A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model

Saturday, February 24, 2024

Solikhin Solikhin, Septia Lutfi, Purnomo Purnomo, Hardiwinoto Hardiwinoto

Abstract


Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.

Keywords


Frequency-based partitioning; Machine learning; Prediction; Rate of change; Transportation public

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DOI: https://doi.org/10.11591/eei.v11i5.3518

Prediction of passenger train using fuzzy time series and percentage change methods

Solikhin Solikhin, Septia Lutfi, Purnomo Purnomo, Hardiwinoto Hardiwinoto

Abstract


In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

Keywords


Double exponential smoothing; Forecasting; Fuzzy time series; Passenger train; Percentage change

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DOI: https://doi.org/10.11591/eei.v10i6.2822

Membangun Sistem Smart Trash Menggunakan Mikrokontroler Motor Servo Panjerino

Yuda Hirmawan1, Eko Riyanto2, Solikhin Solikhin3*

Abstract

To cultivate good behavior and care for the environment, SD Negeri 2 KuwasenJepara promotes proper waste disposal, but in reality, there are still many students who don't do it. The purpose of this research is to build a smart trash can to socialize waste disposal in an attractive way for students. We use a manual trash can that is integrated with the Arduino Uno. This smart trash system is able to open automatically when it detects movement within <50 cm and vice versa, and can emit a "Thank you for not littering" sound. The performance test results show that the ultrasonic sensor device opens and closes within 3.07 seconds at a distance of 15 centimeters and 3.06 seconds at a distance of 30 centimeters. The feasibility test of the tool obtained a score of ≥76% and an ease of use score of 87.7%.

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References


S. Sukarjadi, A. Arifiyanto, D. T. Setiawan, & M. Hatta, “Perancangan dan Pembuatan Smart Trash Bin di Universitas Maarif Hasyim Latif,” Teknika: Engineering and Sains Journal, 1(2), (2017),101-110.

A. Ardiyanto, A. Ariman, A., & E. Supriyadi, “Alat Pengukur Suhu Berbasis Arduino Menggunakan Sensor Inframerah Dan Alarm Pendeteksi Suhu Tubuh Diatas Normal,” SINUSOIDA, 23(1), (2021),11-21.

A. N. Trisetiyanto, “Rancang Bangun Alat Penyemprot Disenfektan Otomatis untuk Mencegah Penyebaran Virus Corona,” Journal of Informatics Education, 3(1), (2020), 45-51.

A. Hilal, & S. Manan, “Pemanfaatan Motor Servo Sebagai Penggerak Cctv Untuk Melihat Alat-Alat Monitor Dan Kondisi Pasien Di Ruang Icu”. Gema Teknologi, 17(2), (2015).

Wikipedia, “Pengeras Suara”. [Internet]. Available: https://id.wikipedia.org/wiki/Pengeras_suara

IAVT.2014. IAVT 2014.Liege Belgium. Montefiore Institut

Teknik Elektronika, “Pengertian Speaker dan Prinsip kerjanya”. [Internet]. Available: https://teknikelektronika.com/fungsi-pengertian-speaker-prinsip-kerja-speaker/

S. Beta, & S. Astuti, “Modul Timbangan Benda Digital Dilengkapi Led Rgb Dan Dfplayer Mini,” Orbith: Majalah Ilmiah Pengembangan Rekayasa dan Sosial, 15(1), (2019), 10-15.

Y. Mochtiarsa, “Rancangan kendali lampu menggunakan mikrokontroller ATMega328 berbasis sensor getar,” Jurnal Informatika SIMANTIK, 1(1), (2016), 40-44.

Teknik Komputer Universitas Pendidikan Indonesia, “Bahasa C dan C++”, (2021). [Internet]. Available: https://tekkom.upi.edu/2021/04/bahasa-c-dan-c/

I. Budiman, S. Saori, R. N. Anwar, F. Fitriani, & M. Y. Pangestu, “Analisis Pengendalian Mutu Di Bidang Industri Makanan (Studi Kasus: Umkm Mochi Kaswari Lampion Kota Sukabumi),” Jurnal Inovasi Penelitian, 1(10), (2021), 2185-2190.

