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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Artificial Intelligence and Digital Business
Aryanto, Fajar Hanggoro Dwi
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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.
Pengembangan Sistem Prediksi Saham Menggunakan Model Hybrid Gated Recurrent Unit–Long Short-Term Memory Berbasis Integrasi Indikator Teknikal Konvensional Aryanto, Fajar Hanggoro Dwi; Sejati, Rr. Hajar Puji; Sanjaya, Fadil Indra
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8988

Abstract

Stock price prediction is a crucial aspect of investment decision-making in the Indonesian capital market. This study aims to design a hybrid Gated Recurrent Unit–Long Short-Term Memory (GRU–LSTM) model architecture integrated with technical indicators such as Moving Average Convergence Divergence, Moving Average, Exponential Moving Average, and Relative Strength Index to improve the accuracy and objectivity of predictions. Additionally, this study aims to optimize model performance through grid search and implement it into a Flask-based web application as a decision support system for investors. The system was developed using a research and development approach at the Yogyakarta University of Technology. Historical data on PT Bank Rakyat Indonesia (Persero) Tbk. (BBRI.JK) shares for the period from January 2, 2020, to October 17, 2025, was obtained through the Yahoo Finance API as the main dataset. The model was optimized to determine the best combination of hyperparameters. Evaluation was performed using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The test results show that the model achieved MAE 0.0241, MSE 0.0012, RMSE 0.0346, and MAPE 2.7%, indicating a high level of accuracy. The web application provides interactive visualization dashboard features, model development, and educational documentation. These findings confirm that the integration of deep learning with technical indicators is an effective solution for more measurable and systematic stock analysis.
Co-Authors Fadil Indra Sanjaya Fajar Permana Putra Jayanegara, Adi Prabu Mahardika, Setiawan Putra Syuhada, Arya Firgi