Perguruan Tinggi Indonesia yang memiliki rumpun Ilmu Komputer dan Teknologi Informasi yang berperan dalam pengembangan kurikulum, standar pendidikan dan sertifikasi professional di bidang Teknologi Informasi (TI).Prediksi jumlah kehadiran peserta Rakornas APTIKOM dapat memberikan perkiraan jumlah peserta di tahun selanjutnya untuk memudahkan panitia penyelenggara Rakornas dalam merencanakan kegiatan secara lebih terukur dan berbasis data dengan menggunakan metode Least Square. Hasil prediksi dianalisis dan dikategorikan menjadi tingkat keaktifan berdasarkan frekuensi kehadiran peserta. Hasil evaluasi model menunjukkan bahwa metode Least Square dapat digunakan secara efektif untuk memprediksi pola kepesertaan dan menghasilkan analisis kategori yang bermanfaat sebagai dasar pengambilan keputusan oleh pihak APTIKOM.
Penerapan Algoritma K-Nearest Neighbors dalam Mengklasifikasi Penyakit Multiple Sclerosis Sitompul, Andrew Efraim Nicholas; Margaretha Yohanna; Arina Prima Silalahi
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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Abstract

The central nervous system is impacted by multiple sclerosis (MS), a chronic autoimmune disease that requires early identification for successful treatment. Because of its many symptoms and similarities to other neurological disorders, MS can be difficult to diagnose. Artificial intelligence techniques like the K-Nearest Neighbors (KNN) algorithm can be used to help with quicker and more precise classification in order to solve this problem. The goal of this study is to classify MS using the KNN technique and assess how well it performs in this regard. The Kaggle platform provided the dataset, which consists of 273 patient records with 18 clinical characteristics. With k = 3 as the number of neighbors, the data was split into 80% for training and 20% for testing. The Python programming language was used to implement the classification procedure. According to the findings, the KNN algorithm classified MS with an accuracy of 81.82%. The precision, recall, and f1-score for class 1 were 0.83, 0.76, and 0.79, respectively, according to additional analysis utilizing a classification report, whereas the scores for class 2 were 0.81, 0.87, and 0.84. These findings suggest that the KNN method has the potential to serve as a supportive tool in the diagnosis of Multiple Sclerosis.
PREDIKSI KEHADIRAN PESERTA RAKORNAS APTIKOM MENGGUNAKAN METODE LEAST SQUARE Cristina Adelia Putri Silaban; Manalu, Darwis Robinson; Margaretha Yohanna
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

APTIKOM (Asosiasi Pendidikan Tinggi Informatika dan Komputer) is an association that brings together Indonesian Universities offering Computer Science and Information Technology programs, playing a role in curriculum development, educational standards, and professional certification in the field of Information Technology (IT). Predicting the number of participants at the APTIKOM National Conference can provide an estimate of the number of participants for the following year, helping the conference organizing committee plan activities more effectively anda data-driven using the Least Square method. The prediction results are analyzed and categorized into activity levels based on participant attendance frequency. The model evaluation results indicate that the Least Square method can be effectively used to predict participation patterns and generate useful category analyses as a basis for decision-making by APTIKOM.
Co-Authors Cristina Adelia Putri Silaban Darwis Robinson Manalu Silaban, Cristina Adelia Putri Silalahi, Arina Prima Sitompul, Andrew Efraim Nicholas