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All Journal JITEK (Jurnal Ilmiah Teknosains) Jurnal SOLMA Jusikom : Jurnal Sistem Komputer Musirawas Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA JUSIM (Jurnal Sistem Informasi Musirawas) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Ekonomi Informatics and Enginering Dedication Dulang: Jurnal Pengabdian Kepada Masyarakat Knowbase : International Journal of Knowledge in Database Jurnal Kaganga : Jurnal Ilmiah Sosial dan Humaniora Health Research Journal of Indonesia Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Bulletin of Social Informatics Theory and Application Sanus Medical Journal The Indonesian Journal of Computer Science
Puspita, Desi
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : The Indonesian Journal of Computer Science

 1 
Implementasi Algoritma FP-Growth Pada E-Commerce Kopi Pagar Alam Menggunakan Framework Codeigniter Dewi, Nadiya Citra; Putrawansyah, Ferry; Puspita, Desi
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v10i2.3012

Abstract

Penelitian ini bertujuan untuk menghasilkan Implementasi Algoritma FP-Growth pada E-commerce menggunakan metode Business to Consumer (B2C) khusus kopi Pagar Alam Penelitian ini dilatarbelakangi oleh kurangnya daya jual kopi selama pandemi, dimana proses penjualan kopi yang hanya sebatas lokal sehingga dibutuhkan E-commerce yang membantu proses transaksi jual beli yang leboh kompleks. Metode pengembangan yang digunakan Rapid, Application dan Development. E-commerce ini dibuat menggunakan bahasa pemrograman PHP 7 yang include dengan framework Codeigniter 3 dengan menggunakan API raja ongkir dan virtual payment menggunakan Midtrans. Hasil penelitian ini adalah E-commerce dinyatakan layak pada uji alpha yakni Ahli Database, Interface, Fungsional dan Algoritma dengan nilai rata-rata 3,8 sehingga Layak di implementasikan dan pada uji betha ke 10 user didapatkan skor 4,3 dengan kategori sangat valid selanjutnya pada pengujian algoritma FP-Growth pada aspek Pengelompokan dan penyeleksian data dinyatakan berhasil sehingga disimpulkan bahwa E-commerce Kopi Pagar Alam layak dan sangat valid untuk di implementasikan sebagai start-up bisnis bagi petani.
Implementasi Learning Vector Quantization untuk Klasifikasi Jenis Buah Kelapa menggunakan Image Processing Puspita, Desi
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3108

Abstract

Coconut fruit is a versatile plant because all parts from the stem to the coconut fruit have their benefits. Coconut fruit is the most valuable part of the economy. The problem so far that has occurred is that the process of classifying coconut species is still done manually and has not been computerized, namely the classification of coconut types is still based on experience, color, and shape of the coconut. This of course takes a long time and errors still occur frequently. So this research can help classify coconuts with Learning Vector Quantization (LVQ). The purpose of this research is to organize the types of coconuts with image processing and Learning Vector Quantization (LVQ) by using mean extraction from RGB (Red, Green, Blue) and standard deviation from RGB (Red, Green, Blue). The results of the study were taken from 2 different types of coconuts against the 80 training data, the accuracy of the training data was 83.75%. The evaluation results with the Confusion Matrix with a test accuracy value of 90% of the 20 test data.
Co-Authors Agustina Agustriyani, Feri Andi Susanto Andrean, Andrean Andy Alfatih Anggraini, Lesi Ardinata Ariansyah, Ricky Arif, Alfis ariska, sendy Asminah Asminah, Asminah Dedi Setiadi Dewi, Nadiya Citra Dwi Astuti, Vitri Ferry Putrawansyah Ferry Putrawansyah, Ferry Fikry Aransyah, Muhammad Finnah Fourqoniah Fitria Rahmadayanti Gusmaliza, Debi Inda Anggraini Iswandi Idris Leon Andretti Abdillah Masdalipa, Risnaini Mukti, Yogi Rina Sampurno, Edi Siti Aminah Suhartini Suhartini Syahri, Riduan Tri Cahaya, Della Wira bharata Wulandari, Robeca Yogi Isro Mukti, Yogi Isro