Garuda - Garba Rujukan Digital

Analysis of Factors Influencing the Acceptance and Use of AdmApp at Cahaya Al-Qur’an Islamic Boarding School Using the UTAUT Model Mulia Ningsih; Muhammad Romadhona Kusuma; Supriadi Panggabean; Ali Ahmad
Journal of Innovation and Computer Science Vol. 2 No. 2 (2026): Journal of Innovation and Computer Science (in the process)
Publisher : Yayasan Mitra Peduli Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57053/jics.v2i2.157

The development of information technology encourages educational institutions to adopt digital solutions to improve administrative efficiency and service quality. This study aims to analyze the factors influencing the acceptance and use of Administration Application (AdmApp), a digital financial application implemented at Cahaya Al-Qur’an Islamic Boarding School, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This quantitative study involved 29 staff respondents using a total sampling technique. Data were collected via questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0. The results show that Performance Expectancy (PE) significantly influences Behavioral Intention (BI) (β = 0.440; p = 0.006), Behavioral Intention significantly influences Use Behavior (UB) (β = 0.504; p = 0.000), and Facilitating Conditions (FC) significantly influence Use Behavior (UB) (β = 0.466; p = 0.000). Meanwhile, Effort Expectancy (EE) and Social Influence (SI) do not significantly influence Behavioral Intention. The research model explains 60.6% of the variance in Behavioral Intention and 83.2% of the variance in Use Behavior. Despite the limited sample size, the model demonstrates substantial explanatory power in understanding technology acceptance behavior. These findings indicate that perceived usefulness and the availability of technical support are important factors in the adoption of digital administrative systems in pesantren environments.

Sentiment Analysis of Netflix Reviews Using Word2Vec and Multiple Machine Learning Models Based on Streamlit Niko Purnomo; Muhammad Romadhona Kusuma; Riadi Marta Dinata; Rengga Gumilar
Journal of Innovation and Computer Science Vol. 2 No. 2 (2026): Journal of Innovation and Computer Science (in the process)
Publisher : Yayasan Mitra Peduli Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57053/jics.v2i2.158

This study aims to analyze the sentiment of user reviews on the Netflix application obtained from the Google Play Store using machine learning and deep learning approaches. The research data were collected through web scraping using the Instan Data Scraper tool and produced 1,661 review data. The dataset was processed through several preprocessing stages, including cleaning, case folding, tokenization, stemming, and stopword removal. Word2Vec was applied to transform text data into numerical vector representations. The classification process was carried out using six algorithms, namely Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Logistic Regression, and Long Short-Term Memory (LSTM). Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that the LSTM algorithm achieved the highest accuracy of 88%, outperforming other machine learning models. These findings indicate that deep learning methods are more effective in capturing contextual information in textual data. This research concludes that the LSTM algorithm is the best method for classifying sentiment in Netflix user reviews due to its superior performance in understanding sequential text patterns.

Teknik Watermarking Citra Digital Berbasis Hybrid DCT-Hessenberg-SVD dengan Diffusion dan Adaptive Embedding untuk Perlindungan Hak Cipta Dokumentasi BAZNAS Romadhona Kusuma, Muhammad; Kusuma, Muhammad Romadhona; Dwiza Riana; Ferda Ernawan; Yudhiarma; Ropi, Muhamad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Peningkatan distribusi media digital menyebabkan risiko pelanggaran hak cipta dan manipulasi citra semakin tinggi, khususnya pada dokumentasi digital institusi. Namun, metode watermarking konvensional masih menghadapi keterbatasan dalam menjaga keseimbangan antara ketidaktampakan (imperceptibility) dan ketahanan watermark (robustness) terhadap berbagai serangan citra. Penelitian ini mengusulkan metode watermarking citra digital berbasis hybrid Discrete Cosine Transform (DCT), Hessenberg Decomposition (HD), Singular Value Decomposition (SVD), dan diffusion refinement untuk meningkatkan stabilitas watermark sekaligus mempertahankan kualitas visual citra. Proses embedding dilakukan pada domain frekuensi DCT melalui modifikasi singular value hasil dekomposisi Hessenberg, kemudian diperkuat menggunakan diffusion refinement berbasis DPM-Solver++ multistep untuk meningkatkan kualitas rekonstruksi citra dan stabilitas ekstraksi watermark. Metode diimplementasikan menggunakan Python dan diuji pada delapan citra dokumentasi digital berwarna menggunakan berbagai skenario serangan, meliputi Gaussian blur, JPEG compression, histogram equalization, sharpening, dan salt-and-pepper noise. Hasil eksperimen menunjukkan rata-rata PSNR sebesar 49,29 dB, SSIM sebesar 0,9488, dan NC sebesar 0,9472 pada kondisi normal. Pada serangan JPEG compression, metode masih mempertahankan nilai SSIM sebesar 0,937 dan PSNR sebesar 40 dB. Hasil tersebut menunjukkan bahwa metode yang diusulkan mampu mempertahankan kualitas visual citra sekaligus meningkatkan ketahanan watermark terhadap berbagai distorsi citra digital. Abstract The increasing distribution of digital media has raised the risk of copyright infringement and image manipulation, particularly in institutional digital documentation. However, conventional watermarking methods still face limitations in maintaining a balance between imperceptibility and robustness against various image attacks. This study proposes a hybrid digital image watermarking method based on Discrete Cosine Transform (DCT), Hessenberg Decomposition (HD), Singular Value Decomposition (SVD), and diffusion refinement to improve watermark stability while preserving image visual quality. The embedding process is performed in the DCT frequency domain through singular value modification derived from Hessenberg decomposition, followed by DPM-Solver++ multistep diffusion refinement to enhance image reconstruction quality and watermark extraction stability. The proposed method was implemented using Python and evaluated on eight color digital documentation images under various attack scenarios, including Gaussian blur, JPEG compression, histogram equalization, sharpening, and salt-and-pepper noise. Experimental results achieved an average PSNR of 49.29 dB, SSIM of 0.9488, and NC of 0.9472 under normal conditions. Under JPEG compression attacks, the method still maintained an SSIM value of 0.937 and PSNR of 40 dB. These results demonstrate that the proposed method effectively preserves image visual quality while improving watermark robustness against various digital image distortions.

