Tesis Mahasiswa NIM 14240013

• By Nurul Adi Prawira;
• Tesis
• 1

Transaksi Ethereum dapat mengalami kegagalan akibat berbagai faktor yang berkaitan dengan penggunaan gas, biaya transaksi, dan karakteristik transaksi. Penelitian ini bertujuan membangun model klasifikasi keberhasilan transaksi Ethereum menggunakan Machine Learning dan Explainable Artificial Intelligence (XAI). Dataset diperoleh dari Google BigQuery Public Datasets sebanyak 111.617 transaksi. Tahapan penelitian meliputi preprocessing, feature engineering, penanganan imbalanced dataset, pembangunan model Logistic Regression, Random Forest, XGBoost, dan LightGBM, hyperparameter tuning, Stratified Cross Validation, AutoML menggunakan FLAML, interpretasi SHAP, serta McNemar Test. Hasil penelitian menunjukkan bahwa tanpa balancing memberikan performa terbaik. Random Forest menghasilkan Accuracy 98,37%, Precision 98,47%, Recall 99,88%, F1-Score 99,17%, dan ROC-AUC 93,93%. FLAML memilih XGBoost dengan Accuracy 98,28% dan ROC-AUC 94,80%. SHAP menunjukkan gasUsed, gas_efficiency, dan gasLimit sebagai fitur paling berpengaruh. McNemar Test menghasilkan p-value 0,377494, sehingga tidak terdapat perbedaan signifikan antara Random Forest dan XGBoost

• Katakunci Ethereum, Klasifikasi Keberhasilan Transaksi, Machine Learning, AutoML, SHAP, Imbalanced Dataset, Stratified Cross Validation, McNemar Test.
• URL http://
• Penerbit UNIV Nusamandiri,

REFERENSI

DAFTAR REFERENSI

[1]       S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System”.

[2]       Z. Zheng, S. Xie, H. N. Dai, X. Chen, and H. Wang, “Blockchain challenges and opportunities: a survey,” Int. J. Web Grid Serv., vol. 14, no. 4, p. 352, 2018, doi: 10.1504/IJWGS.2018.095647.

[3]       F. Casino, T. K. Dasaklis, and C. Patsakis, “A systematic literature review of blockchain-based applications: Current status, classification and open issues,” Telemat. Inform., vol. 36, pp. 55–81, Mar. 2019, doi: 10.1016/j.tele.2018.11.006.

[4]       V. Buterin, “Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform.”.

[5]       D. G. Wood, “ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER”.

[6]       A. Antonopoulos, Mastering Ethereum, 1st edition. Erscheinungsort nicht ermittelbar: O’Reilly Media, Inc, 2018.

[7]       Etherscan, “‘Ethereum Charts & Statistics,’ 2026. [Online]. Available: https://etherscan.io/charts. [Accessed: 31-Jul-2026].”.

[8]       G. Palaiokrassas, S. Bouraga, and L. Tassiulas, “Machine learning on blockchain data: A systematic mapping study. An Annual Review of Information Science and Technology (ARIST) paper,” J. Assoc. Inf. Sci. Technol., vol. 77, no. 1, pp. 224–271, Jan. 2026, doi: 10.1002/asi.70009.

[9]       V. C. Oliveira et al., “Analyzing Transaction Confirmation in Ethereum Using Machine Learning Techniques,” ACM SIGMETRICS Perform. Eval. Rev., vol. 48, no. 4, pp. 12–15, May 2021, doi: 10.1145/3466826.3466832.

[10]     H. J. Singh and A. S. Hafid, “Transaction Confirmation Time Prediction in Ethereum Blockchain Using Machine Learning,” 2019, arXiv. doi: 10.48550/ARXIV.1911.11592.

[11]     T. M. Mitchell, Machine learning, Nachdr. in McGraw-Hill series in Computer Science. New York: McGraw-Hill, 2013.

[12]     C. M. Bishop, Pattern recognition and machine learning. in Information science and statistics. New York: Springer, 2006.

[13]     “Aurélien-Géron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O’Reilly-Media-2019.”

