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All Journal EDU CIVIC Jurnal Kajian Ekonomi Promotif: Jurnal Kesehatan Masyarakat ILKOM Jurnal Ilmiah Efektor Medical Technology and Public Health Journal Gema Wiralodra Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal JURNAL PENELITIAN PERAWAT PROFESIONAL Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Peduli Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Halu Oleo Legal Research Journal of Applied Data Sciences Jurnal Kesehatan Tambusai LOSARI: Jurnal Pengabdian Kepada Masyarakat Journal of English Language and Education Jurnal Penelitian Inovatif Jurnal Bhakti Civitas Akademika Jurnal Keperawatan Klinis dan Komunitas (Clinical and Community Nursing Journal) Journal of Community Engagement Research for Sustainability GENIUS JOURNAL (General Nursing Science Journal) Indonesian Journal of Cancer International Journal Of Economics Social And Technology Pharmaceutical Sciences and Research (PSR) Journal Of Safety and Health Malcom: Indonesian Journal of Machine Learning and Computer Science DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Journal of Artificial Intelligence and Digital Business Citra Delima Scientific journal of Citra Internasional Institute Prosiding Seminar Nasional Inovasi Teknologi Terapan Jurnal Pustaka Keperawatan Jurnal PGSD Indonesia PENG: Jurnal Ekonomi dan Manajemen ZIJEn: Zabags International Journal of Engagement Widya Katambung Jurnal Filsafat Agama Hindu Prosiding Konferensi Nasional Ilmu Kesehatan STIKES Adi Husada
Agustin Agustin
Bagian Farmasetika Fakultas Farmasi Universitas Airlangga Surabaya
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Hubungan Pola Makan dan Kelengkapan Imunisasi Terhadap Kejadian Stunting di Desa Tanjung Gunung Syafitri, Ririn; Agustin, Agustin
Jurnal Pustaka Keperawatan (Pusat Akses kajian Keperawatan) Vol 4 No 1 (2025): Jurnal Pustaka Keperawatan
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakakeperawatan.v4i1.1531

Abstract

Stunting remains a crucial issue during the golden age. In the Bangka Belitung Islands Province, stunting has only decreased by 0.005% despite the region being rich in nutritious marine resources such as fish. One of the affected areas is Tanjung Gunung Village. Low utilization of local food potential and inadequate immunization coverage are contributing factors to the high stunting rate. Immunization plays a crucial role in preventing infections that cause malnutrition. This study aims to determine the relationship between dietary patterns and immunization coverage with stunting incidence in Tanjung Gunung Village. This study is an observational analytical quantitative study using a case-control method to determine the relationship between independent and dependent variables. The sample size was 21 toddlers with stunting (cases) and 21 toddlers without stunting (controls) using a purposive sampling technique. Using the chi-square test with a 95% confidence level (? = 0.05). The results of this study, found a relationship between dietary patterns (p = 0.000; OR = 0.020), completeness of immunization (p = 0.000; OR = 0.018) and the incidence of stunting in Tanjung Gunung village. Conclusion: This study found that completeness of basic immunizations was the most influential factor in stunting among toddlers in Tanjung Gunung Village. These findings are supported by various scientific journals showing that complete basic immunization plays a crucial role in reducing the risk of stunting in toddlers, through protection against infectious diseases that can disrupt a child's growth process and nutritional status.
Predicting Mental Health Status using a Fine-Tuned CNN-LSTM Hybrid Model Agustin, Agustin; Junadhi, Junadhi; Erlinda, Susi; Arita Fitri, Triyani; Efrizoni, Lusiana
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5882

Abstract

Mental health has become a critical global concern in the digital era, particularly as social media platforms increasingly serve as spaces where users express psychological conditions, emotions, and personal struggles. This study aims to predict mental health status from Twitter text using a fine-tuned hybrid CNN–LSTM deep learning model. A total of 12,214 tweets were collected, cleaned, and labeled into five categories: Normal, Stress, Anxiety, Depression, and High-Risk Condition. The dataset was split using stratified sampling into 70% training, 15% validation, and 15% testing portions. Text was transformed into numerical representations through tokenization, padding, and 100-dimensional word embeddings. The hybrid CNN–LSTM architecture combines the CNN’s ability to extract local linguistic features with the LSTM’s strength in capturing long-term contextual dependencies, supported by dropout, early stopping, and hyperparameter fine-tuning. Experimental results show that the hybrid model achieves superior performance compared to standalone CNN and LSTM architectures, obtaining an overall accuracy of 0.892, macro precision of 0.874, macro recall of 0.861, and a macro F1-score of 0.865. Class-wise evaluation indicates that the Normal category achieves the highest accuracy (0.960), followed by Anxiety (0.884) and High-Risk Condition (0.808). Meanwhile, Stress (0.751) and Depression (0.745) show lower accuracies due to semantic overlap in linguistic expressions commonly found on social media. The training process demonstrates stable convergence without significant overfitting, confirming the effectiveness of the selected architecture and training strategy. Overall, this study highlights the effectiveness of the hybrid CNN–LSTM model for early mental health detection based on text data. The findings provide a strong foundation for developing scalable and data-driven mental health monitoring systems in digital environments and contribute to advancing natural language processing approaches for mental health analysis.
Analisis Sentimen Kesehatan Mental Pemuda di Media Sosial Menggunakan Deep Learning Agustin, Agustin; Junadhi, Junadhi; Zoromi, Fransiskus; Kudadiri, Parlindungan
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol. 6 No. 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.8112

