Exploring Energy Data through Clustering: A Hyperparameter Approach to Mapping Indonesia's Primary Energy Supply

Authors

  • Agus Perdana Windarto STIKOM Tunas Bangsa
  • Yerika Puspa Rosanti Institut Teknologi Bandung
  • Mesran Mesran Universitas Budi Darma

DOI:

https://doi.org/10.26555/jiteki.v10i2.29032

Keywords:

Energy Data, Clustering, Hyperparameter Tuning, K-Means, Indonesia Energy Supply, Davies-Bouldin Index, Data Analysis

Abstract

The rapid economic growth and population development in Indonesia have significantly increased the demand for energy, presenting complex challenges in managing the primary energy supply due to geographical variability and dispersed natural resources. This study addresses these challenges by applying clustering techniques with a hyperparameter approach to explore and map Indonesia's primary energy supply. The research contributes to the field by offering an effective method for analyzing energy data patterns and optimizing energy management. Secondary data on energy production, consumption, and distribution from reliable sources such as the Ministry of Energy and Mineral Resources were collected and analyzed. Various clustering algorithms, including K-Means, Fast K-Means, X-Means, and K-Medoids, were applied to identify energy supply patterns across different regions. The Davies-Bouldin Index was used to evaluate the effectiveness of the clustering algorithms. The results indicate that distance measures such as Euclidean Distance and Chebychev Distance consistently show excellent clustering performance. The study found that the choice of distance measure significantly impacts the clustering quality. The insights gained from this analysis provide valuable information for stakeholders involved in energy planning and policy-making, enhancing the efficiency and sustainability of energy management in Indonesia. This research establishes a foundation for further detailed and holistic energy data analysis, supporting better decision-making in energy planning and development.

References

A. Sapitri, “The Impact Of Population Factors On Economic Growth Of Bangka Belitung Islands - Indonesia,” J. Ilmu Ekon. Terap., Vol. 6, No. 2, P. 280, 2021, Doi: 10.20473/Jiet.V6i2.28689.

N. W. Handayani, H. Z. Hadibasyir, And A. A. Sigit, Analysis Of The Population Density Correlation With The Temperature Changes In The Semarang Area From 1999 To 2019, Vol. 2035. Atlantis Press Sarl, 2023. Doi: 10.2991/978-2-38476-066-4_10.

H. Pratiwi Et Al., “Sigmoid Activation Function In Selecting The Best Model Of Artificial Neural Networks,” J. Phys. Conf. Ser., Vol. 1471, No. 1, 2020, Doi: 10.1088/1742-6596/1471/1/012010.

Nabil Ibrahim El-Sawalhi, “Support Vector Machine Cost Estimation Model For Road Projects,” J. Civ. Eng. Archit., Vol. 9, No. 9, Pp. 1115–1125, 2015, Doi: 10.17265/1934-7359/2015.09.012.

A. Wanto Et Al., “Analysis Of The Accuracy Batch Training Method In Viewing Indonesian Fisheries Cultivation Company Development,” J. Phys. Conf. Ser., Vol. 1255, No. 1, 2019, Doi: 10.1088/1742-6596/1255/1/012003.

C. Li, S. Lin, F. Xu, D. Liu, And J. Liu, Short-Term Wind Power Prediction Based On Data Mining Technology And Improved Support Vector Machine Method: A Case Study In Northwest China, Vol. 205. Elsevier Ltd, 2018. Doi: 10.1016/J.Jclepro.2018.09.143.

T. Nurhikmat, “Implementasi Deep Learning Untuk Image Classification Menggunakan Algoritma Convolutional Neural Network (Cnn) Pada Citra Wayang Golek,” J. Phys. A Math. Theor., Vol. 44, No. 8, Pp. 1–15, 2011, Doi: 10.1088/1751-8113/44/8/085201.

F. Yu And X. Xu, “A Short-Term Load Forecasting Model Of Natural Gas Based On Optimized Genetic Algorithm And Improved Bp Neural Network,” Appl. Energy, Vol. 134, Pp. 102–113, 2014, Doi: 10.1016/J.Apenergy.2014.07.104.

S. Kumar, V. Kumar-Solanki, S. K. Choudhary, A. Selamat, And R. Gonzalez-Crespo, “Comparative Study On Ant Colony Optimization (Aco) And K-Means Clustering Approaches For Jobs Scheduling And Energy Optimization Model In Internet Of Things (Iot),” Int. J. Interact. Multimed. Artif. Intell., Vol. 6, No. 1, P. 107, 2020, Doi: 10.9781/Ijimai.2020.01.003.

S. M. Ashhar, “Comparison Of Deep Learning Convolutional Neural Network (Cnn) Architectures For Ct Lung Cancer Classification,” Int. J. Adv. Technol. Eng. Explor., Vol. 8, No. 74, Pp. 126–134, 2021, Doi: 10.19101/Ijatee.2020.S1762126.

X. Ji, “Exploration Of Supply Chain Financing Model And Virtual Economic Risk Control Using The Backpropagation Neural Network,” J. Glob. Inf. Manag., Vol. 31, No. 9, 2023, Doi: 10.4018/Jgim.333605.

