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All Journal Scientica: Jurnal Ilmiah Sains dan Teknologi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Lestari, Danur
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences Other

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AUDIT TATA KELOLA SISTEM INFORMASI PADA SISTEM AGENDA DINAS PERKEBUNAN PROVINSI RIAU MENGGUNAKAN FRAMEWORK COBIT 2019 Lestari, Danur; Mubarak , Haykal Alya; Megawati, Megawati
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 8 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

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Abstract

Dengan menggunakan kerangka kerja COBIT 2019, penelitian ini menyelidiki tata kelola teknologi informasi (TI) pada sistem informasi agenda di Dinas Perkebunan Provinsi Riau. Studi ini berfokus pada domain BAI01 (Managed Program) dan APO07 (Managed Human Resources). Sumber daya manusia (SDM) sangat penting untuk mencapai tujuan organisasi, terutama dalam penggunaan TI yang efisien dan efektif. Dinas Perkebunan Provinsi Riau telah menggunakan TI dalam berbagai aspek untuk meningkatkan transparansi, akuntabilitas, dan layanan publik. Penelitian ini menggunakan metode yang didasarkan pada pedoman metodologi COBIT 2019, yang mencakup perencanaan, pengumpulan data, dan analisis tingkat kapabilitas proses BAI01 dan APO07. Hasil penelitian menunjukkan bahwa domain BAI01 mencapai tingkat kapabilitas rata-rata 90,4% dan APO07 mencapai tingkat kapabilitas rata-rata 91,6%. Kedua domain tersebut berada pada level Fully Achieved (F). Kesimpulan ini menunjukkan bahwa sistem informasi agenda Dinas Perkebunan Provinsi Riau telah mencapai tujuannya dan berfungsi dengan baik. Dengan melakukan evaluasi dan pengukuran yang berkelanjutan pada subdomain BAI01 dan APO07, penelitian ini diharapkan akan memberikan rekomendasi konkret untuk meningkatkan tata kelola TI di instansi tersebut..
Applying A Supervised Model for Diabetes Type 2 Risk Level Classification Dhani, Ahmad; Lestari, Danur; Ningrum, Meriana Prihati; Fakhrizal, M. Andhika; Gandini, Ganis Lintang
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1105

Abstract

Diabetes can lead to heart attacks, kidney failure, blindness, and increased risk of death. This research was conducted with the aim of classifying a diabetes risk dataset. In this context, performance comparison was carried out on three supervised learning algorithms: K-Nearest Neighbor, Naive Bayes, and Random Forest, against a dataset containing information on specific indicators related to diabetes risk. Additionally, this study also aimed to evaluate the accuracy comparison of the results produced by these three algorithms. The results of this research show that Random Forest performs very well in detecting diabetes, prediabetes, and non-diabetes, with high precision, recall, and F1-score levels. Meanwhile, although the results are still below Random Forest, both Naive Bayes and K-NN still demonstrate significant performance, especially regarding prediabetes cases. In conclusion, from the comparison results, the Random Forest algorithm shows the highest accuracy level at 99%, followed by K-Nearest Neighbor with an accuracy of 85%, while Naive Bayes has the lowest accuracy rate of 74%. This research indicates that the Random Forest algorithm excels in classifying data compared to the other two algorithms.
Deep Learning for Pneumonia Detection in Chest X-Rays using Different Algorithms and Transfer Learning Architectures Lestari, Danur; Mulya, Anggi; Tatamara, Aghnia; Haiban, Ryando Rama; Khalifah, Habibah Dian
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.1553

Abstract

Pneumonia is one of the lung conditions brought on by bacterial infections. An accurate diagnosis is necessary for successful treatment. A radiologist can typically diagnose the condition based on images from a chest X-ray. The diagnosis may be arbitrary for a variety of reasons, such as the indistinctness of certain diseases on chest X-ray pictures or the possibility of the illness being mistaken for another. Consequently, clinicians require guidance from computer-aided diagnosis tools. We diagnosed pneumonia using two algorithms CNN and GAN, as well as two architectures ResNet50V2 and InceptionV3. The test results show that the ResNet50V2 architecture is superior to the InceptionV3 architecture on the CNN algorithm with an accuracy of 94% versus 93%. In addition, the test results on the GANs algorithm show that the ResNet50V2 architecture is superior to the InceptionV3 architecture with an accuracy of 96%, while the InceptionV3 architecture achieves an accuracy of 92%.
Co-Authors Dhani, Ahmad Fakhrizal, M. Andhika Gandini, Ganis Lintang Haiban, Ryando Rama Khalifah, Habibah Dian Megawati - Mubarak , Haykal Alya Mulya, Anggi Ningrum, Meriana Prihati Tatamara, Aghnia