Implementation of Artificial Neural Networks to Predicte Laptop Sales Using the Backpropagation Algorithm

Edi Saputra Pradana Rumahorbo; Imelda Sri Dumayanti; Samuel Manurung

Authors

Edi Saputra Pradana Rumahorbo Universitas Methodist
Imelda Sri Dumayanti Universitas Methodist
Samuel Manurung Universitas Methodist

Keywords:

Jaringan Saraf Buatan, Penjualan Laptop, Algoritma Backpropagation

Abstract

Penelitian ini dilakukan untuk memprediksi penjualan laptop di X Computer Elektronik, Kota Medan. Metode yang digunakan oleh peneliti adalah metode Jaringan Saraf Buatan dengan Algoritma Backpropagation. Dalam metode penelitian ini, proses ilmiah untuk memperoleh data yang akan digunakan beserta proses pengolahannya dalam memecahkan suatu masalah akan dijelaskan. Gambaran umum objek penelitian menjelaskan lokasi, waktu penelitian, logo, tujuan, visi dan misi di X Computer Elektronik. Dalam menyelesaikan tesis ini, penulis melakukan penelitian di X Computer Elektronik, yang berlokasi di Jl. Ps. 7 No. 125, Tembung, Kecamatan Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara. Sumber data yang digunakan adalah data penjualan X Computer Elektronik dari Juli hingga Desember 2023, yang dapat dilihat pada [tautan]. Dari uraian yang telah dibahas pada bab sebelumnya, dapat disimpulkan: Model jaringan saraf tiruan dengan metode backpropagation yang dikembangkan untuk memprediksi penjualan laptop menunjukkan kinerja yang konsisten antara fase pelatihan dan pengujian, dengan nilai Mean Absolute Error (MAE) sebesar 4,3400. Struktur optimal model terdiri dari 3 unit input, 4 unit tersembunyi, dan 3 unit output (3-4-3). Kinerja model bervariasi secara signifikan antar merek laptop, dengan MAE individual berkisar antara 0,9462 (terbaik, untuk Acer Aspire 3 Slim) hingga 9,1163 (terburuk, untuk Apple MacBook Air 2020). Hal ini menunjukkan bahwa model memiliki tingkat akurasi yang bervariasi tergantung pada karakteristik penjualan masing-masing merek.

References

Aini, D. H. N., Kurniasari, D., Nuryaman, A., & Usman, M. (2023). Implementation Of Artificial Neural Network With Backpropagation Algorithm For Rating Classification On Sales Of Blackmores In Tokopedia. Jurnal Teknik Informatika (Jutif), 4(2), 365–372.

Aini, H., Haviluddin, H., Budiman, E., Wati, M., & Puspitasari, N. (2019). Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network. Sains, Aplikasi, Komputasi Dan Teknologi Informasi, 1(1), 24. Https://Doi.Org/10.30872/Jsakti.V1i1.2261

Atina, A. (2019). Aplikasi Matlab Pada Teknologi Pencitraan Medis. Jurnal Penelitian Fisika Dan Terapannya (Jupiter), 1(1), 28. Https://Doi.Org/10.31851/Jupiter.V1i1.3123

Cardoso, L. E. P., De Carvalho, A. C. P. De L. F., & Quiles, M. G. (2024). Applying Lstm Recurrent Neural Networks To Predict Revenue. International Conference On Computational Science And Its Applications, 198–212.

Cavalcante, E. S., Vasconcelos, L. G. S., Brito, K. D., & Brito, R. P. (2023). Neural Network Predictive Control Applied To Automotive Paint Drying And Curing Processes. Progress In Organic Coatings, 183, 107773.

Chen, W. (2025). A Fuzzy-Neural Network Approach To Market Supervision And Product Recall Prediction. International Journal Of Advanced Computer Science & Applications, 16(3).

Chopra, M., Chopra, R., Reddy, R., & Chopra, S. (2023). Leveraging Lstm Neural Networks And Arima Models For Enhanced Real-Time Sales Forecasting In Dynamic Retail Environments. Journal Of Ai Ml Research, 12(2).

Chungyoun, M., Au, G., Carpentier, B., Puvada, S., Thomas, C., & Gray, J. J. (2025). Dl4proteins Jupyter Notebooks Teach How To Use Artificial Intelligence For Biomolecular Structure Prediction And Design. Arxiv Preprint Arxiv:2511.02128.

De Castro Moraes, T., Yuan, X., & Chew, E. P. (2024). Hybrid Convolutional Long Short‐Term Memory Models For Sales Forecasting In Retail. Journal Of Forecasting, 43(5), 1278–1293.

