PERAMALAN PENJUALAN SAHAM NIKEL MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY (LSTM)

  • Firhan Abdillah Mahbubi Teknik Informatika, Sekolah Tinggi Teknologi Wastukancana, Purwakarta, Jawa Barat
  • Teguh Iman Hermanto Teknik Informatika, Sekolah Tinggi Teknologi Wastukancana, Purwakarta, Jawa Barat
  • Chandra Dewi Lestari Teknik Informatika, Sekolah Tinggi Teknologi Wastukancana, Purwakarta, Jawa Barat

Abstract

Indonesia has the world's largest nickel resources, with production of 1.6 million tons out of a global total of 328 million tons by 2022. In 2020, the Indonesian government imposed a ban on nickel ore exports to increase domestic processing and attract investment. Nickel supply reached 26 billion tons with reserves of 11,887 million tons. Mineral and coal investment in 2021 reached US$35 billion. The government plans 53 smelters until 2024, with 19 operating in 2021. PT Resource Alam Indonesia Tbk is active in the industry and faces fluctuations in nickel stock prices, which create problems, namely uncertainty for investors in making investment decisions due to fluctuations in nickel prices on the world market. So, effective stock price forecasting is needed using time series data analysis. This research uses a deep learning algorithm approach: Long Short Term Memory (LSTM). The research method uses CRISP-DM, which includes business understanding, data understanding, data preparation, model building, model evaluation, and deployment. Experimentation uses Python, and visualization uses the Streamlit Framework. This study uses optimal technical parameters to evaluate the LSTM model's effectiveness in predicting Nickel stock prices at PT Resource Alam Indonesia Tbk. The results showed that the Long Short Term Memory (LSTM) model could predict the sale of Nickel shares at PT. Resource Alam Indonesia Tbk (password: KKGI.JK) well, with an MAE value of 33.15, RMSE value of 48.14, MSE value of 2317.33, and MAPE value of 7.39. The best combination of the parameter combinations tested is with batch size 32, epochs 150, and optimizer Adam. The findings provide valuable insights for investors in making more informative and effective investment decisions.

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Author Biographies

Firhan Abdillah Mahbubi, Teknik Informatika, Sekolah Tinggi Teknologi Wastukancana, Purwakarta, Jawa Barat

saya adalah mahasiswa akhir dari STT. WASTUKANCANA PURWAKARTA

Teguh Iman Hermanto, Teknik Informatika, Sekolah Tinggi Teknologi Wastukancana, Purwakarta, Jawa Barat

dosen pebimbing sari STT.WASTUKANCANA PURWAKARTA

Chandra Dewi Lestari, Teknik Informatika, Sekolah Tinggi Teknologi Wastukancana, Purwakarta, Jawa Barat

