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All Journal Jurnal Sistem Informasi dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi
Nilam Kurnia Sari
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Diukur Dengan Data Mining C4.5 Menggunakan Metode Decision Tree Dan Naïve Bayes Nilam Kurnia Sari; Mardiana Rafa Alzena; Fakhrudin Fakhrudin
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 1 No. 4 (2023): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v1i4.1777

Abstract

The goal of this data mining C4.5 implementation is to improve student performance in academic coursework in the computer science department at Teknik Fakultas and Pancasakti University in Tegal. Use a limited number of dimensions to assess the following: nyata, jaminan, keandalan, empatia, dan bukti nyata. It is difficult to determine which quality standard has to be raised because the aforementioned kelima aspek cannot be changed in an objective manner. Utilizing the algorithm C4.5 method, the authors consider reducing the sample size to the point where the keputusan is reduced. After manual perhitungan, pembuktian is also carried out using an application called RapidMiner.The analysis's conclusions show that the most important factor in determining the mahasiswa's tingkat kepuasan is the style of teaching.
Analisis Perbandingan Algoritma Random Forest dan Algoritma Naive Bayes untuk Memprediksi Penyakit Paru-Paru di Indonesia Eka Wulansari Fidayanthie; Asep Sayfulloh; Mardiana Rafa Alzena; Nilam Kurnia Sari
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 3 No. 3 (2025): Juli : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v3i3.956

Abstract

Lungs are vital organs in the human respiratory system, responsible for fulfilling the body's oxygen needs. If the lungs experience health problems, it can have adverse effects on the human respiratory system. Common causes of lung diseases are usually due to inhaling air contaminated by dust, smoke, viruses, and bacteria. This study aims to compare the performance of two classification algorithms, namely Random Forest and Naive Bayes, in predicting lung diseases. The data used was obtained from the Kaggle website and processed using RapidMiner software. The attributes involved include smoking habits, pre-existing conditions, staying up late, exercise activities, age, and outcomes. Based on the test results, the Random Forest algorithm demonstrated the best performance with an accuracy of 93%, while the Naive Bayes algorithm achieved an accuracy of 87%. These findings indicate that the Random Forest algorithm outperforms the Naive Bayes algorithm in terms of lung disease prediction accuracy.
Co-Authors Asep Sayfulloh Eka Wulansari Fidayanthie Fakhrudin Fakhrudin Mardiana Rafa Alzena