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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sistem Cerdas Bulletin of Computer Science Research
Rahmawita M, Medyantiwi
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression Idriani R, Nova; Permana, Inggih; Salisah, Febi Nur; Megawati, Megawati; Rahmawita M, Medyantiwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7694

Abstract

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression Idriani R, Nova; Permana, Inggih; Salisah, Febi Nur; Megawati, Megawati; Rahmawita M, Medyantiwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7694

Abstract

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Alphabet Learning Media Using Image Classification for Speech-Impaired Students in Special Education Schools Novita, Rice; Rahmawita M, Medyantiwi; Safiq Tama, Naufal
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.478

Abstract

This research aims to develop an image classification-based learning medium for teaching the alphabet to students with speech impairments in special schools (SLB). The technique used in image classification is Random Forest with a dataset of 5,400 images, including 1 default image and 26 alphabet classes. The software development follows the waterfall model, including requirements analysis, system design, implementation, and testing, with system design utilizing object-oriented analysis and design (OOAD). Evaluation metrics, including accuracy (100.00%), precision (1.00), recall (1.00), and F1 score (1.00), indicate the model’s outstanding performance. The system was tested on 10 students with speech impairments, showing an average improvement in ability from 5.9 in the pretest to 12.8 in the posttest, demonstrating consistent gains among participants. This image classification-based learning medium is expected to support the learning process for students with speech impairments in SLB effectively
Analisa Pengaruh Technology Readiness Index Pada Penggunaan E-Wallet Hardianto, Arvin; Fronita, Mona; Rahmawita M, Medyantiwi; Marsal, Arif
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.632

Abstract

Current technological developments have also driven transformation in payment systems, one of which is through the implementation of a non-cash lifestyle digital wallet, such as DANA, an application used as a medium for storing and transacting electronic money via smartphone devices. Although the number of DANA users and transaction values ??have shown a significant increase, the adoption of technology does not always have a positive impact. There are still problems, one of which is that some users report delays in incoming balances. The success of implementing technological innovation is influenced by various factors, one of which can be measured using the Technology Readiness Index (TRI) instrument developed by Parasuraman. This study aims to identify factors that inhibit the use of DANA Digital Wallets in Indonesia. The results of the analysis using SPSS version 26 software show that the TRI value is at 3.00994 which is included in the moderate category, proving that the resulting influence is standard, with the highest value on the Discomfort variable (0.77892) and the lowest value on the Insecurity variable (0.72978).
Co-Authors Arif Marsal Febi Nur Salisah, Febi Nur Hardianto, Arvin Idriani R, Nova Inggih Permana Megawati - Mona Fronita, Mona Rice Novita Safiq Tama, Naufal