Article Per Year (5 Year)
p-Index From 2021 - 2026
1.126
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika dan Teknologi Informasi
Sindhu Rakasiwi
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Penggunaan Metode Rapid Application Development (RAD) untuk Merancang Aplikasi Absensi QR Code Berbasis Website Endri Mujiono; Yani Parti Astuti; Etika Kartikadarma; Edy Mulyanto; Erlin Dolphina; Sindhu Rakasiwi
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5604

Abstract

This study aims to design and develop a QR Code-based attendance application using the Rapid Application Development (RAD) method, which is implemented at Bina Utama Kendal Vocational High School. The application was created to overcome the limitations of the manual attendance system currently in use, such as vulnerability to data loss, difficulties in maintaining accurate records, and inefficiency in attendance recapitulation. The RAD method was chosen because it emphasizes user involvement throughout the development process, ensuring that the resulting system meets user needs. The development stages included requirement planning, design, system development, and implementation. This web-based attendance application uses QR Code technology to record student attendance quickly, accurately, and in real time. Each student’s QR Code is scanned to mark their presence, which minimizes errors and prevents attendance fraud. In addition, the system includes features for managing student data, generating automatic attendance reports, and providing real-time monitoring for teachers and administrators.The system was tested using the Black Box method, which confirmed that all features function correctly and meet the requirements specified in the design phase. Furthermore, a user satisfaction survey conducted with 150 respondents (teachers, students, and staff) indicated a very high level of acceptance, with an average of 94.25% respondents strongly agreeing on the ease of use, accuracy, and benefits of this application. Overall, the study demonstrates that the QR Code-based attendance application significantly improves the efficiency, reliability, and accuracy of attendance management at Bina Utama Kendal Vocational High School.
Optimalisasi Perilaku Hidup Bersih dan Sehat Melalui Aplikasi Kesehatan di SMP Ibu Kartini Egia Rosi Subhiyakto; Sindhu Rakasiwi; Ika Novita Dewi; Junta Zeniarja; Dhita Aulia Octaviani; Abu Salam; Shelomita Fitriyani; Almira Zuhrotus Safira
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3229

Abstract

Program Perilaku Hidup Bersih dan Sehat (PHBS) merupakan upaya penting dalam mendorong penerapan pola hidup sehat guna menjaga, merawat, serta meningkatkan derajat kesehatan. Penerapan gaya hidup sehat dapat mencegah berbagai penyakit yang berpotensi muncul di masyarakat. PHBS sangat tepat dikenalkan sejak usia sekolah, karena anak-anak termasuk kelompok yang rentan terhadap gangguan kesehatan akibat berbagai faktor. Perkembangan teknologi dalam bidang pendidikan telah terbukti mampu mengubah proses interaksi dan pembelajaran di kelas menjadi lebih efektif, efisien, mudah diakses, serta mendukung pengembangan keterampilan yang dibutuhkan di era digital, baik saat ini maupun di masa mendatang. Pemanfaatan aplikasi digital sebagai hasil perkembangan teknologi telah banyak diterapkan di bidang kesehatan dan pendidikan, yang keduanya saling berkaitan dan mendukung satu sama lain. Penyampaian informasi kesehatan membutuhkan peran pendidikan, sementara proses pendidikan juga tidak dapat berjalan optimal tanpa lingkungan yang sehat. Oleh karena itu, keberadaan teknologi dalam kedua bidang tersebut menjadi sangat krusial. Berdasarkan uraian tersebut, diperlukan pemberian pengetahuan mengenai PHBS kepada para siswa. Selain pemahaman secara teori, santri juga perlu mendapatkan pendampingan dalam penerapan PHBS secara langsung, serta dukungan teknologi berupa aplikasi digital agar proses pembelajaran menjadi lebih menarik dan efektif. Sebelum penerapan aplikasi tersebut, diperlukan sosialisasi dan pelatihan bagi pengasuh pondok pesantren terkait penggunaannya. Atas dasar pertimbangan tersebut, tim berinisiatif melaksanakan kegiatan Pengabdian Kepada Masyarakat dengan tema Pendampingan PHBS pada Siswa melalui Sosialisasi Aplikasi Digital yang berlokasi di SMP Ibu Kartini. Kegiatan ini diharapkan mampu membentuk kebiasaan PHBS dalam kehidupan sehari-hari santri serta mendorong mereka untuk menularkan perilaku positif tersebut kepada lingkungan sekitarnya.
Deteksi URL phishing menggunakan kombinasi model XGBoost dan Random Forest Ilham Maulana Hadinanda; Sindhu Rakasiwi
AITI Vol 23 No 3 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i3.363-378

