Analisis Data Artikel Sistem Pakar Menggunakan Metode Systematic Review

Helen Sastypratiwi, Rudy Dwi Nyoto


Sistem pakar memiliki berbagai keunggulan dibandingkan kepakaran manusia karena sistem pakar terjangkau, permanen, konsisten, proses yang cepat, dan dapat digandakan. Hal ini menyebabkan sistem pakar berkembang diberbagai bidang. Perlu adanya kajian tentang data implementasi maupun metode yang digunakan dalam sistem pakar. Penelitian ini bertujuan untuk menganalisis data pada artikel sistem pakar dengan mengguankan metode systematic review serta menentukan string yang cocok dalam pengumpulan data. Langkah penelitian menggunakan flowchart PRISMA dengan penentuan string yang sesuai berdasarkan lingkup sistem pakar dan teknik dalam data mining. Pencarian dilakukan pada database online. Metode systematic review ini, dapat menjadi salah satu alternatif metode dalam penulisan karya ilmiah berdasarkan artikel sebelumnya. Dimana hasil dalam artikel sistem pakar sebagai studi dalam penelitian bahwa bidang yang masih unggul adalah computer science serta prediction adalah teknik yang paling banyak digunakan.


Analisis Data; Artikel; Data Mining; Sistem Pakar; String; Systematic Review

Full Text:



E. Turban, T.-P. Liang, and J. E. Aronson, Decision Support Systems and Intelligent Systems:(International Edition). Pearson Prentice Hall, 2005.

J. Durkin, “Research review: Application of expert systems in the sciences,” 1990.

H. Liao, Z. Xu, X. Zeng, and J. M. Merigó, “Framework of Group Decision Making With Intuitionistic Fuzzy Preference Information,” IEEE Trans. Fuzzy Syst., vol. 23, no. 4, pp. 1211–1227, 2015.

M. Chen, S. Mao, and Y. Liu, “Big data: A survey,” Mob. networks Appl., vol. 19, no. 2, pp. 171–209, 2014.

J. Manyika et al., “Big data: The next frontier for innovation, competition, and productivity,” 2011.

A. C. M. SIGKDD, “Data mining curriculum.” ed, 2012.

I. K. Crombie and H. T. Davies, “What is meta-analysis,” What is, pp. 1–8, 2009.

A. H. Eagly and W. Wood, “Using research syntheses to plan future research.,” 1994.

I. Chalmers and P. Glasziou, “Avoidable waste in the production and reporting of research evidence,” Lancet, vol. 374, no. 9683, pp. 86–89, 2009.

D. Dang and S. Pekkola, “Systematic Literature Review on Enterprise Architecture in the Public Sector,” Electron. J. e-Government, vol. 15, no. 1, p. 25, 2016.

V. Welch et al., “Extending the PRISMA statement to equity-focused systematic reviews (PRISMA-E 2012): explanation and elaboration,” J. Dev. Eff., vol. 8, no. 2, pp. 287–324, 2016.

L. Alton, “The 7 Most Important Data Mining Techniques,” 2017. [Online]. Available: [Accessed: 28-Oct-2019].

Y. Zhang and Y. Zhao, “Astronomy in the Big Data Era,” Data Sci. J., vol. 14, no. 0, p. 11, 2015.

H. Zaugg, R. E. West, I. Tateishi, and D. L. Randall, “Mendeley: Creating communities of scholarly inquiry through research collaboration,” TechTrends, vol. 55, no. 1, pp. 32–36, 2011.

N. Basias and Y. Pollalis, “Quantitative and qualitative research in business & technology: Justifying a suitable research methodology,” Rev. Integr. Bus. Econ. Res., vol. 7, pp. 91–105, 2018.

S. B. Merriam and E. J. Tisdell, Qualitative research: A guide to design and implementation. John Wiley & Sons, 2015.

M. Hennink, I. Hutter, and A. Bailey, Qualitative research methods. SAGE Publications Limited, 2020.

M. B. Miles and A. M. Huberman, Qualitative data analysis: An expanded sourcebook. sage, 1994.

J. A. Rodger, “Discovery of medical Big Data analytics: Improving the prediction of traumatic brain injury survival rates by data mining Patient Informatics Processing Software Hybrid Hadoop Hive,” Informatics Med. Unlocked, vol. 1, pp. 17–26, 2015.

L. Cao, “Coupling learning of complex interactions,” Inf. Process. Manag., vol. 51, no. 2, pp. 167–186, 2015.

W. S. Lee, E. J. Han, and S. Y. Sohn, “Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents,” Technol. Forecast. Soc. Change, vol. 100, pp. 317–329, 2015.

M. Reder, N. Y. Yürü?en, and J. J. Melero, “Data-driven learning framework for associating weather conditions and wind turbine failures,” Reliab. Eng. Syst. Saf., vol. 169, pp. 554–569, 2018.

K. Coussement, D. F. Benoit, and M. Antioco, “A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection,” Decis. Support Syst., vol. 79, pp. 24–32, 2015.

I. Ahmed, R. Ali, D. Guan, Y.-K. Lee, S. Lee, and T. Chung, “Semi-supervised learning using frequent itemset and ensemble learning for SMS classification,” Expert Syst. Appl., vol. 42, no. 3, pp. 1065–1073, 2015.

