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All Journal Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmu Komputer
Restu Rakhmawati
Universitas Tidar
Computer Science & IT

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KOMPARASI NEURAL NETWORK DAN SUPPORT VECTOR MACHINE UNTUK DATA TIME SERIES DAN NON-TIME SERIES Suamanda Ika Novichasari; Restu Rakhmawati
Multimatrix Vol. 5 No. 1 (2023): Jurnal Multimatrix Juli 2023
Publisher : Universitas Ngudi Waluyo

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Abstrak— Neural Network dan Support Vector Machine merupakan metode datamining yang sering digunakan. Penelitian ini bertujuan untuk mengetahui performa dari Neural Network dan Support Vector Machine yang diterapkan pada data time series dan non-time series. Sehingga terlihat perbedaan dan keunggulan dari kedua metode tersebut. Data yang digunakan merupakan dataset publik, “Australian Credit Approval dan Polar Ice Data”. Untuk tahap validasi model menggunakan 10fold cross-validation dan proses evaluasi model menggunakan Root Mean Square Error (RMSE). Hasil percobaan membuktikan bahwa pada data time series SVM lebih unggul dari NN dilihat dari kinerja dan waktu eksekusinya, sedangkan pada data non-time series NN lebih unggul. Hasil akhir evaluasi percobaan data time series berbanding terbalik dengan hasil percobaan data non-time series.. Kata kunci— Time series, Non-time series, Neural Nerwork, Support Vector Machine, klasifikasi kelayakan kredit, Prediksi Polar Es.
Segmentasi Kepuasan Mahasiswa Terhadap Dosen Menggunakan K-Means Clustering dan Identifikasi Faktor Dominan Dengan Random Forest Restu Rakhmawati; Suamanda Ika Novichasari; Imam Adi Nata; Fadhila Syahida Wibowo; Zharifa Nur Majidah; Meily Adenia
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10395

Abstract

This study aims to analyze student satisfaction patterns regarding lecturer teaching performance by integrating K-Means Clustering and Random Forest algorithms. The research data includes 5,260 observations analyzed based on service quality dimensions and academic attributes. The results using the Elbow Method and Silhouette Score established three optimal clusters representing Very Satisfied (score 4.84), Moderately Satisfied (score 4.04), and Dissatisfied (score 3.04) segments. Furthermore, the Random Forest algorithm demonstrated an accuracy of 47% on 1,576 test data and successfully identified that Semester Credit Load (SKS) is the most dominant determinant influencing satisfaction, with an importance value of 47.46%. A unique finding shows that students with the highest academic load (average 20.81 SKS) are actually in the Very Satisfied segment. This study concludes that more intensive student academic engagement correlates positively with appreciation for lecturer teaching quality. These results provide strategic guidance for university management to improve services based on student academic profiles.
Co-Authors Fadhila Syahida Wibowo Imam Adi Nata Meily Adenia Suamanda Ika Novichasari Suamanda Ika Novichasari Zharifa Nur Majidah