Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Infotek : Jurnal Informatika dan Teknologi EXPLORE Modem : Jurnal Informatika dan Sains Teknologi
Fathoni Dwiatmoko
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Preprocessing Tranformasi Data Menggunakan K-Means Clustering Fathoni Dwiatmoko
Jurnal Explore Vol 11, No 2 (2021): JULI
Publisher : Universitas Teknologi Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (322.573 KB) | DOI: 10.35200/explore.v11i2.544

Abstract

Abstrak – Perkembangan teknologi informasi telah menjadi hal yang tidak dapat dipisahkan dari kehidupan manusia. Banyaknya jumlah data di dunia menyebabkan pengolahan data menjadi pesat karena jumlah data yang semakin bertambah. Salah satu bidang yang terdapat pertumbuhan data yang pesat adalah bidang pertanian. Data pertanian dapat di proses dengan data mining untuk dapat di jadikan bahan pembelajaran maupun pengambilan keputusan. Pada data mining terdapat berbagai macam teknik salah satunya adalah klasifikasi, dalam klasifikasi terdapat preprocesing data salah satunya yaitu transformasi data, pada data pertanian di perlukan transformasi data numerik menjadi interval untuk itu diperlukan metode clustering yang digunakan untuk mengkategorikan data. K-means clustering merupakan salah satu metode yang dapat mempartisi setiap data sehingga pada setiap cluster memiliki karakteristik data yang sama. Tujuan dari penelitian ini adalah untuk mengkategorikan data dalam proses transformasi data pada data mining. Hasil dari clustering menggunakan k-means clustering didapatkan data cls_0 dengan 50 data dengan kategori ringan, cls_1 dengan 9 data kategori sedang dan cls_2 dengan 13 data dengan kategori berat. Pengujian dilakukan menggunakan data uji sebanyak 72 data dan memiliki tingkat kemiripan data pada cluster sebesar 82,3%. Kata kunci: K-means Clustering; data mining;
Analisis Pengaruh Bayesian Optimization Terhadap Kinerja SVM Dalam Prediksi Penyakit Diabetes Dwi Utami; Fathoni Dwiatmoko; Nuari Anisa Sivi
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.28468

Abstract

Diabetes is a prevalent and serious chronic illness that impacts millions of individuals globally. Early detection of diabetes is essential to mitigate severe health complications. This study investigates the application of Support Vector Machine (SVM) enhanced by Bayesian Optimization for the early prediction of diabetes. While SVM is a robust machine learning algorithm, its performance heavily depends on the proper selection of parameters. Bayesian Optimization is an efficient approach to fine-tune SVM parameters, such as the regularization parameter (C) and the kernel parameter (gamma). The research utilizes a Kaggle dataset that includes various diabetes risk factors. The study compares the performance of SVM optimized using Bayesian Optimization against SVM without optimization. The findings reveal that SVM with Bayesian Optimization achieves an accuracy of 95%, surpassing the 94% accuracy of the unoptimized SVM. These results highlight that Bayesian Optimization enhances SVM's effectiveness in predicting diabetes early
Komparasi Algoritma Machine Learning dengan Hyperparameter Tuning pada Prediksi Level Obesitas Dwi Utami; Fathoni Dwiatmoko; Alinsa Novsagita
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 4 No. 3 (2026): Juli : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v4i3.955

Abstract

Obesity has become a growing global health concern due to its association with various chronic diseases, including diabetes mellitus, hypertension, and cardiovascular disorders. This study investigates the performance of Support Vector Machine (SVM), Random Forest, and Gradient Boosting algorithms enhanced through hyperparameter tuning for obesity level prediction. The study utilized the Obesity Level dataset, which contains 2,111 instances, 16 input features, and one target class. Following the data preprocessing stage, 2,087 instances were included in the analysis. Correlation analysis was conducted to identify the nine most relevant features used for model development. Experimental results demonstrate that the Random Forest model achieved the best predictive performance compared to SVM and Gradient Boosting, with an accuracy of 90.91%, precision of 91.19%, recall of 90.91%, and an F1-score of 90.94%. The dataset was partitioned into training and testing subsets using an 80:20 split. Furthermore, the optimal model was deployed in a Streamlit-based web application to facilitate obesity level prediction. These findings suggest that Random Forest, when optimized through hyperparameter tuning, provides a reliable and effective approach for multiclass obesity classification.
Co-Authors Alinsa Novsagita DWI UTAMI Muhammad Yusron Maulana El-Yunusi Nuari Anisa Sivi