Priya Pedamkar, “Prototype Model”, [Internet]. Available: https://www.educba.com/prototype-model/




DOI: https://doi.org/10.26877/jiu.v9i1.15444

JSON and MySQL Databases for Spatial Visualization of Polygon and Multipolygon Data in Geographic Information Systems: A Comparative Study

M. Zakki Abdillah1*, Devi Astri Nawangnugraeni2, Solikhin Solikhin3, Toni Wijanarko Adi Putra4


 Abstract

Purpose: Spatial data is used to display digital maps. Geographic information systems' access performance depends on spatial data formats. This study compared JSON and MySQL database data display speeds. Open-source RDBMSs work with various programming languages. JSON displays data in text format. The purpose of this study is to select spatial data for polygon and multipolygon Geographic Information Systems (GIS).

Design of study: access speed to the GIS determined the method. This study evaluated how effectively JSON and MySQL displayed digital maps in GIS using two types of geographical data. JSON was in the server directory, and MySQL was on the database server. To measure performance, these two spatial data sets were compared using the same server parameters. Testers employed various tools, operating systems, devices, and browsers.

Result: JSON data is stored on a live server and is easier to access while having more data. This test compares file size and speed on three online devices. This test generates JSON as the fastest geographic data, with an average access time of 3.9 seconds and 8.5 MB loaded. MySQL, which averages 9.7 seconds, loads 6.3 MB of files. Despite its larger file size, JSON is faster for spatial data, according to tests.

Originality: Its comparison of JSON and MySQL databases based on its application for geographical data display in GIS is unique. This test offers geographic data in JSON faster than MYSQL. JSON can be used to choose location data that GIS can readily access.


References

A. A. Nurdin, G. N. Salmi, K. Sentosa, A. R. Wijayanti, and A. Prasetya, “Utilization of Business Intelligence in Sales Information Systems,” J. Inf. Syst. Explor. Res., vol. 1, no. 1, pp. 39–48, 2022, doi: 10.52465/joiser.v1i1.101.

R. Naufalia, C. Lateefa, and D. Yassar, “Usefulness factors to predict the continuance intention using mobile payment, case study: GO-Pay, OVO, Dana,” J. Soft Comput. Explor., vol. 2, no. 2, 2021, doi: 10.52465/joscex.v2i2.50.

W. A. Teniwut, C. L. Hasyim, and F. Pentury, “Towards smart government for sustainable fisheries and marine development: An intelligent web-based support system approach in small islands,” Mar. Policy, vol. 143, no. May, p. 105158, 2022, doi: 10.1016/j.marpol.2022.105158.

M. Kulawiak, A. Dawidowicz, and M. E. Pacholczyk, “Analysis of server-side and client-side WebGIS data processing methods on the example of JTS and JSTS using open data from OSM and geoportal,” Comput. Geosci., vol. 129, no. April, pp. 26–37, 2019, doi: 10.1016/j.cageo.2019.04.011.

J. Shi, Z. Pan, L. Jiang, and X. Zhai, “An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT,” Adv. Eng. Informatics, vol. 57, no. November 2022, p. 102114, 2023, doi: 10.1016/j.aei.2023.102114.

S. Sularno, R. Astri, P. Anggraini, D. Prima Mulya, and D. Mulya, “Geographical Information System of Bus and Travel Counter in Padang City Using BFS Method Based on Mobile Web,” Sci. J. Informatics, vol. 8, no. 2, pp. 304–313, 2021, doi: 10.15294/sji.v8i2.33117.

J. L. Amaro-Mellado, L. Melgar-García, C. Rubio-Escudero, and D. Gutiérrez-Avilés, “Generating a seismogenic source zone model for the Pyrenees: A GIS-assisted triclustering approach,” Comput. Geosci., vol. 150, no. February, p. 104736, 2021, doi: 10.1016/j.cageo.2021.104736.

M. Z. Abdillah, D. A. Nawangnugraeni, and A. H. P. Yuniarto, “Geographic Information System (GIS) For Maping Greenpark Using Leaflet JS,” J. Tek. Inform. Kaputama, vol. 5, no. 2, pp. 259–266, 2021.

S. Singh and S. N. Behera, Advances in Waste Management, no. January. Springer Singapore, 2019. doi: 10.1007/978-981-13-0215-2.

P. Du and H. Hu, “Optimization of tourism route planning algorithm for forest wetland based on GIS,” J. Discret. Math. Sci. Cryptogr., vol. 21, no. 2, pp. 283–288, 2018, doi: 10.1080/09720529.2018.1449300.

S. W. Mulvenon, K. Wang, S. McKenzie, and T. Anderson, “Spatially Referenced Educational Achievement Data Exploration: A Web-Based Interactive System Integration of GIS, PHP, and MySQL Technologies,” J. Educ. Technol. Syst., vol. 34, no. 3, pp. 243–256, Mar. 2006, doi: 10.2190/2VUC-CCJN-LHB3-EU7J.

