Tugas Akhir Periode 1 2025

Tugas Akhir Periode 1 2025

• By Salsabila Dwi Irmanti, Kanita;
• Tugas Akhir
• 29
• 1-103

Pengelolaan limbah radioaktif jangka panjang, khususnya pada fasilitas Near Surface Disposal (NSD), memerlukan pendekatan prediktif dan sistem pemantauan adaptif untuk mengantisipasi risiko terhadap kualitas air tanah. Penelitian ini bertujuan mengembangkan model deret waktu untuk memprediksi parameter muka air tanah meliputi kedalaman, pH, dan tds serta mengintegrasikannya dengan sistem klasifikasi risiko ESG berbasis aturan dan machine learning. Metode yang digunakan meliputi model deret waktu Prophet untuk prediksi parameter air tanah dalam 50 tahun ke depan. Hasil prediksi diklasifikasikan menggunakan rule-based classification yang kemudian dievaluasi menggunakan algoritma Random Forest. Aplikasi akhir dikembangkan berbasis web menggunakan Streamlit. Model Prophet memberikan performa prediksi terbaik untuk kedalaman MAE: 0,71; MAPE: 7,41% dan pH MAE: 0,21; MAPE: 4,89%, namun kurang akurat untuk TDS MAE: 12,16; MAPE: 31,62%. Model Random Forest menghasilkan akurasi klasifikasi hingga 98% dan mampu mereplikasi sistem klasifikasi berbasis aturan dengan baik. Integrasi model ini dapat menghasilkan sistem prediktif yang mendukung pengambilan keputusan dalam pengelolaan limbah radioaktif berkelanjutan.

• Katakunci Near Surface Disposal, machine learning, peramalan deret waktu, klasifikasi
• URL https://drive.google.com/drive/folders/14O5RllZVFiLKFR2CbUqyndRlGt5gTDhm?usp=sharing
• Penerbit ,

REFERENSI

[1]      B. Suhartawan et al., Pengelolaan Limbah Padat, Limbah Industry dan B3, 1st ed. Padang: GET PRESS INDONESIA, 2023.

[2]      Fariz Aditya, Raja Songkup Pratama, Florensia Silaban, Mantasia Hasibuan, Rahmi Siregar, and Mario Fany Manurung, “Krisis Lingkungan Dan Implikasinya Terhadap Keamanan Manusia,” Student Research Journal, vol. 1, no. 6, pp. 210–219, Dec. 2023, doi: 10.55606/srjyappi.v1i6.829.

[3]      M. I. Ojovan and H. J. Steinmetz, “Approaches to Disposal of Nuclear Waste,” Energies (Basel), vol. 15, no. 20, p. 7804, Oct. 2022, doi: 10.3390/en15207804.

[4]      I. N. Pratama and H. Hidayatullah, “Strategi dan Kebijakan Pemerintah Dalam Mengatasi Masalah Pencemaran Air Tanah,” Journal of Enviromental Policy and Technology, no. 2, pp. 105–112, Oct. 2023.

[5]      S. Handayani, S. Sudarti, and Y. Yushardi, “Analisis Kualitas Air Minum Berdasarkan Kadar pH Air Mineral dan Rebusan Sebagai Sumber Energi Terbarukan,” OPTIKA: Jurnal Pendidikan Fisika, vol. 7, no. 2, pp. 385–395, 2023.

[6]      R. H. Pratiwi et al., Kesehatan Lingkungan, 1st ed. Bandung: WIDIANA BHAKTI PERSADA BANDUNG, 2022.

[7]      F. N. Nabih, A. Takwanto, and M. Rahayu, “Pengaruh Konsentrasi Ozon Terhadap Nilai pH dan Total Dissolved Solid (TDS) Produk Air Minum dalam Kemasan (AMDK),” DISTILAT: Jurnal Teknologi Separasi, vol. 7, no. 2, pp. 347–352, May 2023, doi: 10.33795/distilat.v7i2.239.

[8]      M. E. Pramulyana, “Pengukuran Kualiatas Air di Masjid Dengan Menggunakan Parameter TSS, PH, DHL, TDS, dan Turbidity,” Universitas Islam Negeri Ar-Raniry, Banda Aceh, 2024.

[9]      M. Arya Revansyah et al., “Analisis TDS, PH, dan COD untuk Mengetahui Kualitas Air Warga Desa Cilayung,” Jurnal Material dan Energi Indonesia, vol. 12, no. 02, pp. 43–49, Aug. 2022.

