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Wargijono Utomo
Universitas Krisnadwipayana
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The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia Wargijono Utomo
ILKOM Jurnal Ilmiah Vol 13, No 1 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i1.763.31-35

Abstract

The coronavirus spreads quickly through human-to-human transmission via close contact and respiratory droplets such as coughing or sneezing. Various studies have been carried out to deal with Covid-19. However, the cure for this virus has not been found until now. Based on data from the covid19.go.id page retrieved on January 1st, 2021, which was updated by the Ministry of Health, the overall number of confirmed cases was 1,078,314 active cases reaching 175,095 or 16.2% of confirmed cases, recovered 873,221 or 81.0% of confirmed cases, and death 29,998 or 2.8% of the confirmed cases. This study compares the two algorithms of data groups to analyze clustering patterns to determine the best data processing method. The data in this study sourced from the Ministry of Health, contained 4 attributes, including confirmed cases, treatment, recovery, and death cases. In this study, only 2 attributes were used: the confirmed and death cases. From the data analysis and processing results through a comparison between the K-Means method and the K-Medoids for clustering the spread of the coronavirus in Indonesia, a conclusion is derived. With the Davies Boulden index value from K2 to K9 values, it turns out that the K-Means method gets the smallest value at the K-5 of 0.064, while K-Medoids at the k-2 value of 0.411. Thus, from the two methods used, it can be concluded that the best method for clustering the spread of the coronavirus outbreaks in Indonesia is the K-Means method.
Development of Drainage Status Prediction Model Based on Internet of Things and Long Short Term Memory Algorithm Ahmad Pahrul Rodji; Wargijono Utomo; Ali Khumaidi; Hudzaifah Al jihad
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The capacity of drainage can overflow due to inadequate conditions and high rainfall intensity. Several incidents in Bekasi City due to poor drainage resulted in inundation of water on the roads which resulted in damaged roads and flooding in residential areas. Several previous studies have discussed the evaluation of the drainage system using the analytical method hydrology in modeling water discharge. In most cases, the minimum capacity of the drainage canal is caused by the high intensity of rain, so the research focuses on the volume of drainage and the intensity of the rain. However, based on observations and interviews with the cleaning service, it turns out that many drainage channels are in a non-optimal condition, where there is a lot of garbage and sedimentation that hinders the flow of water when it rains. This study combines hydrological analysis modeling with drainage channel conditions whose real time data is obtained by using sensors through the internet of things (IoT). IoT devices have been able to send data well in the cloud, by combining rainfall data and then predictive modeling using RNN LSTM with training model parameters used are two layers and 20 cells with each layer given a Dropout layer with a probability of 10%. In the metric evaluation, four functions are used, namely mean squared error, Mean absolute, Nash-Sutcliffe Efficiency and Coefficient of Determination. The model has been able to see the occurrence of an increase or decrease in height and discharge. However, if you look at the results of metric calculations, the predictions generated by the model are not very good.
Prediction of Electricity Usage in The Food and Beverage Department Using Recurrent Neural Network Lukman Aditya; Wargijono Utomo; Ali Khumaidi; Rahmat Hidayat; Hudzaifah Al jihad
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The Food and Beverage (F&B) department is one of the sources of income for the company. F&B uses a variety of equipment and machines with large enough power consumption to support operations. F&B can be a disadvantage because of the wasteful use of electrical energy. This research designs and builds an Internet of Things (IoT) prototype that can monitor electricity usage in electrical equipment using sensors then from the data sent by the sensor and additional data predictions are made. The electrical equipment studied included walk-in chillers, blower wheels, exhaust fans, freezers, dishwashers, water heaters and under chillers. To build IoT devices, Arduino nano, AC Current Module, SIM 800L and humidity and temperature sensors are used. Prediction model built using RNN LSTM. IoT devices have succeeded in sending data well after cloud architecture. With 8 neurons in LSTM with lookback has the best performance. The error values ??for the test data are 51,085 and 18,886 for RMSE and MAE.
SISTEM INFORMASI PENGARSIPAN BERBASIS WEBSITE DENGAN PEMANFAATAN QR CODE STUDI KASUS PADA FAKULTAS TEKNIK UNIVERSITAS KRISNADWIPAYANA Wargijono Utomo; Risanto Darmawan
TEKNOKRIS Vol 23 No 1 (2020): Jurnal Teknokris Edisi Juni
Publisher : Fakultas Teknik Unkris Jakarta

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Abstract

Fakultas Teknik Universitas Krisnadwipayana berdiri sejak tahun 1963 yang memiliki 7 program studi. Fakultas Teknik Unkris belum memiliki Aplikasi Pengarsipan Berbasis Website, saat ini dalam proses pengarsipannya belum terkomputerisasi. Sistem Informasi Arsip Berbasis Website Dengan Pemanfaatan QR Code untuk Scan Data diharapkan dapat membantu admin dan user di Fakultas Teknik Universitas Krisnadwipayana dalam mengelola arsip data. Hasil dari perancangan ini telah dibuat, admin dan mahasiswa menggunakan QR Code sebagai data scan untuk menunjukkan bahwa file telah diambil. Aplikasi ini menggunakan database sebagai tempat penyimpanan data - data penilaian. Aplikasi ini dirancang dengan menggunakan model Unified Modeling Language (UML). Dengan adanya perancangan sistem informasi kearsipan ini diharapkan dapat membantu kinerja admin dalam proses pengarsipan karena sistem email telah diverifikasi ke setiap mahasiswa. Pemanfaatan QR Code dirancang untuk membantu admin saat mahasiswa akan mengambil ijazah
Clustering the Impacts of The Russia-Ukraine War on Personnel and Equipment Wargijono Utomo
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.215

Abstract

In post-pandemic recovery efforts, uncertainty arose due to the unresolved conflict between the Russia-Ukraine war. This conflict impacts world security stability and affects the economic, energy, and food sectors. This conflict also impacts humanity by causing death to civilians and military personnel, including children in Ukraine. The clustering analysis results of the impact of the Russian-Ukrainian war show losses and losses in personnel and war equipment, with three cluster optimization methods used through k-means. Of the two methods that can be recommended, namely elbow and Silhouette, both produce K=3. The profiling results show that losses or losses in Ukrainian personnel and war equipment are categorized into three clusters, with cluster one being the lowest category, cluster two being the very high category, and cluster three being the moderate category. This research is helpful for state agencies, international organizations (NGOs), and other stakeholders.
Co-Authors Achmad Pahrul Rodji Ali Khumaidi Hudzaifah Al jihad Lukman Aditya Rahmat Hidayat Risanto Darmawan