Klasifikasi Kualitas Redaman Kabel Fiber Optik Berdasarkan Terminasi Menggunakan Algoritma K-Means

Naufal Galfan Syah; Arif Senja Fitrani; Irwan Alnarus Kautsar; Cindy  Taurusta

doi:10.32664/smatika.v15i02.1899

Penulis

Naufal Galfan Syah Universitas Muhammadiyah Sidoarjo
Arif Senja Fitrani Universitas Muhammadiyah Sidoarjo
Irwan Alnarus Kautsar Universitas Muhammadiyah Sidoarjo
Cindy Taurusta Universitas Muhammadiyah Sidoarjo

DOI:

https://doi.org/10.32664/smatika.v15i02.1899

Kata Kunci:

K-Means, Klasifikasi, Data Mining, Kualitas Redaman, Fiber Optik

Abstrak

Penelitian ini bertujuan untuk mengklasifikasikan kualitas redaman pada jaringan fiber optik menggunakan algoritma K-Means. Model klasifikasi dibangun untuk mengelompokkan data berdasarkan karakteristik parameter jaringan fiber optik yang diperoleh dari hasil pengukuran lapangan. Penelitian ini juga membandingkan performa model berdasarkan dua pendekatan, yaitu dengan menggunakan seluruh fitur dan dengan menggunakan fitur yang telah diseleksi. Evaluasi dilakukan menggunakan dua metrik utama, yaitu Sum of Squared Errors (SSE) dan Silhouette Coefficient, guna menilai kualitas hasil klasterisasi. Pengujian menunjukkan bahwa penggunaan fitur terpilih memberikan hasil klaster yang lebih baik, dengan nilai SSE sebesar 91.75 dan Silhouette Coefficient sebesar 0.5923, dibandingkan dengan penggunaan seluruh fitur yang menghasilkan SSE sebesar 101.98 dan Silhouette Coefficient sebesar 0.4977. Hasil ini menunjukkan bahwa pemilihan fitur yang tepat dapat memberikan dampak positif terhadap kualitas klaster, meskipun bukan satu-satunya faktor penentu. Secara keseluruhan, K-Means terbukti mampu mengelompokkan kualitas redaman secara efektif, sehingga dapat dimanfaatkan untuk mendukung pemeliharaan dan pemantauan jaringan fiber optik secara lebih efisien.

Referensi

[1] P. Yusman, D. U. Dari, D. Al Farabi, and R. Ardana, “ANALISIS MANAGEMENT DAN REDAMAN FIBER OPTIC PADA PT . JALUR NET INFOTEK,” BHAKTI NAGORI : Jurnal Pengabdian kepada Masyarakat, vol. 4, pp. 31–37, 2024, doi: 10.36378/bhakti_nagori.v4i1.3886.

[2] Muhammad Ridhwan and Lela Nurpulaela, “Analisis Penggunaan Jaringan Fiber Optik Di Area Kawasan Bijb Kertajati,” Jurnal Ilmiah Wahana Pendidikan, vol. 9, no. 14, pp. 467–479, 2023.

[3] M. A. Rahmatulloh, D. Hanto, M. Yantidewi, Agitta Rianaris, and R.A. Firdaus, “Analisis Redaman Fiber Optik dengan Menggunakan Pemodelan Software Optisystem,” Jurnal Kolaboratif Sains, vol. 6, no. 7, pp. 630–639, 2023, doi: 10.56338/jks.v6i7.3795.

[4] M. Nurwahidah, U. A. Ahmad, R. E. Saputra, and P. Y. Pangestu, “Analisis Jarak Jangkauan Jaringan Fiber To The Home ( Ftth ) dengan Teknologi Gigabit Passive Optical Network ( Gpon ) Berdasarkan Link Power Budget,” Prosiding Seminar Nasional Teknik Elektro dan Informatika, vol. 8, no. September, pp. 203–207, 2021.

[5] D. Setiawan, O. Yuliani, P. Studi Teknik Elektro, and F. Teknologi dan Industri ITNY, “Triple Play (3P) Di Kirana Garden Residence Yogyakarta,” Jmte, vol. 04, no. 02, pp. 20–29, 2023.

