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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika
Umy Ramadhani Senga
Universitas Muhammadiyah Kendari
Computer Science & IT

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EVALUASI UI/UX WEBSITE INFORMASI DESA UNA MENDAA MENGGUNAKAN PRINSIP HEURISTIK NIELSEN Umy Ramadhani Senga; Muhammad Rifqi Geroda Lawan; Waode Rina Harmawati; Nopal Agusan Rian; Muhammad Rizky; Zila Razilu
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 2 (2026): JATI Vol. 10 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i2.17757

Abstract

Kemajuan teknologi digital menjadikan website desa sebagai media strategis untuk mendukung transparansi dan pelayanan publik digital. Namun, efektivitas website tidak hanya bergantung pada kelengkapan informasi, tetapi juga pada kualitas desain antarmuka (User Interface) dan pengalaman pengguna (User Experience) yang menentukan kemudahan akses masyarakat. Penelitian ini bertujuan mengevaluasi kualitas UI/UX Website Informasi Desa Una Mendaa menggunakan sepuluh prinsip heuristik Nielsen untuk mengidentifikasi hambatan usability. Metode penelitian menggunakan pendekatan deskriptif kuantitatif dan kualitatif melalui observasi delapan halaman utama serta penyebaran kuesioner kepada 20 responden. Hasil penelitian menunjukkan tingkat kepuasan pengguna sebesar 85,2% dengan rata-rata skor heuristik 4,6 dalam kategori sangat baik. Skor tertinggi dicapai pada prinsip konsistensi (90%), sedangkan terendah pada pencegahan kesalahan (78%). Secara umum, desain website dianggap menarik dan relevan, namun diperlukan perbaikan pada sistem navigasi dan validasi formulir untuk mengoptimalkan tata kelola desa berbasis digital.
Comparative Analysis of Naïve Bayes Variants for Predicting Stunting-Risk Families Alwas Muis; Zila Razilu; Umy Ramadhani Senga; Hutri Wulandari; Reva Andriyani
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i1.11910

Abstract

Stunting is a chronic nutritional condition that adversely affects children’s physical growth and cognitive development, highlighting the need for effective early detection, particularly at the household level. This study proposes a comparative analysis of three Naïve Bayes variants Gaussian, Multinomial, and Bernoulli to identify families at risk of stunting using machine learning techniques. The dataset used in this study consists of family-level records obtained from the National Population and Family Planning Agency (BKKBN) of Southeast Sulawesi Province, comprising demographic, socioeconomic, and health-related attributes. Data preprocessing involved handling missing values, removing irrelevant attributes, and transforming categorical variables. The dataset was divided into training and testing sets using an 80:20 ratio. The main contribution of this study lies in evaluating the effectiveness of different Naïve Bayes variants for family-based stunting risk prediction, which has been rarely explored in previous studies. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Bernoulli Naïve Bayes model achieved the best performance, with an accuracy of 88% and balanced evaluation metrics across both classes. These findings suggest that the Bernoulli Naïve Bayes model is the most suitable approach for predicting family-level stunting risk and can support data-driven early intervention strategies.
Co-Authors Alwas Muis Hutri Wulandari Muhammad Rifqi Geroda Lawan Muhammad Rizky Nopal Agusan Rian Razilu, Zila Reva Andriyani Waode Rina Harmawati