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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Inspiration Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JUSIM (Jurnal Sistem Informasi Musirawas) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Digital Teknologi Informasi Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Bangkit Indonesia Inspiration: Jurnal Teknologi Informasi dan Komunikasi International Journal of Computer Science and Information Technology Journal of Innovation and Computer Science Journal of Applied Computing and Digital Information
Muhammad Romadhona Kusuma
Darunnajah University, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Penerapan Finite State Automata Pada Validasi Permohonan Pengajuan Layanan Akun Digital Menara Masjid Muhammad Romadhona Kusuma; Windu Gata; Laela Kurniawati; Frieyadie Frieyadie; Ahmad Baihaqi
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 12, No 1 (2022): Jurnal Inspiration Volume 12 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v12i1.2676

Abstract

DMI Kota Bekasi dalam melaksanakan visi dan misinya yaitu “Optimalisasi peran dan fungsi masjid dalam pemberdayaan umat di era industri 4.0”, memberikan fasilitas berupa penyediaan layanan penggunaan aplikasi digital menara masjid. Pengelolaan pengajuan akun apabila dilakukan secara manual akan menghabiskan waktu yang cukup lama serta kemungkinan terjadi resiko  kehilangan data pengajuan. Maksud Penelitian ini dilaksanakan  dengan tujuan untuk  menghemat  waktu,  serta memperkecil resiko kehilangan data dan meminimalkan kesalahan pada proses pencatatan permohonan pengajuan akun yang diterima, Pada perancangan sistem ini akan melakukan pengecekan berdasarkan hasil inputan pemohon dan sistem akan secara otomatis melakukan proses validasi, sebelum data tersebut akan disimpan ke dalam basis data menerapkan konsep FSA sehingga data yang akan disimpan sesuai dengan format yang telah ditentukan. Dengan menerapkan hasil penelitian ini, diharapkan dapat membantu DMI Kota Bekasi mengurangi waktu dan memperkecil terjadinya kehilangan data pengajuan, serta meminimalkan terjadinya kesalahan pada proses pencatatan  permohonan pengajuan akun  karena data pengajuan akan disimpan berdasarkan format yang telah ditentukan.
Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking Kusuma, Muhammad Romadhona; Windu Gata; Sigit Kurniawan; Dedi Dwi Saputra; Supriadi Panggabean
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i2.58

Abstract

This research aims to improve the software quality and effectiveness of zakat management by the National Amil Zakat Agency (BAZNAS) through the development of a software defect prediction model (SDPM). We used machine learning techniques and ensemble stacking approach on the "Masjid Tower" dataset containing 228 records and 34 attributes. The preprocessing process involved label encoding, feature selection with Pearson correlation, standard normalization, and the use of SMOTE to handle data imbalance. We performed hyperparameter tuning with grid search CV on Machine Learning algorithms such as Ada Boost and Gradient Boosting. The results showed that the ensemble stacking approach with a combination of Gradient Boosting, Ada Boost, Decision Tree, Bayesian Ridge, and LightGBM meta learner algorithms provided high accuracy with R2 score reaching 0.97, MAE of 0.037, and MSE of 0.006. This finding proves that the ensemble stacking approach is able to overcome the problem of software defects with accurate prediction results, provide useful guidance in the management of zakat and other software applications, and has the potential to improve software quality and the effectiveness of BAZNAS in managing zakat.
Analisis sentimen masyarakat terhadap informasi penerapan PPN atas renovasi dan membangun rumah sendiri pada media sosial youtube dengan metode svm dan naive bayes kusuma, muhammad romadhona; gata, windu; kurniawati, laela; rivan, almay faiz; kurniawan, triadi
Jurnal Digital: Telnologi Informasi Vol 6, No 2 (2023): Jurnal Digital Teknologi informasi
Publisher : Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/digital.v6i2.4758

