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All Journal Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Nusantara Journal of Computers and its Applications
Sulthan, M Burhanis
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Komparasi Kinerja Algoritma SVM-RFE dan RNN-LSTM untuk Analisis Sentimen Review Mahasiswa Sulthan, M Burhanis; Nafisah, Tizanatun; Suhartini, Luluk
Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Vol 5 No 2 (2024): Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Oktober 2024
Publisher : Fakultas Teknik Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/jatim.v5i2.3274

Abstract

Analisis sentimen terhadap ulasan mahasiswa berperan penting dalam meningkatkan kualitas layanan biro akademik. Penelitian ini membandingkan kinerja algoritma Support Vector Machine (SVM) dengan Recursive Feature Elimination (SVM-RFE) dan Recurrent Neural Network dengan Long Short-Term Memory (RNN-LSTM) dalam menganalisis sentimen ulasan mahasiswa terkait pelayanan Biro Akademik Universitas Annuqayah. Data dikumpulkan melalui Google Form dan diproses menggunakan tahapan cleaning, case folding, stopword removal, tokenization, dan stemming. Hasil evaluasi menunjukkan bahwa SVM mencapai akurasi tertinggi sebesar 93,75%, diikuti oleh SVM-RFE (90,63%), RNN-LSTM (87,50%), dan RNN (81,25%). Temuan ini mengindikasikan bahwa SVM lebih unggul dalam mengklasifikasikan sentimen pada dataset ini, sementara SVM-RFE menunjukkan performa yang kompetitif dengan seleksi fitur yang tepat. Meskipun RNN dan RNN-LSTM dikenal efektif dalam menangani data sekuensial, pada penelitian ini, SVM dan SVM-RFE terbukti lebih optimal untuk analisis sentimen ulasan mahasiswa. Hasil penelitian ini memberikan wawasan bagi pemilihan algoritma analisis sentimen di lingkungan pendidikan tinggi. Pemilihan model yang tepat bergantung pada karakteristik data serta tujuan analisis yang diinginkan.
Use of Deep Learning and k-Nearest Neighbor Algorithms for Recognition of Fruit Types Sulthan, M Burhanis; Hartiansyah, Fiqih Rahman; Hafin, Aqid Fahri
NJCA (Nusantara Journal of Computers and Its Applications) Vol 10, No 1 (2025): Edisi Juni 2025
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v10i1.293

Abstract

fruit recognition was done in this research specifically for fruit image. The recognition of fruit in this study can be implemented to know the number of fruits that exist. Fruit image trained into several labels (fruit types) that are classified by data testing. There are several processes and methods undertaken in this research until the classification process, one of this i.e. Gaussian filter to improve the quality of fruit image recognition. Furthermore, the feature extraction process uses Gabor filter and for feature selection, PCA technic is respectively used to select some of the best features. The selected feature will be classified using deep learning and k-nearest neighbor (k-NN) method. Moreover, the results of the processes done carried out in achieving an accuracy of 95.01%.
Co-Authors Argus Argus, Argus Hafin, Aqid Fahri Hartiansyah, Fiqih Rahman Mahrus Ali Nafisah, Tizanatun Suhartini, Luluk Syafiqah, Lu’luatus