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All Journal VISIKES Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Pengabdian Masyarakat Bidang Kesehatan
Nurmandhani, Ririn
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Computer Science & IT Dentistry Education Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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Pemetaan Distribusi Wasting dan Stunting di Wilayah Lokus Stunting Kabupaten Temanggung Nurmandhani, Ririn; Iqbal, Muhammad; Pradana, Firmansyah Kholiq; Wardoyo, Agung; Rimawati, Eti; Setyawati, Vilda Ana Veria
Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Vol 9, No 2 (2023): JMK Yayasan RS.Dr.Soetomo, Kedua 2023
Publisher : STIKES Yayasan RS.Dr.Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29241/jmk.v9i2.1599

Abstract

Survei Status Gizi Indonesia (SSGI) tahun 2022 menunjukkan bahwa Kabupaten Temanggung menempati posisi kedua sebagai kabupaten di Provinsi Jawa Tengah dengan prevalensi stunting tertinggi yaitu prevalensi wasting sebesar 6,1% dan prevalensi stunting sebesar 28,9%. Tujuan dari penelitian ini adalah untuk memetakan distribusi puskesmas lokus stunting. Penelitian ini merupakan penelitian kuantitatif dengan pendekatan cross sectional dan metode analisis deskriptif melalui analisis gap dan kuadran terhadap data sekunder prevalensi wasting dan stunting yang bersumber dari E-PPGBM (Elektronik-Pencacatan dan Pelaporan Gizi Berbasis Masyarakat) pada triwulan keempat tahun 2022 hingga triwulan ketiga tahun 2023 di 17 puskesmas lokus stunting. Hasil gap analysis terkait capaian kinerja penanganan wasting didapatkan masih ditemukan 2 puskesmas dengan prevalensi wasting > 7% yaitu Puskesmas Pringsurat (10%) dan Puskesmas Kledung (8%). Hasil gap analysis untuk kinerja penanganan stunting didapatkan bahwa hanya 2 puskesmas yang berhasil memenuhi target prevalensi stunting ≤14%, yaitu Puskesmas Ngadirejo dan Puskesmas Kedu. Hasil analisis kuadran menunjukkan bahwa Puskesmas Kedu adalah puskesmas yang berhasil menurunkan angka prevalensi stunting dan wasting. Puskesmas Bejen, Gemawang, dan Kledung adalah puskesmas yang membutuhkan perhatian khusus untuk pelaksanaan kinerja program penanggulangan stunting.
ANALISIS PENGUKURAN KINERJA PUSKESMAS PEGIRIAN KOTA SURABAYA DENGAN MENGGUNAKAN PENDEKATAN BALANCED SCORECARD Nurmandhani, Ririn; Ayu Pradita, Dhea; Rimawati, Eti; Ana Veria Setyawati, Vilda
VISIKES Vol. 23 No. 1 (2024): VISIKES
Publisher : Dian Nuswantoro Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60074/visikes.v23i1.11472

Abstract

Puskesmas Pegirian, Surabaya, in several aspects was still in the less than good category in 2022. This research aims to determine the performance of Puskesmas Pegirian based on four balanced scorecard perspectives. This type of research was quantitative descriptive with a sampling technique using a quota sampling technique, namely with predetermined inclusion and exclusion criteria. The data collection technique was carried out using a questionnaire distributed to officer respondents and patient respondents. The results of the study show that the performance of the Puskesmas Pegirian when viewed from four balanced scorecard perspectives was in the fairly good category, namely 50%. The results of this study were influenced by the financial perspective and customer perspective as internal aspects and the internal business process perspective and the growth and income perspective as external aspects. In this study, there was an imbalance where the health center tended to focus more on the internal aspects, namely with a total score of 3 while the external aspects had a total score of 2. Therefore, it is necessary to evaluate and improve the four perspectives that have not yet reached the target. So that in further research there can be improvements in each aspect.
Optimasi Model Extreme Gradient Boosting Dalam Upaya Penentuan Tingkat Risiko Pada Ibu Hamil Berbasis Bayesian Optimization (BOXGB) Kusuma, Edi Jaya; Nurmandhani, Ririn; Aryani, Lenci; Pantiawati, Ika; Shidik, Guruh Fajar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129001