Y.-N. Park, Y.-S. Lee, J.-J. Kim, and T. S. Lee, “The structure and knowledge flow of building information modeling based on patent citation network analysis,” Autom. Constr., vol. 87, pp. 215–224, 2018.

O. Ben-Assuli, T. Heart, N. Shlomo, and R. Klempfner, “Bringing big data analytics closer to practice: A methodological explanation and demonstration of classification algorithms,” Heal. Policy Technol., vol. 8, no. 1, pp. 7–13, 2019.

H.-J. Kim, N.-O. Jo, and K.-S. Shin, “Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction,” Expert Syst. Appl., vol. 59, pp. 226–234, 2016.

W. Ectors et al., “Optimizing copious activity type classes based on classification accuracy and entropy retention,” Futur. Gener. Comput. Syst., 2018.

A. De Mauro, M. Greco, M. Grimaldi, and P. Ritala, “Human resources for Big Data professions: A systematic classification of job roles and required skill sets,” Inf. Process. Manag., vol. 54, no. 5, pp. 807–817, 2018.

V. Bolon-Canedo, D. Fernández-Francos, D. Peteiro-Barral, A. Alonso-Betanzos, B. Guijarro-Berdiñas, and N. Sánchez-Maroño, “A unified pipeline for online feature selection and classification,” Expert Syst. Appl., vol. 55, pp. 532–545, 2016.

D. Arunachalam and N. Kumar, “Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making,” Expert Syst. Appl., vol. 111, pp. 11–34, 2018.

J. G. Enríquez, F. J. Domínguez-Mayo, M. J. Escalona, M. Ross, and G. Staples, “Entity reconciliation in big data sources: A systematic mapping study,” Expert Syst. Appl., vol. 80, pp. 14–27, 2017.

B. Hosseini and K. Kiani, “FWCMR: A scalable and robust fuzzy weighted clustering based on MapReduce with application to microarray gene expression,” Expert Syst. Appl., vol. 91, pp. 198–210, 2018.

A. Ijadi Maghsoodi, A. Kavian, M. Khalilzadeh, and W. K. M. Brauers, “CLUS-MCDA: A novel framework based on cluster analysis and multiple criteria decision theory in a supplier selection problem,” Comput. Ind. Eng., vol. 118, pp. 409–422, 2018.

W. Qadadeh and S. Abdallah, “Customers Segmentation in the Insurance Company (TIC) Dataset,” Procedia Comput. Sci., vol. 144, pp. 277–290, 2018.

G. Manco et al., “Fault detection and explanation through big data analysis on sensor streams,” Expert Syst. Appl., vol. 87, pp. 141–156, 2017.

N. Mehdiyev, J. Krumeich, D. Enke, D. Werth, and P. Loos, “Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques,” Procedia Comput. Sci., vol. 61, pp. 395–401, 2015.

R. M. Salgado, T. C. Machado, and T. Ohishi, “Intelligent Models to Identification and Treatment of Outliers in Electrical Load Data,” IEEE Lat. Am. Trans., vol. 14, no. 10, pp. 4279–4286, 2016.

V. C. Pezoulas et al., “Medical data quality assessment: On the development of an automated framework for medical data curation,” Comput. Biol. Med., vol. 107, pp. 270–283, 2019.

W. Gu, K. Foster, J. Shang, and L. Wei, “A game-predicting expert system using big data and machine learning,” Expert Syst. Appl., vol. 130, pp. 293–305, 2019.

B. Ait Hammou, A. Ait Lahcen, and S. Mouline, “An Effective Distributed Predictive Model with Matrix Factorization and Random forest for Big Data Recommendation systems,” Expert Syst. Appl., 2019.

B. Weng, L. Lu, X. Wang, F. M. Megahed, and W. Martinez, “Predicting short-term stock prices using ensemble methods and online data sources,” Expert Syst. Appl., vol. 112, pp. 258–273, 2018.

P. Tahmasebi, F. Javadpour, and M. Sahimi, “Data mining and machine learning for identifying sweet spots in shale reservoirs,” Expert Syst. Appl., vol. 88, pp. 435–447, 2017.

Y. Kaneda and H. Mineno, “Sliding window-based support vector regression for predicting micrometeorological data,” Expert Syst. Appl., vol. 59, pp. 217–225, 2016.

D. U. Pfeiffer and K. B. Stevens, “Spatial and temporal epidemiological analysis in the Big Data era,” Prev. Vet. Med., vol. 122, no. 1, pp. 213–220, 2015.

E. Shortliffe, Computer-based medical consultations: MYCIN, vol. 2. Elsevier, 2012.

V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos, “Recent advances and emerging challenges of feature selection in the context of big data,” Knowledge-Based Syst., vol. 86, pp. 33–45, 2015.

S. I. Park, G. Lee, H. M. Kim, N. Hur, S. Kwon, and J. kim, “ADT-Based UHDTV Transmission for the Existing ATSC Terrestrial DTV Broadcasting,” IEEE Trans. Broadcast., vol. 61, no. 1, pp. 105–110, 2015.



  • There are currently no refbacks.