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All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis International Journal of Advances in Data and Information Systems
Adriyendi Adriyendi
Informatic Research Group, IAIN Batusangkar, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Klasifikasi Menggunakan Naïve Bayes Dan K-Nearest Neighbor Pada Manajemen Layanan Teknologi Informasi Adriyendi Adriyendi; Yeni Melia
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 2 No 2 (2020): Juli 2020
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v2i2.121

Abstract

Riset ini bertujuan untuk melakukan prediksi standar layanan teknologi informasi atau Information Technology Service (ITS) dengan klasifikasi menggunakan Naïve Bayes Classifier (NBC) dan K-Nearest Neighbor (KNN). ITS terhadap arus data yang sangat besar dari setiap titik akses memerlukan standar kelas layanan. Peningkatan produktivitas merupakan konsekuensi ITS. Peningkatan keuntungan merupakan tujuan ITS. Untuk memperoleh produktivitas dan keuntungan, service menjadi kata kuncinya. Oleh karena itu, service untuk kebutuhan bisnis, maka diperlukan ITS. Layanan yang berkualitas dan manfaat maksimal bagi customer (standar dan non standar) merupakan tujuan yang ingin dicapai ITS. Oleh karena itu, perlu dilakukan klasifikasi untuk menentukan kelas layanan berdasarkan ITS. Klasifikasi menghasilkan kelas layanan yang memenuhi standar manajemen layanan teknologi informasi. Klasifikasi menggunakan KNN dengan input numeric, formula jarak (Euclidean Distance), kalkulasi (classified by rank), dan output (determined by majority). Klasifikasi menggunakan NBC dengan input alpha numeric, formula (Bayes Theorem), kalkulasi (classified by probability), dan output (determined by high value).
Multi-Attribute Decision Making using Hybrid Approach based on Benefit-Cost Model for Sustainable Fashion Adriyendi Adriyendi; Yeni Melia
International Journal of Advances in Data and Information Systems Vol. 2 No. 1 (2021): April 2021 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v2i1.1200

Abstract

Multi-Attribute Decision Making (MADM) is used to select the best alternative from multi-alternatives based on multi-attribute (fashion material) and multi-criteria (sustainable fashion). Multi-alternatives are cotton, linen, silk, wool, acrylic, nylon, polyester, rayon, spandex, and mixed. Multi-attributes are material, texture, color, characteristic, comfort, and wearability. Multi-criteria are material fiber, smooth texture, faded color, elastic clothing, useful long, chilly and comfortable. Hybrid approaches and optimal solutions are needed to determine the best choice in decision making for both producers and consumers. The hybrid approach in MADM used is Simple Multi-Attribute Rating (SMART), Multi-Factor Evaluation Process (MFEP), Multi-Object Optimization based on Ratio Analysis (MOORA), Simple Additive Weighting (SAW), and Weighted Product (WP). SMART and MFEP are based on the Non-Benefit Cost Model while MOORA, SAW, and WP are based on a Benefit-Cost Model. The experimental results show that the SMART model with the best alternative is the rayon with the highest value (2.8333). The selection of the MFEP Model with the best alternative is rayon with the highest value (2.8330). The choice of MOORA model with the best alternative is rayon with the highest value (0.2595). The selection of the SAW Model with the best alternative is rayon with the highest value (0.8932). The selection of the WP Model with the best alternative is rayon with the highest value (0.1285). MADM using SMART, MFEP, MOORA, SAW, and WP for sustainable fashion yields the best alternative for consumption and production for the middle-class population in Indonesia.
Co-Authors Yeni Melia