Abstract

Phishing merupakan salah satu bentuk dari serangan siber era digital sekarang. Serangan ini memanfaatkan teknik manipulasi untuk menipu, sehingga secara tidak sadar pengguna akan mengungkapkan data sensitif melalui web palsu yang meniru tampilan situs resmi. Deteksi terhadap URL phishing menjadi langkah penting dalam meningkatkan keamanan siber, serta mengurangi kerugian pengguna. Dalam penelitian ini digunakan pendekatan berbasis ensemble learning dengan mengombinasikan dua model, yaitu Extreme Gradient Boosting (XGBoost) dan Random Forest, dalam satu model gabungan. Kedua model selanjutnya dilatih menggunakan dataset yang terdiri dari 11430 URL dan terbagi sama rata antara kelas phishing dan legitimate. Model diuji menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa model gabungan XGBoost dan Random Forest menghasilkan akurasi tertinggi, sebesar 0,944. Hasil ini mengungguli hasil yang didapatkan jika menggunakan model tunggal XGBoost (0,942) dan Random Forest (0,928). Temuan ini memperkuat bukti bahwa pendekatan ensemble memberikan generalisasi yang lebih baik dibanding model tunggal untuk deteksi phishing.
ANALISIS PERBANDINGAN KINERJA ARSITEKTUR RESNET50 DAN EFFICIENTNETB1 MENGGUNAKAN METODE FINE-TUNING UNTUK KLASIFIKASI PNEUMONIA Daiyan Akbar Setiyadi; Sindhu Rakasiwi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7273

Abstract

Early detection of pneumonia through chest X-ray images is a crucial step in medical treatment but is often hampered by class imbalance issues in datasets, leading to biased deep learning models. This research aims to conduct a holistic performance evaluation of two modern Convolutional Neural Network (CNN) architectures, ResNet50 and EfficientNetB1, to determine the optimal model under imbalanced data conditions. The methodology employed is transfer learning with an optimized two-phase fine-tuning protocol, supported by data augmentation techniques and class_weight strategies to address data imbalance. Evaluation was performed on the public "Chest X-Ray Images (Pneumonia)" dataset using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results indicate that although ResNet50 achieved the highest total accuracy (89%) with low False Negatives (21 cases), the EfficientNetB1 model (87% accuracy) proved to be fundamentally more balanced. This superiority is demonstrated by a significant increase in the recall of the minority class (NORMAL) to 0.84, along with a 24% reduction in False Positive errors. This study concludes that a clinical trade-off exists where architecture selection must align with specific needs: ResNet50 for high-sensitivity screening, or EfficientNetB1 for prediction reliability and balance.
Flood Status Prediction Based on Water Level Data Using Machine Learning Models Aisyah Putri Widyastuti; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12809

Abstract

Flooding is one of the hydrometeorological disasters that frequently occurs in Indonesia and causes various social and economic losses. This study aims to compare the performance of five machine learning algorithms, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression, as well as one Long Short-Term Memory (LSTM) deep learning model in predicting flood status based on water level data from seven observation posts in the DKI Jakarta area and its surroundings. The research stages include data preprocessing, handling unbalanced data using ADASYN, hyperparameter tuning, and evaluation using accuracy, precision, recall, and F1-score. To avoid data leakage, the data division process is carried out before preprocessing and oversampling. The results show that XGBoost produces the best performance with 96.0% accuracy, 95.5% precision, 96.9% recall, and 96.2% F1-score after hyperparameter tuning. The LSTM model also demonstrated competitive performance with an accuracy of 94.5% and an F1-score of 94.5%. Learning curve analysis showed that all models exhibited normal learning patterns with no indication of data leakage. The results indicate that XGBoost and LSTM have good potential for application in flood early warning systems based on water level data.
Anti-Data Leakage Pipeline for Differentiated Thyroid Cancer Recurrence Prediction: Integrating SMOTE, Optuna-based Optimization, and Bootstrap BCa Validation Deri Rosadi; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12922

