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MULTIMODALITAS PADA LAGU “RUMAH” KARYA SALMA SALSABIL (KAJIAN ANALISIS WACANA MULTIMODAL) Pratiwi, Dini; M. Bayu Firmansyah; Ilmiyatur Rosidah
Jurnal Tinta Vol. 6 No. 2 (2024): Jurnal Tinta
Publisher : Universitas Al-Qolam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35897/jurnaltinta.v6i2.1468

Abstract

Penelitian ini bertujuan untuk menganalisis multimodalitas dalam lagu “Rumah” melalui pendekatan analisis wacana multimodal. Multimodalitas merujuk pada penggunaan berbagai mode komunikasi seperti teks, visual, dan audio yang bersinergi untuk menyampaikan makna secara lebih komprehensif. Lagu “Rumah” dipilih sebagai objek kajian karena lagu ini menggambarkan ekspresi emosional yang mendalam melalui lirik, musik, dan elemen visual yang terkandung dalam video musiknya. Penelitian ini menggunakan metode kualitatif dengan menganalisis lirik, melodi, instrumen musik, serta elemen visual yang terdapat dalam video musik “Rumah”. Hasil analisis menunjukkan bahwa lagu ini memanfaatkan multimodalitas secara efektif untuk menyampaikan pesan tentang pentingnya rumah sebagai tempat kembali yang penuh makna dan emosi. Penggunaan warna, ekspresi vokal, dan komposisi visual dalam video musik turut memperkuat makna yang ingin disampaikan oleh Salma Salsabil. Temuan ini menunjukkan bagaimana elemen-elemen multimodal dalam lagu dan video musik dapat bekerja secara sinergis untuk menciptakan pengalaman emosional yang mendalam bagi pendengar.
Rambak Product Differentiation Training and Mentoring to Improve Branding in Kandangsapi Subdistrict Ulfah, Fitriyah; Maslukhah, Yulina Lailatul; Pratiwi, Dini; Aini, Karina Nur; Laili, Shofiyatul
Bakti Cendana Vol 9 No 1 (2026): Bakti Cendana: Jurnal Pengabdian Masyarakat
Publisher : LPPM Universitas Timor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/bc.v9i1.10643

Abstract

To increase the potential of small businesses in Kandapi Village, Panggungrejo District, Pasuruan City. In community service activities, students strive to improve the quality and marketing of the product "Rambak Ibu Ulfah", this local business is 20 years old. This product differentiation activity includes: creating new packaging designs, innovating flavor variants, and implementing online marketing through social media platforms. The results of this activity include online marketing training, creating promotional content, and establishing an outlet at the Business Center of SMKN 1 Pasuruan. The evaluation showed a positive response to product innovation. From the results of the product differentiation activities that have been carried out, the profit obtained reached IDR 56,000 from the sale of 30 small-packaged rambak and 15 large-packaged rambak. In addition, this activity was able to create branding for rambak products by providing elements of the Pasuruan City logo to attract consumers from all walks of life outside the city
Karakteristik Jamur Kontaminan Pascapanen pada Beberapa Jenis Umbi – umbian Rahmiati, Rahmiati; Situmorang, Toberni S; Simanjuntak, Helen Anjelina; Pratiwi, Dini; Sari, Indah
Herbal Medicine Journal Vol 9 No 1 (2026): Herbal Medicine Journal
Publisher : Program Studi S1 Farmasi, STIKES Senior, Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58996/hmj.v9i1.207

Abstract

The quality of tuber crops frequently deteriorates as a result of post-harvest fungal contamination. Fungal contamination of potatoes, sweet potatoes, and taro tubers during post-harvest handling and storage can significantly reduce their economic value and pose potential health risks through the production of mycotoxins. This study aimed to evaluate the post-harvest shelf life of potato, sweet potato, and taro tubers and to identify the fungal species contaminating these commodities. A qualitative descriptive approach was employed using the scratch plate isolation method. Fungal contaminants were characterized based on macroscopic colony morphology and microscopic features. The research procedures included sample preparation, assessment of physical quality changes during storage, fungal isolation, and identification. The results demonstrated that taro tubers maintained the highest physical quality after 14 days of storage, whereas potato tubers exhibited the most pronounced physical deterioration and the highest incidence of fungal contamination. A total of eight fungal isolates with distinct macroscopic and microscopic characteristics were obtained, designated as T1SP1, T1SP2, U1SP1, U1SP2, T2SP1, T2SP2, T3SP1, and T3SP2. These isolates were identified as belonging to the genera Aspergillus, Trichoderma, Colletotrichum, and Curvularia.
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.