Teknik Watermarking Citra Digital Berbasis Hybrid DCT-Hessenberg-SVD dengan Diffusion dan Adaptive Embedding untuk Perlindungan Hak Cipta Dokumentasi BAZNAS Romadhona Kusuma, Muhammad; Kusuma, Muhammad Romadhona; Dwiza Riana; Ferda Ernawan; Yudhiarma; Ropi, Muhamad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Peningkatan distribusi media digital menyebabkan risiko pelanggaran hak cipta dan manipulasi citra semakin tinggi, khususnya pada dokumentasi digital institusi. Namun, metode watermarking konvensional masih menghadapi keterbatasan dalam menjaga keseimbangan antara ketidaktampakan (imperceptibility) dan ketahanan watermark (robustness) terhadap berbagai serangan citra. Penelitian ini mengusulkan metode watermarking citra digital berbasis hybrid Discrete Cosine Transform (DCT), Hessenberg Decomposition (HD), Singular Value Decomposition (SVD), dan diffusion refinement untuk meningkatkan stabilitas watermark sekaligus mempertahankan kualitas visual citra. Proses embedding dilakukan pada domain frekuensi DCT melalui modifikasi singular value hasil dekomposisi Hessenberg, kemudian diperkuat menggunakan diffusion refinement berbasis DPM-Solver++ multistep untuk meningkatkan kualitas rekonstruksi citra dan stabilitas ekstraksi watermark. Metode diimplementasikan menggunakan Python dan diuji pada delapan citra dokumentasi digital berwarna menggunakan berbagai skenario serangan, meliputi Gaussian blur, JPEG compression, histogram equalization, sharpening, dan salt-and-pepper noise. Hasil eksperimen menunjukkan rata-rata PSNR sebesar 49,29 dB, SSIM sebesar 0,9488, dan NC sebesar 0,9472 pada kondisi normal. Pada serangan JPEG compression, metode masih mempertahankan nilai SSIM sebesar 0,937 dan PSNR sebesar 40 dB. Hasil tersebut menunjukkan bahwa metode yang diusulkan mampu mempertahankan kualitas visual citra sekaligus meningkatkan ketahanan watermark terhadap berbagai distorsi citra digital. Abstract The increasing distribution of digital media has raised the risk of copyright infringement and image manipulation, particularly in institutional digital documentation. However, conventional watermarking methods still face limitations in maintaining a balance between imperceptibility and robustness against various image attacks. This study proposes a hybrid digital image watermarking method based on Discrete Cosine Transform (DCT), Hessenberg Decomposition (HD), Singular Value Decomposition (SVD), and diffusion refinement to improve watermark stability while preserving image visual quality. The embedding process is performed in the DCT frequency domain through singular value modification derived from Hessenberg decomposition, followed by DPM-Solver++ multistep diffusion refinement to enhance image reconstruction quality and watermark extraction stability. The proposed method was implemented using Python and evaluated on eight color digital documentation images under various attack scenarios, including Gaussian blur, JPEG compression, histogram equalization, sharpening, and salt-and-pepper noise. Experimental results achieved an average PSNR of 49.29 dB, SSIM of 0.9488, and NC of 0.9472 under normal conditions. Under JPEG compression attacks, the method still maintained an SSIM value of 0.937 and PSNR of 40 dB. These results demonstrate that the proposed method effectively preserves image visual quality while improving watermark robustness against various digital image distortions.

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