Abstract

Thyroid cancer recurrence prediction remains a critical clinical challenge, as early identification of high-risk patients enables targeted monitoring and intervention. This study presents a comparative evaluation of six machine learning classifiers (XGBoost, LightGBM, CatBoost, Logistic Regression, Random Forest, and Decision Tree) using the UCI Differentiated Thyroid Cancer Recurrence dataset which consists of 383 patient records and 16 clinical features. To prevent performance overestimation, a rigorous anti-data leakage pipeline was implemented, encapsulating SMOTE, Optuna-based hyperparameter optimization, and Isotonic Calibration within the cross-validation process. Furthermore, model stability was assessed using Bias-Corrected and accelerated (BCa) Bootstrap validation with 2,000 iterations. Experimental results demonstrate that XGBoost achieved the best overall performance with an F1-score of 0.9545, an AUC-ROC of 0.9967, and the lowest Brier Score of 0.0183. Bootstrap BCa analysis confirmed XGBoost as the most stable model, with a 95% CI F1-score width of 0.1429 and unbiased estimation. These findings suggest that XGBoost, integrated within a zero-leakage pipeline and validated through Bootstrap BCa, is a promising candidate for post-treatment clinical decision support in differentiated thyroid cancer management.
Evaluating LSB and MSB Steganography in Retinal Fundus Images Through Image Quality Assessment and VGG19-Based Classification Gilang Faturrahman; Muhammad Naufal; Wahyu Aji Eko Prabowo; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13198

Abstract

The security of medical image data within electronic medical record systems has become a critical issue due to the increasing threat of health data breaches. Steganography is a promising technique for protecting patient information by concealing secret data within medical images without significantly altering their visual appearance. However, the application of steganography to retinal fundus images, which carry high diagnostic value, has never been comprehensively evaluated in terms of image quality or its impact on artificial intelligence-based diagnostic model performance. This study compares Least Significant Bit (LSB) and Most Significant Bit (MSB) steganography methods applied to 3,200 retinal fundus images from the Retinal Fundus Multi-disease Image Dataset (RFMiD) dataset across four payload levels (0.1-0.4 bpp), evaluated using PSNR, SNR, SSIM, and FSIM for image quality, and VGG19 classification accuracy and AUC for diagnostic impact. Results show LSB achieves substantially superior image quality (PSNR: 59.97-65.93 dB; SNR: 49.34-55.30 dB; SSIM: 0.9981-0.9997; FSIM: 0.9999-1.0000) compared to MSB (PSNR: 12.98-18.99 dB; SNR: 2.35-8.37 dB; SSIM: 0.5979-0.9003; FSIM: 0.5342-0.7500), while VGG19 classification accuracy remains stable for both methods (LSB: 0.8938-0.9000; MSB: 0.8953-0.9031) with a maximum difference of 0.62% from baseline. This study demonstrates that LSB is the more appropriate steganography method for retinal fundus images, delivering superior visual quality while preserving VGG19 diagnostic capability.
Co-Authors Abu Salam Aisyah Putri Widyastuti Almira Zuhrotus Safira Daiyan Akbar Setiyadi Deri Rosadi Dhita Aulia Octaviani Edy Mulyanto Egia Rosi Subhiyakto Endri Mujiono Erlin Dolphina Etika Kartikadarma Gilang Faturrahman Ika Novita Dewi Ilham Maulana Hadinanda Junta Zeniarja Muhammad Naufal Shelomita Fitriyani Wahyu Aji Eko Prabowo Yani Parti Astuti