The Implementation of Rough Set Algorithm to Classify Student Comfort Level Using Rosetta
DOI:
https://doi.org/10.55123/jomlai.v2i3.2884Keywords:
Rough Set , Classification, Comfort Level , College Student, RosettaAbstract
Student comfort in the campus environment is an integral aspect in creating optimal learning conditions. Students who feel comfortable are more likely to be involved in academic and social activities. Several students were identified as frequently not attending class, and their interest in learning appeared to be lacking. This creates serious challenges in creating an optimal learning environment and meeting student needs. The research classifies student comfort levels and also provides a basis for developing more targeted campus policies. The data collection method uses a questionnaire method. The data processing method uses the Rough Set algorithm. Data processing uses Rosetta software. Based on the analysis carried out from 154 rules, the number of occurrences of the rest level attribute was 94 times, the class environment attribute was 110 times, the assignment difficulty level attribute was 114 times, the lecturer's teaching method attribute was 98 times, the campus facilities attribute was 136 times. So it can be seen that the campus facility attribute is the most influential because it has the highest number of occurrences. The next influential attribute after facilities is the level of difficulty of assignments, class environment, lecturer's teaching method and level of rest and reduce statistics show that campus facilities are a condition attribute that is very influential in student comfort levels, namely with an occurrence of 90,9%.
References
M. Sihite, K. Nadapdap, R. Gultom, and A. Saleh, ‘Peran Mutu Dalam Meningkatkan Daya Saing Perguruan Tinggi’, Jurnal Ilmiah Methonomi, vol. 5, no. 1, pp. 35–48, 2019.
S. Safira et al., ‘Humantech Jurnal Ilmiah Multi Disiplin Indonesia Pendidikan Islam Dalam Era Globalisasi’, vol. 2, no. 7, 2023.
K. Karningsih, ‘Hubungan Motivasi Belajar Dengan Prestasi Belajar Mahasiswa Berbasis Daring Di Masa Pandemi Covid-19’, Public Service and Governance Journal, vol. 3, no. 1, pp. 53–66, 2022.
Paradita, ‘Pentingnya Media Pembelajaran dalam Meningkatkan Prestasi Belajar’, ECIE journal, vol. 3, no. 1, pp. 73–85, 2022.
F. Budi Raharjo, F. Maradita, and H. Sri Nuryani, ‘Analisis Faktor-Faktor Yang Mempengaruhi Keputusan Mahasiswa Asal Kabupaten Sumbawa Dalam Memilih Perguruan Tinggi Universitas Teknologi Sumbawa’, Jurnal Manajemen dan Bisnis, vol. 2, no. 2, pp. 96–104, 2019.
M. Masrizal and A. P. Juledi, ‘Analisis Algoritma Roughset Pada Penerimaan Beasiswa’, Journal of Information System Research (JOSH), vol. 3, no. 4, pp. 564–570, 2022.
R. I. Salam and S. Defit, ‘Penentuan Tingkat Kerusakan Peralatan Labor Komputer Menggunakan Data Mining Rough Set’, vol. 1, no. 4, pp. 36–41, 2019.
Masriza and M. H. Muanandar, ‘Identifikasi Siswa Bermasalah Dengan Menggunakan Metode Rough Set (Studi Di SMA N 4 Pariaman)’, Jurnal Ilmiah Fakultas Sains dan Teknologi, vol. 7, no. 2, pp. 90–99, 2019.
R. J. Zega, ‘Penerapan Algoritma Rough Set Untuk Mengidentifikasi Faktor-Faktor Resignnya Karyawan (Study Kasus : PT. Sumber Alfaria Trijaya, Tbk )’, Pelita Informatika : Informasi dan Informatika, vol. 9, no. 4, pp. 296–302, 2021.
S. R. Ningsih, R. Wulansari, D. Hartama, A. P. Windarto, and A. Wanto, ‘Analysis of PROMETHEE II Method on Selection of Lecturer Community Service Grant Proposals’, in Journal of Physics: Conference Series, Aug. 2019, p. 012004. doi: 10.1088/1742-6596/1255/1/012004.
P. Alkhairi, L. P. Purba, A. Eryzha, A. P. Windarto, and A. Wanto, ‘The Analysis of the ELECTREE II Algorithm in Determining the Doubts of the Community Doing Business Online’, in Journal of Physics: Conference Series, Institute of Physics Publishing, Sep. 2019, p. 012010. doi: 10.1088/1742-6596/1255/1/012010.
R. Watrianthos, W. A. Ritonga, A. Rengganis, A. Wanto, and M. Isa Indrawan, ‘Implementation of PROMETHEE-GAIA Method for Lecturer Performance Evaluation’, Journal of Physics: Conference Series, vol. 1933, no. 1, p. 012067, 2021, doi: 10.1088/1742-6596/1933/1/012067.
S. Sundari, A. Wanto, Saifullah, and I. Gunawan, ‘Sistem Pendukung Keputusan Dengan Menggunakan Metode Electre Dalam Merekomendasikan Dosen Berprestasi Bidang Ilmu Komputer (Study Kasus di AMIK & STIKOM Tunas Bangsa)’, in Seminar Nasional Multi Disiplin Ilmu, 2017, pp. 1–6. doi: 10.17605/OSF.IO/4TWG6.
M. Widyasuti, A. Wanto, D. Hartama, and E. Purwanto, ‘Rekomendasi Penjualan Aksesoris Handphone Menggunakan Metode Analitycal Hierarchy Process (AHP)’, Konferensi Nasional Teknologi Informasi dan Komputer (KOMIK), vol. I, no. 1, pp. 27–32, 2017.
