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INDONESIA
Teknik: Jurnal Ilmu Teknik dan Informatika
ISSN : 28088751     EISSN : 27982513     DOI : 10.51903
Core Subject : Science,
Jurnal Ilmu Teknik dan Informatika (TEKNIK) menerbitkan satu-satunya makalah yang secara ketat mengikuti pedoman dan template TEKNIK untuk persiapan naskah. Semua manuskrip yang dikirimkan akan melalui proses peer review double-blind. Makalah tersebut dibaca oleh anggota redaksi (sesuai bidang spesialisasi) dan akan disaring oleh Redaktur Pelaksana untuk memenuhi kriteria yang diperlukan untuk publikasi TEKNIK. Naskah akan dikirim ke dua reviewer berdasarkan pengalaman historis mereka dalam mereview naskah atau berdasarkan bidang spesialisasi mereka. TEKNIK telah meninjau formulir untuk menjaga item yang sama ditinjau oleh dua pengulas. Kemudian dewan redaksi membuat keputusan atas komentar atau saran pengulas. Reviewer memberikan penilaian atas orisinalitas, kejelasan penyajian, kontribusi pada bidang/ilmu pengetahuan. Jurnal ini menerbitkan artikel penelitian (research article), artikel telaah/studi literatur (review article/literature review), laporan kasus (case report) dan artikel konsep atau kebijakan (concept/policy article), di semua bidang : Network Computer and Security Computer Architecture Design Data Mining Human Computer Interaction Sistem pakar (Expert System) Jaringan syaraf tiruan (Artificial Neural Network) Algoritma genetic. Penalaran komputer berbasis kasus (Case Based Reasoning) Agen Cerdas (Intelligent Software Agents) Geographical Information System
Articles 111 Documents
Analisis Komparatif Metode MABAC, TOPSIS, dan SAW pada Sistem Pendukung Keputusan Pemilihan Tools AI Taufik Kurnialensya; Rohmad Abidin
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1354

Abstract

Selecting appropriate artificial intelligence (AI) tools poses a significant challenge for individuals and organizations given the numerous alternatives available with varying characteristics. This study aims to compare three Multi-Criteria Decision Making (MCDM) methods, namely MABAC (Multi-Attributive Border Approximation area Comparison), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighting), in a decision support system for AI tools selection. Criteria weighting is performed objectively using the Entropy method. Four alternatives are evaluated: ChatGPT, Gemini, Claude, and Copilot, based on ten criteria: price, ease of use, features, integration, support, security, speed, accuracy, templates, and learning curve. The results show that all three methods produce consistent rankings, with ChatGPT ranked first, followed by Copilot second, Claude third, and Gemini fourth. Consistency between methods is measured using Spearman correlation, yielding rs values of MABAC-TOPSIS = 1.0000, MABAC-SAW = 1.0000, and TOPSIS-SAW = 1.0000. These results indicate that all three methods provide highly consistent outcomes and can complement each other. This research contributes methodologically to the application of MCDM in the AI tools selection domain and serves as a practical reference for users in determining the most suitable AI tools for their needs.

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