[14]     K. P. Murphy, Machine learning: a probabilistic perspective, 4. print. (fixed many typos). in Adaptive computation and machine learning series. Cambridge, Mass.: MIT Press, 2013.

[15]     Haibo He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, Sep. 2009, doi: 10.1109/TKDE.2008.239.

[16]     Haibo He, Yang Bai, E. A. Garcia, and Shutao Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China: IEEE, Jun. 2008, pp. 1322–1328. doi: 10.1109/IJCNN.2008.4633969.

[17]     T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, Jun. 2006, doi: 10.1016/j.patrec.2005.10.010.

[18]     J. Bergstra, J. Bergstra, Y. Bengio, and Y. Bengio, “Random Search for Hyper-Parameter Optimization”.

[19]     C. Wang, Q. Wu, M. Weimer, and E. Zhu, “FLAML: A Fast and Lightweight AutoML Library”.

[20]     D. Gunning and D. W. Aha, “DARPA’s Explainable Artificial Intelligence Program,” AI Mag., vol. 40, no. 2, pp. 44–58, Jun. 2019, doi: 10.1609/aimag.v40i2.2850.

[21]     C. Molnar, Interpretable machine learning: a guide for making black box models explainable, Second edition. Munich, Germany: Christoph Molnar, 2022.

[22]     A. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 6, pp. 52138–52160, 2018, doi: 10.1109/ACCESS.2018.2870052.

[23]     S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” 2017, arXiv. doi: 10.48550/ARXIV.1705.07874.

[24]     S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nat. Mach. Intell., vol. 2, no. 1, pp. 56–67, Jan. 2020, doi: 10.1038/s42256-019-0138-9.

[25]     Q. McNemar, “Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages,” Psychometrika, vol. 12, no. 2, pp. 153–157, Jun. 1947, doi: 10.1007/BF02295996.

[26]     D. K. Pramudito, J. Na’am, and F. Ernawan, “Exploring Blockchain and AI in Digital Banking: A Literature Review on Transactions Enhancement, Fraud Detection, and Financial Inclusion,” vol. 14.

[27]     K. Falgenti, Y. Arkeman, K. Syamsu, and E. Hambali, “Enhancing Traceability and Sustainability in Smallholder Oil Palm Plantations Through Gap Analysis and Machine Learning,” ASEAN J. Sci. Technol. Dev., vol. 43, no. 1, Jan. 2025, doi: 10.61931/2224-9028.1637.

[28]     S. Kayikci and T. M. Khoshgoftaar, “Blockchain meets machine learning: a survey,” J. Big Data, vol. 11, no. 1, p. 9, Jan. 2024, doi: 10.1186/s40537-023-00852-y.

[29]     C. Jatoth, R. Jain, U. Fiore, and S. Chatharasupalli, “Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning,” Future Internet, vol. 14, no. 1, p. 16, Dec. 2021, doi: 10.3390/fi14010016.

[30]     J. Crisostomo, F. Bacao, and V. Lobo, “Machine learning methods for detecting smart contracts vulnerabilities within Ethereum blockchain − A review,” Expert Syst. Appl., vol. 268, p. 126353, Apr. 2025, doi: 10.1016/j.eswa.2024.126353.

[31]     A. Nugroho, Wiyanto, and D. Maulana, “COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS IN HANDLING IMBALANCED DATA WITH SMOTE OVERSAMPLING APPROACH,” JITK J. Ilmu Pengetah. Dan Teknol. Komput., vol. 11, no. 2, pp. 487–495, Nov. 2025, doi: 10.33480/jitk.v11i2.6956.

[32]     N. V. Chawla, “Data Mining for Imbalanced Datasets: An Overview,” in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds., Boston, MA: Springer US, 2009, pp. 875–886. doi: 10.1007/978-0-387-09823-4_45.

[33]     N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, Jun. 2002, doi: 10.1613/jair.953.

[34]     S. Rahmadani, A. Subekti, and M. Haris, “Improving Classification Performance on Imbalance Medical Data using Generative Adversarial Network,” J. Ilmu Komput. Dan Inf., vol. 17, no. 1, 2024.

