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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Sri Yulianto
Satya Wacana Christian University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Desain Jaringan Fiber to The Home di Desa Butuh Kidul Imana Yoga Prastowo; Sri Yulianto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3353

Abstract

This study aims to design a Fiber to the Home (FTTH) network in Butuh Kidul Village, Tengaran District, Semarang Regency. In today's digital era, access to fast and stable internet is a fundamental need for communities. However, many rural areas in Indonesia, including Butuh Kidul Village, still lack adequate internet access. FTTH networks provide a solution by utilizing fiber optic cables that can transmit data at high speeds and with large bandwidth capacity. This research employs the Passive Optical Network (PON) method as the network architecture to enhance efficiency and scalability. The expected outcome is that the local community will benefit from improved internet access, supporting digital village programs and enhancing the quality of life and economic potential. This research is anticipated to serve as a reference for similar network developments in other rural areas.
Pendekatan Machine Learning untuk Deteksi Stunting pada Balita Menggunakan K-Nearest Neighbors Ade Putra Tupu Djoru; Sri Yulianto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3436

Abstract

Stunting is a chronic nutritional problem affecting the physical growth and cognitive development of toddlers, especially in early childhood. This study employs the K-Nearest Neighbors (K-NN) algorithm to determine stunting status based on anthropometric variables such as age, weight, and height. The algorithm categorizes data using proximity between samples. Data from the Salatiga City Health Department in 2024 were normalized and encoded for analysis. K-NN was chosen for its ability to provide high-accuracy nutritional classification. Results show that the algorithm achieved 100% accuracy, precision, and recall at certain K values, particularly in the small K range (2-8), demonstrating its effectiveness in identifying nutritional status in toddlers. This study is expected to serve as a reference for utilizing technology and data analysis in early stunting detection, aiding healthcare professionals in designing more targeted and effective interventions. Additionally, it opens opportunities for further development to enhance diagnostic accuracy using machine learning technology.
Co-Authors Ade Putra Tupu Djoru Imana Yoga Prastowo