Abstract

Kehamilan pada ibu hamil memiliki beragam risiko selama prosesnya seperti preeklampsia, diabetes dan hipertensi gestational. Seiring dengan perkembangan teknologi dan pemanfaatan data, implementasi machine learning dalam pengembangan early diagnosis system untuk tingkat risiko kehamilan telah banyak dilakukan. Namun kendala dalam penerapan machine learning adalah sulitnya menemukan konfigurasi parameter yang tepat agar model machine learning mampu memberikan akurasi prediksi yang mumpuni. Pada penelitian ini diusulkan metode optimasi berbasis Bayesian untuk mengoptimalisasikan hyper-parameter dari model Decision Tree (DT) dan Extreme Gradient Boosting (XGB). Kedua model teroptimasi tersebut dilatih dan diuji dengan menggunakan data risiko kehamilan yang diperoleh dari hasil pengukuran medis pada ibu hamil. Dari hasil evaluasi diketahui terdapat pengaruh jumlah iterasi pada Bayesian Optimization (BO). Implementasi BO pada model Decision Tree (BODT) menunjukkan adanya sedikit peningkatan nilai performa dibandingan dengan penelitian sebelumnya. Sementara itu, capaian performa tertinggi diperoleh oleh kombinasi model XGB dan Bayesian (BOXGB) dimana capaian nilai akurasi pada model BOXGB yaitu 87% diikuti dengan nilai rata-rata presisi, recall, dan F1-score masing-masing sebesar 88%, 87%, dan 88%. Secara keseluruhan implementasi Bayesian Optimization mampu memberikan setelan hyper-parameter yang dapat meningkatkan kemampuan model machine learning khususnya dalam memprediksi tingkat risiko kehamilan pada ibu hamil berdasarkan data pengukuran klinis.   Abstract During pregnancy process there are various risks such as preeclampsia, gestational diabetes and gestational hypertension. Along with the developments in technology as well as data science, the implementation of machine learning in early diagnosis system for pregnancy risk levels prediction has been widely carried out. However, there is a challenge in implementing machine learning, which is find the suitable yet effective parameter configuration in training machine learning model to provides better prediction accuracy. This research proposes a Bayesian-based Optimization (BO) method to tune up the hyper-parameters of Decision Tree (DT) and Extreme Gradient Boosting (XGB) models. These two optimized models were trained and tested using maternal risk dataset obtained from the clinical-based measurement on pregnant woman. From the evaluation result, it can be found that the number of iterations has high influence on the BO performance. The implementation of BO toward DT model has slight increase in performance result compared to the previous research. Meanwhile, the highest performance result achieved by the combination of BO and XGB (BOXGB) model where the proposed model reaches 87% of accuracy, followed by average value of precision, recall, and F1-score of 88%, 87%, and 88%, respectively. Overall, the implementation of BO is able to direct the hyper-parameter configuration which improves the machine learning performance especially in predicting maternal risk level based on clinical-based measurement data.
Campus Safety Riding: Establishing A Safe Campus Traffic Zone Rimawati, Eti; Nugroho, Bayu Yoni Setyo; Nurmandhani, Ririn; Fani, Tiara; Suhat; Khalid, Asma
Jurnal Ilmiah Pengabdian Masyarakat Bidang Kesehatan (Abdigermas) Vol. 3 No. 2 (2025): Jurnal Ilmiah Pengabdian Kepada Masyarakat Bidang Kesehatan (Abdigermas)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/abdigermas.v3i2.436

Abstract

Traffic-related accidents involving university students, particularly those aged 18–25, have become a growing concern in Indonesia, with many incidents occurring due to inadequate infrastructure and lack of traffic discipline in densely populated campus environments. This community service program was initiated by Universitas Dian Nuswantoro (UDINUS) in Semarang to address these challenges through the establishment of a “Campus Safety Riding Zone.” The program aimed to increase road safety awareness, promote safe behavior among students and residents, and improve campus-area traffic infrastructure. Activities included coordination with local authorities, painting red-marked safe zones and zebra crossings, installing speed limit signs (30 km/h), and distributing an educational pocketbook titled “Crossing Etiquette on Campus” The intervention area, located on Nakula I Street, was selected due to its high traffic volume between the subdistrict office and UDINUS Building A. Post-intervention observations indicated that drivers generally reduced their speed when entering the safety zone, although issues like improper parking persisted. The initiative demonstrated that a combination of infrastructure enhancement, community education, and multi-stakeholder collaboration can effectively foster a safer traffic culture in university environments. This model offers a replicable framework for other higher education institutions to support student and pedestrian safety through community-based strategies.
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.
Co-Authors Ayu Pradita, Dhea Bayu, Yoni Setyo Nugroho Eti Rimawati Firmansyah Kholiq Pradana PH Guruh Fajar Shidik Ika Pantiawati Khalid, Asma Kusuma, Edi Jaya Lenci Aryani Manglapy, Yusthin Meriantti Mochammad Imron Awalludin Suhat Suhat Tiara Fani Vilda Ana Veria Setyawati Wardoyo, Agung Widianawati, Evina