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All Journal Nuansa Informatika Sistemasi: Jurnal Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Technologia: Jurnal Ilmiah Tematik : Jurnal Teknologi Informasi Komunikasi International Journal of Global Operations Research International Journal of Quantitative Research and Modeling Majalah Bisnis & IPTEK Formosa Journal of Applied Sciences (FJAS) International Journal of Ethno-Sciences and Education Research SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
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Fakultas Teknik Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
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Journal : Nuansa Informatika

 1 
Perbandingan Algoritma Klasifikasi Data Mining untuk Penelusuran Minat Calon Mahasiswa Baru Budiman Budiman
NUANSA INFORMATIKA Vol 15, No 2 (2021)
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (536.203 KB) | DOI: 10.25134/nuansa.v15i2.4162

Abstract

During the AMIK HASS pandemic, it was difficult to determine new student candidates. So that to attract public interest, the Marketing Department has implemented several strategies to attract prospective students to become new students. The data mining technique used in predicting is a classification that includes Naïve Bayes, J48 Decision Tree, and K-Nearest Neighbor. This study aims to perform a comparative analysis of data mining classification algorithms using WEKA tools. The method used in this study, using CRISP-DM. The dataset used by the three classifications is 5,934 records with split mode, the percentage of testing is 70% as much as 4154 as training data and 30% as much as 1780 data as test data. Based on the test results on the three classification models, the highest accuracy value is obtained in the J48 Decision Tree classification, which has a value of 90.3%. While the K-Nearest Neighbor classification has a lower accuracy of 87.52% and the Naïve Bayes classification has an accuracy of 87.24%. The comparison of the AUROC J48 Decision Tree test results has the highest value of 0.9654 while the Naïve Bayes results are 0.9461 and the K-Nearest Neighbor results are 0.9343. The three classifications with ABK scores above 0.90 are included in the excellent classification category
Optimalisasi K-Means Berbasis Particle Swarm Optimization untuk Hasil Produksi Tanaman Sayuran di Indonesia Budiman Budiman
NUANSA INFORMATIKA Vol 17, No 1 (2023)
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/fkom uniku.v17i1.6646

Abstract

Sektor pertanian memegang peranan penting dalam memenuhi kebutuhan pokok masyarakat, dengan demikian akan mendongkrak laju perekonomian, sosial, dan perdagangan. BPS pada September 2020 mencatat jumlah penduduk sebesar 270,20 juta jiwa Bertambahnya jumlah penduduk pada setiap tahun, berbanding lurus dengan permintaan kebutuhan pokok yang semakin meningkat. Sedangkan produksi sektor pertanian tidak setiap tahun meningkat. Penelitian ini bertujuan untuk mengelompokan daerah-daerah yang memiliki tingkat hasil produksi tanaman sayuran yang tinggi atau tingkat hasil produksi tanaman sayuran yang masih rendah. Penerapan K-Means clustering memungkinkan untuk mengoptimasi K-Means clustering dengan PSO sehingga dihasilkan informasi yang lebih bermutu. Hasil pemodelan K-Means clustering berbasis PSO untuk hasil produksi tanaman sayuran di Indonesia cluster 0 (sedang) sebanyak 8,82%, cluster 1 (rendah) sebanyak 88,2%, dan cluster 2 (tinggi) sebanyak 2,94%. Provinsi Jawa Barat berada dalam cluster 2, sehingga Provinsi Jawa Barat mendominasi dalam produksi tanaman sayuran di Indonesia. Hasil pengujian berdasarkan rata-rata silhouette coefficient, K-Means berbasis PSO lebih baik daripada K-Means murni meskipun nilai tersebut tidak berbeda signifikan yaitu dengan rata-rata nilai silhouette coefficient sebesar 0,714114. Rata-rata waktu komputasi waktu komputasi K-Means murni lebih cepat dengan rata-rata 0,748 detik dibandingan K-Means berbasis PSO. Waktu yang dibutuhkan lebih lama karena K-Means berbasis PSO memperbaiki kelompok data dari hasil bentukan K-Means clustering.
Co-Authors Aggi Panigoro Sarifiyono Ahmad Fauzi Ramadhan Ahmad Rizqy Hamdy Amos Duan Nugroho Ari Rizki Fauzi Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Darsiti . Della Puspita Sari Dirham Triyadi Elia Setiana Fikri Rizqillah Hasani Gelar, Trisna Hasan Nuraripin Imannudin Akbar Imannudin Akbar Irtsa Yasyfa Ismi Nur Muhamad Jilan Hafizh Ferdiansyah Efendi Putra Karlina, Nichi Hana M. Rizki Ramadhan Niqotaini, Zatin Nur Alamsyah Nur Alamsyah Nursyanti, Reni R. Yadi Rakhman A R. Yadi Rakhman Alamsyah Reni Nursyanti Reynaldy Gimnastiar Sophian Ramadhan Suci Fitriani Setiawan Tiara Sartina Jayanti Titan Parama Titan Parama Yoga Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Kaunang Vani Maharani Nasution Venia Restreva Danestiara Venia Restreya Danestiara Wulandari Wulandari Zein Suna Arfigan Said