[6] I. M. Zukri, “Analisis Pengaruh Penggunaan Passive Splitter Pada Optical Distribution Point (Odp) Terhadap Kinerja Jaringan Di Rumah Pelanggan,” Jurnal Ilmiah Poli Rekayasa, vol. 18, no. 1, p. 32, 2022, doi: 10.30630/jipr.18.1.249.

[7] M. N. Ikhsanto and A. Setiawan, “Jaringan Akses Fiber To The Home (FTTH) Dengan Teknologi Gigabyte Passive Optical Network (GPON) PT. Telkom Kota Metro,” Journal Computer Science and Information Systems : J-Cosys, vol. 4, no. 1, pp. 57–63, 2024, doi: 10.53514/jco.v4i1.497.

[8] Suwandi, Y. Akbar, and D. I. Mulyana, “Optimasi Gigabit Passive Optical Network dan Algoritma Naïve Bayes dalam Analisis Jaringan FTTH,” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 9, no. 1, pp. 186–192, Nov. 2024, doi: 10.35870/jtik.v9i1.3058.

[9] H. Liu, J. Chen, J. Dy, and Y. Fu, “Transforming Complex Problems Into K-Means Solutions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 7, pp. 9149–9168, 2023, doi: 10.1109/TPAMI.2023.3237667.

[10] S. M. Miraftabzadeh, C. G. Colombo, M. Longo, and F. Foiadelli, “K-Means and Alternative Clustering Methods in Modern Power Systems,” IEEE Access, vol. 11, no. September, pp. 119596–119633, 2023, doi: 10.1109/ACCESS.2023.3327640.

[11] A. Yudhistira and R. Andika, “Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering,” Journal of Artificial Intelligence and Technology Information (JAITI), vol. 1, no. 1, pp. 20–28, 2023, doi: 10.58602/jaiti.v1i1.22.

[12] C. Shi, B. Wei, S. Wei, W. Wang, H. Liu, and J. Liu, “A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm,” Eurasip Journal on Wireless Communications and Networking, vol. 2021, no. 1, 2021, doi: 10.1186/s13638-021-01910-w.

[13] D. M. Putri, A. S. Ilmananda, and N. Prisanta, “Penggunaan Algoritma K-Means dan K-Medoids untuk Pengembangan Strategi Promosi Penerimaan Mahasiswa Baru,” SMATIKA JURNAL, vol. 14, no. 02, pp. 388–398, Dec. 2024, doi: 10.32664/smatika.v14i02.1474.

[14] R. Sharma and S. Arora, “Analysis of K-Means Clustering Algorithm,” INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT), vol. 11, no. 06, pp. 150–153, 2022, doi: DOI : 10.17577/IJERTV11IS060047.

[15] R. Cohn and E. Holm, “Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data,” Integrating Materials and Manufacturing Innovation, vol. 10, no. 2, pp. 231–244, 2021, doi: 10.1007/s40192-021-00205-8.

[16] A. Pegado-Bardayo, A. Lorenzo-Espejo, J. Muñuzuri, and A. Escudero-Santana, “A review of unsupervised k-value selection techniques in clustering algorithms,” Journal of Industrial Engineering and Management, vol. 17, no. 3, p. 641, 2024, doi: 10.3926/jiem.6791.

[17] B. Adi Nugroho, A. Endang Jayati, and P. Muliandi, “Analisa Kualitas Jaringan Akses Indihome Berdasarkan Teknologi Msan Dan Gpon Di Sto Majapahit,” Teknik Elektro, pp. 1–6, 2021.

[18] R. D. Dana, D. Soilihudin, R. H. Silalahi, D. A. Kurnia, and U. Hayati, “Competency test clustering through the application of Principal Component Analysis (PCA) and the K-Means algorithm,” IOP Conference Series: Materials Science and Engineering, vol. 1088, no. 1, p. 012038, 2021, doi: 10.1088/1757-899x/1088/1/012038.

[19] C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Frontiers in Energy Research, vol. 9, no. March, pp. 1–17, 2021, doi: 10.3389/fenrg.2021.652801.

[20] S. M. Javidan, A. Banakar, K. A. Vakilian, and Y. Ampatzidis, “Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning,” Smart Agricultural Technology, vol. 3, no. March 2022, 2023, doi: 10.1016/j.atech.2022.100081.

