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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Pengabdian West Science
Januponsa Dio Firizqi
Universitas Pradita
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 Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Other

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GOVERNANCE OF IOT DEVICES USING NODE-RED ORCHESTRATOR AND WEB-BASED DASHBOARD Atmodjo, Dwi; Harianja, Richad; Firizqi, Januponsa Dio; Kurniawan, Rido Dwi
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1336

Abstract

High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment. High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment.
ANALYSIS AND PREDICTION OF HEALTH INSURANCE PREMIUM VALUE USING MACHINE LEARNING ALGORITHM Danica Recca Danendra; Januponsa Dio Firizqi
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 2 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i2.2775

Abstract

Rising healthcare costs and administrative complexity in the health insurance sector underscore the need for an efficient predictive model to anticipate insurance premium prices. The study explores Machine Learning (ML) techniques to predict the value of health insurance premiums. Also, it aims to provide further insights to stakeholders to create strategies in premium pricing and risk management. This study uses the Kaggle.com datasets and a boosting regression algorithm to compare the accuracy and metric evaluation results in predicting the value of insurance premiums. Feature engineering techniques are applied to improve model performance, reduce over-fitting, and interpret the model to ensure the inclusion of relevant predictors by studying the strengths and limitations of each technique. They overcome this through feature selection, model interpret-ability, scalability, and generalization. Through this comprehensive review, the results of this study aim to provide valuable insights for practitioners, researchers, and policymakers, as well as facilitate informed decision-making in the context of determining the value of health insurance premiums through the use of ML methodologies.
Co-Authors Abimanyu, Andiani Amalia, Zalfa Azzah Andrew Andrew, Andrew Aprikasari, Manda Atmodjo, Dwi Clarinda , Marcellina Aurelia Danica Recca Danendra Dazki, Erick Dwi Atmodjo Wismono Prapto Elizabeth Hiu , Jesslyn Juana Fangky Antoneus Sorongan Harianja, Richad Hidayat, Nurul Panna, Ariya Pratama, Arif Putra Setiawan, Valentinus Refgiufi Patria Avrianto Richardus Eko Indrajit Rido Dwi Kurniawan Saiful Azhari Muhammad Subhi Basit, Muhamad Wiryaatmaja , Christopher Louis WP, Dwi Atmodjo