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All Journal Sistemasi: Jurnal Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia Jurnal Riset Informatika Jurnal ICT : Information Communication & Technology Respati Jurnal Abdi Insani Journal of Soft Computing Exploration Jurnal Pendidikan dan Teknologi Indonesia JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Batara Wisnu : Indonesian Journal of Community Services International Journal of Advanced Science Computing and Engineering SmartComp The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat (JURDIMAS) SWAGATI: Journal of Community Service
Mulia Sulistiyono
Universitas Amikom Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : Jurnal Riset Informatika

 1 
Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM) Retzi Yosia Lewu; Slamet Slamet; Sri Wulandari; Widdi Djatmiko; Kusrini Kusrini; Mulia Sulistiyono
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.558

Abstract

Flood disasters can occur at any time when the factors for the amount of river water discharge and rainfall intensity tend to be high, so preparations and ways of handling are needed to anticipate flood disasters quickly, precisely, and accurately for the Surabaya Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is by calculating predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict rainfall and river water discharge on the Jagir River in Surabaya. The LSTM method is a model commonly used for predictions based on time series data. The data obtained are rainfall data and water discharge on the Jagir River, Surabaya, which will be used as training and testing data to make predictions. The results of implementing the LSTM method using data training of 70% and data testing of 30% on rainfall data using the best epoch, namely at epoch ten by producing tests on data testing can have a Mean Absolute Error (MAE) performance of 4.5 and Root Mean Square Error (RMSE) of 9.7. Whereas the water discharge variable uses the best epoch, namely at epoch 75, by producing data testing data which can have a Mean Absolute Error (MAE) performance of 11.49 and a Root Mean Square Error (RMSE) of 9.63.
Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM) Retzi Yosia Lewu; Slamet Slamet; Sri Wulandari; Widdi Djatmiko; Kusrini Kusrini; Mulia Sulistiyono
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.239

Abstract

AbstractFloods can occur at any time if the amount of river water discharge and rainfall intensity tends to be high, so preparations and ways of handling are needed to anticipate flooding quickly, precisely, and accurately for the Surabaya City Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is to calculate predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict using a time series dataset of rainfall and river water discharge in the Jagir River, Surabaya. This data is used to make predictions with the proportion of 70% training data and 30% testing data. Data normalization is performed in intervals of 0 and 1 using a min-max scaler and activated using ReLU (Rectified Linear Unit) and Adam Optimizer. The process continues by repeating the process to enter iterations, or epochs until it reaches the specified epoch (n). The data is then normalized to their original values and visualized. The model was evaluated and produced acceptable performance evaluation results for the rainfall variable, namely at epoch (n) = 75 for training data, namely a score of 0.054 for MAE and 0.099 for RMSE. In contrast, data testing was given a score of 0.041 for MAE and 0.091 for RMSE. As for the water discharge variable, the performance evaluation shows the difference between the training and testing data. Results of training data MAE = 11.10 and RMSE=18RMSE =18.61.61 at epoch (n) = 150. Results of data testing MAE = 11.37 and RMSE = 21.08 at epoch (n) = 100. These results indicate an anomaly that needs to be discussed in further research.
Evaluating the User-Friendliness of a Mobile Application for Outpatient Food Monitoring: A System Usability Scale (SUS) Approach Sulistiyono, Mulia; Habib Dwi Prajoto; Bernadhed
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.264

Abstract

To maintain and enhance the quality of the Mobile Food Intake Control Application for Outpatients in the Hospital, usability testing must be conducted using the System Usability Scale (SUS). This research aims to evaluate usability and analyze user-friendliness for further action by the Hospital. The respondents consist of 138 outpatient patients. Testing is carried out by requesting respondents to perform scenarios on the Mobile Food Intake Control Application, observed directly by the examiner. Subsequently, respondents fill out a questionnaire containing ten statements with Likert scale responses. The average SUS score of 87.0471 indicates excellent acceptance of the application, and the user rating suggests that the application meets user expectations sufficiently. However, user speed in using the application and focusing on its features are still considered normal, and the error rate falls within acceptable limits.
Co-Authors Abdullah Sukri, M Iqbal Acihmah Sidauruk Afifah Nur Latifah Anggit Dwi Hartanto Anik Sri Widowati Ariadi, Muhammad Vicri Aziz Muzani, Ma'ruf Bayu Setiaji Bernadhed Bernadhed, Bernadhed Bimantara, Aria Wangsa Budy Satria Danang Wijayanto DHANI ARIATMANTO Dini Erlinawati Elik Hari Muktafin Eny Nurnilawati Fadil Indra Sanjaya Fansyah, Rizki Hanif Fatah Yasin Febri Liantoni Febri Liantoni, Febri Gagah Gumelar Habib Dwi Prajoto Hari Muktafin, Elik Hasyim, Jaka Wardana Kharisma, Rizqi Sukma Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Majid Rahardi Mohamad Arie Ajharie Muhammad Taufik Hidayat Muktafin, Elik Hari Negara, Putri Abdi Nurcholis, Muhammad Tofa Nurfian Yudhistira Pamungkas, Pantra Rizal Panggih Suseno Raditya Wardhana Retzi Yosia Lewu Rizqi Sukma Kharisma Robert Marco, Robert Saputro, Uyock Anggoro Slamet Slamet Slameto, Andika Agus Sri Wulandari Sumarni Adi Sutarni, Sutarni Uyock Anggoro Saputro Widdi Djatmiko Wirasakti, Lucky Adhikrisna Yoga Pristyanto Yuliana Rizki Ikhsanty