Jurnal Kesehatan Budi Luhur.
Vol.
No.
July 2025 p-ISSN 1978-8479 e-ISSN 2581-0111 IMPROVING COMMUNITY HEALTH WORKERSAo KNOWLEDGE IN EARLY STUNTING DETECTION THROUGH ANTHROPOMETRIC TRAINING AND THE BERAKSI APPLICATION Dyeri Susanti1.
Selvi Putri Octaviani2.
Tazkihatun Nufush2 Bachelor of Midwifery Education Program.
STIKes Budhi Luhur Cimahi Diploma in Midwifery Program.
STIKes Budhi Luhur Cimahi ABSTRACT Stunting remains a critical public health issue that impairs child growth and In Cibeber sub-district, 6.
83% of the total 1,339 preschoolaged children were stunted, with the highest rates observed in RW 08 .
RW 06 .
, and RW 03 .
According to Indonesian Ministry of Health Regulation No.
2/2020, stunting is defined as a height-for-age z-score below Ae2 SD.
This study aimed to evaluate whether anthropometry training using the mobile application BERAKSI can improve community health workersAo knowledge of early stunting detection.
Employing a descriptive quantitative design with a pre-testAepost-test, we involved all 18 community health workers from Posyandu Rose A and B in RW 08.
Cibeber.
Pre-training, 56% .
of community health workers demonstrated good knowledge, 33% .
fair, and 11% .
Posttraining, all community health workers .
%) achieved good knowledge.
The findings indicate that comprehensive anthropometry training, combined with application-based guidance, significantly enhances community health workersAo knowledge.
For accurate growth surveillance and record keeping in maternalAechild health books, community health workers must possess standardized anthropometric skills and proficiency.
Keywords: Anthropometry.
Stunting.
BERAKSI Application.
Community health worker.
Posyandu Correspondence:
Dyeri Susanti Budi Luhur Institute of Health Sciences Jl.
Kerkoff n0 243.
Cimahi.
Jawa Barat dyerisusanti9@gmail.
Anthropometric Training and The Beraksi Application Jurnal Kesehatan Budi Luhur.
Vol.
No.
July 2025
p-ISSN 1978-8479
e-ISSN 2581-0111
INTRODUCTION
UNICEF and IndonesiaAos National Development Planning Agency (Bappena.
evaluated the National Strategy for Accelerating Stunting Prevention, addressing three major nutritional challenges: wasting, stunting, and childhood overweight/obesity (UNICEF, 2.
In 2024.
Indonesia aims to reduce child stunting to 14%.
The national prevalence of stunting dropped from 8% in 2018 to 21.
5% in 2023Aia decline of 9.
3 percentage points, averaging 1.
86 points West JavaAos rate stood at 21.
7%, while Cimahi recorded 2,890 stunted children .
in 2023 (Bappenas, 2018.
Ministry of Health, 2.
Cibeber sub-district, one of CimahiAos 15 administrative areas, exhibits significant variability in child anthropometric measures, with several children classified as AushortAy or Auvery short.
Ay Among 1,339 children, 7.
73% were normal while 6.
83% were stunted (Puskesmas Cibeber dat.
The Indonesian Ministry of Health defines stunting as a height-for-age z-score below Ae2 SD per its Regulation No.
2/2020 (Abeway et al.
, 2018.
Ministry of Health, 2.
Stunting stems from multiple factors, including maternal education and height, economic status, low birth weight, breastfeeding, maternal nutrition, micronutrient deficiencies, infections, parental nutritional knowledge, antenatal/postnatal care quality, and inadequate sanitation.
Its impacts range from impaired immunity and cognitive development to increased risk of obesity, glucose intolerance, cardiovascular problems, hypertension, and osteoporosis (Akbar & Huriah.
Lalu Makripuddin et al.
, 2.
Effective stunting prevention in Indonesia requires integrated multisectoral interventions (Suparyanto & Rosad, 2.
Since Presidential Regulation No.
72/2021, the Family Welfare Movement (PKK) has played a strategic role in stunting taskforces from provincial to village levels.