[10]    International Atom Energy Agency, “Radioactive Waste Management Solutions for a Sustainable Future,” in Radioactive Waste Management, Vienna: International Atomic Energy Agency, Nov. 2023, pp. 1–490.

[11]    S. Gupta and S. K. Gupta, “A critical review on water quality index tool: Genesis, evolution and future directions,” Ecol Inform, vol. 63, p. 101299, Jul. 2021, doi: 10.1016/j.ecoinf.2021.101299.

[12]    D. E. Daniel, Geotechnical Practice for Waste Disposal. Boston, MA: Springer US, 2012. doi: 10.1007/978-1-4615-3070-1.

[13]    K. Tanaka et al., “Microbially Formed Mn(IV) Oxide as a Novel Adsorbent for Removal of Radium,” Chemosphere, vol. 355, p. 141837, May 2024, doi: 10.1016/j.chemosphere.2024.141837.

[14]    T. T. Jäger et al., “Characterization of a mock up nuclear waste package using energy resolved MeV neutron analysis,” Sci Rep, vol. 15, no. 1, Feb. 2025, doi: 10.1038/s41598-025-89879-0.

[15]    S. Dizman and O. Mukhtarli, “Tritium concentrations and consequent doses in bottled natural and mineral waters sold in Turkey and Azerbaijan,” Chemosphere, vol. 267, p. 128721, Mar. 2021, doi: 10.1016/j.chemosphere.2020.128721.

[16]    A. Chhabra et al., “Sustainable and Intelligent Time-Series Models for Epidemic Disease Forecasting and Analysis,” Sustainable Technology and Entrepreneurship, vol. 3, no. 2, May 2024, doi: 10.1016/j.stae.2023.100064.

[17]    H. L. Vu, K. T. W. Ng, A. Richter, J. Li, and S. A. Hosseinipooya, “Impacts of nested forward validation techniques on machine learning and regression waste disposal time series models,” Ecol Inform, vol. 72, p. 101897, Dec. 2022, doi: 10.1016/j.ecoinf.2022.101897.

[18]    M. J. A. Shohan, M. O. Faruque, and S. Y. Foo, “Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model,” Energies (Basel), vol. 15, no. 6, pp. 1–18, Mar. 2022, doi: 10.3390/en15062158.

[19]    Fariz Aditya, Raja Songkup Pratama, Florensia Silaban, Mantasia Hasibuan, Rahmi Siregar, and Mario Fany Manurung, “Krisis Lingkungan Dan Implikasinya Terhadap Keamanan Manusia,” Student Research Journal, vol. 1, no. 6, pp. 210–219, Dec. 2023, doi: 10.55606/srjyappi.v1i6.829.

[20]    H. Susiati et al., Pembangkit Listrik Tenaga Nuklir di Indonesia Upaya Berkelanjutan Menuju Net Zero Emission. Malang: Unisma Press, 2023. doi: 10.5281/zenodo.7905583.

[21]    X. Wang et al., “Identification of Key Degradation Processes of Cement-Based Materials in Low-Level Radioactive Waste Near-Surface Disposal Sites over a Large Time Scale,” 2024, pp. 799–805. doi: 10.3233/ATDE240708.

[22]    R. Gelar Guntara, “Pemanfaatan Google Colab Untuk Aplikasi Pendeteksian Masker Wajah Menggunakan Algoritma Deep Learning YOLOv7,” Jurnal Teknologi  Dan Sistem Informasi Bisnis, vol. 5, no. 1, pp. 55–60, Feb. 2023, doi: 10.47233/jteksis.v5i1.750.

[23]    C. Schröer, F. Kruse, and J. M. Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” Procedia Comput Sci, vol. 181, pp. 526–534, 2021, doi: 10.1016/j.procs.2021.01.199.

[24]    C. Zhang, W. Ding, and L. Zhang, “Impacts of Missing Buoy Data on LSTM-Based Coastal Chlorophyll-a Forecasting,” Water (Basel), vol. 16, no. 21, p. 3046, Oct. 2024, doi: 10.3390/w16213046.

[25]    J. Brownlee, Deep Learning for Time Series Forecasting, 1.9. 2018.

[26]    R. P. Masini, M. C. Medeiros, and E. F. Mendes, “Machine learning advances for time series forecasting,” J Econ Surv, vol. 37, no. 1, pp. 76–111, Feb. 2023, doi: 10.1111/joes.12429.

