Article Per Year (5 Year)
p-Index From 2021 - 2026
5.524
P-Index
This Author published in this journals
All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TEKNIK INFORMATIKA Jurnal TIMES Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Informasi MURA Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Applied Science, Engineering, Technology, and Education U-NET Jurnal Teknik Informatika Jurnal Teknik Informatika (JUTIF) JPM: JURNAL PENGABDIAN MASYARAKAT Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Minfo Polgan (JMP) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) PUBLIDIMAS CSRID SISFOTENIKA
Rika Rosnelly, Rika
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 41 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 41 Documents
Search

 1   2   3   4   5 
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification Gea, Muhammad Nasri; Wanayumini, Wanayumini; Rosnelly, Rika
JURNAL TEKNIK INFORMATIKA Vol. 18 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.v18i1.44147

Abstract

This study examines the impact of hyperparameter tuning on the performance of Convolutional Neural Networks (CNN) in classifying brain tumors using MRI images. The dataset, sourced from Kaggle, underwent preprocessing techniques such as normalization, augmentation, and resizing to enhance consistency and diversity. The study evaluates five hyperparameter configurations, analyzing their effects on classification accuracy, precision, recall, and F1-score. The optimal configuration (batch size: 16, epochs: 10, learning rate: 0.001) achieved an accuracy of 86%, precision of 81%, recall of 85%, and an F1-score of 0.83. Other configurations showed trade-offs, where larger batch sizes increased recall but reduced precision. These findings emphasize the importance of careful hyperparameter tuning to optimize medical imaging classification performance.
A Comparative Analysis on the Evaluation of KNN and SVM Algorithms in the Classification of Diabetes Limas, Agus Fahmi; Rosnelly, Rika; Hartono, Hartono; Nursie, Aly
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.44269

Abstract

Purpose: Diabetes has received a great deal of attention in medical research because of its profound effect on human health. Many factors cause this disease in the human body. Can be from food or drink that is often consumed by the human body. Diabetes cannot be cured and can only be controlled.Methods: In this study, using 2 data mining techniques namely Support Vector Machine and K-Nearest Neighbor were applied to predict diabetes. In this study, 768 diabetes data were used as trial data, consisting of training data that had been pre-processed data and 400 data cleaning data, 278 data testing data, and 50 diabetes data samples used as samples in the calculation.Result: The performance of each algorithm is analyzed differently, the results of each best algorithm will be analyzed to determine which algorithm can provide better results for predicting diabetes. The results obtained in this study get a value of 0 where the predicted value of the target class for new data is the negative class (Suffer).Novelty: This study compares the SVM and K-NN methods for diabetes classification. So, successfully implemented for data on the classification target
Decision Support System Application Evaluation of Transformer Isolation Condition with Simple Additive Weighting (SAW) Method Rosnelly, Rika; Gunawan, Teddy; Paramitha, Cindy; Sadikin, Muhammad
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 1 No 1 (2020): APRIL 2020
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/abdimastek.v1i1.914

Abstract

The use of computer technology has spread among workers and companies. Therefore researchers recommend a system that can overcome the problem of assessing the conditions of transformer insulation at PT. Electricity System Cemerlang uses a computer system. The system that researchers use is a decision support system. Decision Support System or often called Decision Support System (DSS) is a model-based system that consists of procedures in data processing and consideration to assist managers in making decisions. In order to succeed in achieving its objectives, the system must be simple, robust, easy to control, easily adaptable to important things and easy to communicate with. Implicitly also means that this system must be computer-based and used as an addition to someone's problem solving capabilities. But to be able to use a decision support system properly, a method or an appropriate method is needed to get the right results. Therefore researchers recommend the method of Simple Additive Weighting (SAW). Simple Additive Weighting (SAW) method is often also known as the weighted sum method. The basic concept of the method of Simple Additive Weighting (SAW) is to find a weighted sum of performance ratings on each alternative on all attributes.
Rancang Bangun dan Implementasi Sistem Antrian Customer Pada PT. Infomedia Solusi Humanika Rosnelly, Rika; Sari, Dian Maya; Paramitha, Cindy
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 2 No 1 (2021): VOLUME 2. NO 1. APRIL 2021
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/abdimastek.v2i1.1109

