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All Journal Syntax Jurnal Informatika Teknika: Jurnal Sains dan Teknologi Format : Jurnal Imiah Teknik Informatika Jurnal Ilmiah Sains dan Teknologi JIKA (Jurnal Informatika) Jurnal Abdimas Bina Bangsa Jurnal Pengabdian Dinamika
Sukarna, Royan Habibie
Universitas Sultan Ageng Tirtayasa
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Journal : Jurnal Ilmiah Sains dan Teknologi

 1 
IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAIVE BAYES DENGAN FEATURE SELECTION UNTUK PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU Royan Habibie Sukarna; Yulian Ansori
Jurnal Ilmiah Sains dan Teknologi Vol 6 No 1 (2022): Jurnal Ilmiah Sains dan Teknologi
Publisher : Teknik Informatika Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1101.395 KB) | DOI: 10.47080/saintek.v6i1.1467

Abstract

The Education Efficiency Rate (AEE) is one of the parameters of the quality of the education program. The quality is measured based on 7 main standards, one of which is students and graduates. Meanwhile, to predict students' graduation rates accurately based on manually owned data set characteristics is very difficult. Data Mining by Naïve Bayes method was chosen to find patterns in analyzing and predicting timely graduation of students. As for the test will be done by comparing the initial dataset and dataset characteristics using the algorithm attribute selector Gain Ratio Attribute with the help of tools WEKA. The results showed that there was a difference to the accuracy of the results, and the larger ROC or AUC curves on the dataset characteristics using the selector attribute by using the Gain Ratio Attribute, although not very significant. And the result of this research yield 81% accuracy level with precision equal to 83.563% and recall 88.41%. The method used is included in Good Classification and will become the reference of the college management side, to address the problems that may arise in the decrease of the quality of education (e.g. decrease ratio of lecturers with students).
Perancangan Aplikasi Surat Keteranggan Pengantar Ijazasah Berbasis Web Sukarna, Royan Habibie; Krisdianto, Nanang; Hilman, Mohamad; Holilah, Holilah; Januriana, Andi Moch; Umam, Ahmad Khaerul
Jurnal Ilmiah Sains dan Teknologi Vol 8 No 1 (2024): Jurnal Ilmiah Sains dan Teknologi
Publisher : Teknik Informatika Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/saintek.v8i1.2977

Abstract

This study explains how to create and implement a web-based application for Certificate of Introduction to Diplomas (SKPI) using the Laravel framework and the Extreme Programming (XP) method. With SKPI as a confirmation of competency after graduation, this application aims to record student competency during the course. The XP method is used to ensure flexibility, collaboration and sustainability in application development. The Laravel framework was chosen for its ease of use and strength in backend/API creation, which allows integration with other applications. The research results show that the SKPI application was successfully created with features that meet the need for recording and verifying student competency. This research contributes to a practical understanding of web-based application development with a focus on student competency track records
Analisis Prediksi Kelulusan Mahasiswa Universitas Sultan Ageng Tirtayasa Menggunakan Algoritma Machine Learning dan Feature Selection Sukarna, Royan Habibie; Holilah, Holilah; Damyati, Fitri; Hilman, Mohamad
Jurnal Ilmiah Sains dan Teknologi Vol 8 No 2 (2024): Jurnal Ilmiah Sains dan Teknologi
Publisher : Teknik Informatika Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/saintek.v8i2.3468

Abstract

The KNN algorithm with feature selection achieved the highest accuracy of 74.44% and an Area Under the Curve (AUC) of 0.8212. This model showed a balanced accuracy improvement compared to its performance using the dataset with complete features, which had an accuracy of 72.83% and an AUC of 0.8071. Similarly, the Random Forest model with feature selection showed an accuracy of 72.00% and an AUC of 0.7741, compared to an accuracy of 70.52% and an AUC of 0.7672 with all features. The SVM model with feature selection also improved, reaching an accuracy of 72.28% and an AUC of 0.7812, compared to an accuracy of 69.80% and an AUC of 0.774 with all features. Logistic Regression showed minimal change, with an accuracy of 69.14% and an AUC of 0.7644 after feature selection, compared to an accuracy of 69.25% and an AUC of 0.7645 with all features.
Co-Authors Agung Susilo Yuda Irawan Ahmad Khoirul Umam Andi Moch Januriana Arina, Faula Budiaji, Weksi Damyati, Fitri Elga Ramdani Fatmasari, Nela hardiansyah, alim Herfandi Herfandi Hilman, Mohamad Hilman, Muhamad Holilah Holilah, Holilah Ikhsan, Aulia Januariana, Andi Moch Krisdianto, Nanang Majahar Ali, Majid Khan Mukhtar, Mukhtar Oktarisa, Yuvita Pertiwi, Dinda Dwi Anugrah Robby Zidny Saruni Dwiasnati Syarif Abdullah Umam, Ahmad Khaerul Wicaksono, Agung Satrio Yuliadi Yuliadi Yulian Ansori Yulian Ansori