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All Journal Jurnal Informatika Universitas Pamulang SPIRIT Indonesian Journal of Information Technology and Computing (IMAGING)
kholik, Moh abdul
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Computer Science & IT

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Application of the K-Nearest Neighbor (KNN) Algorithm for Stunting Diagnosis in Infants Aged 1-12 Months kholik, Moh abdul; Pratomo, Cucut Hariz; Gustina, Sapriani
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.40983

Abstract

Stunting in toddlers must be addressed immediately because it has a negative impact on their growth and development. Stunting is a disorder where toddlers experience chronic malnutrition, thus their physical growth and height do not match their age. According to the Indonesian Nutritional Status Survey (SSGI), stunting is more common among toddlers from aged 0 to 1 year than overall. Stunting can have short-term and long-term impacts. This research examines data from the Temanggung District Health Service on 3,999 toddlers aged 0 to 12 months between 2019 and 2022.  Many studies have exclusively looked at stunting in children aged one to five years, especially research on stunting using the KNN method, even though stunting can actually be recognized from an early age. Therefore, researchers are more specific in using the KNN method for cases of babies 1 to 12 months so as to differentiate it from previous researchers. The aim of this research is to use the K-Nearest Neighbor (KNN) algorithm to detect stunting nutritional status in toddlers. K-Nearest Neighbor (KNN) is a classification algorithm that uses a set of K values ​​from the closest data (its neighbors) as a reference to determine the class of incoming data. KNN classifies data based on its similarity or closeness to other data. The dataset used includes parameters of age, gender and height. The research approach is the CRISP-DM (Cross Industry Standard Process for Data Mining) method, which begins with business knowledge, followed by EDA and modeling, evaluation, testing and report preparation. The result shows that the KNN algorithm can accurately categorize children as stunted or not based on age (U) and height (TB), with the maximum level of accuracy and the lowest error rate at k = 5. At this optimal value (k), this algorithm has an accuracy of 99.87%, Recall 99.84%, and precision 99.73.
Interoperabilitas (SIMRS) dengan Bridging Antrian Online BPJS v3 dengan RESTful API dan JSON Web Service Yuliawan, Endra; Kusumaningrum, Danik; Kholik, Moh Abdul
Indonesian Journal of Information Technology and Computing (IMAGING) Vol 5, No 1 (2025): 30 Juni 2025
Publisher : Politeknik Harapan Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52187/img.v5i1.355

Abstract

Transformasi digital di bidang layanan kesehatan Indonesia mendorong penggabungan sistem informasi agar layanan lebih efisien dan nyaman. Salah satu inovasi yang dikembangkan adalah sistem antrean online BPJS Kesehatan versi 3 (V3), yang memungkinkan pendaftaran secara daring dengan menggunakan teknologi komunikasi RESTful API dan format data JSON. Penelitian ini bertujuan untuk menerapkan dan mengevaluasi kemampuan sistem informasi manajemen rumah sakit (SIMRS) Khanza dalam berintegrasi dengan sistem antrean online BPJS Kesehatan V3. Metode pengembangan menggunakan pendekatan berulang dengan fokus pada pengujian keamanan data melalui enkripsi HMAC-SHA256 dan AES-256. Hasil integrasi menunjukkan bahwa SIMRS Khanza mampu memproses permintaan layanan secara baik, tanpa mengalami hambatan dalam format data atau komunikasi antar sistem. Implementasi ini mendukung upaya peningkatan kualitas layanan publik serta memenuhi regulasi terkait digitalisasi kesehatan nasional.
OTOMATISASI PEMBERIAN NUTRISI BERDASARKAN TDS DAN PEMANTAUAN PH PADA HIDROPONIK BERBASIS IOT Riyanto, Tegar; Rianto, Agus; Noviyanto, Hendri; Kholik, Moh Abdul
SPIRIT Vol 17, No 2 (2025): SPIRIT
Publisher : LPPM ITB Yadika Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53567/spirit.v17i2.396

Abstract

Hidroponik khususnya telah menjadi metode mutakhir untuk meningkatkan produktivitas dan efisiensi pertanian melalui pemanfaatan teknologi. Pertumbuhan tanaman sangat dipengaruhi oleh parameter seperti pH dan Total Dissolved Solids (TDS). Menggunakan mikrokontroler ESP32 dan aplikasi Blynk, penelitian ini menciptakan sistem pemberian nutrisi otomatis berdasarkan kadar TDS dan pemantauan pH berbasis Internet of Things (IoT). Sistem ini secara otomatis menjaga kadar TDS dengan mengendalikan pompa peristaltik dan pompa air bersih menggunakan modul relai. pH dipantau, dan ketika terjadi variasi, pesan peringatan akan diberikan. Google Sheets merekam data secara real-time, yang kemudian ditampilkan di aplikasi Blynk. Berdasarkan hasil pengujian, sistem berhasil menjaga TDS dalam rentang yang diinginkan, meskipun pH sebagian besar tetap konstan dan perlu disesuaikan secara manual. Metode ini meningkatkan efektivitas pemantauan dan pengendalian dalam pertanian hidroponik skala kecil sekaligus mengurangi kebutuhan akan intervensi manual.
Co-Authors Agus Rianto Kusumaningrum, Danik Noviyanto, Hendri Pratomo, Cucut Hariz Riyanto, Tegar sapriani gustina, sapriani Yuliawan, Endra