Abstract

The service process carried out at the customer care center is currently still using a manual service system. Therefore, the researcher tries to implement a customer queuing system at the care center to simplify the service process. In this final result the researcher will use the ATMega 16 microcontroller minimum system module for the design and manufacture of a minimum system to simplify the customer queuing service process at the care center. Microcontroller programming is widely used for service system display functions on seven segment displays as well as print out queue no. The process begins with the visitor pressing the push button which then the system will issue a print out of the visitor queue no. If the customer care servant presses the push button in the system used by the customer service, it is used for seven segment displays. Then the data from the push button results will be sent by the microcontroller to print out the queue no on the printer, and then enter the data into the Personal Computer. After that the waiter at the customer care presses the button then the data is sent by the microcontroller to be output on the seven segment display.
Pendekatan Explainable Deep Learning pada Klasifikasi Citra Sampah Menggunakan MobileNetV2 dan Teknik Grad-CAM serta SHAP Al Adib, Muhammad; Siregar, Andri Armaginda; Raj, Bill; Hasibuan, Rahmat Humala Putra; Nasution, Jalaluddin; Parapat, Andreas Jorghy; Rosnelly, Rika
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.739

Abstract

The increasing volume of waste resulting from urbanization and population growth poses significant challenges to waste management systems, particularly in the sorting stage. Deep learning approaches, especially Convolutional Neural Networks (CNNs), have been widely employed for waste image classification due to their ability to automatically extract complex visual features. However, a major limitation of these approaches lies in their limited interpretability, which may hinder user trust and real-world adoption. This study proposes an Explainable Deep Learning Framework for organic and inorganic waste image classification by integrating the MobileNetV2 architecture with Explainable Artificial Intelligence (XAI) methods, namely Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). MobileNetV2 is utilized as a feature extractor due to its computational efficiency and suitability for deployment on resource-constrained devices. The dataset used in this study consists of a combination of a public benchmark dataset and field-acquired waste images, processed using a transfer learning approach. Model performance is evaluated using accuracy, precision, recall, and f1-score metrics. Experimental results demonstrate that the proposed model achieves a validation accuracy of 90.25% with balanced performance across both classes. Furthermore, interpretability analysis using Grad-CAM and SHAP reveals that the model focuses on semantically relevant visual features and provides explainable feature contributions. These findings confirm that integrating lightweight CNN architectures with XAI techniques can produce waste classification systems that are accurate, transparent, and accountable.
Klasifikasi Tingkat Kedisiplinan Siswa Menggunakan Algoritma Machine Learning: Decision Tree, KNN, dan Naive Bayes Hutabalian, Damri Mulia; Hutabarat, Pebruarianto; Mhd Prasetyo; Irnanda, Mhd Agung; Dalimunthe, Naufal Dhiya Putra; Rosnelly, Rika
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.788

Abstract

Discipline is a crucial factor influencing the effectiveness of learning processes and the quality of graduates in vocational education. SMK Swasta RK Bintang Timur Pematangsiantar maintains records of student attendance and academic performance that have the potential to be analyzed as indicators of student discipline. However, these data have not been optimally utilized as a basis for decision-making to provide early detection of students who are at risk of declining discipline. This research aims to develop a predictive model of student discipline by identifying patterns of attendance and academic achievement using a data mining approach.The study employs the CRISP-DM framework, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset includes daily attendance records, semester academic grades, and documented disciplinary behavior used as class labels. Several classification algorithms—Decision Tree (C4.5), KNN, Naive Bayes were implemented to compare model performance. Model evaluation was conducted using confusion matrix, accuracy, precision, recall, and F1-score, with k fold cross-validation.The results show that attendance and academic performance patterns significantly influence the prediction of student discipline levels. The Random Forest algorithm produced the highest performance results, with consistent F1-scores for at-risk student categories. The most influential features include attendance percentage, the number of unexcused absences, and average academic scores. The resulting model is implemented as a decision support prototype dashboard to assist counseling teachers and homeroom teachers in monitoring potential disciplinary violations and planning early intervention. This research is expected to support the development of data-driven discipline monitoring systems in schools and provide practical benefit in preventive actions to improve student behavior quality at SMK Swasta RK Bintang Timur Pematangsiantar.
Optimalisasi Pembelajaran Coding Berbasis Kecerdasan Buatan untuk Meningkatkan Literasi Digital Siswa Wahyuni, Linda; Rizal, Chairul; Rosnelly, Rika; Sari, Rita Novi; Hardianto, Hardianto; Harahap, Charles Bronson
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i3.2785

