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All Journal JUSTI (Jurnal Sistem dan Teknik Industri) Sainteks: Jurnal Sain dan Teknik
Armando Tirta Dwilaga
Universitas Gunadarma
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Implementasi Model Artificial Intelligence dalam Warehouse: Systematic Literature Review Armando Tirta Dwilaga
JUSTI (Jurnal Sistem dan Teknik Industri) Vol 3 No 2 (2022): justi
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/justicb.v3i2.5250

Abstract

Tujuan dari penulisan ini adalah untuk mengkaji gambaran dan mendeskripsikan berbagai variabel penerapan model kecerdasan buatan di gudang dengan melakukan kajian literatur. Basis data indeks ScienceDirect hanya digunakan untuk tahun 2018, 2019, 2020, 2021, dan 2022, dan salah satu dasar pemilihannya adalah pemahaman mendasar tentang teknologi kecerdasan buatan. Hasil dari 318 artikel dikumpulkan menjadi 40 artikel yang memiliki keterkaitan erat hingga terpilih 14 artikel, berdasarkan kerangka metode PRISMA ( Preferred Reporting Items for Systematic Review and Meta-analyses) yang telah dimodifikasi untuk menghitung kriteria inklusi dan eksklusi. Hasil artikel-artikel tersebut lebih spesifik diidentifikasi dengan menggunakan deskripsi deskriptif daftar jurnal dari IFAC-PapersOnLine yang dijadikan referensi paling dominan, penerbit masing-masing jurnal didominasi Elsevier, negara pembuat jurnal didominasi China, model pengukuran penelitian yang paling umum menggunakan data dari sistem algoritma seperti pengelolaan fuzzy, k-means, dan metode yang digunakan didominasi kuantitatif. Fokus ringkasan literatur ini sebagian besar pada model kecerdasan buatan yang digunakan di gudang .
Decision Model and Industry Optimization in Production: A Systematic Literature review Armando Tirta Dwilaga
Sainteks: Jurnal Sain dan Teknik Vol 5 No 1 (2023): Maret
Publisher : Universitas Insan Cendekia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37577/sainteks.v5i1.528

Abstract

This article aims to discover the modeling and optimization options relevant to production-related industrial sectors. PRISMA (Preferred Reporting Items for Systematic Review and Meta-analyses) is a preferred submission method with inclusive and exclusive criteria, one of the bases for the selection made from the ScienceDirect index database only for 2018, 2019, 2020, 2021, and 2022 is understanding decision models and optimization with production keywords. As a result, 823 articles were converted to 100 articles and 16 articles adjacent to the final selection of 10 articles were used. The detailed results of the list of journals used as the most common references from the journal Computers & Industrial Engineering are used to identify the results of this publication in more detail. The most common research model is the adaptive decision model, and the most common research methodology is quantitative. Advanced research with sophisticated applications from the latest technologies such as AI (Machine Learning) to (Deep Learning), this wider and varied use of data includes unstructured or unorganized data so that new concepts will lead to new decision model system innovations, still relatively little additional research that can be used in the realm of production in assembly, process quality, and the environment.
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