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All Journal Jurnal Teknik Teknosains: Media Informasi Sains dan Teknologi JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) JURNAL INSTEK (Informatika Sains dan Teknologi) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Aptisi Transactions on Technopreneurship (ATT) Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Proceedings Series on Physical & Formal Sciences Jurnal Teknoif Teknik Informatika Institut Teknologi Padang The Indonesian Journal of Computer Science Research Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Joko Siswanto
Politeknik Keselamatan Transportasi Jalan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Physics Social Sciences

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Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM Joko Siswanto; Sri Yulianto Joko Prasetyo; Sutarto Wijono; Evi Maria; Untung Rahardja
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p03

Abstract

Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation.
Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model Joko Siswanto; Irwan Sembiring; Adi Setiawan; Iwan Setyawan
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20210

Abstract

The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.
eParticipation Keselamatan Transportasi Jalan Joko Siswanto; Suprapto Hadi; Brasie Pradana Sela Bunga Riska Ayu
The Indonesian Journal of Computer Science Research Vol. 3 No. 1 (2024): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v3i1.89

Abstract

Keselamatan transportasi jalan merupakan isu yang sangat penting dan mendesak dalam masyarakat modern. eParticipation memainkan peran penting dalam meningkatkan kesadaran masyarakat tentang pentingnya keselamatan transportasi jalan. Konsep e-partisipasi telah menjadi solusi yang menarik untuk meningkatkan kesadaran dan keselamatan dalam transportasi jalan. eParticipation Framework yang digunakan terdiri dari 3 level yaitu area partisipasi, kategori alat, dan teknologi. Area partisipasi terbatas pada penumpang transportasi umum bus di terminal tipe A yang bertindak sebagai partisipan. Kategori Tools menggunakan aplikasi berbasis website berupa eParticipation SQA yang dibangun dan dapat dioperasikan oleh 2 aktor yaitu administrator dan peserta. Data yang dikumpulkan dengan rata-rata jenis jawaban adalah Worse (2%), Poor (2%), Good (59%), dan Very Good (37%). Teknologi yang digunakan untuk mengimplementasikan Aplikasi eParticipation SQA dengan berbasis website yang memerlukan beberapa perangkat seperti perangkat lunak(CodeIgniter, Bootstrap, MySql, CodeRunner, dan Web Browser), perangkat keras(komputer server, komputer, dan smartphone), dan perangkat jaringan(akses internet dan piranti penghubung). Aplikasi eParticipation yang diusulkan dapat berkontribusi dalam menjaga keselamatan transportasi jalan transportasi umum bus di terminal bus, serta membantu menciptakan lingkungan transportasi jalan yang lebih aman dan berkelanjutan.
Co-Authors Adi Setiawan Ainun Rahmawati Alfianti, Pramesti Gita Anas Azhimi Qalban Anggari Nuryono, Aninditya Aprianto, Rizal Bagus Ikapraja Benny Hamdi Rhoma Putra Brasie Pradana Sela Bunga Riska Ayu Danny Manongga Erick Alfons Lisangan Evi Maria Evi Maria Febrianta, Arjuna Rizky Frans Tohom Hendry Humami, Faris I Made Suartika Immanuel, Candra Great Irianti, Faizah Irwan Sembiring Iwan Setyawan Joko Siswanto Lestari, Astri Mifta, Eka Moh. Adam Asshofi Mohamad, Ar Rofiqi Muhammad Iman Nur Hakim Mustaqbal, Zhilal Akbar Nurul Fitriani Pramesti, Meisya Nira Prasetiyo, Wahyu Jati Pratma, Najwan Putro, Dimas Nugroho Rahadian Satya Mahaddhi Ramadhan Dwi Prasetyo Siti Shofiah Sri Nilawaty Lahay Sri Yulianto Joko Prasetyo Sugiyanto, Mohammad Ibrahim Suprapto Hadi Sutarto Wijono Tri Susila Hidayati Tsani, Mokhammad Rifqi Untung Rahardja Untung Rahardja Varadinta, Salsabilla Widyaningrum, Shiska Wahyu Yoga, Mahendra Yogi Oktopianto Zaenudin Zaenudin