Company Profit Prediction Based On Forecasting Of Port Throughput Using Time Series-Adaptive Neuro Fuzzy Inference System

Victory Tyas Pambudi Swindiarto, Mohammad Isa Irawan

Abstract


As a maritime country, ports play an important role in economic development in Indonesia. Throughput is an important factor affecting Port Profits. This prediction is needed in an effort to find out the company's prospects, help estimate the long-term profitability of representatives, predict earnings, and estimate risk in investment. In this research, forecasting data throughput will be carried out, such as container traffic, number of ships, export traffics, goods traffic, animal flow and passenger traffic for the next year using Time Series-Adaptive Neuro Fuzzy Inference System (TS-ANFIS) as an input parameter in the decision support system. Before predicting the benefits of the port using the ANFIS method, principal component analysis (PCA) was applied to reduce parameters that did not sufficiently affect the profits of the port. The data used are time series data from 2009 to 2018. From the system built it is expected to be able to provide good results in predicting the value of port throughput using TS-ANFIS and to predict profit values using the ANFIS method. The best results from profit prediction using ANFIS obtained R2 of 0.947, RMSE of 28524582.39, MAPE of 14.74% and MAAPE of 0.145. From the prediction results, it can be used as a reference for company projections in investing, managing cash flow, managing assets and global bonds.

Keywords


ANFIS; PCA; Time Series; Throughput; Port; Profit

Full Text:

PDF

References


D. A. Yeremia, “Peranan Sektor Maritim Dalam Perekonomian Indonesia,” Universitas Airlangga, 2017.

C. Zhang, L. Huang, and Z. Zhao, “Research on combination forecast of port cargo throughput based on time series and causality analysis,” vol. 6, no. 1, pp. 124–134, 2013.

X. Zha, Y. Chai, F. Witlox, and L. Ma, “Container Throughput Time Series Forecasting Using a Hybrid Approach,” vol. 281, pp. 639–650, 2016.

H. K. Chan, S. Xu, and X. Qi, “A comparison of time series methods for forecasting container throughput,” Int. J. Logist. Res. Appl., vol. 0, no. 0, pp. 1–10, 2018.

Z. Jin and Y. Ding, “Prediction of Container Throughput in China,” pp. 12–20, 2018.

X. Dong, W. Fan, and J. Gu, “Predicting LTE Throughput Using Traffic Time Series,” ZTE Commun., vol. 13, pp. 61–64, 2015.

P. Ghosh, “Machine Learning TrendsNo Title,” Dataversity, 2017. .

J. Singh and P. Tripathi, “Time Series Forecasting Using Back Propagation Neural Network with ADE Algorithm,” vol. 0869, no. 5, pp. 19–23, 2017.

M. A. Boyacioglu and D. Avci, “An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange,” Expert Syst. Appl., vol. 37, no. 12, pp. 7908–7912, 2010.

D. A. Adyanti, A. H. Asyhar, D. C. R. Novitasari, A. Lubab, and M. Hafiyusholeh, “Forecasts marine weather on java sea using hybrid methods: Ts-anfis,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 4, no. September, pp. 492–497, 2017.

I. Svalina, V. Galzina, R. Lujić, and G. Šimunović, “An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices,” Expert Syst. Appl., vol. 40, no. 15, pp. 6055–6063, 2013.

S. Chabaa, “ANFIS Method for Forecasting Internet Traffic Time Series,” 2009.

R. Hidayat, “Meminimalisasi nilai error peramalandengan algoritma,” pp. 187–192, 2012.

M. S. Ubay, “Peramalan Harga Saham Dengan Menggunakan Jaringan Syaraf Tiruan Metode Extreme Learning Machine,” Unoversitas Airlangga, 2012.

S. Sehgal, H. Singh, M. Agarwal, and V. B. Shantanu, “Data Analysis Using Principal Component Analysis,” no. 2, pp. 45–48, 2014.

D. Kashyap, A. Somani, and J. Shekhar, “Cervical Cancer Detection And Classification Using Independent Level Sets And Multi SVMs,” pp. 523–528, 2016.

N. A. Qureshi, V. Suthar, H. Magsi, and M. J. Sheikh, “Application of Principal Component Analysis ( PCA ) to Medical Data,” vol. 10, no. May, 2017.

F. M. Kusuma and A. Wibowo, “Principal Component Analysis (PCA) Untuk Mengatasi Multikolinieritas Terhadap Faktor Angka Kejadian Pneumonia Balita Di Jawa Timur tahun 2014,” J. Biometrika dan Kependud., vol. 6, no. 2, pp. 89–97, 2017.

K. and K. P. P.M., “Daily rainfall forecasting using Adaptive Neuro-Fuzzy inference system (ANFIS) models.,” İnternational J. Sci. Nature., vol. 6., no. 3, pp. 382–388, 2015.

G. Evren, “Adaptive Neuro Fuzzy Inference System (ANFIS) Applications in Chemical Processes,” The Middle Eact Technical University, 2003.

D. C. R. Novitasari, M. H. Bisri, and A. H. Asyhar, “Classification of EEG Signals using Fast Fourier Transform ( FFT ) and Adaptive Neuro-Fuzzy Inference System ( ANFIS ),” vol. 5, no. 1, pp. 36–45, 2019.

W. Chen, M. Panahi, K. Khosravi, H. Reza, and F. Rezaie, “Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization,” J. Hydrol., vol. 572, no. February, pp. 435–448, 2019.

E. Özer, A. Akgündo, and A. Ersoy, “Prediction of dust particle size e ff ect on e ffi ciency of photovoltaic modules with ANFIS : An experimental study in Aegean region , Turkey,” vol. 177, no. December 2018, pp. 690–702, 2019.

N. K. Singh, Y. Singh, S. Kumar, and A. Sharma, “Materials Today : Proceedings Predictive analysis of surface roughness in EDM using semi-empirical , ANN and ANFIS techniques : A comparative study,” Mater. Today Proc., no. xxxx, 2019.




DOI: http://dx.doi.org/10.12962/j23546026.y2020i1.11949

Refbacks

  • There are currently no refbacks.


View my Stat: Click Here

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