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All Journal Coreid Journal Journal of Elektronik Sistem InformasI JuSTISe: Journal Data Science, Technology, Informatics and Security
Nana Suryana
Universitas Kebangsaan Republik Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Mendesain Antarmuka Pengguna Sistem Informasi Manajemen Aset TI menggunakan Pendekatan Desain Berpikir Yogi Saputra; Agung Pratama; Nana Suryana; Popon Dauni; Novianti Indah Putri
JESII: Journal of Elektronik Sistem InformasI Vol 4 No 1 (2026): Journal of Elektronik Sistem InformasI - JESII (DECEMBER)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v4i1.4798

Abstract

It is crucial to create an information system, especially for asset management, to design a good user interface (UI). The purpose of this study is to test the application of the Design Thinking method in the UI design process for the IT Asset Management Information System at PT Niaga Handal Cemerlang located in Bandung City. The Design Thinking method was chosen because of its user-focused approach, which allows for the creation of a functional and easy-to-use interface. Empathy, problem definition, ideation, prototyping, and testing are the steps in this study. This study found that applying Design Thinking can improve the quality of the user interface, which impacts how efficient the company's asset management is. These results support that a user-oriented approach is crucial when building complex information systems.
Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes Classification di Kelurahan Padasuka Ciomas Bogor Muhammad Lutfi; Nana Suryana; Isep Saepudin; Adam Husain; Sri Handayani
Journal Data Science, Technology, Informatics and Security Vol 3 No 1 (2025): Journal Data Science, Technology, Informatics and Security (Juni 2025)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v3i1.4265

Abstract

Life is characterized by symptoms of growth and development. The health status of each individual is different. In this case, one of the efforts to improve health status is to improve nutritional status. Nutritional status is a state of the body related to food consumption patterns and the use of nutrients that are tailored to the body's needs. Improving nutritional status is useful for increasing body resistance and making normal growth. In actualizing the daily nutritional status of children under five at the posyandu, it is usually obtained through anthropometric measurements, namely by using the BW/U index or body weight compared to age to determine nutritional status. However, in anthropometric measurements, it was found that there was confusion in the determination of nutritional quality, so that in order to get accurate results, a data mining method was needed, namely the Naive Bayes Classification (NBC) Algorithm which would be implemented in the study. This research is expected to help posyandu cadres in Padasuka sub-district, Ciomas sub-district, Bogor district in determining the nutritional status of toddlers better and more accurately.
Natural Language Processing and Random Forest for Mental Health Symptom Identification Using Social Media Data Sigit Sugara; Popon Dauni; Novianti Indah Putri; Yogi Saputra; Nana Suryana
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.145

Abstract

This study explores the implementation of machine learning models, specifically Natural Language Processing (NLP) and Random Forest, for detecting mental health symptoms based on text analysis of web-sourced data. The research addresses the challenges of analyzing highly subjective and dynamic text in social media content to identify patterns associated with anxiety, depression, and stress. The methodology involves several preprocessing steps including case folding, cleansing, language normalization, negation conversion, stopword removal, and tokenization, followed by TF-IDF weighting and Random Forest classification. The model evaluation revealed a high accuracy rate of approximately 80%, although achieving a confidence level of 75% proved challenging. This research demonstrates that despite the inherent difficulties in predicting subjectively variable text, the machine learning approaches employed show satisfactory performance in identifying mental health symptoms, offering potential for early detection and intervention systems.
Co-Authors Adam Husain Agung Pratama Isep Saepudin Muhammad Lutfi Novianti Indah Putri Popon Dauni Sigit Sugara Yogi Saputra Yogi Saputra