Dini, P., & Paolini, D. (2025). Exploiting Artificial Neural Networks For The State Of Charge Estimation In Ev/Hv Battery Systems: A Review. Batteries, 11(3), 107.

Endahti, L., & Faturahman, M. S. (2025). Predicting Demand For Msme Products Using Artificial Neural Networks (Ann) Based On Historical Sales Data. International Journal Of Informatics And Information Systems, 8(4), 212–220.

Famil Alamdar, P., & Seifi, A. (2024). Dynamic Pricing Of Differentiated Products Under Competition With Reference Price Effects Using A Neural Network-Based Approach. Journal Of Revenue And Pricing Management, 23(6), 575–587.

Gozali, L., Zulfan, P. M., Sukania, I. W., Ismail, S., Cheng, W. H., Masrom, M., & Robin, C. (2026). Implementation Of Business Intelligence For Sales Prediction And Inventory Optimization And Maintenance In Sme Carton Box Company With Open-Source Application. Production Engineering, 34(1), 85–96.

Hansel, K. C., Tanoto, V. A., Suri, P. A., & Fajar, M. (2024). Comparative Analysis Of Machine Learning Algorithms For Laptop Value Estimation. Procedia Computer Science, 245, 825–833.

Haryanto, D., & Argadila, D. (2019). Sistem Informasi Pengarsipan Data Konsumen Di Pt. Dinasti Pertiwi “Perumahan Dewasari.” Jurnal Teknik Informatika, 7(1), 1–6.

Hasan, N. F., Kusrini, K., & Fatta, H. A. (2019). Peramalan Jumlah Penjualan Menggunakan Jaringan Syaraf Tiruan Backpropagation Pada Perusahaan Air Minum Dalam Kemasan. Jurnal Teknik Informatika Dan Sistem Informasi, 5(2), 179–188. Https://Doi.Org/10.28932/Jutisi.V5i2.1607

Iman, G. (2026). Artificial Neural Network Applications In Supply Chain Management: A Literature Review And Classification. Applied System Innovation, 9(3), 55.

Inala, S., Aleshwari, N., & Swamy, G. N. (2024). Design And Implementation Of Autopilot Using Jetson Nano. Grenze International Journal Of Engineering & Technology (Gijet), 10.

Jafari, N., & Lewison, M. (2024). Forecasting Air Passenger Traffic And Market Share Using Deep Neural Networks With Multiple Inputs And Outputs. Frontiers In Artificial Intelligence, 7, 1429341.

Janah, S. N., Hastono, T., Nurmala, N., & Azman, M. N. A. (2023). Implementation Network Nerves Imitation Using The Backpropagation Method For Prediction Sale Groceries. Applied Science And Technology Reaserch Journal, 2(2), 56–66.

Khrais, L. T., & Shidwan, O. S. (2023). The Role Of Neural Network For Estimating Real Estate Prices Value In Post Covid-19: A Case Of The Middle East Market. International Journal Of Electrical & Computer Engineering (2088-8708), 13(4).

Luo, C. C. (2026). Application Of Recurrent Neural Networks In Demand Forecasting: A Teaching Brief. Decision Sciences Journal Of Innovative Education, 24(1–2), E70017.

Malik, S., Khan, M., Abid, M. K., & Aslam, N. (2024). Sales Forecasting Using Machine Learning Algorithm In The Retail Sector. Journal Of Computing & Biomedical Informatics, 6(02), 282–294.

Maulidah, S. (2020). Peramalan Teknologi.

Mubarokh, M. F., Nasir, M., & Komalasari, D. (2020). Jaringan Syaraf Tiruan Untuk Memprediksi Penjualan Pakaian Menggunakan Algoritma Backpropagation. Journal Of Computer And Information Systems Ampera, 1(1), 29–43. Https://Doi.Org/10.51519/Journalcisa.V1i1.3

Odja, M. O., Likadja, F. J., Ina, W. T., & Pella, S. I. (2021). Penggunaan Microsoft Excel Untuk Kemudahan Pengolahan Data Nilai Hasil Belajar Siswa. Jurnal Pengabdian Kepada Masyarakat Undana, 22–29.

Padilah, P. (2023). Prediksi Luas Lahan Pertanian Kecamatan Haurwani Menggunakan Program Matlab Dan Jaringan Saraf Tiruan. Jurnal Informatika Dan Teknik Elektro Terapan, 11(2), 152–158. Https://Doi.Org/10.23960/Jitet.V11i2.2890

Rahmadani, F., Pardede, A. M. H., & Nurhayati. (2021). Jaringan Syaraf Tiruan Prediksi Jumlah Pengiriman Barang Menggunakan Metode Backpropagation. Jurnal Teknik Informatika Kaputama (Jtik), 5(1), 100–106.

