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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Artificial Intelligence and Digital Business
Jayanegara, Adi Prabu
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Deteksi Tingkat Stres Mahasiswa Dengan Logika Fuzzy Tsukamoto Aryanto, Fajar Hanggoro Dwi; Syuhada, Arya Firgi; Putra, Fajar Permana; Mahardika, Setiawan Putra; Jayanegara, Adi Prabu; Sanjaya, Fadil Indra
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1042

Abstract

Stres pada mahasiswa merupakan permasalahan serius yang dapat berdampak negatif terhadap kesehatan mental dan performa akademik apabila tidak ditangani dengan baik. Dampak tersebut tidak hanya terbatas pada penurunan prestasi akademik, tetapi juga dapat mengarah pada gangguan psikologis jangka panjang yang menghambat proses pembelajaran dan perkembangan pribadi mahasiswa. Penelitian ini bertujuan untuk mengembangkan sistem deteksi tingkat stres mahasiswa menggunakan metode logika fuzzy Tsukamoto. Sistem ini dirancang untuk mengelola data subjektif dan tidak pasti berdasarkan lima parameter utama, yaitu kualitas tidur, performa akademik, hubungan mahasiswa-dosen, dukungan sosial, dan kondisi lingkungan. Proses inferensi dilakukan melalui pembentukan himpunan fuzzy dan 48 aturan IF-THEN yang disusun berdasarkan kombinasi kelima parameter tersebut. Penelitian ini melibatkan penyebaran kuesioner kepada mahasiswa Program Studi Informatika Universitas Teknologi Yogyakarta untuk memperoleh data yang kemudian diproses dalam sistem fuzzy. Hasil akhir dari sistem ini berupa nilai crisp yang menunjukkan tingkat stres mahasiswa, yang dikategorikan dalam level normal hingga tinggi. Diharapkan sistem ini dapat menjadi alat bantu bagi institusi pendidikan dalam mendeteksi dan menangani gejala stres mahasiswa secara cepat dan tepat.
Deteksi Kematangan Buah Tomat Menggunakan Convolutional Neural Network Berdasarkan Ekstraksi Fitur Warna dan Tekstur Jayanegara, Adi Prabu; Sejati, Rr. Hajar Puji; Sanjaya, Fadil Indra
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8580

Abstract

Determining the ripeness level of tomatoes is a crucial aspect in ensuring consumption quality. However, the methods commonly used by the public are still manual and subjective, relying on visual observations of color and surface texture. This limitation leads to a high rate of errors in selecting fruits suitable for consumption or processing purposes, potentially resulting in food waste, economic loss, and decreased efficiency within the agricultural supply chain. Without the development of a technology-based assistance system, these impacts will continue to recur and may threaten food security on both micro and macro scales. As a solution, this study develops an intelligent system based on digital image processing to detect tomato ripeness levels. The system utilizes color feature extraction using the HSV histogram and texture feature extraction using the Local Binary Pattern (LBP), which are then processed through a Convolutional Neural Network (CNN) model for image classification. The results show that the system achieves an accuracy of 95.12%, outperforming (or matching) state-of-the-art end-to-end CNN-based methods on the same or similar datasets, demonstrating the effectiveness of HSV-LBP features. The implementation of this system is expected to help users make more accurate decisions when selecting tomatoes according to their needs, reduce waste, and improve consumption efficiency.
Co-Authors Aryanto, Fajar Hanggoro Dwi Fadil Indra Sanjaya Mahardika, Setiawan Putra Putra, Fajar Permana Syuhada, Arya Firgi