[35]     Z. Rahmatinejad et al., “A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department,” Sci. Rep., vol. 14, no. 1, p. 3406, Feb. 2024, doi: 10.1038/s41598-024-54038-4.

[36]     L. Rokach, “Ensemble-based classifiers,” Artif. Intell. Rev., vol. 33, no. 1–2, pp. 1–39, Feb. 2010, doi: 10.1007/s10462-009-9124-7.

[37]     Z.-H. Zhou, ENSEMBLE METHODS: foundations and algorithms, Second edition. in Chapman & Hall/CRC machine learning & pattern recognition. Erscheinungsort nicht ermittelbar: CHAPMAN & HALL CRC, 2025.

[38]     O. Sagi and L. Rokach, “Ensemble learning: A survey,” WIREs Data Min. Knowl. Discov., vol. 8, no. 4, p. e1249, Jul. 2018, doi: 10.1002/widm.1249.

[39]     N. Septiani and Y. Rianto, "Implementation of Ensembled Voting for Classification Performance Improvement," Telematika, vol. 21, no. 2, 2024, doi: 10.31515/telematika.v21i2.13004.

[40]     “Improving Obesity Classification with Advanced Machine Learning and Feature Selection Methods,” Int. J. Intell. Eng. Syst., vol. 18, no. 7, pp. 373–387, Aug. 2025, doi: 10.22266/ijies2025.0831.26.

[41]     L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.

[42]     T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA: ACM, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785.

[43]     A. R. Al Musyaffa, Y. Pristyanto, and N. Mauliza, “COMPARISON OF ENSEMBLE METHODS FOR DECISION TREE MODELS IN CLASSIFYING E. COLI BACTERIA,” JITK J. Ilmu Pengetah. Dan Teknol. Komput., vol. 10, no. 3, pp. 514–522, Feb. 2025, doi: 10.33480/jitk.v10i3.5972.

[44]     G. Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree”.

[45]     G. Türkmen and A. Sezen, “A Comparative Analysis of XGBoost and LightGBM Approaches for Human Activity Recognition: Speed and Accuracy Evaluation,” Int. J. Comput. Exp. Sci. Eng., vol. 10, no. 2, Jun. 2024, doi: 10.22399/ijcesen.329.

[46]     R. Hidayat and A. Subekti, “Evaluasi Kinerja Model Machine Learning dalam Cross-Project Defect Prediction Menggunakan Library PyCaret,” vol. 24, no. 2, 2025.

[47]     A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion, vol. 58, pp. 82–115, Jun. 2020, doi: 10.1016/j.inffus.2019.12.012.

[48]     T. Saito and M. Rehmsmeier, “The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets,” PLOS ONE, vol. 10, no. 3, p. e0118432, Mar. 2015, doi: 10.1371/journal.pone.0118432.

[49]     L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar, “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization,” 2016, doi: 10.48550/ARXIV.1603.06560.

[50]     S. Szeghalmy and A. Fazekas, “A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning,” Sensors, vol. 23, no. 4, p. 2333, Feb. 2023, doi: 10.3390/s23042333.

[51]     M. K. Mayangsari, I. Syarif, and A. Barakbah, “Evaluation of Stratified K-Fold Cross Validation for Predicting Bug Severity in Game Review Classification,” Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, Jul. 2023, doi: 10.22219/kinetik.v8i3.1740.

[52]     S. Raschka, “Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,” 2018, arXiv. doi: 10.48550/ARXIV.1811.12808.

[53]     M. Nuutinen and R.-L. Leskelä, “Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system,” Health Technol., vol. 13, no. 4, pp. 557–570, Jul. 2023, doi: 10.1007/s12553-023-00763-1.

[54]     R. Hidayat and A. Subekti, “Evaluasi Kinerja Model Machine Learning dalam Cross-Project Defect Prediction Menggunakan Library PyCaret,” vol. 24, no. 2, 2025.

[55]     S. Cao, “Research on Illegal Transaction Detection in Ethereum Network Based on Machine Learning,” Appl. Comput. Eng., vol. 125, no. 1, pp. 254–261, Mar. 2025, doi: 10.54254/2755-2721/2025.21206.