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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Indonesian Journal of Public Administration Review
Kurniawan, Defri
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : Infotekmesin

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Optimasi Algoritma K-Nearest Neighbors Menggunakan GridSearchCV untuk Klasifikasi Penyakit Diabetes Yaqin, Ainul; Kurniawan, Defri; Zeniarja, Junta
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2557

Abstract

Diabetes is a chronic disease that has a significant impact on global health, with prevalence increasing every year. Therefore, early detection is crucial to prevent further complications and save lives. The utilization of technology, such as machine learning, offers innovative solutions to improve the accuracy of predicting this disease. This research develops a diabetes prediction model using the K-Nearest Neighbors (KNN) algorithm with the Pima Indians Diabetes Database dataset. Given the class imbalance in the dataset, Random Over-Sampling technique was applied to balance the data distribution. The results showed that the KNN model optimized with GridSearchCV resulted in 88% accuracy, 89% precision, 75% recall, and 82% F1-score. This approach is expected to produce a more accurate and efficient model to support early detection of diabetes, and shows the great potential of machine learning technology in improving the effectiveness of disease diagnosis and control.
Pengembangan Sistem Modul Komisi Dinamis pada Modul Penjualan ERP - Odoo12 Wahyu Utomo, Danang; Kurniawan, Defri; Rosi Subhiyakto, Egia
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.729

Abstract

The improvement of the sales system not only focuses on the advantage result of the sales transaction but also can use another parameter to improve it. One of a parameter used is commission. Giving commissions to the salesperson can improve their work performance and have an impact on increasing sales targets. Based on the study literature, the problem faced by the company is the discrepancy of commission. It canbe affected by several factors such as the commission system are not integrated with the main system, improper formula, or there are many systems used in the company so it the staff are difficult to integrate the system. For example, the company using Odoo ERP to support sales transaction and use commission information system separately. The salesperson must integrate sales data into both of the systems. It can affect the time delay of decision commission. Based on the problem above, we propose a prototype commission system that integrates with Odoo12. The salesperson does not need to integrate data manually into the system because it automatically integrates into the system. This study uses a prototyping model as a software development method. The results show that the commission system can implement on the Odoo12 ERP to decide commission to the salesperson. 70% of respondent agree that system has able to use in order to setting up commission module on Odoo
Optimasi Algoritma K-Nearest Neighbors Menggunakan GridSearchCV untuk Klasifikasi Penyakit Diabetes Yaqin, Ainul; Kurniawan, Defri; Zeniarja, Junta
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2557

Abstract

Diabetes is a chronic disease that has a significant impact on global health, with prevalence increasing every year. Therefore, early detection is crucial to prevent further complications and save lives. The utilization of technology, such as machine learning, offers innovative solutions to improve the accuracy of predicting this disease. This research develops a diabetes prediction model using the K-Nearest Neighbors (KNN) algorithm with the Pima Indians Diabetes Database dataset. Given the class imbalance in the dataset, Random Over-Sampling technique was applied to balance the data distribution. The results showed that the KNN model optimized with GridSearchCV resulted in 88% accuracy, 89% precision, 75% recall, and 82% F1-score. This approach is expected to produce a more accurate and efficient model to support early detection of diabetes, and shows the great potential of machine learning technology in improving the effectiveness of disease diagnosis and control.
Co-Authors Abu Salam Ainul Yaqin Alzami, Farrikh Amartha, Putri Ananta, Devanda Radya Azizah, Hidayatul Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fahmi, Muhammad Aqil Fauziah, Mitha Fikri Budiman Hana, Rohima Choirul Haresta, Alif Agsakli Ida Ayu Putu Sri Widnyani Ika Novita Dewi Junta Zeniarja Kamarudin, Fatkhurridlo Pranoto Kuncoro, Aneira Vicentiya Kurniawan Aji Saputra Megantara, Rama Aria Muhammad Naufal, Muhammad Muljono Muljono Muslih Muslih Ni'mah, Laila Maulin Ningrum, Novita Kurnia Nurrohman, Slamet Hudha Prajanto Wahyu Adi, Prajanto Wahyu Pranata, Eka Arya Purwanto Purwanto Putra Sadewa, Fastabyq Putro Utomo, Satrio Allam Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rosi Subhiyakto, Egia Sabrina, Della Septian Enggar Sukmana Sindhu Rakasiwi Sri Winarno Utomo, Danang Wahyu