S. Yang, “Aero: Design Space Exploration Framework For Resource-Constrained Cnn Mapping On Tile-Based Accelerators,” Ieee J. Emerg. Sel. Top. Circuits Syst., Vol. 12, No. 2, Pp. 508–521, 2022, Doi: 10.1109/Jetcas.2022.3171826.

Y. Cheng And Y. Yu, “K-Means Clustering Algorithm-Based Functional Magnetic Resonance For Evaluation Of Regular Hemodialysis On Brain Function Of Patients With End-Stage Renal Disease,” Comput. Math. Methods Med., Vol. 2022, 2022, Doi: 10.1155/2022/1181030.

Y. Ji, “Deep Mining Of Mobile Learning Data Based On Multi-Scale Clustering Analysis,” Int. J. Contin. Eng. Educ. Life-Long Learn., Vol. 33, No. 4, Pp. 456–467, 2023, Doi: 10.1504/Ijceell.2023.132414.

Y. Wang, F. Sibaii, K. Lee, M. J. Gill, And J. L. Hatch, “Clusa: Clustering-Based Spatial Analysis Framework Through Graph Neural Network For Chronic Kidney Disease Prediction Using Histopathology Images,” Medrxiv, Vol. 1, No. 165, Pp. 1–13, 2021.

M. Moradi Fard, T. Thonet, And E. Gaussier, “Deep K-Means: Jointly Clustering With K-Means And Learning Representations,” Pattern Recognit. Lett., Vol. 138, No. 2016, Pp. 185–192, 2020, Doi: 10.1016/J.Patrec.2020.07.028.

Saiyedfaiayazwaris And S. Koteeswaran, “Early Prediction Of Heart Conditions By K-Means Consensus Clustering And Convolution Neural Network,” Vol. 25, No. 3, Pp. 6623–6640, 2021, [Online]. Available: Http://Annalsofrscb.Ro

H. Zhang, “Clustering Mining Method Of Large-Scale Network Abnormal Data Based On Selective Collaborative Learning,” J. Comput. Methods Sci. Eng., Vol. 23, No. 1, Pp. 9–21, 2023, Doi: 10.3233/Jcm-226537.

E. Bakhshizadeh, “Customer Clustering Based On Factors Of Customer Lifetime Value With Data Mining Technique (Case Study: Software Industry),” Int. J. Ind. Eng. Prod. Res., Vol. 33, No. 1, 2022, Doi: 10.22068/Ijiepr.33.1.1.

A. Jasinska-Piadlo Et Al., “Data-Driven Versus A Domain-Led Approach To K-Means Clustering On An Open Heart Failure Dataset,” Int. J. Data Sci. Anal., Vol. 15, No. 1, Pp. 49–66, 2023, Doi: 10.1007/S41060-022-00346-9.

D. Novaliendry, “Optimizing Patient Medical Records Grouping Through Data Mining And K-Means Clustering Algorithm: A Case Study At Rsud Mohammad Natsir Solok,” Int. J. Online Biomed. Eng., Vol. 19, No. 12, Pp. 144–155, 2023, Doi: 10.3991/Ijoe.V19i12.42147.

V. Duhoon, “Canopy, Cobweb, Dbscan Clustering Data Mining Techniques: Study And Analysis,” Ital. J. Pure Appl. Math., Vol. 47, Pp. 438–448, 2022.

F. Y. Wattimena, “Data Mining Application For The Spread Of Endemic Butterfly Cenderawasih Bay Using The K-Means Clustering Algorithm,” Int. J. Online Biomed. Eng., Vol. 19, No. 9, Pp. 108–121, 2023, Doi: 10.3991/Ijoe.V19i09.40907.

S. Basar, M. Ali, G. Ochoa-Ruiz, M. Zareei, A. Waheed, And A. Adnan, “Unsupervised Color Image Segmentation: A Case Of Rgb Histogram Based K-Means Clustering Initialization,” Plos One, Vol. 15, No. 10 October, Pp. 1–21, 2020, Doi: 10.1371/Journal.Pone.0240015.

H. Li, “Summary Of Clustering Research In Time Series Data Mining,” Dianzi Keji Daxue Xuebao/Journal Univ. Electron. Sci. Technol. China, Vol. 51, No. 3, Pp. 416–424, 2022, Doi: 10.12178/1001-0548.2022055.

S. T. Ahmed, “Data Mining For Non-Redundant Big Data Using Dynamic Kmean Clustering,” Int. J. Comput. Digit. Syst., Vol. 14, No. 1, Pp. 243–251, 2023, Doi: 10.12785/Ijcds/140121.

T. Wahyudi, “Implementation Of Data Mining Using K-Means Clustering Method To Determine Sales Strategy In S&Amp;R Baby Store,” J. Appl. Eng. Technol. Sci., Vol. 4, No. 1, Pp. 93–103, 2022, Doi: 10.37385/Jaets.V4i1.913.