Village community health workersAiincluding PKK youth and family planning community health workersAiassist in growth monitoring at Posyandu (Permendagri, 2.
Community health workers must ensure precise anthropometric measurements to avoid misleading growth assessments (Yusran et al.
, 2.
Previous community service projects .
Indriati, 2.
revealed persistent misunderstandings regarding accurate anthropometry techniques.
To address this, we integrated the BERAKSI mobile applicationAia free Android tool available on the Play StoreAithat standardizes data entry and categorizes growth outcomes via WHO growth charts based on the Minister of HealthAos standards (Susanti et al.
, 2.
METHODS
This descriptive quantitative study used a one-group pre-testAepost-test design.
The total sample comprised 18 Posyandu community health workers (Rose A: n = 10.
Rose B: n = .
in RW 08.
Cibeber.
The instrument, adapted from IndonesiaAos anthropometry and early childhood development screening standards (Minister of Health Decree No.
HK.
07/MENKES/51/2.
was validated .
= 0.
and reliable ( = 0.
in Posyandu Lestari.
RW 10.
Following ethical approval (KEPK-STIKes 9/D/KEPK-STIKes/I/2.
, community health workers completed pretraining questionnaires, participated in theoretical and practical training using the national anthropometry kit, and logged measurements via the BERAKSI app.
A post-test knowledge assessment followed.
Anthropometric Training and The Beraksi Application Jurnal Kesehatan Budi Luhur.
Vol.
No.
July 2025
p-ISSN 1978-8479
e-ISSN 2581-0111
RESULT
In this study, data analysis used descriptive methods which are presented in the form of frequency distribution in the following table:
Table 1.
Distribution of Community Health WorkersAo Knowledge on Anthropometry Community health worker Knowledge of Anthropometry Good Fair Poor Total Pre-test Percentage (%) Post test Percentage (%) Based on the table above, it is known that before the training, community health workers who had good knowledge were 10 respondents .
%), and those who had sufficient knowledge were 6 respondents .
%).
Those who had insufficient knowledge were 2 respondents .
%).
can be concluded that most community health workers had good knowledge before the anthropometry training was conducted to conduct early detection of stunting.
After the training, all community health workers .
had increased their knowledge to good .
%).
DISCUSSION
Prior to training, 56% of community health workers already possessed good anthropometry knowledge, likely due to their long service (>5 year.
, routine practice, prior health department training, and supervision (Wijhati et al.
, 2.
Educational background varied: most held highschool diplomas and worked as homemakers, while two held bachelorAos degrees and served as teachersAieducational attainment is associated with enhanced problem-solving and adaptation (Novitasari et al.
, 2.
Post-training results were unanimously positive: all community health workers achieved good knowledge scores.
Practical simulations revealed common issues: failure to remove childrenAos accessories during measurement and difficulties handling non-cooperative childrenAi factors that can bias measurement outcomes and affect growth records.
Ongoing refreshers are essential (Hal et al.
, 2.
The BERAKSI app facilitated accurate data recording and provided real-time growth and development categorizations based on Indonesian anthropometric standards.
It also includes the KPSP digital screening tool aligned with the national SDIDTK guidelines (Ministry of Health, 2.
, enabling community health workers to consult results with health professionals, reinforcing its utility for early detection (Susanti et al.
, 2.
CONCLUSION
Before training, 56% of community health workers demonstrated good knowledge.
were fair and 11% poor.
Following anthropometry training and digital application support, 100% reached a good knowledge level.
Knowledge gains were influenced by training, educational background, community health worker experience, and supportive interventions by health Anthropometric Training and The Beraksi Application Jurnal Kesehatan Budi Luhur.
Vol.
No.
July 2025
p-ISSN 1978-8479
e-ISSN 2581-0111
RECOMMENDATION
Posyandu community health workers should receive regular academic and practical training, digital tools like BERAKSI, and ongoing supervision to standardize anthropometric skills, facilitating accurate growth monitoring and contributing strategically to community-level stunting prevention programs.
REFERENCES