Klasifikasi Ulasan Mahasiswa tentang Fasilitas dan Pelayanan Perguruan Tinggi Swasta PGRI dengan BiLSTM

Nazdaen Akbar Nururrahman

doi:10.70716/alpha.v2i1.358

Authors

Nazdaen Akbar Nururrahman Universitas PGRI Yogyakarta, Indonesia

DOI:

https://doi.org/10.70716/alpha.v2i1.358

Keywords:

Student Satisfaction , Service Quality, Student Reviews, Bidirectional Long Short-Term Memory (BiLSTM), FastText Embedding, Higher Education Services, Text Classification

Abstract

Student satisfaction with campus facilities and services is an important indicator in assessing the quality of higher education. This study develops a system for classifying student reviews related to facilities and services at PGRI Private Universities using the Bidirectional Long Short-Term Memory (BiLSTM) method. The research data consist of 3,067 student reviews collected through Google Forms and Google Maps, covering five service aspects: Physical Facilities and Core Infrastructure; Academic Support Facilities and Learning Resources; Administrative Services and Staff; Facilities and Environment Supporting Non-Academic Activities; and Security and Accessibility.

The BiLSTM method with 300-dimensional FastText word embeddings is employed to classify reviews into service aspect categories and satisfaction levels (Very Satisfied, Satisfied, Moderately Satisfied, Less Satisfied, and Not Satisfied). The model architecture comprises two BiLSTM layers with 128 and 64 units, respectively, along with a dropout mechanism to reduce the risk of overfitting. Model performance is evaluated using a confusion matrix, precision, recall, F1-score, and overall accuracy.

The results indicate that the BiLSTM model is able to classify service aspects with good accuracy, although the performance of satisfaction level classification is still affected by data imbalance and the similarity of expressions across satisfaction categories. Overall, the proposed system can provide automated analysis of student reviews and serve as a decision-support tool for universities in understanding service quality based on review data in a more objective and structured manner.

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References

M. Briyantoro, A. Surya Nugraha Institut Teknologi dan Bisnis Trenggalek, and K. Trenggalek, “Analisis Pengaruh Fasilitas Kampus dan Pelayanan Akademik Terhadap Kepuasan Mahasiswa Institut Teknologi dan Bisnis Trenggalek,” vol. 3, no. 1, 2023, doi.org/10.55606/CEMERLANG.V3I1.710 Hari Prasad, K. V. (2021). Role of Natural Enemies in Maize Pest Management. Journal of Biological Control, 35(2), 87–96. https://doi.org/10.18311/jbc/2021/27145

N. S. Ginanjar, D. Resmanasari, R. Mulyani, S. Tinggi, I. Ekonomi, and P. Sukabumi, “Kualitas Pelayanan dan Fasilitas terhadap Kepuasan Mahasiswa STIE PGRI Sukabumi,” 2020. : https://ejournal.stiepgri.ac.id/index.php/ekonomak/article/view/227.

A. Septiani and I. Budi, “KLASIFIKASI ULASAN PENGGUNA APLIKASI: STUDI KASUS APLIKASI IPUSNAS PERPUSTAKAAN NASIONAL REPUBLIK INDONESIA (PNRI).”

E. Budihartono, “Analisa Penerapan Logika Fuzzy dalam Visualisasi Kepuasan Mahasiswa pada Pembelajaran Prodi DIII Teknik Komputer,” Digital Transformation Technology, vol. 3, no. 2, pp. 677–685, Nov. 2023, doi: 10.47709/digitech.v3i2.3242.

D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia,” Jurnal Manajemen Informatika (JAMIKA), vol. 12, no. 2, pp. 89–99, Sep. 2022, doi: 10.34010/jamika.v12i2.7764.

