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All Journal JURNAL PENDIDIKAN TAMBUSAI Journal of Management and Bussines (JOMB) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Tinta: Jurnal Ilmu Keguruan dan Pendidikan Journal Of Human And Education (JAHE) Business Perspective Journal Innovative: Journal Of Social Science Research ROUTERS: Jurnal Sistem dan Teknologi Informasi Horizon: Indonesian Journal of Multidisciplinary
Pratiwi, Dini
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Klasifikasi Kondisi Kesehatan Mental Mahasiswa Menggunakan Algoritma Logistic Regression Pratiwi, Dini; Roslina, Yulia
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 1, Februari 2026
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v5i1.4742

Abstract

Student mental health has become a critical issue in higher education, as it directly affects students’ well-being and academic performance. Academic, social, and psychological pressures faced by university students increase the risk of mental health disorders such as depression and anxiety. This study aims to classify students’ mental health conditions, particularly the risk of depression, using the Logistic Regression algorithm and to compare its performance with a baseline model and the K-Nearest Neighbors (KNN) algorithm. The dataset used in this study is the Student Mental Health dataset obtained from the Kaggle platform, consisting of 101 student records with demographic, academic, and psychological variables. The research process includes data preprocessing, splitting the dataset into training and testing sets with an 80:20 ratio, classification modeling, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that Logistic Regression achieves the best performance compared to the other models, with an accuracy of 0.85, precision of 1.00, recall of 0.57, and an F1-score of 0.73. The baseline model achieves an accuracy of 0.65 but fails to detect any depression cases, while KNN (k = 5) produces a lower accuracy of 0.55. Further analysis indicates that psychological factors such as Marital, Treatment, and Anxiety significantly contribute to the prediction of depression among students. Based on these findings, Logistic Regression is considered an effective and relevant approach for classifying depression risk among university students and has the potential to support early detection of mental health problems in higher education environments.
Pengaruh Manajemen Literasi Keuangan, Adopsi Teknologi, dan Keputusan Investasi Masyarakat Digital Loo, Petrus; Saleh, Muhammad; Utami, Dina; Pratiwi, Dini; Putri Wahyu
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/6mr47d02

Abstract

Penelitian ini bertujuan menganalisis pengaruh manajemen literasi keuangan dan adopsi teknologi terhadap keputusan investasi masyarakat Indonesia di era keuangan digital. Kajian ini dilatarbelakangi oleh meningkatnya akses masyarakat terhadap layanan keuangan digital dan instrumen investasi berbasis aplikasi, namun kualitas keputusan investasi tetap ditentukan oleh kemampuan memahami informasi keuangan, mengevaluasi risiko, serta memanfaatkan teknologi secara bijak. Penelitian ini menggunakan pendekatan kuantitatif eksplanatori dengan unit analisis masyarakat Indonesia pengguna layanan keuangan digital. Sampel penelitian berjumlah 385 responden yang dipilih melalui purposive sampling dan diperkuat dengan quota sampling sederhana. Data dikumpulkan menggunakan kuesioner berskala Likert dan dianalisis melalui regresi linear berganda setelah memenuhi uji validitas, reliabilitas, dan asumsi klasik. Hasil penelitian menunjukkan bahwa manajemen literasi keuangan berpengaruh positif dan signifikan terhadap keputusan investasi masyarakat (B = 0,351; t = 7,278; p < 0,001). Adopsi teknologi juga berpengaruh positif dan signifikan terhadap keputusan investasi (B = 0,456; t = 9,756; p < 0,001). Secara simultan, kedua variabel berpengaruh signifikan terhadap keputusan investasi dengan nilai F = 221,639 dan R Square sebesar 0,537. Temuan ini menegaskan bahwa kualitas keputusan investasi masyarakat Indonesia tidak hanya dipengaruhi oleh kemudahan akses teknologi, tetapi juga oleh kemampuan mengelola pengetahuan keuangan secara rasional. Penelitian ini memberikan implikasi bagi regulator, lembaga keuangan, dan penyedia platform investasi digital untuk memperkuat edukasi investasi berbasis literasi keuangan digital dan keamanan teknologi.
Co-Authors Adawiyah, Sabrina Darwin Kesuma, Darwin Dina Utami Faradhilla, Naisha Fytaloka, Vemi Hairul Hairul Hakiki, Suci Miranti Hamzal, Said Handayani, Melia Heri Fitriadi Hestin, Hestin Ilmiyatur Rosidah Kalalo, Rieneke Ryke Kamalludin KASINEM, KASINEM Kasron Nst M. Bayu Firmansyah Mahdi Mahdi, Mahdi Muhammad Saleh Mujari Nahdah, Alifah Nawawi, M Kholil Ningsih, Tri Widyastuti Pahlan, M. Petrus Loo Putri Wahyu Raden Mohamad Herdian Bhakti Riko Mersandro Permana Roslina, Yulia Sasono, Heri Winata, Bagas Anggi