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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) ENSAINS JOURNAL JURNAL KOMUNIKASI DAN BISNIS Bulletin of Information Technology (BIT) Tedc Amare Coreid Journal Journal of Elektronik Sistem InformasI JURNAL PENGABDIAN KEPADA MASYARAKAT (JPKM) KEBANGSAAN JuSTISe: Journal Data Science, Technology, Informatics and Security
Nana Suryana
Program Studi Sistem Informasi, Fakultas Ilmu Komputer & Sistem Informasi, Universitas Kebangsaan Republik Indonesia, Bandung
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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.
Penentuan Prioritas Bantuan Sosial Dengan Metode Combined Compromise Solution (CoCoSo) Darmansyah Darmansyah; Yessy Yanitasari; Yudiana Yudiana; Agus Nugraha; Nana Suryana
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2447

Abstract

Penyaluran bantuan sosial yang tepat sasaran merupakan kunci dalam mendukung kesejahteraan masyarakat, terutama di tengah keterbatasan sumber daya. Penelitian ini bertujuan untuk menentukan prioritas penerima bantuan sosial dengan menggunakan metode Combined Compromise Solution (CoCoSo), sebuah metode pengambilan keputusan multi-kriteria yang mampu menghasilkan solusi kompromi optimal dengan mempertimbangkan berbagai kriteria secara seimbang. Metode CoCoSo digunakan untuk mengevaluasi dan mengkombinasikan nilai dari setiap alternatif penerima bantuan berdasarkan kriteria yang telah ditentukan, sehingga menghasilkan peringkat prioritas yang objektif dan efisien. Penerapan metode ini diharapkan dapat membantu dalam proses seleksi penerima bantuan sosial yang lebih transparan dan tepat sasaran, terutama dalam kondisi di mana terdapat konflik atau perbedaan kepentingan antar kriteria. Hasil penelitian menunjukkan bahwa metode CoCoSo efektif dalam memberikan rekomendasi prioritas penerima bantuan sosial dengan solusi yang seimbang dan dapat diandalkan.
Co-Authors Adam Husain Agung Pratama Agus Nugraha Aziz, Faisal Darmansyah Darmansyah Datau, Sjahriani Erwin Teguh Arujisaputra Husain, Adam Isep Saepudin Leni Apriliyani Muhammad Lutfi Novianti Indah Putri Popon Dauni Pratiwi Pratiwi Purwadi Purwadi Rizki Tri Prasetio, Rizki Tri Sigit Sugara Taufik, Cevi Mochamad Wafa Pamulasari Yessy Yanitasari Yogi Saputra Yogi Saputra Yogi Saputra, Yogi Yudiana Yudiana