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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Riset Informatika
Christina Juliane
STMIK LIKMI
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Engineering Materials Science & Nanotechnology Physics

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Application of Data Mining Using the K-Means Clustering Algorithm for Opening Industrial Classes in Vocational High Schools Aan Rosydiana; Dian Sediana; Christina Juliane
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.19172

Abstract

Vocational High School has a goal to enter the world of work, meaning that it must have a skill program to be relevant to the industrial world. However, adapting to the industrial world is difficult, one of the things that is happening between industries is increasing. Various efforts continue to be made, among others, by establishing an industrial class, the formation of an industrial class is expected to produce students who have competencies in accordance with the standards required by the collaborating industries. The formation of an industrial class can be done by applying data mining methods, in order to form the right industrial class and in accordance with predetermined criteria. This study aims to classify new student registration data at State Vocational Schools at the Regional Education Office XIII Branch of West Java Province in 2022 and the results of the grouping are used to form industrial classes. The clustering process is carried out using the K-Means algorithm and cluster analysis is carried out with the help of RapidMiner software. The results showed that the data clustering was formed into 4 clusters. The cluster that has the highest number is cluster 1 and the cluster that has the lowest number is cluster 0. There are variables used for data grouping, including school variables and expertise programs, from these variables it is obtained that the schools selected by students are based on the highest order and have expertise programs contained in their clusters, which need to be considered when opening industrial classes.
Pemanfaatan Manajemen Pengetahuan untuk Membantu Persiapan Data pada Proses Data Mining Yusuf Bayu Wicaksono; Christina Juliane
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 1 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i1.2424

Abstract

The data mining process always involves a data preparation stage. Based on the experience of IBM data mining practitioners, 40-70% of data mining project time is spent on data preparation. This is because not everyone knows what the content of the available data is, so it will take time just to understand the data itself. The research method used adopts an information systems research framework, by comparing the knowledge base (data mining) with environmental facts (the duration of data preparation). Design/research is made using a knowledge management approach designed for data. Two qualitative and quantitative tables containing data related knowledge are used as an explicit form of data. With this knowledge the data preparation process can be shortened because miners are not mining data from zero knowledge.
Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction Yusuf Bayu Wicaksono; Christina Juliane
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4492

Abstract

By 2022 it is estimated that 29 billion devices have been connected to the internet so that cybercrime will become a major threat. One of the most common forms of cybercrime is infection with malicious software (malware) designed to harm end users. Microsoft has the highest number of vulnerabilities among software companies, with the Microsoft operating system (Windows) contributing to the largest vulnerabilities at 68.85%. Malware infection research is mostly done when malware has infected a user's device. This study uses the opposite approach, which is to predict the potential for malware infection on the user's device before the infection occurs. Similar studies still use single algorithms, while this study uses ensemble algorithms that are more resistant to bias-variance trade-off. This study builds models from data on computer features that affect the possibility of malware infection on computer devices with Microsoft Windows operating system using ensemble algoritms, such as Bagging Classifier, Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting Machine, Category Boosting, and Stacking Classifier. The best model is Stacking Classifier, which is a combination of Light Gradient Boosting Machine and Category Boosting Classifier, with training and test results of 0.70665 and 0.64694. Important features have also been identified as a reference for taking policies to protect user devices from malware infections.
THE EFFECT OF AMOUNT OF DATA ON RESULTS OF ACCURACY VALUE OF C4.5 ALGORITHM ON STUDENT ACHIEVEMENT INDEX DATA Anton Sunardi; Sienny Rusli; Christina Juliane
Jurnal Riset Informatika Vol. 4 No. 2 (2022): March 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i2.157

Abstract

Of the many academic data, data in the form of an achievement index needs to be used in-depth so that it does not become a display of numbers and information only. This achievement index evaluation data reflects the educational process students and teaching staff carries out in an educational process. This study aims to measure the accuracy of data mining processing based on differences in test data by analyzing the C4.5 algorithm using RapidMiner as a data processing tool and determining the decisions students can make and academic institutions in developing study strategies and educational curricula to be maximized. The data processing is carried out by classifying the student achievement index data at a private university using data analysis test equipment. The data source comes from kaggle.com, which consists of 1687 data that have been processed and processed. The conclusion from the results of this study is that the amount of data turns out to have a significant influence on the accuracy value of the C4.5 algorithm, where an accuracy rate of 91.69% is obtained from the test results of 1687 data with four main attributes, namely IPK1, IPK2, IPK3, IPK4 and correctly or not as a label.
Analysis of Digital Transformation Readiness in State-Owned Construction Enterprises Based on the INDI 4.0 Measurement Framework Ernawan; Christina Juliane
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.13138

Abstract

Digital transformation is a strategic necessity for state-owned construction companies in the face of global competition and the demands of operational efficiency. This study aims to evaluate the readiness of digital transformation of PT. PQR uses the Indonesia Industry 4.0 Readiness Index (INDI 4.0) framework which includes five pillars: Management and Organization, People and Culture, Products and Services, Technology, and Business Operations. The research method used a descriptive quantitative approach with an action research design through questionnaires of 53 respondents (executives, managers, and specialists) and in-depth interviews, analyzed using descriptive statistics and thematic analysis. The results of the study showed an average score of 3.51 from a target of 4.00, placing PT. PQR in the category of ready readiness towards full implementation. The pillar scores show: Management and Organization (3.92), People and Culture (3.75), Products and Services (3.00), Technology (3.83), and Business Operations (2.89). The biggest gap is in the Business Operations pillar with partial automation and suboptimal intelligent maintenance systems. Key challenges include limited human digital competencies, organizational cultural resistance, budget constraints, and weak external collaboration. The research recommends strengthening digital governance, increasing human resource capacity through structured programs, investment in enabler technologies (cloud, AI, IoT), and ecosystem collaboration. Academically, the research contributes to the literature on the implementation of INDI 4.0 in the SOE construction sector; Practically, it is a reference for a sustainable digital transformation strategy.
Co-Authors Aan Rosydiana Anton Sunardi Dian Sediana Ernawan Lukito Angga Prasakti Sienny Rusli Yusuf Bayu Wicaksono Yusuf Bayu Wicaksono