dosen pembimbing 2

References

[1] M. Agung and E. A. W. Adi, “Peningkatan Investasi Dan Hilirisasi Nikel Di Indonesia,” JISIP (Jurnal Ilmu Sos. dan Pendidikan), vol. 6, no. 2, pp. 4009–4020, 2022, doi: 10.58258/jisip.v6i2.3085.
[2] T. Bastian Sianturi, I. Cholissodin, and N. Yudistira, “Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, pp. 1101–1107, 2023, [Online]. Available: http://j-ptiik.ub.ac.id
[3] ESDM Ministry, “Investment Opportunities in Nickel in Indonesia,” The Ministry of Energy and Mineral Resources of the Republic of Indonesia. pp. 1–40, 2020. [Online]. Available: https://www.esdm.go.id/id/booklet/booklet-tambang-nikel-2020
[4] G. Budiprasetyo, M. Hani’ah, and D. Z. Aflah, “Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM),” J. Nas. Teknol. dan Sist. Inf., vol. 8, no. 3, pp. 164–172, 2023, doi: 10.25077/teknosi.v8i3.2022.164-172.
[5] P. H. Gunawan, D. Munandar, and A. Z. Farabiba, “Long Short-Term Memory Approach for Predicting Air Temperature In Indonesia,” J. Online Inform., vol. 5, no. 2, p. 161, 2020, doi: 10.15575/join.v5i2.551.
[6] N. Faridah and B. Sugiantoro, “Analisis Optimasi Pada Algoritma Long Short Term Memory Untuk Memprediksi Harga Saham,” J. Media Inform. Budidarma, vol. 7, no. 1, pp. 575–582, 2023, doi: 10.30865/mib.v7i1.5421.
[7] F. Qotrunnada, “Implementasi Long Short Term Memory Pada Optimalisasi Prediksi Harga Saham Menggunakan Parameter Analisis Teknikal,” UIN Maulana Malik Ibrahim Malang, Malang, Jun. 2022. Available: http://etheses.uin-malang.ac.id/37135/1/15610115.pdf.
[8] D. Diandra, F. Atsila, R. Hanif, S. Akhdan, and N. Yudistira, “Prediksi Perubahan Iklim di Indonesia pada Tahun 2013-2014 Menggunakan LSTM,” | J. Litbang Edusaintech, vol. 3, no. 2, pp. 101–106, 2022, [Online]. Available: http://dx.doi.org/10.51402/jle.v3i2.49
[9] D. A. Manalu and G. Gunadi, “Implementasi Metode Data Mining K-Means Clustering Terhadap Data Pembayaran Transaksi Menggunakan Bahasa Pemrograman Python Pada Cv Digital Dimensi,” Infotech J. Technol. Inf., vol. 8, no. 1, pp. 43–54, 2022, doi: 10.37365/jti.v8i1.131.
[10] F. Husaini, I. Permana, and M. Afdal, “Application of Long Short-Term Memory Algorithm for Palm Oil Production Prediction P,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 366–374, 2024.
[11] Namora and Jan Everhard Riwurohi, “Prediction of Water Levels on Peatland using Deep Learning,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 2, pp. 234–239, 2022, doi: 10.29207/resti.v6i2.3919.
[12] M. Owen, V. Vincent, R. Br Ambarita, and E. Indra, “Implementasi Metode Long Short Term Memory Untuk Memprediksi Pergerakan Nilai Harga Emas,” J. Tek. Inf. dan Komput., vol. 5, no. 1, p. 96, 2022, doi: 10.37600/tekinkom.v5i1.507.
[13] D. D. Pramesti, D. C. R. Novitasari, F. Setiawan, and H. Khaulasari, “Long-Short Term Memory (Lstm) for Predicting Velocity and Direction Sea Surface Current on Bali Strait,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 2, pp. 451–462, 2022, doi: 10.30598/barekengvol16iss2pp451-462.
[14] A. Rasikha, M. H. Fatihurrahman, and A. Munandar, “Teknik Long Short-Term Memory Untuk Analisa Mengatasi Fenomena Pump and Dump,” J. Investasi, vol. 8, no. 3, pp. 13–18, 2022, doi: 10.31943/investasi.v8i3.211.
[15] A. Rosyd, A. I. Purnamasari, and I. Ali, “Penerapan Metode Long Short Term Memory (LSTM) Dalam Memprediksi Harga Saham Pt Bank Central Asia,” JATI (Jurnal Mahasiswa Teknik Informatika).,vol. 8, no. 1, pp. 501–506, 2024.
[16] D. P. Sari, L. Karlitasari, and F. D. Wihartiko, “Clean Water Demand Prediction Model Using The Long Short Term Memory (LSTM) Method,” Komputasi J. Ilm. Ilmu Komput. dan Mat., vol. 20, no. 2, pp. 160–168, 2023, doi: 10.33751/komputasi.v20i2.8060.
[17] Z. S. C. Viqri, E. Kurniati, and Respitawulan, “Perbandingan Penerapan Metode Fuzzy Time Series Model Chen-Hsu dan Model Lee dalam Memprediksi Kurs Rupiah terhadap Dolar Amerika,” DataMath J. Stat. Math., vol. 1, no. 1, pp. 19–26, 2023.
[18] C. Chandra and S. Budi, “Analisis Komparatif ARIMA dan Prophet dengan Studi Kasus Dataset Pendaftaran Mahasiswa Baru,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 278–287, 2020, doi: 10.28932/jutisi.v6i2.2676.
[19] Moch Farryz Rizkilloh and Sri Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 25–31, 2022
Published
2025-02-04
How to Cite
[1]
F. Mahbubi, T. Hermanto, and C. Lestari, “PERAMALAN PENJUALAN SAHAM NIKEL MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY (LSTM)”, IDEALIS, vol. 8, no. 1, pp. 138-149, Feb. 2025.