Ramos, F. R., Martinez, L. M., Martinez, L. F., Abreu, R., & Rubio, L. (2025). Mapping E-Commerce Trends In The Usa: A Time Series And Deep Learning Approach: Fr Ramos Et Al. Journal Of Marketing Analytics, 13(3), 606–634.

Ramos, F. R., Pereira, M. T., Oliveira, M., & Rubio, L. (2023). The Memory Concept Behind Deep Neural Network Models: An Application In Time Series Forecasting In The E-Commerce Sector. Decision Making: Applications In Management And Engineering, 6(2), 668–690.

Ray, S., Marjani, M., Ihsan, U., & Fergina, A. (2025). A Machine Learning-Based Price Prediction Application For Second-Hand Laptop Market Using Historical Sales Data. 2025 International Conference On Metaverse And Current Trends In Computing (Icmctc), 1–13.

Sajawal, M., Usman, S., Alshaikh, H. S., Hayat, A., & Ashraf, M. U. (2022). A Predictive Analysis Of Retail Sales Forecasting Using Machine Learning Techniques. Lahore Garrison University Research Journal Of Computer Science And Information Technology, 6(04), 33–45.

Satria, W. (2021). Jaringan Syaraf Tiruan Backpropagation Untuk Peramalan Penjualan Produk (Studi Kasus Di Metro Electronic Dan Furniture). Djtechno: Jurnal Teknologi Informasi, 1(1), 14–19. Https://Doi.Org/10.46576/Djtechno.V1i1.966

Setyani, T. P. H., & Gunadi, W. (2020). Pengaruh Kualitas Produk Dan Citra Merek Terhadap Keputusan Pembelian Mobil All New Rio Kia Motors Indonesia (Pada Pt. Radita Autoprima). Jurnal Ilmiah Mahasiswa Manajemen Unsurya, 1(1), 92–102.

Shaik, M. A., Varshith, M., Srivyshnavi, S., Sanjana, N., & Sujith, R. (2022). Laptop Price Prediction Using Machine Learning Algorithms. 2022 International Conference On Emerging Trends In Engineering And Medical Sciences (Icetems), 226–231.

Sillberg, C., & Rungratanaubon, T. (2022). Approach Of Deep Learning Model Based Multi-Layer Feed-Forward Artificial Neural Network With Backpropagation Algorithm For Water Quality Prediction. Environmentasia, 15(1), 2022–2023.

Solano Meza, J. K., Orjuela Yepes, D., Rodrigo-Ilarri, J., & Rodrigo-Clavero, M.-E. (2023). Comparative Analysis Of The Implementation Of Support Vector Machines And Long Short-Term Memory Artificial Neural Networks In Municipal Solid Waste Management Models In Megacities. International Journal Of Environmental Research And Public Health, 20(5), 4256.

Srivastava, M. A., & Books, A. (2026). Neural Networks And Deep Learning: Concepts And Applications: Practical Guide To Ai| Agph Books.

Surbakti, N. K. (2021). Data Mining Pengelompokan Pasien Rawat Inap Peserta Bpjs Menggunakan Metode Clustering (Studi Kasus : Rsu.Bangkatan). Journal Of Information And Technology, 1(2), 47–53. Https://Doi.Org/10.32938/Jitu.V1i2.1470

Syafiq, M., Hartama, D., Kirana, I. O., Gunawan, I., & Wanto, A. (2020). Prediksi Jumlah Penjualan Produk Di Pt Ramayana Pematangsiantar Menggunakan Metode Jst Backpropagation. Jurikom (Jurnal Riset Komputer), 7(1), 175. Https://Doi.Org/10.30865/Jurikom.V7i1.1963

Tambunan, H. T. B., Hartama, D., & Gunawan, I. (2021). Implementasi Jaringan Syaraf Tiruan ( Jst ) Untuk Memprediksi Jumlah Penjualan Gas 3kg Menggunakan Metode Backpropagation. Tin: Terapan Informatika Nusantara, 1(9), 479–488.

Teguh Pradnyana Yoga, G., Dyana Arjana, G., & Mataram, I. M. (2020). Perbandingan Kombinasi Fungsi Pelatihan Jaringan Syaraf Tiruan Backpropagation Pada Peramalan Beban. Jurnal Spektrum, 7(1), 41. Https://Doi.Org/10.24843/Spektrum.2020.V07.I01.P6

Vishwakarma, S., & Kumari, R. (2024). Video Game Sales Prediction Model Using Regression Model.

Wang, F., Li, J., & Yin, X. (2025). Decoding Key Drivers Of Information-Sensitive Waste Electrical And Electronic Equipment Recycling In China Using A Dematel-Bp Neural Network Approach. The Global Environmental Engineers, 12, 99–114.