Sentiment Analysis of Customer Satisfaction with the Services of the Waikelo Port Class III Administrative Office Using the CAN Order Method

Authors

  • Kamalia Ahmad University of Stella Maris Sumba Author
  • Friden Elefri Neno University of Stella Maris Sumba Author
  • Alexander Adis University of Stella Maris Sumba Author

DOI:

https://doi.org/10.64472/jciet.v2i1.14

Keywords:

Customer Satisfaction, Sentiment Analysis, CAN Order, Public Services, Waikelo Port

Abstract

This study aims to analyze customer satisfaction levels with the services provided by the Waikelo Port Class III Administrative Office using the CAN (Cumulative Agreement Normalized) Order method and sentiment analysis. Data was collected from customer comments on service aspects such as speed of service, staff friendliness, facilities, and registration procedures. The comments were processed through text pre-processing, including case folding, tokenization, stopword removal, and stemming, then analyzed using the VADER Sentiment Analyzer to obtain positive, neutral, and negative scores. These sentiment values were used as input to calculate the CAN value, which determines the satisfaction ranking of each alternative objectively. The results show that service alternatives focusing on speed and staff friendliness have the highest CAN value of 0.83, while alternatives related to facilities and queues have the lowest CAN value of 0.33, indicating aspects that need improvement. Evaluation of the consistency of the CAN ranking with the respondent ranking using Spearman Rank Correlation yields a value of 0.92, indicating a high degree of conformity between the CAN method and customer preferences. This analysis proves that the integration of sentiment analysis and the CAN Order method can provide a quantitative picture of customer satisfaction levels and serve as a basis for practical recommendations to improve service quality.

Downloads

Download data is not yet available.

References

[1] T. Shaik, X. Tao, C. Dann, H. Xie, Y. Li, and L. Galligan, “Sentiment analysis and opinion mining on educational data: A survey,” Natural Language Processing Journal, vol. 2, p. 100003, Mar. 2023, doi: https://doi.org/10.1016/j.nlp.2022.100003

[2] C. Adiwinata and A. Afiyati, “Sentiment Analysis of X Users,” 2025. JUITA : Jurnal Informatika. doi : https://doi.org/10.30595/juita.v13i2.26152

[3] O. A. Irmawan, I. Budi, A. B. Santoso, and P. K. Putra, “Improving Sentiment Analysis and Topic Extraction in Indonesian Travel App Reviews Through BERT Fine-Tuning,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 13, no. 2, pp. 359–370, Jul. 2024, doi: https://doi.org/10.23887/janapati.v13i2.77028

[4] R. A. Rudiyanto and E. B. Setiawan, “Sentiment Analysis Using Convolutional Neural Network (CNN) and Particle Swarm Optimization on Twitter,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 9, no. 2, pp. 188–195, Feb. 2024, doi: https://doi.org/10.33480/jitk.v9i2.5201

[5] Maghfiroh Maulani and W. Gata, “SENTIMENT ANALYSIS OF JAKLINGKO APP REVIEWS USING MACHINE LEARNING AND LSTM,” Jurnal Techno Nusa Mandiri, vol. 22, no. 1, pp. 51–60, Mar. 2025, doi: https://doi.org/10.33480/techno.v22i1.6375

[6] A. A. Firdaus et al., “Application of Sentiment Analysis as an Innovative Approach to Policy Making: A Review,” 2024, Department of Agribusiness, Universitas Muhammadiyah Yogyakarta. doi: https://doi.org/10.18196/jrc.v5i6.22573

[7] R. Khairunnas, J. A. Pagua, G. Fitriya, and Y. Ruldeviyani, “User sentiment dynamics in social media: a comparative analysis of X and Threads,” IAES International Journal of Artificial Intelligence, vol. 14, no. 1, pp. 447–456, Feb. 2025, doi: https://doi.org/10.11591/ijai.v14.i1.pp447-456

[8] R. Isa, M. Fauzan Nordin, R. Othman, and H. M. Mohadis, “Sentiment Analysis in Public Health Crisis Applications: A Conceptual Framework,” 2022.

[9] N. Widaad and D. Anggraini, “SENTIMENT ANALYSIS OF CHATGPT APP USER REVIEWS USING SVM AND CNN METHODS,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 6, pp. 1687–1700, Dec. 2024, doi: https://doi.org/10.52436/1.jutif.2024.5.6.4010

[10] R. Mehmood, D. Pamucar, F. Smarandache, and A. R. Krishnan, “Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey.” doi : https://doi.org/10.3389/fdata.2022.990699

[11] Salsabila Septiani, Nabila Putri, Dara Jessica, and Arya Saputra, “Sentiment Analysis Of Social Media Data Using Deep Learning Techniques,” International Journal of Computer Technology and Science, vol. 1, no. 2, pp. 08–14, Apr. 2024, doi: https://doi.org/10.62951/ijcts.v1i2.59

[12] D. Anggraini, S. Rahmawati, and R. Kurniawan, “Natural Language Processing For Automatic Sentiment Analysis In Social Media Data,” International Journal of Information Engineering and Science, vol. 1, no. 1, pp. 16–19, 2024, doi: https://doi.org/10.62951/ijies.v1i2.54.

