Segmentation of Waste Management of All Provinces in Indonesia Using K-Means Clustering
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Abstract
The amount of waste in Indonesia continues to increase along with the increasing population and welfare. Waste data there are so many waste data throughout Indonesia that it is difficult to determine which managed waste data from provinces will be taken so a recommendation is needed to determine it. Mapping waste management based on the results of waste managed into animal feed raw materials, compost raw materials, recycled raw materials, up-cycle raw materials and energy source raw materials is expected to help the government (or local government) make more appropriate policies. Therefore, this research uses a clustering method, namely k-means clustering. Based on the results of the analysis using the elbow method, the optimal number of clusters selected in this study is k=2. Next, the process of clustering managed waste is carried out using the K-Means clustering algorithm. The clustering results on waste management data display data information with a low level of proportion of waste management volume consisting of 28 provinces and a high level of proportion of waste management volume consisting of 6 provinces. Based on the evaluation of the k-means clustering results, the maximum value of the silhouette coefficient = 0.940 and the Davies-Bouldin index value = 0.430. The concrete recommendations are to make the province with the highest proportion of waste management as a pilot project for the construction of PLTSa, develop a Public-Private Partnership scheme for investment in waste-to-energy processing technology and accelerate licensing and local regulations that support the operationalization of WtE.
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Almotairy, B. M., Abdullah, M., & Alahmadi, D. H. (2024). Dataset for detecting and characterizing Arab computation propaganda on X. Data in Brief, 53. https://doi.org/10.1016/j.dib.2024.110089
Assef, F. M., Steiner, M. T. A., & Lima, E. P. de. (2022). A review of clustering techniques for waste management. In Heliyon (Vol. 8, Issue 1). Elsevier Ltd. https://doi.org/10.1016/j.heliyon.2022.e08784
Budiyarto, A., Clarke, B., & Ross, K. (2024). Overview of waste bank application in Indonesian regencies. In Waste Management and Research. SAGE Publications Ltd. https://doi.org/10.1177/0734242X241242697
Fatimah, Y. A., Govindan, K., Murniningsih, R., & Setiawan, A. (2020). Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: A case study of Indonesia. Journal of Cleaner Production, 269. https://doi.org/10.1016/j.jclepro.2020.122263
Firman Ashari, I., Banjarnahor, R., Farida, D. R., Aisyah, S. P., Dewi, A. P., & Humaya, N. (2022). Application of Data Mining with the K-Means Clustering Method and Davies Bouldin Index for Grouping IMDB Movies. In Journal of Applied Informatics and Computing (JAIC) (Vol. 6, Issue 1). http://jurnal.polibatam.ac.id/index.php/JAIC
Garcia-Fossa, F., Cruz, M. C., Haghighi, M., de Jesus, M. B., Singh, S., Carpenter, A. E., & Cimini, B. A. (2023). Interpreting Image-based Profiles using Similarity Clustering and Single-Cell Visualization. Current Protocols, 3(3). https://doi.org/10.1002/cpz1.713
Gratsos, K., Ougiaroglou, S., & Margaris, D. (2023). A web tool for k-means clustering. In Novel & Intelligent Digital Systems Conferences, 783, 91–101. https://doi.org/https://doi.org/10.30871/jaic.v6i1.3485
Gujski, L. M., Di Filippo, A., & Limongiello, M. (2022). MACHINE LEARNING CLUSTERING FOR POINT CLOUDS OPTIMISATION VIA FEATURE ANALYSIS IN CULTURAL HERITAGE. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 46(2/W1-2022), 245–251. https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-245-2022
Haider, M. M., Hossin, Md. A., Mahi, H. R., & Arif, H. (2020). Automatic Text Summarization Using Gensim Word2Vec and K-Means Clustering Algorithm. 2020 IEEE Region 10 Symposium (TENSYMP), 283–286. https://doi.org/https://doi.org/10.1109/TENSYMP50017.2020.9230670
Setiyawati, N., Bangkalang, D., & Purnomo, H. (2023). Comparison between K-Means and K-Means++ Clustering Models Using Singular Value Decomposition (SVD) in Menu Engineering. JOIV: International Journal on Informatics Visualization, 7(3), 871–877. https://doi.org/https://doi.org/10.30630/joiv.7.3.1053
Jahanian, M., Karimi, A., & Zarafshan, F. (2021). Summer and Autumn. In Journal of Computer & Robotics (Vol. 14, Issue 2).
