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All Journal Jurnal Riset Informatika Jurnal Mantik JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) JURNAL EKONOMI BISNIS DAN MANAJEMEN AMMA : Jurnal Pengabdian Masyarakat Teknokris Jurnal Ekonomi Bisnis dan Manajemen
Wargijono Utomo
Universitas Krisnadwipayana
Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing

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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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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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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.
Rancang Bangun Aplikasi Monitoring Tumbuh Kembang Anak Balita Pada Posyandu Kelurahan Duren Sawit Junaidi; Elmi Devia; Mega Wahyu Rhamadani; Wargijono Utomo
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 9 : Oktober (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Children are one of the valuable assets for a family. Children's growth and development can run well if their nutritional needs are met. Stunting is one of the disorders caused by poor parenting practices. Lack of parental knowledge about child growth and development can increase the risk of stunting. Duren Sawit Village is a village located in Duren Sawit District, East Jakarta Administrative City, DKI Jakarta Province. From interviews conducted with sources at the RW. 015 Posyandu, Duren Sawit Village, it is known that the process of recording child growth and development data is still done manually by recording child data in the Maternal and Child Health (KIA) record book. Based on the statement above, we are interested in creating an application for monitoring the growth and development of toddlers at the RW. 015 Posyandu, Duren Sawit Village, to make it easier for Posyandu cadres to send toddler nutritional status data to the Duren Sawit Sub-dept.
INTEGRASI BUSINESS INTELLIGENCE DAN ARTIFICIAL INTELLIGENCE DALAM PENGELOLAAN SAMPAH DI INDONESIA: TINJAUAN SISTEMATIS BERBASIS PRISMA Heni Lestari; Wargijono Utomo
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 6, No 2 (2026): JURNAL JRIS EDISI JULI 2026
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol6no2.1285

Abstract

Indonesia faces a waste management crisis, with only 39% of waste managed properly. This study aims to identify gaps in the integration of Business Intelligence (BI) and Artificial Intelligence (AI) in waste management in Indonesia. This study employed a Systematic Literature Review (SLR) using the PRISMA protocol for 20 scientific articles that met the inclusion criteria for the 2020–2025 period. The results indicate the absence of an integrated BI-AI model, persistent data fragmentation, and a lack of studies on the socio-economic impact of waste management. A future research agenda is proposed for adaptive systems tailored to the Indonesian context.Indonesia menghadapi krisis pengelolaan sampah dengan hanya 39% sampah dikelola secara layak. Penelitian ini bertujuan mengidentifikasi kesenjangan integrasi Business Intelligence (BI) dan Artificial Intelligence (AI) dalam pengelolaan sampah di Indonesia. Penelitian ini menggunakan metode Systematic Literature Review (SLR) menggunakan protokol PRISMA terhadap 20 artikel ilmiah yang memenuhi kriteria inklusi pada periode 2020–2025. Hasil penelitian menunjukkan belum adanya model terintegrasi antara BI dan AI, masih adanya fragmentasi data, serta minimnya studi dampak sosial-ekonomi pengelolaan sampah. Agenda riset masa depan diusulkan untuk sistem adaptif yang sesuai konteks Indonesia.
Penerapan Business Intelligence Berbasis Data Mining dan Predictive Analytics untuk Analisis Pola dan Prediksi Churn Driver Transportasi Online Berbasis Dashboard Interaktif Yulius Rodolob Sahelatua; Wargijono Utomo
JURNAL EKONOMI BISNIS DAN MANAJEMEN Vol. 4 No. 2 (2026): April : JURNAL EKONOMI BISNIS DAN MANAJEMEN
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jise.v4i2.2174

Abstract

This study aims to develop a Business Intelligence system based on Data Mining and Predictive Analytics to analyze the behavioral patterns of online transportation drivers, predict potential driver churn, and improve the operational efficiency of transportation companies through an interactive dashboard. The research employs a quantitative approach involving the development of a Data Warehouse, the implementation of the Extract, Transform, Load (ETL) process, the application of the Random Forest algorithm for driver Churn Prediction, clustering analysis to classify drivers based on their operational behavior, and the visualization of analytical results using an interactive Business Intelligence dashboard for real-time monitoring. The dataset includes drivers' daily activities, number of completed orders, income levels, customer ratings, order cancellation rates, operating hours, and driver activity status history. The results demonstrate that the proposed system improves operational reporting efficiency by up to 92% and achieves a driver Churn Prediction accuracy of 89%. Furthermore, the system provides analytical insights that assist management in identifying the key factors influencing driver performance decline and the likelihood of driver attrition. The interactive dashboard also facilitates real-time performance monitoring, order distribution evaluation, identification of high-churn regions, and the formulation of data-driven driver retention strategies. The novelty of this research lies in the integration of Business Intelligence, Data Mining, Predictive Analytics, and an interactive dashboard into a unified analytical platform that supports comprehensive strategic decision-making for online transportation companies. This integrated approach contributes to improved resource management, enhanced operational efficiency, and better service quality through evidence-based decision support.
Penerapan Business Intelligence Berbasis Data Mining dan Predictive Analytics untuk Analisis Pola dan Prediksi Churn Driver Transportasi Online Berbasis Dashboard Interaktif Yulius Rodolob Sahelatua; Wargijono Utomo
JURNAL EKONOMI BISNIS DAN MANAJEMEN Vol. 4 No. 2 (2026): April : JURNAL EKONOMI BISNIS DAN MANAJEMEN
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jise.v4i2.2174

Abstract

This study aims to develop a Business Intelligence system based on Data Mining and Predictive Analytics to analyze the behavioral patterns of online transportation drivers, predict potential driver churn, and improve the operational efficiency of transportation companies through an interactive dashboard. The research employs a quantitative approach involving the development of a Data Warehouse, the implementation of the Extract, Transform, Load (ETL) process, the application of the Random Forest algorithm for driver Churn Prediction, clustering analysis to classify drivers based on their operational behavior, and the visualization of analytical results using an interactive Business Intelligence dashboard for real-time monitoring. The dataset includes drivers' daily activities, number of completed orders, income levels, customer ratings, order cancellation rates, operating hours, and driver activity status history. The results demonstrate that the proposed system improves operational reporting efficiency by up to 92% and achieves a driver Churn Prediction accuracy of 89%. Furthermore, the system provides analytical insights that assist management in identifying the key factors influencing driver performance decline and the likelihood of driver attrition. The interactive dashboard also facilitates real-time performance monitoring, order distribution evaluation, identification of high-churn regions, and the formulation of data-driven driver retention strategies. The novelty of this research lies in the integration of Business Intelligence, Data Mining, Predictive Analytics, and an interactive dashboard into a unified analytical platform that supports comprehensive strategic decision-making for online transportation companies. This integrated approach contributes to improved resource management, enhanced operational efficiency, and better service quality through evidence-based decision support.
Co-Authors Achmad Pahrul Rodji Ali Khumaidi Elmi Devia Heni Lestari Hudzaifah Al jihad Junaidi Lukman Aditya Mega Wahyu Rhamadani Rahmat Hidayat Risanto Darmawan Yulius Rodolob Sahelatua