Abstract

This community service program aims to enhance the professional capacity of teachers through mastery of modern educational technologies. The implementation of this program is based on several issues identified within the partner institution, including teachers’ limited understanding of artificial intelligence, their low ability to integrate coding into learning activities, and the suboptimal use of digital tools and media in the classroom. These challenges contribute to students’ low digital literacy and the insufficient application of project-based learning that aligns with current technological developments. The training program was designed to strengthen teachers’ competencies in implementing AI-based coding instruction through participatory approaches and hands-on practice. The learning modules were developed contextually to meet the specific needs of the partner school, enabling teachers to adapt them easily into their teaching practices. The results of the program indicate significant improvements in teachers’ understanding of fundamental AI concepts, their ability to integrate coding into the learning process, and their capacity to apply digital technologies ethically and effectively. Enhanced student engagement was also observed, as learners became more enthusiastic and active in participating in project-based learning activities involving AI applications. Although the program faced several limitations, such as inadequate technological equipment and limited implementation time, it has established a strong foundation for the development of more structured and sustainable follow-up activities. Overall, the outcomes demonstrate that strengthening teachers’ competencies in artificial intelligence plays a crucial role in fostering an innovative digital learning ecosystem aligned with the demands of 21st-century education.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SUPPLIER MENGGUNAKAN METODE SAW PADA APOTEK HALOMOAN Lubis, Dela Aventi Oktavia Br; Rosnelly, Rika
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8358

Abstract

Proses pemilihan supplier pada banyak instansi farmasi sering dilakukan secara manual sehingga keputusan menjadi subjektif, memakan waktu, dan berisiko menimbulkan masalah seperti keterlambatan pengiriman atau ketidaksesuaian kualitas barang. Kondisi ini menunjukkan perlunya sistem evaluasi yang lebih objektif dan terstruktur. Penelitian ini menerapkan metode Simple Additive Weighting (SAW) dalam Sistem Pendukung Keputusan (SPK) untuk menilai dan menentukan supplier terbaik berdasarkan beberapa kriteria terukur. Enam kriteria digunakan dalam penelitian ini, yaitu waktu pengiriman, harga, kualitas produk, ketersediaan stok, tempo pembayaran, dan layanan keluhan. Sistem dikembangkan menggunakan PHP dan MySQL, kemudian diuji secara fungsional untuk memastikan akurasi perhitungan dan performa sistem. Hasil penelitian menunjukkan bahwa metode SAW mampu menghasilkan rekomendasi supplier secara objektif, dengan salah satu alternatif memperoleh nilai tertinggi sebesar 0,913. Temuan ini membuktikan bahwa penerapan SAW efektif dalam meningkatkan kualitas pengambilan keputusan, mempercepat proses evaluasi, serta memastikan keputusan yang dihasilkan dapat dipertanggungjawabkan secara transparan.
EKSPLORASI PADA PEMETAAN KLASIFIKASI RADIOGRAF TORAKS PENYAKIT PARU-PARU MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Zai, Andreas Rezeki; Suhardi, Bambang; Nowo, Surya Tri; Rosnelly, Rika; Setiawan, Adil
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 2 (2025): Desember 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i2.7296

Abstract

ABSTRAKAbstrak— Radiograf toraks (CXR) merupakan alat penting dalam diagnosis penyakit paru, namun interpretasinya memerlukan keahlian khusus dan berpotensi menimbulkan bias. Penelitian ini bertujuan mengeksplorasi kinerja lima arsitektur Convolutional Neural Network (CNN) berbasis transfer learning, yaitu VGG16, ResNet50, EfficientNetB0, DenseNet121, dan MobileNetV2, dalam mengklasifikasikan lima kelas penyakit paru-paru: bacterial pneumonia, COVID-19, tuberculosis, viral pneumonia, dan normal. Dataset yang digunakan dilengkapi dengan preprocessing CLAHE-RGB, augmentasi data, serta penanganan ketidakseimbangan kelas menggunakan class weighting. Evaluasi dilakukan dengan empat skenario epoch (5, 10, 15, dan 30), serta menggunakan metrik akurasi, precision, recall, F1-score, dan confusion matrix. Hasil menunjukkan bahwa model VGG16 pada epoch ke-15 memberikan performa terbaik dengan akurasi 93,95% dan F1-score 0,94. Penelitian ini menunjukkan bahwa kombinasi preprocessing yang tepat dan arsitektur CNN yang sesuai mampu meningkatkan akurasi klasifikasi penyakit paru secara signifikan. Kata Kunci— Convolutional Neural Network, Citra CXR, VGG16, Transfer Learning, CLAHE, Penyakit Paru. ABSTRACTAbstract— Chest radiography (CXR) is a vital tool in diagnosing pulmonary diseases, yet its interpretation often requires expert analysis and may involve subjectivity. This study explores the performance of five Convolutional Neural Network (CNN) architectures: VGG16, ResNet50, EfficientNetB0, DenseNet121, and MobileNetV2 for classifying five categories of lung conditions: bacterial pneumonia, COVID-19, tuberculosis, viral pneumonia, and normal. The dataset underwent preprocessing using CLAHE-RGB enhancement, data augmentation, and class balancing with class weighting. Each model was trained using four epoch scenarios (5, 10, 15, and 30) and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. The results indicate that VGG16 with 15 epochs achieved the best performance, reaching 93.95% accuracy and 0.94 F1-score. This study demonstrates that combining appropriate preprocessing techniques with suitable CNN architectures significantly enhances classification performance for pulmonary disease detection. Keywords— Convolutional Neural Network, CXR images, VGG16, Transfer Learning, CLAHE, Lung Disease.
Optimized KNN Performance with PCA and K-Fold Cross-Validation for Colorectal Cancer Survival Prediction Manza, Yuke; Rosnelly, Rika; Furqan, Mhd; Riza, Bob Subhan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5422

