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All Journal Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan
Nur Hafsari, Lintang
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Transportation

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Jumlah Kepala Sekolah Dan Guru Menurut Kelompok Umur Provinsi Bali, Jambi, Papua Barat, Dan Papua Tengah Tahun 2023/2024 Rosyani, Perani; Gisma Ripan, Fitrah; Bunga, Azaria; Nur Hafsari, Lintang; Saputro, Bagus
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 2 No. 4 (2024): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

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Abstract

The study aims to analyze the distribution of school principals and teachers based on age groups in the provinces of Bali, Jambi, West Papua, and Central Papua. This research employs a descriptive quantitative approach using data sourced from the 2023/2024 BPS reports and related academic journals. The findings indicate that the age distribution of educators varies across provinces. Bali and Jambi show higher mean and median ages, indicating a dominance of experienced educators. On the other hand, West Papua and Central Papua have lower mean and median ages, suggesting a predominance of younger educators. These findings offer a better understanding of the age composition of educators in each province and highlight the importance of educational policy planning tailored to the demographic conditions of each region. This data is expected to serve as a foundation for stakeholders in enhancing the quality and sustainability of education in Indonesia.
Penerapan Teknik SVM untuk Klasifikasi Wine Berdasarkan Komposisi Kimia Zakaria, Arief Reza; Hamzah, Amri; Nur Hafsari, Lintang; Rosyani, Perani
AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Vol. 3 No. 2 (2025): Jurnal AI dan SPK : Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan
Publisher : CV. Shofanah Media Berkah

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Abstract

Kualitas wine merupakan aspek penting dalam industri wine yang dipengaruhi oleh komposisi kimia. Penelitian ini bertujuan untuk mengklasifikasikan kualitas wine berdasarkan komposisi kimia menggunakan teknik Support Vector Machine (SVM). Dataset wine yang digunakan dalam penelitian ini diperoleh dari Kaggle, yang terdiri dari 5320 sampel wine dengan 11 fitur kimia. Hasil penelitian menunjukkan bahwa teknik SVM dapat mengklasifikasikan kualitas wine dengan akurasi yang tinggi, yaitu lebih dari 75%. Penelitian ini membuktikan bahwa teknik SVM dapat digunakan sebagai metode yang efektif untuk klasifikasi wine berdasarkan komposisi kimia.
Co-Authors Bunga, Azaria Gisma Ripan, Fitrah Hamzah, Amri Perani Rosyani Saputro, Bagus Zakaria, Arief Reza