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All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Inspiratif Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control UICELL Conference Proceeding Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA Hearty : Jurnal Kesehatan Masyarakat JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Biomedika dan Kesehatan Psikologi Konseling: Jurnal Kajian Psikologi dan Konseling Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Health Information : Jurnal Penelitian International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL LA MEDIHEALTICO Journal of Gender and Social Inclusion in Muslim Societies MAHESA : Malahayati Health Student Journal Fitrah: Journal of Islamic Education Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Data Science and Software Engineering Journal of Embedded Systems, Security and Intelligent Systems Jurnal Pengabdian Kepada Masyarakat Itekes Bali JUKEJ: Jurnal Kesehatan Jompa Jurnal Informatika Polinema (JIP) Jurnal Ilmiah Kesehatan Mandala Waluya Inovasi Kesehatan Global Jurnal Kesehatan Masyarakat Perkotaan Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Holistik Jurnal Kesehatan
Fatma Indriani
Program Studi Ilmu Komputer, Fakultas Matematika Dan Pengetahuan Alam, Universitas Lambung Mangkurat, Indonesia
Religion Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Faktor-Faktor yang Berhubungan dengan Kelelahan Kerja pada Petugas Pengamanan Lingkungan Pelabuhan Ferry Mulia Raja Napitupulu Kota Balige Nur Chairani Rizki Nasution; Fatma Indriani
Inovasi Kesehatan Global Vol. 3 No. 1 (2026): Februari : Inovasi Kesehatan Global
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/ikg.v3i1.2860

Abstract

Work fatigue is one of the occupational health problems that may affect productivity, concentration, and increase the risk of workplace accidents. Security officers at Ferry Mulia Raja Napitupulu Port, Balige, are at high risk of experiencing fatigue due to shift work systems, physical and mental workload, and poor sleep quality. This study aims to identify factors associated with work fatigue among security officers. A quantitative method with a cross-sectional design was applied. The sample consisted of 45 security officers selected using total sampling technique. Data were collected through questionnaires on individual characteristics, nutritional status, sleep quality, workload, and environmental temperature, and analyzed using bivariate tests. The results revealed significant relationships between age, nutritional status, sleep quality, workload, and temperature with work fatigue levels (p<0.05). It can be concluded that both individual and work environment factors contribute to fatigue among security officers. The study recommends that port management pay more attention to shift arrangements, rest patterns, and occupational health interventions to reduce fatigue risk and enhance officers’ productivity.
Enhancing Classification of Self-Reported Monkeypox Symptoms on Social Media Using Term Frequency-Inverse Document Frequency Features and Graph Attention Networks Rizian, Rizailo Akfa; Budiman, Irwan; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Ahmad, Umar Ali
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5482

Abstract

Early detection of infectious diseases plays a crucial role in minimizing their spread and enabling timely intervention. In the digital era, social media has emerged as a valuable source of real-time health information, where individuals often share self-reported symptoms that can serve as early warning signals for disease outbreaks. However, textual data from social media is typically unstructured, noisy, and contextually diverse, posing challenges for conventional text classification methods. This study proposes a hybrid model combining Term Frequency–Inverse Document Frequency (TF-IDF) feature representation with a Graph Attention Network (GAT) to enhance the early detection of Monkeypox-related self-reported symptoms on Indonesian social media. A dataset of 3,200 tweets was collected through Tweet-Harvest and subsequently preprocessed and manually labeled, producing a balanced distribution between positive (51%) and negative (49%) samples. TF-IDF vectors were used to construct a document similarity graph via the k-Nearest Neighbors (k-NN) method with cosine similarity, enabling GAT to leverage both textual and relational information across posts. The model’s performance was evaluated using accuracy, precision, recall, and macro-F1, with macro-F1 serving as the primary indicator. The proposed TF-IDF + GAT model achieved 93.07% accuracy and a macro-F1 score of 93.06%, outperforming baseline classifiers such as CNN (92.16% macro-F1), SVM (85.73%), Logistic Regression (84.89%). These findings demonstrate the effectiveness of integrating classical text representations with graph-based neural architectures for improving social media based disease surveillance and supporting early epidemic response strategies.
Improving Neutral Sentiment Classification in Indonesian E-Wallet Reviews Using Word2Vec and Easy Data Augmentation (EDA) Muhammad Fattah Edric Camilo; Fatma Indriani; Mohammad Reza Faisal; Dwi Kartini; Dodon Turianto Nugrahadi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29770

Abstract

The rapid expansion of digital payments has produced massive volumes of user-generated reviews, making manual analysis impractical. This study focuses on the challenge of neutral sentiment classification in Indonesian e-wallet reviews, where neutral comments often contain ambiguous language and are underrepresented relative to positive and negative classes. A total of 26,537 preprocessed DANA application reviews were used to evaluate whether Word2Vec embeddings and Easy Data Augmentation (EDA) can improve neutral sentiment detection when combined with Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures. Experiments comparing eight model configurations showed that the combination of Word2Vec, EDA, and LSTM achieved the best performance, with 0.861 accuracy, 0.841 macro-F1, and 0.749 F1-score for the neutral class. These findings demonstrate that semantic representations and controlled lexical variation can jointly enhance minority-class recognition in short informal Indonesian text and highlight the importance of aligning embedding strategies with sequence architectures.
Performance and Training-Time Comparison of Five Pretrained CNN Architectures for South Kalimantan Food Image Classification Ahmad Balya Al Erpat; Dwi Kartini; Fatma Indriani; Andi Farmadi; Muliadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13850