[21] E. U. Oti, M. O. Olusola, F. C. Eze, and S. U. Enogwe, “Comprehensive Review of K-Means Clustering Algorithms,” International Journal of Advances in Scientific Research and Engineering, vol. 07, no. 08, pp. 64–69, 2021, doi: 10.31695/ijasre.2021.34050.

[22] A. A. Aldino, D. Darwis, A. T. Prastowo, and C. Sujana, “Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency,” Journal of Physics: Conference Series, vol. 1751, no. 1, 2021, doi: 10.1088/1742-6596/1751/1/012038.

[23] Z. Ning, J. Chen, J. Huang, U. J. Sabo, Z. Yuan, and Z. Dai, “WeDIV – An improved k-means clustering algorithm with a weighted distance and a novel internal validation index,” Egyptian Informatics Journal, vol. 23, no. 4, pp. 133–144, 2022, doi: 10.1016/j.eij.2022.09.002.

[24] L. P. Refialy, H. Maitimu, and M. S. Pesulima, “Perbaikan Kinerja Clustering K-Means pada Data Ekonomi Nelayan dengan Perhitungan Sum of Square Error (SSE) dan Optimasi nilai K cluster,” Techno.Com, vol. 20, no. 2, pp. 321–329, 2021, doi: 10.33633/tc.v20i2.4572.

[25] J. Wala, H. Herman, R. Umar, and S. Suwanti, “Heart Disease Clustering Modeling Using a Combination of the K-Means Clustering Algorithm and the Elbow Method,” Scientific Journal of Informatics, vol. 11, no. 4, pp. 903–914, Dec. 2024, doi: 10.15294/sji.v11i4.14096.

[26] W. B. Syamhuri, M. T. Furqon, and C. Dewi, “Pengelompokan Film Berdasarkan Alur Cerita menggunakan Metode Self Organizing Maps dan Silhouette Coefficient,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 12, pp. 5898–5904, 2022.

[27] S. Suraya, M. Sholeh, and U. Lestari, “Evaluation of Data Clustering Accuracy using K-Means Algorithm,” International Journal of Multidisciplinary Approach Research and Science, vol. 2, no. 01, pp. 385–396, 2023, doi: 10.59653/ijmars.v2i01.504.

[28] A. S. Ritonga and I. Muhandhis, “Clustering Data Tweet E-Commerce Menggunakan Metode K-Means (Studi Kasus Akun Twitter Blibli Indonesia),” Smatika Jurnal, vol. 12, no. 01, pp. 75–84, 2022, doi: 10.32664/smatika.v12i01.665.

[29] T. Seyam et al., “Next-Generation K-Means Clustering: Mojo-Driven Performance for Big Data,” International Journal of Intelligent Information Systems, vol. 14, no. 1, pp. 7–19, Mar. 2025, doi: 10.11648/j.ijiis.20251401.12.

[30] V. No, D. Prasetyawan, A. Mulyanto, and R. Gatra, “Edumatic : Jurnal Pendidikan Informatika Pemetaan Lintasan Karir Alumni Berdasarkan Analisis Cluster : Kombinasi K-Means dan Reduksi Dimensi Autoencoder,” Edumatic: Jurnal Pendidikan Informatika, vol. 9, no. 1, pp. 198–207, 2025, doi: 10.29408/edumatic.v9i1.29713.

[31] Muh. A. Amri, A. A. N. Risal, Muh. F. Bakri, and D. F. Surianto, “Mini-Batch K-Means Clustering Untuk Pengelompokan Kemandirian Daerah Di Sulawesi Selatan,” Jurnal Informatika Polinema, vol. 11, no. 2, pp. 235–244, Feb. 2025, doi: 10.33795/jip.v11i2.6871.

[32] D. Hartama and M. Anjelita, “Analysis of Silhouette Coefficient Evaluation with Euclidean Distance in the Clustering Method (Case Study: Number of Public Schools in Indonesia),” Jurnal Mantik, vol. 6, no. 3, pp. 3667–3677, 2022.

[33] H. Mulyani, R. A. Setiawan, and H. Fathi, “Optimization of K Value in Clustering Using Silhouette Score (Case Study: Mall Customers Data),” Journal of Information Technology and Its Utilization, vol. 6, no. 2, pp. 45–50, 2023, doi: 10.56873/jitu.6.2.5243.