J. Jumadi and S. Widiadi, “Pengembangan Aplikasi Sistem Informasi Geografis (SIG) berbasis Web untuk Manajemen Pemanfaatan Air Tanah menggunakan PHP, Java dan MySQL Spatial (Studi Kasus di Kabupaten Banyumas),” Forum Geogr., vol. 23, no. 2, p. 1236, Dec. 2009, doi: 10.23917/forgeo.v23i2.5006.

S. Q. Khairunisa et al., “Characterization of spatial and temporal transmission of HIV infection in Surabaya, Indonesia: Geographic information system (GIS) cluster detection analysis (2016–2020),” Heliyon, vol. 9, no. 9, p. e19528, 2023, doi: 10.1016/j.heliyon.2023.e19528.

C. Quiros, P. K. Thornton, M. Herrero, A. Notenbaert, and E. Gonzalez-Estrada, “GOBLET: An open-source geographic overlaying database and query module for spatial targeting in agricultural systems,” Comput. Electron. Agric., vol. 68, no. 1, pp. 114–128, Aug. 2009, doi: 10.1016/j.compag.2009.05.001.

G. Zodiatis, E. Zhuk, V. Krylenko, and M. Krylenko, “Dolgaya spit dynamics visualization by using Black Sea GIS regional module,” in Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), Aug. 2018, p. 61. doi: 10.1117/12.2326496.

A. Azzam, G. Samy, M. A. Hagras, and R. ElKholy, “Geographic information systems-based framework for water–energy–food nexus assessments,” Ain Shams Eng. J., p. 102224, Mar. 2023, doi: 10.1016/j.asej.2023.102224.

F. Medjani, T. Derradji, F. Zahi, M. Djidel, S. Labar, and L. Bouchagoura, “Assessment of soil erosion by Universal Soil Loss Equation model based on Geographic Information System data: a case study of the Mafragh watershed, north-eastern Algeria,” Sci. African, vol. 21, p. e01782, Sep. 2023, doi: 10.1016/j.sciaf.2023.e01782.

J. Penny, D. Khadka, P. B. R. Alves, A. S. Chen, and S. Djordjević, “Using multi criteria decision analysis in a geographical information system framework to assess drought risk,” Water Res. X, vol. 20, p. 100190, Sep. 2023, doi: 10.1016/j.wroa.2023.100190.

L. Vankova, Z. Krejza, G. Kocourkova, and J. Laciga, “Geographic Information System Usage Options in Facility Management,” Procedia Comput. Sci., vol. 196, pp. 708–716, 2022, doi: 10.1016/j.procs.2021.12.067.

S. W. Chan, S. K. Abid, N. Sulaiman, U. Nazir, and K. Azam, “A systematic review of the floodvulnerability using geographic information system,” Heliyon, vol. 8, no. 3, p. e09075, Mar. 2022, doi: 10.1016/j.heliyon.2022.e09075.

F. J. Fliegner and D. Möst, “High-resolution scenario building support for offshore grid development studies in a geographical information system,” Energy Strateg. Rev., vol. 48, p. 101110, Jul. 2023, doi: 10.1016/j.esr.2023.101110.

G. Villacreses, J. Martínez-Gómez, D. Jijón, and M. Cordovez, “Geolocation of photovoltaic farms using Geographic Information Systems (GIS) with Multiple-criteria decision-making (MCDM) methods: Case of the Ecuadorian energy regulation,” Energy Reports, vol. 8, pp. 3526–3548, Nov. 2022, doi: 10.1016/j.egyr.2022.02.152.

J. Zhang, X. Zhang, A. Rentizelas, C. Dong, and J. Li, “Optimisation of Logistic Model Using Geographic Information Systems: A Case Study of Biomass-based Combined Heat & Power Generation in China,” Appl. Energy Combust. Sci., vol. 10, p. 100060, Jun. 2022, doi: 10.1016/j.jaecs.2022.100060.

S. Boroushaki and J. Malczewski, “ParticipatoryGlS: a web-based collaborative GIS and multicriteria decision analysis,” Urisa J., vol. 22, no. 1, p. 23, 2010.

C.-O. Truică, E.-S. Apostol, J. Darmont, and T. B. Pedersen, “The Forgotten Document-Oriented Database Management Systems: An Overview and Benchmark of Native XML DODBMSes in Comparison with JSON DODBMSes,” Big Data Res., vol. 25, p. 100205, Jul. 2021, doi: 10.1016/j.bdr.2021.100205.