Y. N. Fernando, A. Y. Rahman, and R. P. Putra, “Analisis Sentimen pada Ulasan Konsumen Ayam Goreng Nelongso di Google Maps Menggunakan Metode Bidirectional Long Short-Term Memory (Bi-LSTM) Sentiment Analysis of Consumer Reviews on Ayam Goreng Nelongso in Google Maps Using the Bidirectional Long Short-Term Memory (Bi-LSTM) Method.”

“JITE (Journal of Informatics and Telecommunication Engineering) Sentiment Analysis to Evaluate Public Service Perception among Surakarta City Residents Using the BiLSTM Model,” 2025, doi: 10.31289/jite.v9i1.15498.

F. A. Sihombing, “Kajian Fuzzy Metode Mamdani dan Fuzzy Metode Sugeno serta Implementasinya,” Copyright @ Firginia Astuti Sihombing INNOVATIVE: Journal Of Social Science Research, vol. 4, pp. 4940–4955.

N. J. Aruan and A. H. Hasugian, “Implementasi Fuzzy Mamdani Menentukan Tingkat Kepuasan Penumpang Trans Metro Deli Kota Medan,” Jurnal Teknologi Sistem Informasi dan Aplikasi, vol. 6, no. 3, pp. 334–342, Jul. 2023, doi: 10.32493/jtsi.v6i3.30382.

E. Subowo, F. Adi Artanto, I. Putri, and W. Umaedi, “BLTSM untuk analisis sentimen berbasis aspek pada aplikasi belanja online dengan cicilan”.

M. Dzaki, A. Nasir, and S. Hidayat, “Terbit online pada laman web jurnal: https://ejurnalunsam.id/index.php/jicom/ Analisis Sentimen Ulasan Film Menggunakan Metode BiLSTM”, [Online]. Available: https://ejurnalunsam.id/index.php/jicom/

R. Illahi, S. Agustian, F. Yanto, P. H. Studi Teknik Informatika Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau Jl Soebrantas, S. Baru, and K. Pekanbaru, “KLASIFIKASI SENTIMEN MENGGUNAKAN BIDIRECTIONAL LSTM DAN INDOBERT DENGAN DATASET TERBATAS,” 2025.

A. H. Prihamayu, “PENGGUNAAN LOGIKA FUZZY METODE MAMDANI UNTUK MENGETAHUI TINGKAT KEPUASAN PELAYANAN TENAGA KEPENDIDIKAN OLEH MAHASISWA”.

Z. N. Jati, T. Hastono, and F. Andrian, “Prediksi Produksi Bawang Merah di Kota Yogyakarta menggunakan Metode Fuzzy Mamdani,” vol. 2, no. 2, pp. 129–137, doi: 10.55606/jupikom.v3i1.

N. J. Aruan and A. H. Hasugian, “Implementasi Fuzzy Mamdani Menentukan Tingkat Kepuasan Penumpang Trans Metro Deli Kota Medan,” Jurnal Teknologi Sistem Informasi dan Aplikasi, vol. 6, no. 3, pp. 334–342, Jul. 2023, doi: 10.32493/jtsi.v6i3.30382.

Khoirunisa Khoirunisa, Steven Ronis Pangaribuan, Bitsy Naviri Ismaniar, Beby Dwi Febriyanti, and Dheanita Sekarini Octanisa, “Sistem Inferensi Fuzzy Mamdani dalam Pengukuran Rata-Rata Tingkat Kepuasan Wisatawan Green Gumuk Candi,” Gemawisata: Jurnal Ilmiah Pariwisata, vol. 21, no. 2, pp. 196–205, May 2025, doi: 10.56910/gemawisata.v21i2.687.

J. Amalia, J. Pakpahan, M. Pakpahan, Y. Panjaitan, F. Informatika dan Teknik Elektro, and I. Teknologi Del, “Model Klasifikasi Berita Palsu Menggunakan Bidirectional LSTM Dan Word2Vec Sebagai Vektorisasi,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 4, 2022, [Online]. Available: http://jurnal.mdp.ac.id

A. Prayoga Permana, T. Chamidy, and C. Crysdian, “Klasifikasi Ulasan Fasilitas Publik Menggunakan Metode Naïve Bayes dengan Seleksi Fitur Chi-Square,” MEI, 2023.

