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All Journal Technologia: Jurnal Ilmiah Journal of Computer Networks, Architecture and High Performance Computing JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Hen Hen Lukmana
Universitas Siliwangi
Computer Science & IT Education

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PERANCANGAN SISTEM INFORMASI DETEKSI DINI STUNTING BERBASIS WEBSITE MENGGUNAKAN METODE USER CENTER DESIGN Hen Hen Lukmana; Muhammad Al-Husaini; Irani Hoeronis; Luh Desi Puspareni
Technologia : Jurnal Ilmiah Vol 14, No 3 (2023): Technologia (Juli)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v14i3.12025

Abstract

Stunting merupakan masalah serius dalam pembangunan kesehatan di Indonesia. Stunting merujuk pada kondisi dimana pertumbuhan dan perkembangan anak terhambat karena faktor-faktor seperti kurang gizi, infeksi berulang, dan kurangnya stimulasi psikososial yang memadai. Stunting memiliki dampak besar terhadap kehidupan dan perkembangan anak, termasuk penurunan kemampuan kognitif, keterampilan motorik, dan daya tahan tubuh yang lemah. Deteksi dini stunting sangat penting untuk mencegah dampak jangka panjang yang merugikan. Pengembangan sistem informasi deteksi dini stunting dapat menjadi solusi efektif dalam mengurangi keterlambatan pendeteksian stunting pada anak. Dengan menerapkan metode UCD pengembang dapat memastikan bahwa sistem yang dikembangkan mudah digunakan dan dimengerti oleh pengguna, termasuk petugas kesehatan dan orang tua yang terlibat dalam pendeteksian stunting. Penelitian ini bertujuan untuk mengembangkan sistem informasi pendeteksi dini stunting menggunakan metode User Center Design di Kota Tasikmalaya. Pendekatan ini diharapkan dapat meningkatkan keterlibatan pengguna, memenuhi kebutuhan mereka, dan memungkinkan pengguna untuk memahami fungsi sistem hanya dengan satu kali penggunaan. Metode pengujian yang dilakukan yaitu usability testing dan blackbox testing untuk mengevaluasi kegunaan dan fungsionalitas sistem. Hasil pengujian menunjukkan bahwa sistem memenuhi persyaratan fungsional dan memiliki kegunaan yang baik.
Visualisasi Skyline Query untuk Distribusi Tenaga Kesehatan COVID-19 Vega Purwayoga; Muhammad Al Husaini; Hen Hen Lukmana
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 1 (2023): JuTISI (in progress)
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i1.5624

Abstract

The use of health workers in areas with minimal risk can be a solution to help areas with a high risk of spreading COVID-19. Selection of low risk areas can be done by measuring the level of risk in an area. One solution is to use the skyline query algorithm. Skyline query is able to recommend which areas are potential to serve as supporting areas for health workers. Skyline query is able to produce a recommendation model for determining the supporting area for health workers, however, in the process of reading the information, it is necessary to extract the information. Extraction is carried out by developing a system for visualizing the skyline query as a recommendation system for health personnel assistance. This study develops a visualization system using a hybrid approach, which combines the Rapid GIS Development Cycle (RGDC) and Navigational Development Techniques (NDT) methods. The system was developed using R and the shiny library, ggplot2, rpref and leaflets. The system can work as expected, such as displaying a map of the recommended area to become a supporting area, visualizing data with ggplots and visualizing dominance testing on the skyline query.
Sentiment Analysis on Short Social Media Texts Using DistilBERT Asyaky, Muhammad Sidik; Muhammad Al-Husaini; Hen Hen Lukmana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5836

Abstract

Sentiment analysis on short texts from social media, such as tweets, presents unique challenges due to their brevity and informal language. This study explores the effectiveness of transformer-based models, particularly DistilBERT, in performing sentiment analysis on short texts compared to traditional machine learning approaches including Support Vector Machine, Logistic Regression, and Naive Bayes. The objective is to assess whether DistilBERT not only enhances sentiment classification accuracy but also remains efficient enough for quick social media analysis. The models used in this study were trained and evaluated on stratified samples of 10,000, 30,000, and 50,000 tweets, drawn from the Sentiment140 dataset while preserving the original class distribution. The methodology involved data collection and sampling, data splitting, data cleaning, feature extraction, model training, and evaluation using accuracy and F1-score. Experimental results showed that DistilBERT consistently outperformed traditional models in both accuracy and F1-score, and demonstrated competitive results against BERT while requiring significantly less training time. Specifically, DistilBERT trained approximately 1.8 times faster than BERT on average, highlighting its computational efficiency. The best result was achieved by DistilBERT trained on the 50k subset, reaching an accuracy of 85% and an F1-score of 84%. These findings suggest that lightweight transformer models like DistilBERT are highly suitable for real-world sentiment analysis tasks where both speed and performance are critical.
Co-Authors Asyaky, Muhammad Sidik Irani Hoeronis Muhammad Al Husaini Muhammad Al-Husaini Muhammad Al-Husaini Purwayoga, Vega Puspareni, Luh Desi