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All Journal Jurnal Pendidikan Sains dan Komputer Karimah Tauhid
Ananda Alfiah
Universitas Djuanda
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Neuroscience Physics Social Sciences Other

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SISTEM INFORMASI PELAYANAN PUBLIK BERBASIS APLIKASI DALAM KONSEP SMART CITY KOTA BOGOR Abu Bakar Basri; Agung Restu Ramadhan; Ananda Alfiah; Wildan Khoirul Fikri; Muhammad Risyad Fadilah; Mulil Khaira; Muhammad Encep
KARIMAH TAUHID Vol. 2 No. 1 (2023): Karimah Tauhid
Publisher : Universitas Djuanda

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Abstract

Pelayanan public dalam konsep smart city yang di terapkan di kabupaten Bogor saat ini merupakan sistem tatakelola pemerintahan yang berbasis teknologi, ini merupakan pergeseran dikarenakan era globalisasi yang terus berkembang. smart city di harapkan menjadi solusi untuk menjawab segala permaslahan yang ada di kota Bogor, seperti di amanatkan pada undang-undang No. 25 Tahun 2009 tentang pelayanan publik. Pemerintah kotasebagai salah satu sarana penyelenggara pelayanan publik mempunyai peran yang sangat penting dalam memberi pelayanan yang baik kepada masyarakat. Kota Bogor terus tumbuh dan berbenah menyongsong era persaingan bebas menuju Masyarakat Ekonomi Asean. Aplikasi berbasis mobile dapat diakses dengan mudah menggunakan smartphone pribadi masyarakat yang dapat digunakan untuk melaporkan informasi terkait pelayanan publik, mengetahui informasi dan kebijakan pemerintah dan dapat juga di akses oleh pemerintah dan stakeholder di pemerintahan yang dapat memberikan feedback secara langsung kepada masyarakat.(Riska Chyntia Dewi & Suparno Suparno, 2022)
Analisis Kinerja Decision Tree dan Random Forest Menggunakan Dataset Breast Cancer: Performance Analysis of Decision Tree and Random Forest Using Breast Cancer Dataset Uus Firdaus; Ananda Alfiah; Lorina Mohdo
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 01 (2026): Artikel Riset, February 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i01.7892

Abstract

Breast cancer is a disease with a high mortality rate in women worldwide, making early detection a crucial factor in increasing the chances of successful treatment and patient survival. Advances in computing technology, particularly machine learning, have created opportunities to use medical data to inform decision-making in the diagnostic process. This study aims to analyze and compare the performance of the Decision Tree and Random Forest algorithms in classifying breast cancer using the Wisconsin Breast Cancer Dataset. The dataset comprises 569 data points with 30 numeric attributes representing cancer cell characteristics and a class label indicating benign or malignant cancer. The research stages include data preprocessing, splitting the data into training and test sets (80:20), applying the classification algorithm, and evaluating model performance using accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm outperforms the Decision Tree. Random Forest achieved an accuracy of 98.68% on the training data and 95.61% on the test data, while Decision Tree achieved an accuracy of 96.92% on the training data and 91.23% on the test data. This difference indicates that Random Forest has better generalization capabilities and is more resistant to overfitting. The findings of this study indicate that Random Forest is more effective for data-based breast cancer classification than Decision Tree. Therefore, the Random Forest algorithm is recommended as a more reliable method to support decision support systems in early breast cancer detection.
Co-Authors Abu Bakar Basri Agung Restu Ramadhan Lorina Mohdo Muhammad Encep Muhammad Risyad Fadilah Mulil Khaira Uus Firdaus Wildan Khoirul Fikri