Abstract

Purpose - This study analyzes the classification performance and computational efficiency of five pretrained Convolutional Neural Network (CNN) architectures for identifying South Kalimantan traditional food images as an expanded benchmark. Design/methods/approach - The models were trained on a curated traditional food image dataset using a two-stage transfer learning strategy consisting of linear probing and full fine-tuning with frozen Batch Normalization, supported by a multi-technique data augmentation pipeline. Evaluation was conducted under a fixed stratified data-splitting scenario across repeated runs with different random seeds to assess model stability and reproducibility. Findings - EfficientNetV2B0 achieved the strongest overall performance among the evaluated architectures and provided the most favorable balance between classification accuracy and computational efficiency. Its performance was comparable to other high-performing CNN architectures while requiring substantially lower training time than deeper residual and hybrid networks. The results indicate that greater architectural complexity does not necessarily translate into better recognition performance for a relatively small traditional food image dataset. Research implications/limitations - The findings provide practical guidance for selecting efficient CNN architectures for traditional food recognition. However, the evaluation was conducted on a curated dataset under controlled conditions, and the absence of an ablation study prevents isolating the individual effects of data augmentation and two-stage fine-tuning. Originality/value - This expanded benchmark highlights the critical trade-off between reliable classification performance and computational cost, offering practical guidance for selecting efficient deep learning models to support the digital preservation of culinary heritage.
Co-Authors Abdilah, Muhammad Fariz Fata Abdul Azis Abdullayev, Vugar Achmad Rizal Afifa, Ridha Agil Maritho Lauchan Ahmad Balya Al Erpat Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Al Habesyah, Noor Zalekha Amini, Aisah Ananda, Zahra Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Arianti, Tiara Aryanti, Agustia Kuspita Asti, Rahmah Dwi Astuti, Yeni Ayu Astuty, Delfriana Ayu Athavale, Vijay Annant Aulia, Rizky Gunadi Azizah, Azkiya Nur Badali, Rahmat Amin Bagaskara Ridho Vandio Baharuddin Siregar, Baharuddin Baron Hidayat Barus, Nency Utami Br Br Barus, Nency Utami br Damanik, Cici Rahayu Carolina, Ayu DALIMUNTHE, NADIYAH RAHMA Darmansyah, Rendi Daulay, Rangga Muriansyah Dendy Fadhel Adhipratama Dendy Dewi Sri Wahyuni, Dewi Sri Difa Fitria Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Effendi, Khairunnisa Fadilah, Sylva Qamara Nur Fahira Ramadhani Saragih Fahmi Setiawan Fairudz Shahura Faisal, M. Reza Faisal, Mohammad Reza Fajrin Azwary Fitriani, Karlina Elreine Friska Abadi Ghinaya, Helma Gunawan, Muhammad Khair Gustara, Rizki Asih Hafizah, Rini Harahap, Helma Denisah Hasyimi , Ali Hayati, Sera Br Hermiati, Arya Syifa Herteno, Rudi Heru Kartika Chandra I Gusti Ngurah Antaryama Ichwan Dwi Nugraha Ihsan, Muhammad Khairi Irwan Budiman Irwan Budiman Lilies Handayani Lubis, Masruroh M. Apriannur M. Iqbal M. Khairul Rezki Mahmud Mahmud Marwiyah, Syarifatul Mawandri, Dwi Mohammad Mahfuzh Shiddiq Muhammad Alkaff Muhammad Fattah Edric Camilo Muhammad Itqan Mazdadi Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muliadi Muliadi Muliadi Muliadi Aziz Naufal Said Nita Arianty Nofi Susanti Nur Chairani Rizki Nasution Nurhayani nurhayani Nurhayati Octavia, Mayang Dwi Oni Soesanto P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Purnajaya, Akhmad Rezki Putra Apriadi Siregar Putri Safira Radityo Adi Nugroho Ramadhanu, Suhada Rapotan Hasibuan Riadi, Agus Teguh Risma, Ade Ritonga, Egril Rehulina Rizian, Rizailo Akfa Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rudy Herteno Salianto Salianto, Salianto Saragih, Triando Hamonangan Sa’diah, Halimatus Selvia Indah Liany Abdie Setyo Wahyu Saputro Siregar, Nurul Syahputri Soesanto, Oni Sri Rahayu Suci Wulandari Tarigan, David Brando Pratama Triyoolanda, Anggun Umar Ali Ahmad Utami, Tri Niswati Wahyu Caesarendra Wardana, Muhammad Difha Wati, Desi Indriani Rahma Wijaya Kusuma, Arizha YILDIZ, Oktay Yulia Khairina Ashar Yunida, Rahmi Zahra, Fairuz Zakwan, M. Hadin Zali, Muhammad Zata Ismah Zida Ziyan Azkiya