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All Journal Semantik Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Elementer (Elektro dan Mesin Terapan) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA ComTech: Computer, Mathematics and Engineering Applications Jurnal Pengabdian Masyarakat Multidisiplin INSERT: Information System and Emerging Technology Journal Jurnal Akuntansi Keuangan dan Bisnis Proceeding Applied Business and Engineering Conference Proceeding International Applied Business and Engineering Conference Fleksibel: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics
Kartina Diah K. W.
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Penerapan Video Animasi Pembelajaran Pendidikan Agama Islam Kelas 4 di SD Al-Ittihad Pekanbaru Dewi, Meillany; Kartina Diah Kusuma Wardani; Suhatman, Rahmat; Kamila, Aida
FLEKSIBEL: Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2021): Edisi Oktober 2021
Publisher : Fakultas Teknik Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/6a01q591

Abstract

There are many factors that influence the achievement of learning objectives, one of which is learning media. With the use of appropriate learning media, it can make learning more effective so that teachers can create a new, conducive atmosphere among students and something that cannot be presented or raised in class can be presented clearly and easily understood by students.This PKM aims to facilitate the school, especially SDIT AL-Ittihad Pekanbaru, to make learning materials in the form of animated videos as an alternative to existing learning media. This PKM produces animated videos to assist teachers in delivering Al fatihah and Asmaul Husna (Allah SWT Almighty and Allah SWT Most Giver) materials to grade 4 elementary school students so that they can become alternative learning media in schools so that they can become alternative learning media in schools. This activity received a very good response and enthusiasm from the school and from grade 4 elementary school students, so as to improve good relations between PCR and SDT AL-Ittihad Pekanbaru.
Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost) Wardhani, Kartina Diah Kusuma; Akbar, Memen
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.970

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%
Principal Component Analysis for Prediabetes Prediction using Extreme Gradient Boosting (XGBoost) Wardhani, Kartina Diah Kesuma; Novayani, Wenda
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.13416

Abstract

Purpose: The purpose of this study is to increase the accuracy of the model used for prediabetes prediction. This study integrates Principal Component Analysis (PCA) for reducing the dimension of data with Extreme Gradient Boosting (XGBoost). The study contributes to providing a new alternative for prediabetes prediction in patients by reducing the complexity of the dataset with the aim of increasing the accuracy of the obtained model. PCA and XGBoost identify the best features that have the highest correlation with prediabetes so that they are expected to produce a better predictive model. Methods: This study utilizes published data sourced from the UCI Machine Learning Repository consisting of 520 records, 16 attributes and 1 label class. The dataset is data collected through direct questionnaires from patients in Sylhet, Bangladesh at the Sylhet Diabetes Hospital. The research method in this study consists of several stages, namely: Data Collection, Data Preprocessing, Dimension Reduction using PCA to reduce the complexity of dimensions in the dataset, Modeling using XGBoost to identify patterns used to predict prediabetes, and Model evaluation used to measure the performance of the resulting model using evaluation metrics such as accuracy, recall, precision and F1-Score. Result: The current study utilizes XGBoost with Principal Component Analysis for feature selection, resulting in 12 features and a model accuracy of 97.44. Novelty: The study's originality lies in applying PCA as a preprocessing step to enhance the performance of machine learning models by reducing data dimensionality and focusing on the most critical features. By demonstrating how PCA can improve the efficiency and accuracy of prediabetes prediction models, this research provides valuable insights to inform future studies and contribute to the development of more effective diagnostic tools for early detection and prevention of prediabetes.
Modeling and Application of Credit Scoring Based on A Multi-Objective Approach to Debtor Data in PT. Bank Riau Kepri Sugianto, -; Widyasari, Yohana Dewi Lulu; Wardhani, Kartina Diah Kusuma
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1493

Abstract

The development of information technology in Indonesia, marked by the start of Industry 4.0, is very rapid. With the development of technology, many companies use technology to develop their business, one of which is banking, which analyses the process of prospective customers. New employees find it challenging to interpret and tend to agree more easily with prospective customers because they only see the fulfillment of general requirements. This research aims to find an overview of the primary and additional factors to analyze prospective credit customers using The Cross-Industry Standard Process for Data Mining (CRISP-DM). Develop a model in this study using data variables of prospective customers in health insurance as a moderating variable. This model tested the Decision Tree algorithm with an accuracy value of 92.49%, the Random Forest with an accuracy value of 81.72%, the Support Vector Machine (SVM) with an accuracy value of 91.25%, and K-Nearest Neighbor (K-NN) with an accuracy value. 90.58%, Gradient Boosting with an accuracy value of 90.69%, and XGBoost with an accuracy value of 93.27%. The algorithm uses a cross-validation technique at the validation stage by changing the K value to 2, 4, 6, 8, and 10. The results show that the XGBoost Algorithm accuracy is 93.27% with a K value of 8. As the highest model accuracy, this model was implemented using the XGBoost Algorithm.
Co-Authors - Sugianto Akbar, Memen Akbar, Memen Alfin H Anam, M Khairul Ari Azhar Cakra Ramadhana chintia romauli rambe Rambe Dewi, Meillany Dhimas Sena Rahmantara Efrizoni, Lusiana Heri Subagiyo Ike Yunia Pasa Isnaini, Mei Nanda Jan Alif K Jan Alif Kreshna Juni Nurma Sari Kamila, Aida Khairul Umam S Maksum Ro’is Adin Saf Maksum Ro’is Adin Saf Mardhiah Fadhli, Mardhiah Memen Akbar Muhammad Bambang Firdaus Nurmalasari, Dini Puja Hanifah Sari, Juni Sri Devi Anjasmara Sugeng Purwantoro E.S.G.S Suhatman, Rahmat Surya, Ibnu Syaparudin BS Tisa Magrisa Tobi Arfan Tri Wahyuni, Retno Wawan Yunanto Wenda Novayani Weslie Weslie Yohana Dewi Lulu Widyasari Yuli Fitrisia