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All Journal Journal of Dinda : Data Science, Information Technology, and Data Analytics Journal of Intelligent Software Systems Jurnal Sistem Informasi Galuh
Nenen Isnaeni
Telkom University
Computer Science & IT

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Analisa Slope Wilayah Kebakaran Hutan menggunakan Metode Naive Bayes Lisda; Isnaeni, Nenen; Firmansyah, Muhammad Raafi'u
Jurnal Sistem Informasi Galuh Vol 2 No 2 (2024): Journal of Galuh Information Systems
Publisher : Fakultas Teknik Jurusan Sistem Informasi Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/jsig.v2i2.3934

Abstract

Forest and land fires are an economic and environmental problem that can cause serious damage. We can predict what factors cause forest fires. It is undeniable that topographical conditions affect the triggering and propagation of fires. The topographical condition itself is in the form of a slope, where fire propagation will be faster when going up the slope than going down the slope. This study aims to match whether the slope and display locations that are prone to the spread of certain fires with high fire intensity actually have a high fire potential and report the magnitude of the influence of the slope in the prediction of fire potential. One of the common approaches to classifying data is to use data mining. So in this study the researchers used the Naive Bayes Classifier as a classification method by getting the highest accuracy value of 0.99%.
BUILDING DATA WAREHOUSE FOR EMPLOYEE TRAINING MINISTRY OF LAW AND HUMAN RIGHTS Nugroho, Ari Fauzi Mukti; Kartadie, Rikie; Handayani, Latifah Nurrohmah; Isnaeni, Nenen; kholik, Moh. Abdul
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1500

Abstract

In order to improve and expand each employee's competency and knowledge, education and training are crucial for the Ministry of Law and Human Rights. They may also be utilized for employee mapping. In the past, it was necessary to gather data on things like the number of employees who attended a particular training, which training had reached its participant quota, and the number of graduates in each training. This required extensive processing and repeated cross-checking of data sources to make sure the data was accurate and legitimate before it could be compiled into a table for analysis. Information technology may be used to immediately process employee competence data and education and training results into information. Therefore, it is expected that the Nine Step method, which is part of the Kimball & Ross (2010), methodology will simplify and accelerate the process of processing training data into information presented for analysis and reporting purposes at the leadership level in each work unit. Keywords: data warehouse, OLAP, ETL, pentaho, kemenkumham, training 
Comprehensive Lakehouse Data Architecture Model for College Accreditation Nenen Isnaeni; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani; Siti Khomsah
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1759

Abstract

Accreditation is an assessment activity that determines the feasibility of study programs at a university. College accreditation data comes from various sources and includes multiple data types: semi-structured, unstructured, or structured. Over time, the volume of data will continue to grow and develop, so there is a possibility of data redundancy and a long time to collect the data needed for accreditation activities. The solution is integrating data. This research aims to design a data architecture to facilitate the management of university accreditation data using the Lakehouse data architecture model. All data types can be stored on one platform in the Lakehouse data architecture. In this research, the identification, integration, and data transformation process for university accreditation data is carried out. The data used in this research is academic data in which there are with. The study's results provide an overview of the data flow process in the Lakehouse data architecture model to help better manage university accreditation data. This architecture also supports real-time data analysis so that the accreditation process can be carried out more effectively and efficiently. Keywords: accreditation, data analysis, data architecture, data lakehouse, data warehouse
Illegal Motorcycle Parking Detection in The Car Area Isnaeni, Nenen -; Wisesa, Bradika Almandin; Lisda, Lisda; Febrianto, Dany Candra
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1948

Abstract

Illegal motorcycle parking in designated car areas at Politeknik Manufaktur Negeri Bangka Belitung (Polman Babel) disrupts campus parking management, reduces space availability, and poses safety risks. This paper proposes an automated detection system using computer vision and license plate recognition to identify motorcycles parked in car areas and notify their owners via WhatsApp and email alerts. The system integrates CCTV cameras with YOLOv11 for vehicle detection and EasyOCR for license plate recognition, coupled with a database for owner identification. Upon detection, owners receive immediate notifications to rectify the violation. Experiments in Polman Babel’s parking lot show a 94% accuracy in motorcycle detection and 88% in license plate recognition under diverse conditions. The system enhances parking enforcement efficiency, reduces manual intervention, and supports smart campus initiatives. This work offers a scalable, cost-effective solution adaptable to other institutions facing similar parking challenges.
Co-Authors Bambang Purnomosidi Dwi Putranto Bradika Almandin Wisesa Dany Candra Febrianto Firmansyah, Muhammad Raafi'u Handayani, Latifah Nurrohmah kholik, Moh. Abdul Lisda Lisda Nugroho, Ari Fauzi Mukti Rikie Kartadie Siti Khomsah, Siti Widyastuti Andriyani