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All Journal RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Nopalia
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Computer Science & IT

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Diagnosa Penyakit Hipertensi Menggunakan Sistem Pakar Dengan Metode Certainty Factor Ninditama, Ilsa Palingga; Nopalia; Rizki, Fido
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 6 (2024): RESOLUSI July 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i6.2004

Abstract

Hypertension is a disease that has a high mortality rate every year and can attack individuals of all ages. Hypertension is also one of the entry points or risk factors for diseases such as heart disease, kidney failure, diabetes mellitus and stroke. The aim of this research is to be able to help the community when there are limited tools and experts which cause the community to have difficulty in diagnosing hypertension, on the other hand there are also people who have limited time or money to diagnose hypertension at health facilities such as Community Health Centers, Homes. Hospitals, Clinics and other health facilities. The system development method used in this research is the waterfall method, the waterfall method is a system development method that provides systematic and sequential approaches to software development such as requirements specifications, software design implementation, testing and so on, as for the stages in The waterfall system development method starts from analysis, design, coding and testing. The expert system used in this research uses a certainty factor in diagnosing the percentage of hypertension. The certainty factor method is a method for proving the uncertainty of an expert's thinking, where to accommodate this one usually uses a certainty factor to describe the expert's level of confidence in The problem being faced. The results of this research have a contribution, namely that the application of this expert system can quickly and precisely diagnose whether someone is suffering from hypertension or not based on the data set that has been compiled.
Deteksi Kebiasaan Penggunaan Smartphone Menggunakan K-Means Clustering Ahmad Marsehan; Nopalia; Raniyah Ayu Lestari
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/negszr43

Abstract

The rapid development of digital technology has led to a significant increase in smartphone usage in modern society. The intensity and diversity of smartphone usage form different behavioral patterns among individuals, resulting in complex data that are difficult to analyze using conventional methods. This study aims to detect and cluster smartphone usage behavior using the K-Means Clustering method. The data used include daily usage duration, application access frequency, and the most frequently used application categories. The research process begins with data preprocessing, normalization, determining the optimal number of clusters using the elbow method, and applying the K-Means algorithm. The clustering results are evaluated using the silhouette score, Davies-Bouldin Index, and inertia. The results show that the K-Means algorithm is able to group smartphone users into 5 (five) clusters with distinct characteristics, namely Heavy User, Light User, Minimal User, Moderate User, and Normal User. The quality of the resulting clusters is considered good with a Silhouette Score of 0.62 (good category), a Davies-Bouldin Index of 0.85 (low value indicating good separation between clusters), and an Inertia value of 1,245.78, which indicates stable compactness of data within clusters. This study is expected to provide a deeper understanding of smartphone usage patterns and serve as a basis for promoting healthier and more controlled smartphone usage.
Co-Authors Ahmad Marsehan Ninditama, Ilsa Palingga Raniyah Ayu Lestari Rizki, Fido