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All Journal Multitek Indonesia : Jurnal Ilmiah Algoritme Jurnal Mahasiswa Teknik Informatika
Dwi Putra, Tommy
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Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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Implementasi Groq AI untuk Otomatisasi Feedback pada Website Evaluasi Kinerja Dosen Kusumastuti, Rajnaparamitha; Oktafiani, Dewi; Dwi Putra, Tommy
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 3 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i3.13458

Abstract

Lecturer performance evaluation is essential to maintain the quality of higher education, yet traditional methods often lack objectivity and provide limited feedback. This study designed a web-based evaluation system using the Simple Additive Weighting (SAW) method for decision-making, integrated with Groq AI to generate automatic feedback from students. The system was developed with a prototype approach using the Flask framework and tested on 10 courses with a total of 250 randomly selected respondents. Instrument reliability was confirmed using Cronbach’s Alpha (α = 0.84), indicating a high level of reliability. System speed evaluation through 40 trials showed an average processing time of 0.564 seconds. User satisfaction was measured with a 1–4 Likert scale and converted using the Percent of Maximum Possible (POMP), resulting in a 92.4% satisfaction rate. The AI feature successfully provided automated feedback without manual intervention, significantly improving efficiency and effectiveness. These results demonstrate that integrating SAW with Groq AI enhances objectivity, speed, and quality in lecturer performance evaluation.
KLASIFIKASI CITRA PENYAKIT DAUN JAGUNG MENGGUNAKAN ALGORITMA CNN EFFCIENTNET Kusumastuti, Rajnapramitha; Dwi Putra, Tommy; Zulfahmi Yudam, Zaky
MULTITEK INDONESIA Vol 17 No 2 (2023): Desember
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v17i2.10085

Abstract

Tanaman jagung termasuk jenis makanan pokok dengan jumlah konsumsi tinggi sebagai makanan pokok yang ada di Indonesia. Dengan jumlah konsumsi yang tinggi, maka produksi tanaman jagung juga memiliki tingkat tanam yang juga tinggi. Untuk mengidentifikasi jenis penyakit daun dalam skala tanam yang besar tidak mudah dilakukan dengan cara manual karena membutuhkan ketelitian dan memakan waktu yang cukup lama. Penelitian ini bertujuan untuk mengindentifikasi penyakit daun jagung menggunakan metode convolutional neural network (CNN) dengan arsitektur effcientNetB1, effcientNetB2, dan efficienetNetB3. Penelitian menggunakan dataset sebanyak 4118 citra dengan ukuran 260 x 260 dan membagi dataset dengan persentase data pelatihan sebesar 80% dengan 10% data uji dan 10% data validasi. Hasil pengujian menunjukan arsitektur dari efficienNet memiliki nilai akurasi yang tinggi sebesar 97.77%.
Co-Authors Kusumastuti, Rajnaparamitha Kusumastuti, Rajnapramitha Oktafiani, Dewi Zulfahmi Yudam, Zaky