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INDONESIA
Jurnal Teknologi Dan Sistem Informasi Bisnis
Published by Universitas Dharma Andalas
ISSN : -     EISSN : 26558238     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal Teknologi dan Sistem Informasi Bisnis merupakan Jurnal yang diterbitkan oleh Prodi Sistem Informasi Universitas Dharma Andalas untuk berbagai kalangan yang mempunyai perhatian terhadap perkembangan teknologi komputer, baik dalam pengertian luas maupun khusus dalam bidang-bidang tertentu yang terkait dengan teknologi informatika komputer. Naskah yang diterima untuk diterbitkan berupa hasil penelitian lapangan, penelitian kepustakaan, pengamatan serta karya ilmiah yang berhubungan dengan topik yang relevan dengan situasi Teknologi Komputer.Jurnal Teknologi Komputer terbit 2 kali dalam satu tahun yaitu bulan Januari dan Juli.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 492 Documents
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Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest Muhamad Angga Rizki Sabima; Barry Ceasar Octariadi; Rachmat Wahid Saleh Insani
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2415

Abstract

Nutritional problems in children under five remain a major challenge in Indonesia's health development as they can affect growth, cognitive abilities, and future productivity. The Indonesian Nutritional Status Survey (SSGI) serves as an important data source to understand the nutritional condition of children under five. This study aims to classify the nutritional status of children under five using the Random Forest algorithm with a Knowledge Discovery in Database (KDD) approach. The research stages include data cleaning, preprocessing, feature selection, modeling, and model performance evaluation. The data were obtained from Puskesmas Gang Sehat.The results indicate that Random Forest can classify the nutritional status of children under five with high accuracy for the majority class (Normal Nutrition), while performance for the minority class (Abnormal Nutrition) can still be improved. This demonstrates that the Random Forest algorithm is effective for classifying nutritional status, although optimizing data imbalance and adding supporting variables can enhance results for the minority class. This study is expected to contribute to the development of technology-based solutions for addressing nutritional issues in children under five.
Data Mining Dalam Pengelompokkan Intelligence Quotient (IQ) Pada Anak Reterdasi Mental Dengan Menggunakan Algoritma K-Means gushelmi gushelmi; Diana Kemala; Muhammad Afdhal
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2447

Abstract

Traditional methods for classifying children with mental retardation based on fixed IQ score thresholds are often inadequate in capturing the diversity of intellectual abilities. This study proposes the use of data mining techniques, specifically the K-Means clustering algorithm, to group Intelligence Quotient (IQ) data derived from psychological assessments. The research methodology consists of data collection, data preprocessing, selection of the optimal number of clusters, and implementation of the K-Means algorithm. The experimental results demonstrate that the proposed approach can successfully cluster IQ data into multiple groups representing distinct levels of intellectual functioning. The resulting clusters can be utilized as a decision-support mechanism to assist educators and practitioners in selecting appropriate instructional methods and intervention strategies in the field of special education.

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