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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Artificial Intelligence and Robotics (IJAIR)
Martyan Anggadimas, Nanda
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Classification Appropriateness Recipient Help Non-Cash Food Using Learning Vector Quantization (LVQ) Method Lestari, Ayu; Aris Widodo, Anang; Martyan Anggadimas, Nanda
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 1 (2023): May 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i1.6287

Abstract

Help Non-Cash Food is a program from the Government that is used to overcome poverty. The program is not functioning as well as it could because the procedure of receiving aid is not uniform, and individuals responsible for making choices are having trouble determining which families are qualified to receive the assistance. To overcome this problem, a classification system is needed to classify the eligibility of Non-Cash Food Assistance recipients so that the results are more efficient and accurate. This research uses the Learning Vector Quantization (LVQ) method with Python. This research aims to implement the LVQ method for the eligibility classification of non-cash food assistance recipients. System design is a stage that contains the process from start to finish of running this system which is described in the form of a flowchart, including system requirements that support this research, both software and hardware. In the process of analyzing the results and tests that are used as evaluation material in the process of finding a solution to a problem and making decisions in the process of planning activities, it is necessary to assess whether or not the LVQ approach is practicable to apply based on the findings of the research. In this study, 200 datasets were used with three epoch values and a learning rate of 0.1. The data set was randomly divided into a training portion of 80% and a testing portion of 20%. So that the results of this research using the LVQ method on the eligibility classification of recipients of Non-Cash Food Assistance obtain an accuracy of 97.5%.
IMPLEMENTASI DECISION TREE UNTUK KLASIKASI OBESITAS Khikam, Ahlul; Martyan Anggadimas, Nanda; Udin, Muhammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13397

Abstract

Angka obesitas telah meningkat secara signifikan dalam beberapa dekade terakhir di seluruh dunia. Aspek seperti pola makan buruk, kurangnya olahraga, faktor genetik, lingkungan, dan masalah psikologis bisa mempengaruhi berkembangnya obesitas. Obesitas dapat menyebabkan berbagai masalah kesehatan seperti penyakit jantung, diabetes, dan gangguan pernapasan, serta bisa berdampak buruk pada kualitas hidup seseorang. Tujuan penelitian untuk mengimplementasi metode Decision Tree untuk klasifikasi obesitas. Decision Tree merupakan metode yang sangat efektif dalam melakukan klasifikasi. Dataset dalam penelitian ini, dilakukan dengan mengambil data Obesity Levels dari website Arcihive.ics.uci.edu. Dataset yang digunakan terdapat 2111 data dan 17 kolom. Hasil dari prediksi menggunakan metode Decision Tree mendapatkan nilai accuracy sebesar 94%, pecision sebesar 94%, recall sebesar 94%, Dan F1-score sebesar 94%. Meskipun hasil ini cukup memuaskan, penelitian ini juga menunjukkan adanya peluang untuk pengembangan lebih lanjut, seperti mempertimbangkan aspek – aspek lain seperti menambahkan jumlah dan variasi data, teknik preprocessing one-hot encoding, dan menggunakan metode klasifikasi lain untuk membandingkan kinerja model guna mendapatkan hasil yang lebih optimal dalam klasifikasi obesitas.
Co-Authors Anang Aris Widodo AYU LESTARI Khikam, Ahlul Udin, Muhammad