Article Per Year (5 Year)
p-Index From 2021 - 2026
1.503
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Seminar Nasional Informatika (SEMNASIF) Jurnal Eksplora Informatika JURTEKSI CCIT (Creative Communication and Innovative Technology) Journal IT (INFORMATIC TECHNIQUE) JOURNAL TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi PUBLIDIMAS
Rika Rosnelly
Unknown Affiliation
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Penerapan Data Mining dengan Metode K-Means untuk Menentukan Penilaian Kinerja Pengasuh Anak pada Panti Asuhan Eben Heazer Pinta, Elysa; Rika Rosnelly
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp467-477

Abstract

An orphanage is a social institution whose job is to provide care and guidance for children who do not have parents or come from poor families. Caregivers play a crucial role in fostering children's independence across emotional, behavioral, and moral dimensions. At Eben Heazer Orphanage, caregivers function as educators, mentors, and motivators. As educators, they instill ethical values and religious devotion; as mentors, they assist in discovering children's interests and talents; and as motivators, they encourage self-reliance. Nevertheless, the caregivers' contribution to developing children's independence remains suboptimal. Therefore, an evaluation of caregiver performance is essential to continually enhance services for the children. This evaluation not only enables management to assess the extent to which caregivers fulfill their duties but also provides a foundation for further training and development programs. In this way, the quality of care can be progressively improved to meet the children's needs. Furthermore, the application of technologies such as the K-Means algorithm supports transparency and objectivity in the assessment process, ensuring that each caregiver receives a fair evaluation based on actual performance. This initiative is expected to establish a more professional and measurable caregiving system at Eben Heazer Orphanage.
Efficiency vs. Accuracy: A Comparative Analysis of Lightweight MobileNetV2 and VGG16 for Brain Tumor MRI Classification Using Deep Feature Extraction Nasution, Raja Anan; Mhd. Furqan; Rika Rosnelly
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.45002

Abstract

Brain tumor detection using magnetic resonance imaging (MRI) is a crucial task for early diagnosis and treatment planning, requiring models that are not only accurate but also computationally efficient. This study presents a comparative analysis of two Convolutional Neural Network (CNN) architectures, MobileNetV2 and VGG16, combined with Principal Component Analysis (PCA) for deep feature dimensionality reduction. The dataset consists of 253 brain MRI images (155 tumor and 98 non-tumor), which have been preprocessed and divided into training and testing sets using an 80:20 stratification split. Experimental results show that MobileNetV2 with PCA achieves an accuracy of 86.27%, with a precision of 87.50% and a recall of 90.32% for the tumor class, demonstrating balanced performance in classifying tumor and non-tumor images. VGG16 with the same PCA configuration achieves an accuracy of 64.71%, with a recall of 100% for the tumor class but a low recall of 10% for the non-tumor class. These findings suggest that extreme dimensionality reduction affects deep feature representation differently depending on the original feature structure. The results show that MobileNetV2 provides a better balance between accuracy and feature compactness at high dimensionality reduction settings, making it more suitable for resource-constrained medical image classification scenarios.
Co-Authors Charles Bronson Harahap Daniel Ginting Daniel Ginting Doughlas Pardede Edy Victor Haryanto Sianturi Fahmi Syuhada Arif Ichsan Firmansyah LINDA WAHYUNI Meisarah Riandini Meli Handayani Mhd Rozy Pratama Mhd. Furqan Mochammad Imron Awalludin Nasution, Raja Anan Pinta, Elysa Retantyo Wardoyo Retantyo Wardoyo Sugiarto Sugiarto Utawi Handika Sari Wiwi Verina Yudhi Andrian