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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TEKNIK INFORMATIKA Jurnal TIMES Jurnal Teknologi Informasi dan Ilmu Komputer 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 Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) PUBLIDIMAS CSRID SISFOTENIKA
Rika Rosnelly, Rika
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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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.
Co-Authors -, Mubarak Agung Rizky, Muhammad Dipo Agus Fahmi Limas Ptr Aji, Eko Setyo Budi Putra Akbar, Muhammad Barkah Alkhairi, Putrama Amrullah Amrullah Ashari, Annisa Bambang Suhardi Batubara, Ela Roza Bob Subhan Riza, Bob Subhan Daifiria 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 Heru Satria Tambunan, Heru Satria Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Indra Kelana Jaya Junaidi Junaidi Kelvin Leonardi Kohsasih Khairi, Ibni Krismona, Lumi Limas, Agus Fahmi Manza, Yuke Margolang, Khairul Fadhli MARIA BINTANG Mega Christin Morys Lase Mochammad Imron Awalludin Muhammad Sadikin Mulkan Azhari Nasution, Ammar Yasir Nasution, M. Irfan Aldy Naswar, Alvinur Ndruru, Agus F.S. Nur Hayati Nursie, Aly Paramitha, Cindy Putra, Reza Ananda Rahma, Intan Dwi Rahmadi, Diky Ramadhan, Muhammad Yakub Rambe, Lima Hartima Rambe, Lima Hartimar Rofiqoh Dewi Roslina Roslina, Roslina Sagala, Tamado Simon Sari, Rita Novita Setiawan, Adil Simanullang, Maradona Jonas 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 Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.