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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Dimas: Jurnal Pemikiran Agama untuk Pemberdayaan Jurnal Pendidikan Matematika RAFA Journal of Natural Sciences and Mathematics Research Phenomenon : Jurnal Pendidikan MIPA Edu Sains: Jurnal Pendidikan Sains dan Matematika Sinkron : Jurnal dan Penelitian Teknik Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics Delta: Jurnal Ilmiah Pendidikan Matematika Jurnal Informatika dan Rekayasa Perangkat Lunak Square : Journal of Mathematics and Mathematics Education Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Multidisciplinary Applied Natural Science Journal of Mathematics: Theory and Applications Edumatica: Jurnal Pendidikan Matematika Journal Focus Action of Research Mathematic (Factor M) Indonesian Journal of Statistics and Its Applications
Emy Siswanah
Universitas Islam Negeri Walisongo Semarang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Forecasting the Number of Ship Passengers with SARIMA Approach (A Case Study: Semayang Port, Balikpapan City) Multiningsih, Multiningsih; Siswanah, Emy; Saleh, Minhayati
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.10211

Abstract

From year to year, the number of ship passengers at Semayang Port, Balikpapan city tends to fluctuate. It also doubles in certain months and repeats every year. Sea transportation companies need to make forecasts in order to implement policies related to predict the number and capacity of ships that need to be provided as well as the preparation of port facilities. The study aims at obtaining the best model, predicting and determining the accuracy of the forecasting results for the number of passengers arriving and departing at Semayang Port, Balikpapan city using SARIMA method. The SARIMA method is a time series data forecasting method that is able to identify seasonal patterns. The results showed that the best model for predicting the number of passengers departing at Semayang Port, Balikpapan city is the SARIMA (4,1,0)(0,1,2)12 model with a MAPE of 14.05%. It means that the SARIMA model used produces good forecasting. Meanwhile, the best model to predict the number of passengers coming to Semayang Port Balikpapan city is the SARIMA (0,1,1)(2,1,0)12 model with a MAPE value of 3.27% which exposes that the SARIMA model used succeed to provide accurate forecasting. The results of this forecast can be used as a reference for the government or port managers to anticipate a surge in passengers. The government or port management can prepare an adequate amount of transportation in certain months to avoid the accumulation of passengers and to make sea transportation more efficient. 
Comparative Study of Recurrent Neural Network (RNN) and Extreme Learning Machine (ELM) in Predicting Bank Central Asia’s Stock Price Mukharomah, Rizanatul; Siswanah, Emy
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

Predicting stock prices is an important financial topic, especially for investors who want to maximize profit and minimize risk. This research compares two machine-learning capabilities, a Recurrent Neural Network (RNN) and an Extreme Learning Machine (ELM), in predicting Bank Cental Asia (BBCA) stock prices. These two are chosen for their capabilities in handling time-series data. This research uses the data of BBCA’s daily prices over a certain period and involves several steps such as data collecting, data pre-processing, model training, and calculation of accuracy value. This accuracy calculation will be evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). This research shows ELM has better accuracy than RNN in predicting BBCA’s stock prices. ELM shows lower MSE and MAPE values than RNN, indicating the capability of ELM to predict with smaller errors. This research also concludes ELM is better in accuracy than RNN in predicting BBCA’s stock prices. Thus, ELM is the recommended method to predict stock prices.
Co-Authors Abdurakhman Abdurakhman Ahmad Aunur Rohman Ahmad Aunur Rohman Ahmad Mutawaslih Idrus Anggraini, Agustina Di Asih I Maruddani Dwi Noviyanti Dyan Falasifa Tsani Dyan Falasifa Tsani Fara Lukita Umul Amaliah Fika Riza Syifamillah Fitriani, Ulliya Furqon, Syifa'ul Izzatul Yazidah, Izzatul Linda Nur Chabibah Lulu Choirun Nisa M. Ismail M. Ismail Maulida, Via Minhayati Saleh Muhammad Malik Hakim Muhammad Malik Hakim, Muhammad Malik Mukharomah, Rizanatul Multiningsih, Multiningsih Nabila Wafa’, Jihan Nisa, Eva Khoirun Rohman, Ahmad Aunur Rosidah, Zakiyatur Seftina Diyah Miasary Siti Maslihah Sulis Azizahroh Mahmudah Syifa'ul Furqon Tafrikan, Mohamad Tauki Rohman Muzaki Tri Yuliyanti Tri Yuliyanti Tsani, Dyan Falasifa Ulna, Jihan Ramadhani Ar-Raafi’ Umami, Riza Lathifatul Via Maulida Yustisio, Dheva