Karrar, A. E. (2022). The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values. Indonesian Journal of Electrical Engineering and Informatics, 10(2), 375–384. https://doi.org/10.52549/ijeei.v10i2.3730
Kasharjanto, A., Erwandi, Jati Mintarso, C. S., Suyanto, E. M., & Rahuna, D. (2023). Study of Supply Chain Management of Industrial Plan Manufacturing Development of Marine Power Turbine in Indonesia. IOP Conference Series: Earth and Environmental Science, 1166(1). https://doi.org/10.1088/1755-1315/1166/1/012018
Michael, C., & Utama, D. N. (2020). A modified dsm based on social media for treating waste management issue. ICIC Express Letters, Part B: Applications, 11(11), 1001–1010. https://doi.org/10.24507/icicelb.11.11.1001
Ordenshiya, K. M., & Revathi, G. K. (2025). Enhancing air quality index forecast with string reduction, entropy weight and similarity measure using K-means clustering for fuzzy inference system. Engineering Applications of Computational Fluid Mechanics , 19(1). https://doi.org/10.1080/19942060.2024.2439347
Prasasti, R. A., Budihardjo, M. A., & Samadikum, B. P. (2021). Reduction of waste generation to extend the lifetime of landfill: Review. IOP Conference Series: Earth and Environmental Science, 896(1). https://doi.org/10.1088/1755-1315/896/1/012067
Primandari, A. H., Kesumawati, A., & Purwaningsih, T. (2021). Supporting of Waste Management in Indonesia Using Self Organizing Map for Clustering Analysis. Journal of Physics: Conference Series, 1863(1). https://doi.org/10.1088/1742-6596/1863/1/012072
Rochman, E. M. S., Miswanto, & Suprajitno, H. (2022). COMPARISON OF CLUSTERING IN TUBERCULOSIS USING FUZZY C-MEANS AND K-MEANS METHODS. Communications in Mathematical Biology and Neuroscience, 2022. https://doi.org/10.28919/cmbn/7335
Rudnichenko, N., Vychuzhanin, V., Shibaeva, N., Petrov, I., & Otradskya, T. (2023). Intelligent Data Clustering System for Searching Hidden Regularities in Financial Transactions.
RUPTL 2018-2027. (2018). https://web.pln.co.id/statics/uploads/2018/04/RUPTL-PLN-2018-2027.pdf
Saxena, P., Sinha, A., & Singh, S. K. (2024). Computer-assisted interpretation, in-depth exploration and single cell type annotation of RNA sequence data using k-means clustering algorithm. Computer Methods in Biomechanics and Biomedical Engineering. https://doi.org/10.1080/10255842.2023.2300685
SIPSN. (2020). https://sipsn.menlhk.go.id/sipsn/public/home/fasilitas/bsi
Vatresia, A., Rais, R. R., Utama, F. P., & Oktarianti, W. (2022). MINING FIRE HOTSPOTS OVER NUSA TENGGARA AND BALI ISLANDS. Indonesian Journal of Forestry Research, 9(1), 73–85. https://doi.org/10.20886/ijfr.2022.9.1.73-85
Yana, S., Yulisma, A., & Zulfikar, T. M. (2022). Manfaat Sosial Ekonomi Energi Terbarukan: Kasus Negara-negara ASEAN. Serambi Engineering, VII(1). https://doi.org/https://doi.org/10.32672/jse.v7i1.3820
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