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All Journal Journal of Applied Data Sciences Jurnal Komtekinfo
Habib Yuhandri, Muhammad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Perbandingan Tingkat Akurasi SAW-TOPSIS dalam Penilaian Kelayakan Proposal Liga Mayola; Guswandi, Dodi; Safitri, Wifra; Hafizh, M; Habib Yuhandri, Muhammad
Jurnal KomtekInfo Vol. 10 No. 3 (2023): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i3.415

Abstract

Seminar proposal adalah salah satu matakuliah pada kurikulum yang wajib dilalui oleh mahasiswa Teknologi Informasi agar dapat melanjutkan ke tahapan berikutnya yaitu kualifikasi. Penentuan kelayakan kelulusan seminar proposal mahasiswa oleh penguji masih dilakukan dengan manual dan belum menggunakan Sistem Pendukung Keputusan (SPK) dalam mengambil keputusan. Tujuan penelitian ini adalah untuk menganalisis pengambilan keputusan kelayakan sebuah proposal yang diajukan mahasiswa dan menguji metode yang tepat diantara dua metode SPK yang dipilih. Metode yang digunakan dalam penelitian ini adalah metode SAW dan metode TOPSIS. Data sampel yang digunakan berjumlah dua belas data penilaian dosen terhadap mahasiswa. Kriteria yang digunakan dalam penilaian kelayakan proposal yakni kemuktahiran topik dan ketajaman rumusan masalah (C1), relevansi dan kemuktahiran kajian pustaka (C2), ketepatan metode penelitian (C3), manfaat dan kontribusi penelitian (C4), referensi acuan (C5) dan estetika penulisan (C6). Hasil penelitian ini berupa pemodelan Decision Support System (DSS) dalam bentuk perankingan agar dapat mengetahui kelayakan seminar proposal mahasiswa. Berdasarkan perbandingan metode SAW dan TOPSIS pada kasus ini, maka dapat disimpulkan bahwa metode SAW memiliki tingkat akurasi yang lebih tinggi yaitu 41,667 %.
Multimodal Deep Learning and IoT Sensor Fusion for Real-Time Beef Freshness Detection Kurniawan, Bambang; Wahyuni, Refni; Yulanda, Yulanda; Irawan, Yuda; Habib Yuhandri, Muhammad
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.977

Abstract

Beef freshness quality is one of the important indicators in ensuring food safety and suitability. However, conventional methods such as manual visual inspection and laboratory testing cannot be widely applied in real-time and mass scale. To overcome these challenges, this study proposes a meat freshness detection system based on a multimodal approach that combines visual imagery and gas sensor data in a single IoT-based framework. This system is designed by utilizing the YOLOv11 architecture that has been optimized using the Adam optimizer. The dataset consisted of 540 original beef images, expanded into 1,296 images after augmentation. The model is trained on these augmented images and is able to achieve detection performance with a mAP@0.5 value of 99.4% and mAP@0.5:0.95 of 95.7%. As a further improvement, the cropped image features from the YOLOv11 model are processed through a combination of the ViT model and CNN to classify the level of meat freshness into three classes: Fresh, Medium, and Rotten with an accuracy of 99%. On the other hand, chemical data was obtained from the MQ136 and MQ137 gas sensors to detect H₂S and NH₃ levels which are indicators of meat spoilage. Data from visual and chemical data were then combined through a multimodal fusion method and classified using the Random Forest algorithm, producing a final prediction of Fit for Consumption, Need to Check, and Not Fit for Consumption. This multimodal model achieved a classification accuracy of 98% with a ROC-AUC score approaching 1.00 across all classes. While the proposed system achieved very high accuracy, further validation across diverse real-world environments is recommended to establish its generalizability.
Co-Authors Bambang Kurniawan Dodi Guswandi Hafizh, M Liga Mayola Refni Wahyuni Safitri, Wifra Yuda Irawan Yulanda, Yulanda