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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Komputer, Informasi dan Teknologi
Risca Lusiana Pratiwi
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media

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Analisis Nilai Gizi Makanan Berbasis Machine Learning Pendekatan Unsupervised untuk Penentuan Status Gizi Sehat Bagoes Pangestu; Muhammad Annajmuts Tsaqib; Fatih Al Farizi; Risca Lusiana Pratiwi; Euis Widanengsih
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 2 (2025): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i2.3230

Abstract

This study aims to analyze and classify various food items based on their nutritional content using an unsupervised learning approach, specifically the K-Means Clustering algorithm. The increasing complexity of nutritional data requires effective data-driven methods to support accurate and efficient analysis. This research utilizes K-Means to group food items into distinct clusters according to their energy, fat, carbohydrate, protein, and fiber levels. The clustering process successfully identified three main groups that represent different nutritional characteristics. Cluster 1 consists of high-energy and high-fat foods suitable for individuals with high physical activity. Cluster 0 includes balanced-nutrition foods recommended for daily consumption, while Cluster 2 contains low-calorie and high-fiber foods ideal for weight control or diet programs. The results demonstrate that K-Means is effective in simplifying complex nutritional data and providing clear classifications for practical use. This study highlights the potential of machine learning as a valuable tool in nutritional analysis and digital health innovation. The application of this method can support the development of intelligent nutrition-based applications that help individuals manage healthy diets more effectively and contribute to promoting public awareness of balanced nutrition.
Spam Message Classification Using the Naïve Bayes Algorithm Based on RapidMiner Muhamad Yusup; Mochamad Isham Fadillah; Rifky Adinanta Fauzanie; Risca Lusiana Pratiwi; Rani Irma Handayani; Euis Widanengsih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1811

Abstract

This study implements the Naïve Bayes algorithm for classifying spam and non-spam (ham) messages using the RapidMiner Studio platform. The dataset used was obtained from the SMS Spam Collection Dataset on the Kaggle platform, which consists of 5,759 messages with a distribution of 4,075 ham messages and 1,291 spam messages. The research stages included text pre-processing, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results showed that the Naïve Bayes model achieved an accuracy of 89.64% with a precision of 56.93%, a recall of 100%, and an F1-score of 72.56%. The research findings indicate that the Naïve Bayes algorithm is effective in detecting spam messages with adequate accuracy, and prove that RapidMiner is an efficient tool for implementing machine learning methods in text classification.
Clustering Provinces in Indonesia Based on Economic Indicators Using the K-Means Algorithm Ilham Ilyasa; Muhamad Fazri Sugara; Abdul Aziiz; Rani Irma Handayani; Risca Lusiana Pratiwi; Euis Wida Nengsih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1812

Abstract

This study aims to analyze and classify the level of economic development in provinces in Indonesia using the K-Means algorithm. The data used includes three main indicators, namely Gross Regional Domestic Product (GRDP) per capita, percentage of poor population, and Human Development Index (HDI) in 2024 obtained from the Central Statistics Agency (BPS). The data was processed through normalization and analysis using the Elbow method to determine the optimal number of clusters. The results were evaluated using the Davies–Bouldin Index (DBI) to assess the level of separation and compactness between clusters. The results show that the most effective division consists of three groups representing high, medium, and low levels of development. Provinces such as DKI Jakarta and Riau are included in the high development cluster, Central Java and South Sulawesi are in the medium cluster, while Papua and East Nusa Tenggara are in the low cluster. These results show that machine learning methods, particularly K-Means, are capable of identifying patterns of regional economic inequality and provide a useful basis for the government in formulating more targeted and equitable development policies.
Co-Authors Abdul Aziiz Bagoes Pangestu Euis Wida Nengsih Euis Widanengsih Fatih Al Farizi Ilham Ilyasa Mochamad Isham Fadillah Muhamad Fazri Sugara Muhamad Yusup Muhammad Annajmuts Tsaqib Rani Irma Handayani Rifky Adinanta Fauzanie