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All Journal JURNAL PROMOSI KESEHATAN INDONESIA JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknik Informatika (JUTIF) APMa Jurnal Pengabdian Masyarakat
Manglapy, Yusthin Meriantti
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Factors Associated with Community Visits to Integrated Non-Communicable Diseases Development Posts (Posbindu PTM) Nur’Ilmi, Aulia; Manglapy, Yusthin Meriantti; Iftita, Magumi Avrora; Askar, Muhammad Afiq
Jurnal Promosi Kesehatan Indonesia Vol 20 No 2: April 2025
Publisher : Master Program of Health Promotion Faculty of Public Health Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpki.20.2.125-131

Abstract

Background: Non-communicable diseases are encountered quite often in productive age. Early detection efforts through Posbindu-PTM must be optimized, considering many people still need to gain awareness. What factors play a role in POSBINDU PTM visits in the Tambakharjo is unknown.Method: This research is an observational study with a cross-sectional approach. Systematic sampling was used for 158 people from Tambakharjo aged ≥15 years. Sociodemographic data, history of non-communicable diseases, hypertension knowledge, and POSBINDU PTM visits were collected by interview during November 2023.Result: The research results showed that 22.2% of respondents visited Posbindu-PTM. The multiple logistic regression test showed that the factors that contributed to 99% of POSBINDU PTM visits in Tambakharjo District were ownership of medical equipment (OR=0.332; 95%CI. 0.130-0.845), lack of knowledge about hypertension (OR= 2.300; 95%CI. 0.948-5.579), age ≤45 years (OR=2.53; 95%CI. 1.087-5.393), male gender (OR=6.042; 95%CI=1.677-21.778). The results of this study only describe individual factors. Further studies on psychological factors and the social environment are necessary.
A Random Forest and SMOTE-Based Machine Learning Model for Predicting Recurrence in Papillary Thyroid Carcinoma Kusuma, Edi Jaya; Nurmandhani, Ririn; Pantiawati, Ika; Manglapy, Yusthin Meriantti; Widianawati, Evina
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4854

Abstract

PTC (Papillary Thyroid Carcinoma) is one subtype of thyroid cancer occurred most frequently in thyroid cancer cases. Although the prognosis of this cancer is typically positive, its recurrence remains a key challenge requiring early detection. This study proposes machine learning models to predict PTC recurrence, explicitly addressing the inherent class imbalance in the recurrence data. This study implemented three supervised learning algorithms, namely Random Forest (RF), Extreme Gradient Boost (XGB), and Support Vector Machine (SVM) with the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset. SMOTE was chosen for its capacity to generate synthetic minority class samples while minimizing information loss, thus effectively addressing class imbalance and improving classification outcomes. Model performance was assessed using accuracy, precision, recall (sensitivity), and F1-score. Among all approaches tested, RF with SMOTE demonstrated superior performance, achieving 0.98 accuracy, perfect precision (1.0), high recall (sensitivity) (0.95), and a strong F1-score (0.97), outperforming previous methods including SMOTEENN-based approaches. The result of this study demonstrates SMOTE specifically outperforms SMOTEENN in this clinical context, likely due to better preservation of subtle prognostic indicators with minimal information loss. This improvement suggests SMOTE's effectiveness in preserving valuable decision boundary information while addressing class imbalance in PTC recurrence prediction. These findings establish RF with SMOTE as a robust and well-balanced approach for predicting PTC recurrence, contributing significantly to the development of more precise and responsive AI-driven decision support tools for thyroid cancer.
Penguatan Kader Posyandu ILP dalam Skrining PTM Usia Produktif dan Lansia Manglapy, Yusthin Meriantti; Fani, Tiara; Muthoharoh, Nor Amalia; Kusuma, Edi Jaya
APMa Jurnal Pengabdian Masyarakat Vol. 5 No. 2: Juli 2025
Publisher : STIKES Bhakti Husada Mulia Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47575/apma.v5i2.716

Abstract

Kegiatan pengabdian ini bertujuan meningkatkan kapasitas kader posyandu Integrasi Layanan Primer (ILP) dalam melakukan skrining PTM pada kelompok usia produktif dan lanjut usia. Metode kegiatan mencakup perekrutan kader, pelatihan partisipatif, serta evaluasi melalui pre-test dan post-test. Dari 29 pendaftar, 21 kader mengikuti pelatihan secara aktif dan menyerahkan lembar komitmen sebagai bentuk kesiapan untuk terlibat berkelanjutan. Pelatihan mencakup penyampaian materi dan praktik lima meja posbindu menggunakan metode ceramah interaktif dan simulasi. Hasil evaluasi menunjukkan peningkatan rata-rata skor dari 16,76 menjadi 19,35, yang mencerminkan peningkatan signifikan dalam pemahaman dan keterampilan kader. Kegiatan ini menunjukkan bahwa pendekatan pelatihan berbasis praktik efektif dalam membekali kader dengan keterampilan teknis skrining PTM, serta memotivasi mereka untuk berkontribusi aktif di komunitas.
HYBRID ANOVA-SHAP APPROACH FOR MODEL-AGNOSTIC FEATURE SELECTION IN GALLSTONE DISEASE PREDICTION Kusuma, Edi Jaya; Nurmandhani, Ririn; Manglapy, Yusthin Meriantti; Ika Pantiawati; Widianawati, Evina
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.66622

Abstract

Penyakit batu empedu merupakan kondisi yang cukup umum terjadi, namun sering kali tidak terdeteksi hingga muncul komplikasi, sehingga pentingnya deteksi dini menjadi semakin penting. Penelitian ini bertujuan untuk mengusulkan pendekatan seleksi fitur hibrid yang bersifat model-agnostik dengan menggabungkan metode statistik (ANOVA) dan explainable machine learning (multi-model SHAP) untuk meningkatkan kinerja prediksi batu empedu sekaligus tetap menjaga interpretabilitas model. Kerangka yang diusulkan diuji menggunakan dataset klinis yang terdiri dari 319 pasien dengan 38 variabel, yang mencakup data demografis, bioimpedansi, dan parameter laboratorium. Proses seleksi fitur dilakukan melalui beberapa konfigurasi bobot (\alpha=0.3,\ 0.5,\ 0.7) guna melihat keseimbangan antara kontribusi statistik dan berbasis model. Hasil penelitian menunjukkan bahwa fitur seperti C-Reactive Protein (CRP), Vitamin D, Bone Mass, serta parameter fungsi hati secara konsisten berperan dalam prediksi, yang menggambarkan keterkaitan antara inflamasi, metabolisme, dan fungsi hati dalam pembentukan batu empedu. Performa terbaik diperoleh pada \alpha=0.7 khususnya metode Gradient Boosting dengan akurasi 0.8646 dan recall 0.875, sedangkan \alpha=0.5 menghasilkan nilai AUC tertinggi sebesar 0.905. Secara keseluruhan, pendekatan ini memberikan solusi yang cukup andal dan mudah dipahami untuk mendukung deteksi dini.
Co-Authors Askar, Muhammad Afiq Iftita, Magumi Avrora Ika Pantiawati Kusuma, Edi Jaya Muthoharoh, Nor Amalia Nurmandhani, Ririn Nur’Ilmi, Aulia Tiara Fani Widianawati, Evina