Abstract

Kesehatan mental merupakan isu yang semakin penting di kalangan pemuda Indonesia, terutama dengan meningkatnya ekspresi emosi negatif seperti stres, kelelahan, dan kecemasan yang sering diungkapkan melalui media sosial. Penelitian ini bertujuan untuk menganalisis sentimen kesehatan mental pemuda menggunakan pendekatan deep learning berbasis Long Short-Term Memory (LSTM) terhadap unggahan publik berbahasa Indonesia di platform X (Twitter). Data dikumpulkan melalui proses web scraping dengan kata kunci yang relevan dan kemudian melalui tahapan pra-pemrosesan, pelabelan manual, serta pembagian data menjadi 80% untuk pelatihan dan 20% untuk pengujian. Model LSTM dibangun dengan arsitektur yang terdiri atas embedding layer, LSTM layer, dropout layer, dense layer, dan output layer beraktivasi Softmax untuk tiga kelas sentimen (positif, negatif, dan netral). Hasil penelitian menunjukkan distribusi sentimen menunjukkan bahwa emosi negatif mendominasi dengan proporsi 45,8%, diikuti oleh sentimen positif sebesar 35,8%, dan netral sebesar 18,4%.Model mampu mencapai akurasi sebesar 87,4% dengan nilai precision dan recall rata-rata sebesar 0,85, yang menandakan kemampuan tinggi dalam mengenali konteks bahasa informal pemuda di media sosial. Analisis distribusi sentimen menunjukkan dominasi emosi negatif yang berkaitan dengan tekanan akademik dan sosial, sementara sentimen positif menggambarkan semangat dan mekanisme adaptasi diri. Temuan ini membuktikan bahwa LSTM efektif untuk deteksi ekspresi emosional berbasis teks serta berpotensi diterapkan sebagai sistem pemantauan digital bagi kesejahteraan mental generasi muda.
Co-Authors Abdul Wahab Agustian, Fadhlan Hatta Agustin, Ina Agustini, Elina Ainur Rofiq Alfares, Juli Dayan Alhidayah Anam, M Khairul Andesa, Khusaeri Andesla, Askari Andini, Andini ANGGERIYANA, SEPTA Anggraini, Fadhila Anggraini, Rima Berti Annurahman, Arif ARDIANSYAH ARDIANSYAH Arita Fitri, Triyani Atalia, Atalia Bafadhal, M. Asyrafi Bella, Bella Cecep Eli Kosasih Chusjairi, Juni Alfiah Dahnilsyah, Dahnilsyah Darmawan, Flora Honey Dewi Supu, Sri Diana Ulfah Diva, Nisrina Nazhifah Dudini, Amalia Khasanah Ima Efrizoni, Lusiana Elsi Dwi Hapsari Erlinda, Susi Fakhriyah, Anya Bunga Fauta, Ari FERLY OKTRIYEDI Finanta Okmayura Firdaus, Muhammad Bambang Fitri Nurhayati Fitriana Fitriana Fransiskus Zoromi, Fransiskus Gaurifa, Debi F Putra Ghazali, Muh Al Habibie, Dedi Rahman Hairil Akbar Hamza, St. Rahmawati Hamzah, St.Rahmawati Hasdi Aimon Helda Yenni, Helda Herwin Herwin, Herwin Husen, Ratna Andini Hutami, Nurfitria Anisa Ilham Ary Wahyudie Inarotul A’yun Irfandi Irfandi, Irfandi Isjoni, Muhammad Yogi Ryantama Iskandar, Dina Nur Ismanizan, Ryan Jacob, Lodia Antonia Jafar, Kamaruddin JANUARSI, RATNA Jesi Alexander Alim Joleha, Joleha Junadhi Junadhi Junadhi, Junadhi Kudadiri, Parlindungan Kusmiati, Lisa Lastari, Vina Fuji Lestari, Indri Puji Lusiana Lusiana Lusito, Tri Astrit Mahmud Alpusari Manan, Afni Abdul Maryana Maryana, Maryana Matius Paundanan, Matius Mokodompit, Hariansyah Muhammad Rivai Mujahidah, Gita Murtiani, Farida Muslim Muslim Mustaqimah, Arifah Muzayyana, Muzayyana Nanda Indira Nelson Alexander Neni Hermita Ningsih, Septiara Noorma Rosita Norma Norma Nova Mardiana, Nova Novita, Karina Nuraida, Irma Nurhuda, Agus Tri Nurlaili Nurlaili Nurvinanda, Rezka Nuzulullail, Agung Subakti Perumal, Thinagaran Pratiwi, Gessy Ade Pratiwi, Hanameyra Prawira, Ade Bagus Puspitasari, Halfie Zaqiyah Gusti Putra, Jean Riko Kurniawan Putra, Septian Aditya Putri Asilestari, Putri Putri, Wanda Dwi Rahmaddeni Rahmaddeni Rahmiati Rahmiati Retno Sari Rianti, Wida Rianti Riliyantri, Sri Riyati, Riyati Rizky Meilando Rukmini Rukmini Sapitri, Riska Mela Sarifudin, Asri Wati Sarma, Nyoman Sazali, Didik Selwis Raistanti, Sihqina Ramadhani Sinapoy, Muh. Sabaruddin Siti Rokhanah, Siti Sitti Nurul Hikma Saleh Sri Hartini Sri Ulfa Sentosa Sriwahyuningsih, Ade Sudirman Sudirman Sunarti Syafitri, Ririn syahirah, nazhifah Syahrir, Syamsul Syarah, Amalia Tetti Solehati Triyani Arita Fitri Tsauroh, Salsabila Fiqrotu Tumbuan, Ridho Anwar Tussa’diah, Halimah Tutiek Purwanti Viegas, Bonifacio de Jesus Wiratama, Novialita Angga Yanti, Neneng Trisna Yinnie, Celine Yuly Peristiowati Yuwan, Ifan Alif Zahroh, Windy Septiana Zetra Hainul Putra Zuhroh, Siti