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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) TEKNIK INFORMATIKA SITEKIN: Jurnal Sains, Teknologi dan Industri JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Seminar Nasional Teknologi Informasi Komunikasi dan Industri Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Multidisiplin Jurnal Nasional Teknik Elektro dan Teknologi Informasi Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Proceeding International Applied Business and Engineering Conference
Akbar, Memen
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Peat Soil Temperature Monitoring System With IoT Technology Hatta Zulkifli; Agus Urip Ari Wibowo; Memen Akbar
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1307.762 KB)

Abstract

Peat soil is a soil element that is very susceptible to burning if it is dry and when burned peat will be synonymous with giving rise to dense smoke and giving rise to embers. Prevention has been carried out so far by building a monitoring tower to see the condition of a peatland from a certain height, but because this is done by humans, there will certainly be limitations in monitoring quickly and precisely. By utilizing the development of Internet of Things technology, a solution that can be done by building a system called Silahan Gambut (SILAGA), where this system has been tested on peatlands using the DB18S20 sensor calibrated with a DHT 22 sensor Using Internet of Things technology, it has been successfully monitored in real time the condition of a peatland that has the potential to burn. The results of the sensor data are managed using MongoDB noSQL so that the data obtained is well managed on peat soils at a certain time, condition and region.
Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost) Kartina Diah Kusuma Wardani; Memen Akbar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4651

Abstract

Diabetes results from impaired pancreatic function as a producer of insulin and glucagon hormones, which regulate glucose levels in the blood. People with diabetes today are not only experienced adults, but pre-diabetes has been identified since the age of children and adolescents. Early prediction of diabetes can make it easier for doctors and patients to intervene as soon as possible so that the risk of complications can be reduced. One of the uses of medical data from diabetes patients is to produce a model that medical personnel can use to predict and identify diabetes in patients. Various techniques are used to provide the earliest possible prediction of diabetes based on the symptoms experienced by diabetic patients, including the use of machine learning. People can use machine learning to generate models based on historical data from diabetic patients, and predictions are made with the model. In this study, extreme gradient boosting is the machine learning technique for predicting diabetes (xgboost) using XGBoost with importance features. The diabetes dataset used in this study comes from the early stage diabetes risk prediction dataset published by UCI Machine Learning, which has 520 records and 16 attributes. The diabetes prediction model using xgboost is displayed as a tree. The model precision result in this study was 98.71%, for the F1 score was 98.18%. The accuracy obtained based on the best 10 attributes using the importance of the XGBoost feature is 98.72%.
Ekstraksi Data pada Tabel dari Halaman Web Menggunakan Pohon Document Object Model Memen Akbar; Cici Patmala; Dini Nurmalasari
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 5 No 4: November 2016
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (925.883 KB)

Abstract

Data on the web page can be available in various formats, such as table. With the growing of web pages, the need to extract data from tables is increasing. Results of the extraction can be used for integration with other web tables or stored in a database. This study discusses the extraction of data from a table on a web page using a Document Object Model (DOM) tree. The initial step of this extraction process is to transform the HTML document into a DOM tree. Then, by applying search methods Depth First Search (DFS), part of the data in the table is extracted and stored in a CSV file. An engine has been developed using Visual Basic. The results show that the engine can automatically extract data from the table that has the following characteristics: the number of rows and columns are not limited, able to handle all of the table orientation layout, and able to handle tables that are merged cells.
Co-Authors - Robinson Agus Urip Ari Wibowo Ananda Ananda Cici Patmala Fandy Hidayat Fazat Nur Azizah Fella Ulfa Hafidz Hafidz Hatta Zulkifli Heri Subagiyo Kartina Diah K. W. Lisa Chandra Mardhiah Fadhli, Mardhiah Muhammad Arif Fadhly Ridha Nurmalasari, Dini Patmala, Cici Retno Tri Wahyuni Saptawati, G. A. Putri Sri Devi Anjasmara Sugeng Purwantoro E.S.G.S Warnia Nengsih Wenda Novayani Wiwin Styorini Yohana Dewi Lulu Widyasari Yuli Fitrisia