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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Education Technology Building of Informatics, Technology and Science
Nurhidayat, Rifki
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Computer Science & IT Education

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The Testing of E-Module Flip-PDF Corporate to Support Learning: Study of Interests and Learning Outcomes Erniwati, Erniwati; Hunaidah, Hunaidah; Nurhidayat, Rifki; Fayanto, Suritno
Journal of Education Technology Vol. 6 No. 4 (2022): November
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jet.v6i4.43857

Abstract

ICT-based learning resources that are developed still need to be improved, especially in areas where learning facilities are lacking. It causes students difficulty in understanding the learning material. This study aims to develop an e-module on static fluid material based on corporate edition flip pdf software. The type of research used is Research and Development with the ADDIE method. The research object was class XI SMA, with a trial sample of 25 students. The research instrument consisted of validator sheets, teacher and student response questionnaires, and learning achievement test sheets. Data analysis techniques consist of descriptive analysis and hypothesis testing. The results showed that the static fluid physics learning e-module had a validity percentage of 89.95% (very good). The level of practicality of the static fluid physics learning e-module through the results of student responses is 87.42% (very good), and the teacher's response results are 86.25% (very good). The level of effectiveness of the learning e-module through students' learning interest obtains the medium category, and the increase in students' physics learning outcomes obtains the medium category. It indicates that the use of e-modules impacts interest and learning outcomes. The implication of this study is that learning e-modules are available in flip pdf corporate education that can be used by students and teachers as learning resources and can be accessed online online.
Analisis Sentimen Komentar Media Sosial Twitter Terhadap Tes CPNS dengan Algoritma Naive Bayes Nurhidayat, Rifki; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6148

Abstract

The Calon Pegawai Negeri Sipil (CPNS) is one of the most sought-after careers in Indonesia, with the number of applicants increasing every year. The CPNS selection process attracts public attention and triggers various opinions, both positive and negative, which are widely conveyed through social media such as Twitter. This research aims to analyze public sentiment towards the CPNS selection process using the Naive Bayes algorithm. The data used in this study consists of 5,599 comments on Twitter, with a composition of 5,269 negative sentiment data and 323 positive sentiment data. Tests were conducted using several data sharing ratios, namely 80:20, 70:30, 90:10, and 50:50. The results show that the 70:30 ratio provides the best accuracy, which is 95%. However, data imbalance causes the model to focus more on negative sentiment. To address this issue, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, which successfully improved the model's performance in classifying positive data, with precision and recall reaching 85-98%. After the application of SMOTE, the overall accuracy decreased slightly to 91% at 80:20, 70:30, and 90:10 ratios, but the model became more effective in detecting both sentiments. The results of this study provide insight into the public's views on CPNS selection and can be used by the government to improve the selection process in the future. With this approach, it is expected that government agencies can better understand public perceptions and optimize a more transparent and fair recruitment system.
Implementasi XGBoost dalam Klasifikasi Gagal Ginjal Kronis Menggunakan Dataset Chronic Kidney Disease Abdillah, Muhammad; Sarira, Brayen Tisra; Hidayat, Ahmad Nur; Fauzan, Ahmad Nur; Nurhidayat, Rifki; Septiarini, Anindita; Puspitasari, Novianti
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.11546

Abstract

Chronic Kidney Disease (CKD) is a serious health issue that can lead to death if not detected early. To support early detection, this study applies the eXtreme Gradient Boosting (XGBoost) algorithm to classify patients at risk of CKD. The dataset used is the Chronic Kidney Disease Dataset from Kaggle, consisting of 400 patient records and 26 clinical attributes. Preprocessing involved imputing missing values and converting categorical features into numerical form. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that XGBoost achieved 99% accuracy, with 98% precision and 100% recall, indicating excellent performance in binary classification tasks. This study demonstrates that XGBoost is a reliable algorithm for automatic prediction of chronic kidney disease. Keywords: XGBoost, chronic kidney disease, classification, machine learning
Co-Authors Anindita Septiarini, Anindita Erniwati Erniwati Fauzan, Ahmad Nur Fayanto, Suritno Hidayat, Ahmad Nur Hunaidah, Hunaidah Muhammad Abdillah Nirwana Hendrastuty Puspitasari, Novianti Sarira, Brayen Tisra