Abstract

Colorectal cancer remains a leading cause of global mortality, necessitating effective predictive tools for patient survival. While Machine Learning algorithms like K-Nearest Neighbors (KNN) utilize patient data for prediction, standard KNN implementations often suffer from the curse of dimensionality and overfitting, leading to unreliable performance on complex medical datasets. This study aims to evaluate and optimize the performance of the KNN algorithm by integrating Principal Component Analysis (PCA) for dimensionality reduction and K-Fold Cross-Validation (KFCV) to enhance model stability. The research utilized a quantitative approach on a global colorectal cancer dataset, processing demographic and clinical features through a rigorous pipeline of imputation, encoding, and normalization. Three model configurations were systematically compared: Standard KNN, KNN combined with PCA, and an optimized KNN model utilizing both PCA and KFCV across various neighbor values. The results demonstrate a distinct trade-off between predictive sensitivity and model stability. While the Standard KNN and PCA-enhanced models achieved higher recall, indicating a strong ability to identify survivors in a single data split, the fully optimized KNN+PCA+KFCV model provided the most stable and generalized accuracy with minimal deviation. These findings indicate that while PCA effectively reduces computational complexity without information loss, the integration of cross-validation is crucial for obtaining an honest assessment of model performance. This research contributes to clinical informatics by highlighting the necessity of prioritization between high sensitivity and generalization stability when developing survival prediction models for complex, inseparable medical data.
Co-Authors -, Mubarak Agung Rizky, Muhammad Dipo Agus Fahmi Limas Ptr Aji, Eko Setyo Budi Putra Akbar, Muhammad Barkah Al Adib, Muhammad Alkhairi, Putrama Ammar Yasir Nasution Amrullah Amrullah Ashari, Annisa Bambang Suhardi Batubara, Ela Roza Bob Subhan Riza, Bob Subhan Daifiria Dalimunthe, Naufal Dhiya Putra Dian Maya Sari ElisaBeth S, Noprita ElisaBeth S Fahriyani, Tasya Finis Hermanto Laia Gea, Muhammad Nasri Habib Satria Habib, Nurhayati Harahap, Charles Bronson Harahap, Gilang Harahap, Sarwedi HARDIANTO - Hartono Hartono Haryanto S., Edy Victor Hasibuan, Rahmat Humala Putra Heru Satria Tambunan, Heru Satria Hutabalian, Damri Mulia Hutabarat, Pebruarianto Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Indra Kelana Jaya Irnanda, Mhd Agung Junaidi Junaidi Kelvin Leonardi Kohsasih Khairi, Ibni Krismona, Lumi Lili Tanti, Lili Limas, Agus Fahmi Lubis, Dela Aventi Oktavia Br Manza, Yuke Margolang, Khairul Fadhli MARIA BINTANG Mega Christin Morys Lase Mhd Furqan Mhd Prasetyo Mochammad Imron Awalludin Mubarak Mubarak Muhammad Sadikin Mulkan Azhari Nasution, Jalaluddin Nasution, M. Irfan Aldy Naswar, Alvinur Ndruru, Agus F.S. Nowo, Surya Tri Nur Hayati Nursie, Aly Paramitha, Cindy Parapat, Andreas Jorghy Putra, Reza Ananda Rahma, Intan Dwi Rahmadi, Diky Raj, Bill Ramadhan, Muhammad Yakub Rambe, Lima Hartima Rambe, Lima Hartimar Rizal, Chairul Rofiqoh Dewi Roslina Roslina, Roslina Sagala, Tamado Simon Sari, Rita Novi Sari, Rita Novita Setiawan, Adil Simanullang, Maradona Jonas Siregar, Andri Armaginda Siregar, Kiki Putri Ani Situmorang, Zakaria sri lestari rahayu Subhan, Zhafira Nur Sugeng Riyadi Suhada WD, Muhammad Sukriatna Sumantri, Ekoliyono Wahyu Suyono Suyono Syahrian, Achmad Tambunan, Fazli Nugraha Tarigan, Dede Ardian Teddy Gunawan, Teddy Teddy Surya Gunawan Tri Nowo, Suryandika Veronica Wijaya, Veronica Wahyudi, Diky Wahyuni, Linda Wanayaumini, W Wanayumini Zai, Andreas Zai, Andreas Rezeki Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.