Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Jaring SainTek Journal of Informatics and Information Security Journal of Intelligent Systems for Community Development (JISCoDe)
Rakhmat Purnomo
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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

Found 3 Documents
Search

 1 
Analisis Luas Lahan Kelapa Sawit Menggunakan Metode FuzzyAHP Terhadap Resiko Pelaku Usaha Paduloh; Rakhmat Purnomo; Murwan Widyantoro
Jurnal Jaring SainTek Vol. 1 No. 1 (2019): April 2019
Publisher : Fakultas Teknik, Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jaring-saintek.v1i1.182

Abstract

The aim of this study was to identify the risk of oil palm land ownership and the relationship between oil palm land area and farmers' income, the level of welfare of farmers and workers in oil palm plantations. This research can be a reference for businesses who will invest in oil palm plantations. The research was conducted by giving questionnaires to experts, the experts in question were oil palm industry players and were able to make decisions. The results of the questionnaire are processed using Fuzzy AHP to determine the degree of importance of each expert. The results obtained are the biggest risk at the actor level is the farmer, at the level of the highest risk criteria is the increase in the amount of supply while at the alternative level is the risk of quality.
Implementasi Big Data Analytical Untuk Perguruan Tinggi Menggunakan Machine Learning Rakhmat Purnomo; Priatna, Wowon Priatna; Tri Dharma Putra
Journal of Informatic and Information Security Vol. 2 No. 1 (2021): Juni 2021
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/v6cdp268

Abstract

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance.
Detection and Classification of Indonesian Batik Motifs Using YOLOv11 Mochamad Galih Pradipta; Herlawati Herlawati; Rakhmat Purnomo
Journal of Intelligent Systems for Community Development Vol. 1 No. 2 (2026): May 2026
Publisher : Indonesian Artificial Neural Network Society

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The complexity of asymmetric visual patterns and overlapping ornaments in batik motifs presents challenges for automatic identification in modern applications. The limitations of conventional classification methods have created a need for computer vision techniques capable of accurate object localization. This study aimed to detect and classify various Indonesian batik motifs in real time using the YOLOv11 model. The research methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM), consisting of six structured phases, from business understanding to system deployment. The study focused on major variations of batik motifs originating from different regions of Indonesia, with model performance evaluated using the mean Average Precision (mAP) metric. The results demonstrated that: (1) the YOLOv11 model achieved an mAP50 of 77%; (2) data augmentation effectively reduced the risk of model overfitting; and (3) the trained detection model was successfully integrated into a web-based platform, enabling users to perform real-time image testing.
Co-Authors Herlawati Herlawati Mochamad Galih Pradipta Paduloh Paduloh Priatna, Wowon Priatna Tri Dharma Putra Tri Dharma Putra Widyantoro, Murwan