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All Journal Indonesian Journal of Electrical Engineering and Computer Science MAHESA : Malahayati Health Student Journal
Widjaja Putra, Bayu Taruna
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Dentistry Health Professions Nursing Public Health

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Stigma Tuberkulosis Paru di Asia Tenggara: Systematic Literatur Review Pradana, Tasya Lukita Cyndi; Widjaja Putra, Bayu Taruna; Utami, Wiwien Sugih
MAHESA : Malahayati Health Student Journal Vol 5, No 6 (2025): Volume 5 Nomor 6 (2025)
Publisher : Universitas Malahayati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/mahesa.v5i6.18337

Abstract

ABSTRACT Pulmonary tuberculosis is an infectious disease that remains a global health problem. Tuberculosis stigma is determinants of this disease. The purpose of this study was to find out the determinants of tuberculosis stigma in 4 Southeast Asian regions, namely Thailand, Cambodia, Vietnam and Indonesia. The research method used was a systematic literature review using PRISMA guidelines. From the 14 articles reviewed, it was found that tuberculosis stigma can be divided into two things, namely personal and negative views of the surrounding community. The impact of stigma is that patients choose to isolate themselves, feel embarrassed, and are discriminated against by the surrounding community.  The stigma of tuberculosis in four regions make the problem of tuberculosis tend to be high. The author recommends providing education to communities that stigmatize tuberculosis patients. By providing education, the author hopes that the community can change their mindset and provide social support to tuberculosis patients. Keywords: Tuberculosis Stigma, Systematic Literatur Review, Southeast Asia.  ABSTRAK Tuberkulosis paru merupakan penyakit menular yang masih menjadi masalah kesehatan di seluruh dunia. Salah satu faktor determinan dari penyakit ini dan adalah stigma tuberkulosis. Tujuan dari penelitian ini mencari tahu faktor determinan stigma tuberkulosis di 4 wilayah Asia Tenggara yaitu Thailand, Kamboja, Vietnam, dan Indonesia. Metode penelitian yang digunakan adalah systematic literatur review dengan pedoman PRISMA. Dari 14 artikel yang direview didapatkan bahwa stigma tuberkulosis dibedakan menjadi dua hal yaitu personal dan pandangan negatif masyarakat sekitar. Dampak yang ditimbulkan dari stigma yaitu pasien memilih untuk mengisolasi diri, merasa malu, dan terdiskriminasi dari lingkungan sekitar. Adanya stigma tuberkulosis di empat wilayah tersebut menjadikan masalah tuberkulosis cenderung tinggi. Penulis merekomendasikan pemberian edukasi pada masyarakat yang memberikan stigma kepada pasien tuberkulosis. Dengan adanya upaya pemberian edukasi, penulis berharap masyarakat dapat merubah pola pikir dan memberi dukungan sosial kepada pasien tuberkulosis.  Kata Kunci: Stigma Tuberkulosis, Systematic Literatur Review, Asia Tenggara
Agraph neural network framework for vascular streak dieback recognition Slamin, Slamin; Alfanio Atmoko, Rizky; Cahya Prihandoko, Antonius; Ariful Furqon, Muhammad; A’yuni Ar Ruhimat, Qurrota; Maghiroh Harvyanti, Annisa Fitri; Widjaja Putra, Bayu Taruna; Hasni, Roslan
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp194-204

Abstract

Vascular streak dieback (VSD) is one of the most destructive diseases affecting cocoa production in Southeast Asia, including Indonesia, where early visual symptoms are often subtle and spatially distributed across the leaf sur face. Conventional image-based disease recognition approaches, particularly those relying solely on convolutional neural networks (CNNs), are effective in extracting local visual features but remain limited in modeling long-range structural relationships such as venation disruption and lesion spread. To ad dress this limitation, this study investigates a hybrid CNN-graph neural network (CNN-GNN) framework for automated VSD recognition from cocoa leaf im ages. A primary dataset consisting of 1,000 RGB images collected directly from cocoa plantations in Jember Regency was used to reflect realistic field condi tions. In the proposed approach, CNNs are employedfor local feature extraction, while graph-based representations enable GNNs to capture global relational pat terns through message passing. Experimental results demonstrate stable learning behavior and strong classification performance, achieving a maximum validation accuracy of 95.2% and an area under the curve (AUC) of approximately 0.94. Further analysis shows balanced precision and recall across classes, indicating reliable discrimination between Sehat and VSD-infected leaves. These findings suggest that hybrid CNN-GNN modeling provides an effective strategy for cap turing both local and distributed structural characteristics of VSD symptoms and highlights the potential of graph-based reasoning to complement convolutional feature learning in plant disease diagnostics.
Co-Authors Alfanio Atmoko, Rizky A’yuni Ar Ruhimat, Qurrota Cahya Prihandoko, Antonius Furqon, Muhammad Ariful Maghiroh Harvyanti, Annisa Fitri Pradana, Tasya Lukita Cyndi Roslan Hasni, Roslan S Slamin Wiwien Sugih Utami