M.-A. Baazizi, D. Colazzo, G. Ghelli, C. Sartiani, and S. Scherzinger, “Negation-closure for JSON Schema,” Theor. Comput. Sci., vol. 955, p. 113823, Apr. 2023, doi: 10.1016/j.tcs.2023.113823.

Zhuokui Xu and Jianjun Zhu, “Research of WebGIS based on HTML5 and JSON,” in Proceedings of 2011 International Conference on Computer Science and Network Technology, Dec. 2011, pp. 1714–1717. doi: 10.1109/ICCSNT.2011.6182298.

J. Maso, A. Z. Torres, and P. Baumann, “New Model for Geospatial Coverages in JSON,” 2019, pp. 316–357. doi: 10.4018/978-1-5225-8446-9.ch015.

A. A. Frozza and R. dos S. Mello, “JS4Geo: a canonical JSON Schema for geographic data suitable to NoSQL databases,” Geoinformatica, vol. 24, no. 4, pp. 987–1019, Oct. 2020, doi: 10.1007/s10707-020-00415-w.

P. Bourhis, J. L. Reutter, and D. Vrgoč, “JSON: Data model and query languages,” Inf. Syst., vol. 89, p. 101478, Mar. 2020, doi: 10.1016/j.is.2019.101478.

M. Z. Abdillah, “Implementation of AJAX and JSON to improve web application performance,” J. Transform., vol. 14, no. 1, p. 1, Nov. 2016, doi: 10.26623/transformatika.v14i1.363.

F. da Costa Rainho and J. Bernardino, “Web GIS: A new system to store spatial data using GeoJSON in MongoDB,” in 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), Jun. 2018, pp. 1–6. doi: 10.23919/CISTI.2018.8399279.

Gunawan, F. X. Ferdinandus, and E. I. Setiawan, “GeoJSON Web Service based road assets management system for Surabaya city using mobile GPS,” in 2016 International Computer Science and Engineering Conference (ICSEC), Dec. 2016, pp. 1–5. doi: 10.1109/ICSEC.2016.7859915.

Z. Zhu and J. Tan, “A Multi-Source Heterogeneous Vector Space Data Integration Scheme Based on GeoJSON,” in 2018 26th International Conference on Geoinformatics, Jun. 2018, pp. 1–4. doi: 10.1109/GEOINFORMATICS.2018.8557141.

Y. K. Gupta, R. D. Gupta, and K. Kumar, “WebGIS for Planning Infrastructural Facilities at Village Level,” in 13th Annual International Conference and Exhibition on Geospatial Information Technology and Applications, 2010, pp. 19–21.

Y. P. Singh, A. K. Singh, and R. P. Singh, “Web GIS based Framework for Citizen Reporting on Collection of Solid Waste and Mapping in GIS for Allahabad City,” SAMRIDDHI A J. Phys. Sci. Eng. Technol., vol. 8, no. 01, pp. 01–05, Jun. 2016, doi: 10.18090/samriddhi.v8i1.11405.

A. T. Kulkarni, J. Mohanty, T. I. Eldho, E. P. Rao, and B. K. Mohan, “A web GIS based integrated flood assessment modeling tool for coastal urban watersheds,” Comput. Geosci., vol. 64, pp. 7–14, Mar. 2014, doi: 10.1016/j.cageo.2013.11.002.

I. K. G. Sudiartha, I. N. E. Indrayana, I. W. Suasnawa, S. A. Asri, and P. W. Sunu, “Data Structure Comparison Between MySql Relational Database and Firebase Database NoSql on Mobile Based Tourist Tracking Application,” J. Phys. Conf. Ser., vol. 1569, p. 032092, Jul. 2020, doi: 10.1088/1742-6596/1569/3/032092.

M. Ohyver, J. V. Moniaga, I. Sungkawa, B. E. Subagyo, and I. A. Chandra, “The Comparison Firebase Realtime Database and MySQL Database Performance using Wilcoxon Signed-Rank Test,” Procedia Comput. Sci., vol. 157, pp. 396–405, 2019, doi: 10.1016/j.procs.2019.08.231.

E. Zhuk, A. Khaliulin, G. Zodiatis, A. Nikolaidis, and E. Isaeva, “Black Sea GIS developed in MHI,” in Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), Aug. 2016, p. 96881C. doi: 10.1117/12.2241631.

 
 
 
 
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