N. V Bondarev, “Clustering And Classification Of Red Wines According To Physical-Chemical Properties Using Data Mining Methods,” Russ. J. Gen. Chem., Vol. 93, No. 9, Pp. 2325–2339, 2023, Doi: 10.1134/S1070363223090141.

J. Hernández-Rodríguez, “Convolutional Neural Networks For Multi-Scale Lung Nodule Classification In Ct: Influence Of Hyperparameter Tuning On Performance,” Tem J., Vol. 11, No. 1, Pp. 297–306, 2022, Doi: 10.18421/Tem111-37.

K. S. Chong, “Comparison Of Naive Bayes And Svm Classification In Grid-Search Hyperparameter Tuned And Non-Hyperparameter Tuned Healthcare Stock Market Sentiment Analysis,” Int. J. Adv. Comput. Sci. Appl., Vol. 13, No. 12, Pp. 90–94, 2022, Doi: 10.14569/Ijacsa.2022.0131213.

S. Kiziloluk And E. Sert, “Covid-Ccd-Net: Covid-19 And Colon Cancer Diagnosis System With Optimized Cnn Hyperparameters Using Gradient-Based Optimizer,” Med. Biol. Eng. Comput., Vol. 60, No. 6, Pp. 1595–1612, 2022, Doi: 10.1007/S11517-022-02553-9.

E. H. Houssein, M. M. Emam, And A. A. Ali, “An Optimized Deep Learning Architecture For Breast Cancer Diagnosis Based On Improved Marine Predators Algorithm,” Neural Comput. Appl., Vol. 34, No. 20, Pp. 18015–18033, 2022, Doi: 10.1007/S00521-022-07445-5.

R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, And S. Venkatraman, “Deep Learning Approach For Intelligent Intrusion Detection System,” Ieee Access, Vol. 7, Pp. 41525–41550, 2019, Doi: 10.1109/Access.2019.2895334.

J. M. Ede And R. Beanland, “Adaptive Learning Rate Clipping Stabilizes Learning,” Mach. Learn. Sci. Technol., Vol. 1, No. 1, 2020, Doi: 10.1088/2632-2153/Ab81e2.

M. Ahmad Et Al., “A Lightweight Convolutional Neural Network Model For Liver Segmentation In Medical Diagnosis,” Vol. 2022, 2022.

M. Hosseinzadeh, O. Hassan, A. Marwan, And Y. Ghafour, “A Multiple Multilayer Perceptron Neural Network With An Adaptive Learning Algorithm For Thyroid Disease Diagnosis In The Internet Of Medical Things,” J. Supercomput., No. 0123456789, 2020, Doi: 10.1007/S11227-020-03404-W.

W. Sujatmiko And Z. Arifin, “Pengaruh Gross Domestic Product (Gdp) Dan Nilai Tukar Negara Mitra Dagang Terhadap Ekspor Udang Indoneisa Tahun 2009-2015,” J. Ilmu Ekon., Vol. 3, No. 2, Pp. 247–253, 2019.

M. Bordoloi And S. K. Biswas, Sentiment Analysis: A Survey On Design Framework, Applications And Future Scopes, Vol. 56, No. 11. Springer Netherlands, 2023. Doi: 10.1007/S10462-023-10442-2.

C. H. Chan, M. Sun, And B. Huang, “Application Of Machine Learning For Advanced Material Prediction And Design,” Ecomat, Vol. 4, No. 4, Pp. 1–28, 2022, Doi: 10.1002/Eom2.12194.

I. Tlemsani, M. A. Mohamed Hashim, And R. Matthews, “Portfolio Replication: Islamic Vs Conventional,” J. Islam. Account. Bus. Res., Vol. 14, No. 1, Pp. 1 – 20, 2023, Doi: 10.1108/Jiabr-09-2021-0261.

P. Golpour Et Al., “Comparison Of Support Vector Machine, Naïve Bayes And Logistic Regression For Assessing The Necessity For Coronary Angiography,” Int. J. Environ. Res. Public Health, Vol. 17, No. 18, Pp. 1–9, 2020, Doi: 10.3390/Ijerph17186449.

F. Nasari And C. J. M. Sianturi, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokkan Penyebaran Diare Di Kabupaten Langkat,” Cogito Smart J., Vol. 2, No. 2, P. 108, 2018, Doi: 10.31154/Cogito.V2i2.19.108-119.

Mohamad Jajuli Nurul Rohmawati, Sofi Defiyanti, “Implementasi Algoritma K-Means Dalam Pengklasteran Mahasiswa Pelamar Beasiswa,” Jitter 2015, Vol. I, No. 2, Pp. 62–68, 2015.

Downloads

Published

2024-07-03

How to Cite

[1]
A. P. Windarto, Y. P. Rosanti, and M. Mesran, “Exploring Energy Data through Clustering: A Hyperparameter Approach to Mapping Indonesia’s Primary Energy Supply”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 10, no. 2, pp. 359–372, Jul. 2024.

Issue

Vol. 10 No. 2 (2024): June

Section

Articles

License

Authors who publish with JITEKI agree to the following terms:

  1. Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

Lisensi Creative Commons

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

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.