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All Journal Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Cinta Apriliza
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Computer Science & IT

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Penerapan Metode MAUT dalam Menentukan Lokasi Cabang Baru Tokepangsit Medan di Kabupaten Langkat Silvia Amara; Cinta Apriliza; Sherly Rohana; Amirullah Wahid; Safrizal Safrizal
Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 3 No. 1 (2025): Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v3i1.658

Abstract

Every business definitely wants to expand its business by opening new branches in different regions to increase income and become a successful business. To open a new business branch, of course, a lot of consideration is needed before making a decision. So that the decision is correct and in accordance with needs, Research is used in its implementation. Therefore, this research applies a decision support system using the Multi Attribute Utility Theory (MAUT) method in selecting a new branch location for the Tokepangsit Medan business which will open a new branch in Langkat Regency. With this method, the decision result obtained is alternative 4, namely Selesai District, which gets the highest score of 0.640.
Pengelompokkan Penyakit Tuberkulosis Paru Berdasarkan Penyebabnya Menggunakan Metode Clustering: Studi Kasus : UPT Puskesmas Selesai Cinta Apriliza; Relita Buaton; Hermansyah Sembiring
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.995

Abstract

Pulmonary tuberculosis remains a pressing public health problem, particularly in the work area of the Duduk Health Center (UPT Puskesmas). Effective management of this disease requires a thorough understanding of the characteristics of the causes of pulmonary TB in patients. This study aims to classify pulmonary TB cases based on the main causes such as diabetes mellitus, irritant factors, pleural effusion, and family environmental conditions. The research method used is a clustering technique with the K-Means algorithm. The data used are data on pulmonary TB patients in 2020–2025 with variables of age, gender, and causative factors collected from medical records. The analysis process was carried out using MATLAB R2014b software. The clustering model was carried out in 3, 4, and 5 clusters to compare the level of segmentation efficiency. Based on the calculation results, the model with 5 clusters showed the lowest cluster variance value of 0.4889 compared to the 3-cluster model (0.7333) and 4-cluster models (0.6151), which indicates that the division into 5 clusters produces the most compact and representative data group. Each cluster shows a different combination of characteristics of pulmonary TB patients, for example: (1) elderly male patients with comorbid diabetes; (2) adolescent females with the negative influence of environmental factors; (3) adult males exposed to irritants; (4) patients with pleural effusion; and (5) groups with multiple factors. The results of this study can provide strategic input for the Finished Community Health Center UPT in formulating more targeted and targeted intervention policies in order to prevent, control, and handle pulmonary tuberculosis cases in a sustainable and effective manner.  
Co-Authors Amirullah Wahid Buaton, Relita Hermansyah Sembiring Safrizal Safrizal Sherly Rohana Silvia Amara