[13] H. Imaduddin, F. Yusfida A’la, and Y. S. Nugroho, “Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach.” [Online]. doi : https://doi.org/10.14569/IJACSA.2023.0140813

[14] A. Z. R. Adam and E. B. Setiawan, “Social Media Sentiment Analysis Using Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU),” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 9, no. 1, pp. 119–131, Feb. 2023, doi: https://doi.org/10.26555/jiteki.v9i1.25813.

[15] A. Pramana Thenata, D. Sakti, and R. Saputra, “Analysis of Public Sentiment Towards President Prabowo’s Work Program Using The CNN,” 2025. [Online]. doi : https://doi.org/10.30871/jaic.v9i4.9394

[16] C. Xu, M. Wang, Y. Ren, and S. Zhu, “Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Information,” Sep. 2024, [Online]. Available: http://arxiv.org/abs/2409.14997

[17] D. Tiyasya Putra and E. Budi Setiawan, “Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa,” vol. 10, no. 3, pp. 457–563, 2023, doi: https://doi.org/10.29207/resti.v7iX3.4892.

[18] S. Riyadi, N. G. Mahardika, C. Damarjati, and S. Ramli, “Modified Convolutional Neural Network for Sentiment Classification: A Case Study on The Indonesian Electoral Commission,” Scientific Journal of Informatics, vol. 11, no. 2, pp. 493–506, Jun. 2024, doi: https://doi.org/10.15294/sji.v11i2.4929.

[19] A. Z. Abdullah and E. B. Setiawan, “Sentiment Analysis Accuracy for 2024 Indonesian Election Tweets Using CNN-LSTM With Genetic Algorithm Optimization,” INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, vol. 9, no. 1, pp. 1–14, Feb. 2025, doi: https://doi.org/10.29407/intensif.v9i1.22999.

[20] F. S. Nufus and W. Gata, “SENTIMENT ANALYSIS OF PUBLIC OPINION ON TRANSPORTATION SERVICES IN INDONESIA USING MACHINE LEARNING,” Techno Nusa Mandiri : Journal of Computing and Information Technology As an Accredited Journal Rank, vol. 22, no. 2, 2025, doi: https://doi.org/10.33480/techno.v22i2.6577.

[21] M. Alliansa, N. H. Matondang, and R. D. Amalia, “Predicting YouTube Video Viewership Using Multi-Feature Random Forest Modeling: A Case Study on the Warganet Life Official Channel,” Journal of Computing Innovations and Emerging Technologies, vol. 1, no. 2, pp. 71–75, Dec. 2025, doi: https://doi.org/10.64472/jciet.v1i2.23.

[22] D. Listianing Tyas and L. Charifi, “Forecasting Monthly Sales Using Single Exponential Smoothing: An Evaluation and Performance Analysis,” Journal of Computing Innovations and Emerging Technologies, vol. 1, no. 1, pp. 11–15, Jul. 2025, doi: https://doi.org/10.64472/jciet.v1i1.3.

[23] N. A. Arifuddin, I. W. R. Pinastawa, N. Anugraha, and M. G. Pradana, “Classification of Stroke Opportunities with Neural Network and K-Nearest Neighbor Approaches,” SinkrOn, vol. 8, no. 2, pp. 688–693, Apr. 2023, doi: https://doi.org/10.33395/sinkron.v8i2.12228.

Vol. 2 No. 1 (2026)

Downloads

Published

2026-06-05

Issue

Vol. 2 No. 1 (2026): Volume 2 No 1

Section

Articles

License

Copyright (c) 2026 Kamalia Ahmad, Friden Elefri Neno, Alexander Adis (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

PROPOSED POLICY FOR JOURNALS OFFERING OPEN ACCESS

The following conditions must be fulfilled by the Authors:

  1. Copyright and Licensing
    Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.

  2. Non-Exclusive Distribution
    Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., posting it to an institutional repository or publishing it in a book), with an acknowledgment of its initial publication in this journal.

  3. Online Posting and Early Sharing
    Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their personal websites) prior to and during the submission process, as this can lead to productive exchanges, as well as earlier and increased citation of the published work. (See The Effect of Open Access).

How to Cite

Sentiment Analysis of Customer Satisfaction with the Services of the Waikelo Port Class III Administrative Office Using the CAN Order Method. (2026). Journal of Computing Innovations and Emerging Technologies, 2(1), 1-6. https://doi.org/10.64472/jciet.v2i1.14