K. Fatmawati et al., ‘Analysis of Promothee II Method in the Selection of the Best Formula for Infants Under Three Years’, Journal of Physics: Conference Series, vol. 1255, no. 1, p. 012009, Aug. 2019, doi: 10.1088/1742-6596/1255/1/012009.
N. Rofiqo, A. P. Windarto, and A. Wanto, ‘Penerapan Metode VIKOR Pada Faktor Penyebab Rendahnya Minat Mahasiswa Dalam Menulis Artikel Ilmiah’, Seminar Nasional Sains & Teknologi Informasi (SENSASI), vol. 1, no. 1, pp. 228–237, 2018.
S. R. Ningsih, D. Hartama, A. Wanto, I. Parlina, and Solikhun, ‘Penerapan Sistem Pendukung Keputusan Pada Pemilihan Objek Wisata di Simalungun’, in Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), 2019, pp. 731–735.
I. A. R. Simbolon, F. Yatussa’ada, and A. Wanto, ‘Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia’, Jurnal Informatika Upgris, vol. 4, no. 2, pp. 163–169, 2018, doi: 10.26877/jiu.v4i2.2423.
W. Saputra, J. T. Hardinata, and A. Wanto, ‘Resilient method in determining the best architectural model for predicting open unemployment in Indonesia’, IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, p. 012115, Jan. 2020, doi: 10.1088/1757-899X/725/1/012115.
E. Hartato, D. Sitorus, and A. Wanto, ‘Analisis Jaringan Saraf Tiruan Untuk Prediksi Luas Panen Biofarmaka Di Indonesia’, SemanTIK, vol. 4, no. 1, pp. 49–56, 2018, doi: 10.55679/semantik.v4i1.4201.
B. K. Sihotang and A. Wanto, ‘Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Tamu Pada Hotel Non Bintang’, Jurnal Teknologi Informasi Techno, vol. 17, no. 4, pp. 333–346, 2018, doi: 10.33633/tc.v17i4.1762.
I. S. Purba and A. Wanto, ‘Prediksi Jumlah Nilai Impor Sumatera Utara Menurut Negara Asal Menggunakan Algoritma Backpropagation’, Jurnal Teknologi Informasi Techno, vol. 17, no. 3, pp. 302–311, 2018, doi: 10.33633/tc.v17i3.1769.
A. Wanto, S. Defit, and A. P. Windarto, ‘Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana’, RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, pp. 254–264, 2021, doi: 10.29207/resti.v5i2.3031.
A. Wanto and J. T. Hardinata, ‘Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0’, IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, p. 012114, Jan. 2020, doi: 10.1088/1757-899X/725/1/012114.
N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, and A. Wanto, ‘Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms’, IOP Conference Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012018, 2021, doi: 10.1088/1757-899x/1071/1/012018.
T. H. Sinaga, A. Wanto, I. Gunawan, S. Sumarno, and Z. M. Nasution, ‘Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM’, Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 9–20, 2021, doi: 10.47709/cnahpc.v3i1.923.
A. Pradipta, D. Hartama, A. Wanto, S. Saifullah, and J. Jalaluddin, ‘The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm’, IJISTECH (International Journal of Information System & Technology), vol. 3, no. 1, pp. 31–36, 2019, doi: 10.30645/ijistech.v3i1.30.
N. A. Febriyati, A. D. Gs, and A. Wanto, ‘GRDP Growth Rate Clustering in Surabaya City uses the K- Means Algorithm’, International Journal of Information System & Technology, vol. 3, no. 2, pp. 276–283, 2020, doi: 10.30645/ijistech.v3i2.60.
I. Parlina et al., ‘Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category’, in Journal of Physics: Conference Series, Aug. 2019, p. 012031. doi: 10.1088/1742-6596/1255/1/012031.
W. Yulianti, ‘Analisis Persepsi Kepuasan Mahasiswa Tentang Kualitas Pelayanan Administrasi Mahasiswa Dengan Metode Roughset’, Journal of Machine Learning and Data Analytics, vol. 1, no. 1, pp. 65–74, 2022.
S. Lestari, ‘Analisis Tingkat Kepuasan Pelanggan Dalam Membeli Peralatan Kue Dengan Menggunakan Metode Rough Set Pada PT. XYZ’, INSOLOGI: Jurnal Sains dan Teknologi, vol. 1, no. 3, pp. 300–312, 2022, doi: 10.55123/insologi.v1i3.563.
R. Alamsyah and Allwine, ‘Penerapan Metode Rough Set Pada Tingkat Kepuasan Kostumer Terhadap Kualitas Pelayanan Lapangan Futsal’, Jurnal BisantaraInformatika (JBI), vol. 7, no. 1, pp. 1–14, 2023.
A. Sofiyan and A. Azkiya, ‘Penerapan Metode Rough Set Menganalisis Penyakit Yang Sering Dikeluhkan Pasien (Studi Kasus Puskesmas Jaya Mukti Dumai)’, Jurnal Informatika, Manajemen dan Komputer, vol. 14, no. 1, pp. 31–40, 2022, doi: 10.36723/juri.v14i1.348.
S. Samaray, P. Studi, and T. Informatika, ‘Implementasi Algoritma Rough Set dengan Software Rosetta untuk Prediksi Hasil Belajar’, Eksplora Informatika, vol. 11, no. 1, pp. 57–66, 2021.
B. Bangun, R. Pane, A. A. Ritonga, I. Purnama, and L. Hamkimi Siregar, ‘Sistem Keputusan Kinerja Dosen Absensi Data Menggunakan Algoritma Aprirori Studi Kasus Fakultas Sains Dan Teknologi Universitas Labuhanbatu’, Journal Computer Science and Information Technology, vol. 1, no. 1, pp. 16–22, 2020.
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