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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic IJoICT (International Journal on Information and Communication Technology) Informatics for Educators and Professional : Journal of Informatics Jurnal Statistika dan Matematika (Statmat) Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat International Journal of Information Technology and Computer Science Applications (IJITCSA) AMMA : Jurnal Pengabdian Masyarakat
Khairunnisa Fadhilla Ramdhania
Universitas Bhayangkara Jakarta Raya
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Other

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Comparative Analysis of K-Means and Hierarchical Clustering for Regional Welfare Disparity Identification in West Java Province Muhamad Dani Yusuf; Tb Ai Munandar; Khairunnisa Fadhilla Ramdhania
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.213

Abstract

This study aims to cluster regencies/cities in West Java Province based on public welfare indicators using the K-Means Clustering and Hierarchical Clustering methods. The data used includes health, economic, population density, and average length of schooling indicators in 2023. Cluster quality evaluation was performed using the silhouette score. The results show that K-Means Clustering with five clusters yields the highest silhouette score of 0.219. For comparison, Hierarchical Clustering with the Ward Linkage method and eight clusters was chosen, having a silhouette score of 0.202, which is the largest among other Hierarchical Clustering methods. The identification of each cluster's characteristics in K-Means reveals areas with multidimensional challenges (Cluster 1), industrial areas with unemployment issues (Cluster 2), areas with high stunting prevalence despite good access to basic facilities (Cluster 3), densely populated urban areas with good welfare but high unemployment (Cluster 4), and areas with very high health complaints and low welfare (Cluster 5). K-Means clusters (except Cluster 4) tend to have a low average length of schooling, below 12 years. Consistency in cluster patterns was found between K-Means and Ward Linkage, especially in advanced urban areas and areas with multidimensional welfare challenges in southern West Java. These findings are expected to serve as a reference for the government and policymakers in formulating more targeted and effective development strategies.
Team-Teaching-Based Course Scheduling Using Genetic Algorithm Rafika Sari; Khairunnisa Fadhilla Ramdhania; Rakhmat Purnomo
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 10 No. 1 (2022): March 2022
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v10i1.4416

Abstract

Scheduling problems occur in various fields, e.g., education, health institutions, transportation, sports, etc. Main scheduling problems in education is course scheduling which creates schedules for students and lecturers. In this study, course scheduling allocates the lecturers in the form of team teaching and courses into the class and a certain time to even out the workload of lecturers per day and a group of students per day in one week without breaking the constraint. The method used in this research is a genetic algorithm where Universitas Bhayangkara Jakarta Raya as the case study. The genetic algorithm process is done by getting several candidate solutions that undergo a process of selection, mutation, and crossing over to produce chromosomes with the best fitness values. The objective function in this research is minimizing the average variance of the workload of lecturers and students per day in one week. The parameters used in genetic algorithm are determined based on the Design of Experiments mechanism (DOE). The optimal parameter values ​​used to run the program are as: population size = 50, with probability of crossing over = 0.4 and probability of mutation = 0.008. The results of scheduling with genetic algorithms show that the value of the workload variance lecturers and students by considering team teaching is better than actual scheduling. The application of the genetic algorithm method results in a decrease in the standard value deviation of the workload of lecturers and a group of students in one week is 0.114 (3.68%) and 3.11 (55.7%). In addition, course scheduling uses a genetic algorithm with consider team teaching better than genetic algorithm without considering team teaching because there is no class schedule that clashes in real conditions.
Decision Support System Design for Informatics Student Final Projects Using C4.5 Algorithm Rafika Sari; Hasan Fatoni; Khairunnisa Fadhilla Ramdhania
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5954

Abstract

Academic consultation activities between students and academic supervisors are necessary to help students carry out academic activities. Based on the transcript of grades obtained, many students do not choose the appropriate final project/thesis specialization fields based on their academic abilities, resulting in a lot of inconsistencies between the course grades and the final project specialization fields. The purpose of this research is to minimize the subjectivity aspect of students in choosing their final project academic supervisors and minimize the inconsistencies between the course grades and the final project specialization fields. The method used in this research is classification data mining using the Decision Tree and C4.5 Algorithm methods, with the attributes involved being courses, course grades, and specialization courses. The C4.5 Decision Tree algorithm is used to transform data (tables) into a tree model and then convert the tree model into rules. The implementation of the C4.5 Decision Tree algorithm in the specialization field decision support system has been successfully carried out, with an accuracy rate of 70% from the total calculation data. The data used in this research is a sample data from several senior students in the Informatics program at Ubhara-Jaya. The results of the research decision support system can be used as a good recommendation for the Informatics program and senior students to direct their final project research. It is expected that further research will use more sample data so that the accuracy rate will be better and can be implemented in website or mobile-based applications.
Fuzzy Use Case Points as a Basis for Effort Estimation Rakhmi Khalida; Tubagus Maulana Kusuma; Khairunnisa Fadhilla Ramdhania
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.6941

Abstract

Many software development projects encounter problems related to over- or under-estimation of effort. Accurate effort estimation is crucial for successful project management, but it can be challenging when resources are limited, and little is known about the project. The commonly used method for effort estimation is Use Case Points (UCP), which is mainly used for application-based objects and takes use cases as input. However, UCP has weaknesses, particularly in the high variation of weight factor values for Unadjusted Use Case Weight (UUCW). To address this problem, Fuzzy Use Case Points (FUCP), which is a combination of fuzzy logic and use case points, can be used. By applying fuzzy logic to the UUCW category, FUCP derives new weight factor values for UUCW. The implementation of FUCP to calculate effort estimation in ten government-based projects in this research has shown that FUCP yields the closest value to the actual effort required. It has also been demonstrated that FUCP outperforms UCP in terms of accuracy, with an improvement of 6.51%.
Co-Authors Adi Muhajirin Ajif Yunizar Pratama Yusuf Almajid, Nafis Hasan Fatoni Illa Nur’aini Kardinah Indriana Meutia Lisa Adhani Lisa Adhani Mayadi Mayadi Mayadi Muhamad Dani Yusuf Muhammad Ganang Martyana Pilipus Neri Agustima Prima Dina Atika Purnomo, Rakhmat RAFIKA SARI Rakhmi Khalida Rakhmi Khalida Reghita Suryani Putri Renilda Filiandini Rizki Surya Pratama, Daffa SITI SETIAWATI Siti Setiawati Syifa Rahmadhani Tb Ai Munandar Tb Ai Munandar, Tb Ai Tubagus Maulana Kusuma