[27]    A. Ramadhan, A. Huda, Syerlie Annisa, and I. Efendi, “Perancangan Rule-Based Classification bagi Guru Baru Teknik Informatika,” SATIN - Sains dan Teknologi Informasi, vol. 8, no. 2, pp. 142–151, Dec. 2022, doi: 10.33372/stn.v8i2.919.

[28]    A. Abbas et al., “Surface and sub-surface flow estimation at high temporal resolution using deep neural networks,” J Hydrol (Amst), vol. 590, p. 125370, Nov. 2020, doi: 10.1016/j.jhydrol.2020.125370.

[29]    Y. Kim, J. Jeong, H. Park, M. Kwon, C. Cho, and J. Jeong, “Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data,” J Hydrol (Amst), vol. 612, p. 128127, Sep. 2022, doi: 10.1016/j.jhydrol.2022.128127.

[30]    M. A. Alshara, “Stock Forecasting Using Prophet vs. LSTM Model Applying Time-Series Prediction,” IJCSNS International Journal of Computer Science and Network Security, vol. 22, no. 2, pp. 185–192, 2022, doi: 10.22937/IJCSNS.2022.22.2.24.

[31]    M. Barooni, S. Ghaderpour Taleghani, M. Bahrami, P. Sedigh, and D. Velioglu Sogut, “Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications,” Atmosphere (Basel), vol. 15, no. 6, p. 640, May 2024, doi: 10.3390/atmos15060640.

[32]    C. Pires and M. V. Martins, “Enhancing Water Management: A Comparative Analysis of Time Series Prediction Models for Distributed Water Flow in Supply Networks,” Water (Basel), vol. 16, no. 13, p. 1827, Jun. 2024, doi: 10.3390/w16131827.

[33]    A. Zubair, Data Mining Menggunakan Orange. Yogyakarta: Teknosian, 2022.

[34]    T. Fadiyah Basar, D. E. Ratnawati, and I. Arwani, “Analisis Sentimen Pengguna Twitter terhadap Pembayaran Cashless menggunakan Shopeepay dengan Algoritma Random Forest,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 3, pp. 1426–1433, Mar. 2022, [Online]. Available: http://j-ptiik.ub.ac.id

[35]    E. O. Ojetunde, I. H. Denwigwe, P. A. Nwachukwu, and J. A. Adebisi, “A Review of Environmental, Social and Governance Frameworks in Sustainable Disposal of Waste from Renewable Energy Resources,” JDFEWS, vol. 4, no. 2, pp. 1–22, 2023.

[36]    J. Jiao, Y. Shuai, and J. Li, “Identifying ESG types of Chinese solid waste disposal companies based on machine learning methods,” J Environ Manage, vol. 360, p. 121235, Jun. 2024, doi: 10.1016/j.jenvman.2024.121235.

[37]    F. Gholian-Jouybari et al., “Developing environmental, social and governance (ESG) strategies on evaluation of municipal waste disposal centers: A case of Mexico,” Chemosphere, vol. 364, p. 142961, Sep. 2024, doi: 10.1016/j.chemosphere.2024.142961.

[38]    G. Hu and W. Pfingsten, “Data-driven machine learning for disposal of high-level nuclear waste: A review,” Ann Nucl Energy, vol. 180, p. 109452, Jan. 2023, doi: 10.1016/j.anucene.2022.109452.

[39]    M. Kazaryan, E. A. Semenishchev, and V. Voronin, “The underground surface analysis of waste disposal objects based on the neural network image processing methods,” in Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII, D. W. Messinger and M. Velez-Reyes, Eds., SPIE, May 2022, p. 44. doi: 10.1117/12.2620807.

[40]    H. Li, Z. Jin, and S. Krishnamoorthy, “E-Waste Management Using Machine Learning,” in 2021 6th International Conference on Big Data and Computing, New York, NY, USA: ACM, May 2021, pp. 30–35. doi: 10.1145/3469968.3469973.

[41]    S. Sudaryono, Metodologi Riset di Bidang TI, I. 2015.

[42]    H. Mondal, S. M. Swain, and S. Mondal, “How to Conduct Descriptive Statistics Online: A Brief Hands-on Guide for Biomedical Researchers,” Indian Journal of Vascular and Endovascular Surgery, vol. 9, no. 1, pp. 70–76, Jan. 2022, doi: 10.4103/ijves.ijves_103_21.

[43]    P. Hidayatullah and J. K. Kawistara, Pemrograman Web, I. Bandung: Informatika Bandung, 2017.