Y. Sylviani, M. Nugrah Nopiasadi, A. W. Alam, and O. Hijuzaman, “Pengaruh Pelayanan Akademik, Fasilitas dan Kinerja Kampus terhadap Kepuasan Mahasiswa/I di Kabupaten Purwakarta The Influence of Academic Services, Facilities, and Campus Performance on Student Satisfaction in Purwakarta Regency,” vol. 2024, no. 1, pp. 99–108, doi: 10.51132/teknologika.v14i1.

M. Riziq sirfatullah Alfarizi, M. Zidan Al-farish, M. Taufiqurrahman, G. Ardiansah, and M. Elgar, “PENGGUNAAN PYTHON SEBAGAI BAHASA PEMROGRAMAN UNTUK MACHINE LEARNING DAN DEEP LEARNING,” 2023.

O. Mawarni Saputri, “UTILIZING GOOGLE FORM TO FACILITATE EVALUATION PROCESS OF THE ONLINE LEARNING,” Jurnal Pengabdian Kepada Masyarakat, vol. 1, no. 11, 2022, [Online]. Available: http://bajangjournal.com/index.php/J-ABDI

J. Ipmawati, S. Saifulloh, and K. Kusnawi, “Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 247–256, Jan. 2024, doi: 10.57152/malcom.v4i1.1066.

Y. Akkem, B. S. Kumar, and A. Varanasi, “Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation,” Indian J Sci Technol, vol. 16, no. 48, pp. 4688–4702, Dec. 2023, doi: 10.17485/IJST/v16i48.2850.

H. Faisal, A. Febriandirza, and F. N. Hasan, “Analisis Sentimen Terkait Ulasan Pada Aplikasi PLN Mobile Menggunakan Metode Support Vector Machine,” 2024.

C. Cahyaningtyas, Y. Nataliani, and I. R. Widiasari, “Analisis sentimen pada rating aplikasi Shopee menggunakan metode Decision Tree berbasis SMOTE,” AITI: Jurnal Teknologi Informasi, vol. 18, no. Agustus, pp. 173–184, 2021.

J. W. Iskandar and Y. Nataliani, “Comparison of Naïve Bayes, SVM, and k-NN for Aspect-Based Gadget Sentiment Analysis,” Jurnal RESTI, vol. 5, no. 6, pp. 1120–1126, Dec. 2021, doi: 10.29207/resti.v5i6.3588.

A. Pranata and N. Azmi Verdikha, “KLASIFIKASI TEKS QUICK COUNT PEMILIHAN PRESIDEN 2024 PADA TWITTER MENGGUNAKAN METODE TF-IDF DAN NAIVE BAYES,” Jurnal Informatika Terpadu, vol. 10, no. 2, pp. 93–100, 2024, [Online]. Available: https://journal.nurulfikri.ac.id/index.php/JIT

M. Ainur Rohman and T. Chamidy, “Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile Bidirectional GRU with Attention Mechanism on Sentiment Analysis of PLN Mobile.”

M. A. M. Pahendra, S. Anraeni, and L. B. Ilmawan, “Perbandingan Kinerja Word Embedding dalam Analisis Sentimen Ulasan Pengguna Aplikasi Perjalanan,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 11, no. 1, pp. 49–62, Apr. 2025, doi: 10.28932/jutisi.v11i1.9681.

M. Susanty and S. Sukardi, “Perbandingan Pre-trained Word Embedding dan Embedding Layer untuk Named-Entity Recognition Bahasa Indonesia,” PETIR, vol. 14, no. 2, pp. 247–257, Sep. 2021, doi: 10.33322/petir.v14i2.1164.