Abstract

Penelitian ini dilakukan untuk menganalisis komentar masyarakat terhadap informasi penerapan pajak pertambahan nilai atas kegiatan renovasi dan membangun rumah sendiri  yang kini telah diumumkan oleh pemerintah melalui kementrian keuangan. Analisis ini dilakukan untuk mengetahui bagaimana pandangan masyarakat terhadap adanya informasi tersebut dengan memfokuskan pada gaya bahasa yang digunakan masyarakat dalam menyampaikan komentarnya serta maksud dari komentar yang disampaikan atau ditulis.  Karena tidak mudah untuk menganalisis sentimen dari berbagai komentar, hal ini karena komentar mengandung teks yang tidak terstruktur, terutama dalam teks Indonesia. Prapemrosesan teks dalam penambangan teks merupakan bagian penting untuk mendapatkan informasi dasar yang terkandung dalam komentar. Adapun penelitian yang dilakukan menggunakan pra-pemrosesan teks Indonesia dengan Textmining menggunakan rapid minner. Teknik pengumpulan data dilakukan melalui pengumpulan komentar di media sosial youtube kanal berita viva.co.id dari tanggal 10 april 2022 hingga 30 juni 2022 dengan jumlah 1650 komentar . Kemudian dilanjutkan dengan mengekstraksi informasi menggunakan metode klasifikasi Naïve Bayes dan Support Vector Machine yang dioptimalkan menggunakan Particle Swarm Optimization. Hasil penelitian ini menunjukkan bahwa komentar masyarakat mengenai informasi penerapan pajak pertambahan nilai atas kegiatan renovasi dan membangun rumah sendiri beraneka ragam. Ada yang setuju, ada yang tidak setuju, dan ada pula yang tidak tahu menahu. Simpulan dari penelitian ini hendaknya pemerintah melalui kementerian keuangan RI memberikan sosialisasi yang luas kepada masyarakat, agar masyarakat tidak bingung terhadap informasi tersebut sehingga masyarakat tidak akan asal berkomentar dengan pendapatnya masing-masing.
IMPLEMENTASI YOU ONLY LOOK ONCE v8 DALAM DETEKSI MAKANAN WARUNG TEGAL UNTUK SISTEM PERHITUNGAN HARGA OTOMATIS Purnomo, Niko; Gata, Windu; Romadhona Kusuma, Muhammad; Marta Dinata, Riadi; Binti Husna, Modesta
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13465

Abstract

Warung Tegal (Warteg) merupakan usaha kuliner yang populer di Indonesia, tetapi sistem perhitungan harga makanannya masih manual, yang dapat menyebabkan kesalahan transaksi. Penelitian ini bertujuan mengembangkan sistem deteksi makanan otomatis menggunakan YOLO v8 untuk mengotomatisasi perhitungan harga.Dataset terdiri dari berbagai lauk warteg yang diproses dengan teknik augmentasi seperti pemotongan, rotasi, dan pencahayaan guna meningkatkan kinerja model. Hasil penelitian menunjukkan bahwa dalam pengujian terbaik dengan dataset 70:30 (20 epochs, batch size 16, learning rate 0.001), model YOLO v8 mencapai precision 0.602, recall 0.176, F1-score 0.32, box_loss 1.756, dan mAP@0.5 0.229. Tantangan utama meliputi keterbatasan dataset, kompleksitas latar belakang, dan kurangnya perbandingan dengan dataset publik. Meskipun dalam beberapa kondisi akurasi mencapai 75%- 100%, diperlukan dataset lebih besar dan perbandingan model lain untuk meningkatkan akurasi. Sistem ini berpotensi mendukung digitalisasi industri kuliner dan meningkatkan efisiensi transaksi di warteg.
PENDEKATAN ANALISIS PREDIKTIF REGRESI MENGGUNAKAN METODE PEMBELAJARAN MESIN UNTUK MEMPERKIRAKAN EFFORT PENGEMBANGAN PERANGKAT LUNAK PADA APPLIKASI MENARA MASJID BAZNAS RI Jupriyanto, Jupriyanto; Kusuma, Muhammad Romadhona
JUSIM (Jurnal Sistem Informasi Musirawas) Vol 10 No 1 (2025): JUSIM : Jurnal Sistem Informasi Musi Rawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusim.v10i1.2595

Abstract

Suatu proyek pastinya membutuhkan suatu estimasi, pada proyek pengembangan perangkat lunak estimasinya berfokus untuk memperkirakan effort apa saja yang diperlukan pada saat mengembangkan perangkat lunak dengan mencakup sumber daya, kebutuhan tenaga kerja yang diperlukan, waktu serta pengolahan jadwal kegiatannya serta berusaha menekan anggaran menjadi seminimal mungkin. Biasanya seorang Manajer proyek yang akan bertanggung jawab serta memberi keputusan untuk menangani perhitungan proyek estimasi tersebut. Seringkali membuat keputusan di bawah ketidakpastian yang tinggi adalah masalah kritis dalam pengembangan perangkat lunak. Sedangkan dalam hal Memprediksi tentunya membutuhkan suatu pengalaman tingkat lanjut dan juga alat bantu yang dapat digunakan untuk meningkatkan keakuratan prediksi tersebut. Sebuah prediksi berbasis algortima machine learning dapat memprediksi effort pengembangan perangkat lunak secara efisien dan berguna yang keakuratannya membantu memprediksi kinerja berdasarkan data historis metrik pengembangan perangkat lunak, tentu hal ini bagi manajer proyek dapat bermanfaat sebagai salah satu opsi sistem pendukung keputusan untuk meningkatkan ketepatan dalam hal memperkirakan effort pengembangan perangkat lunak. Berdasarkan latar belakang tersebut maka pada penelitian ini adalah bagaimana melakukan pengukuran untuk meningkatkan efisiensi dalam memperkirakan effort pembangunan perangkat lunak. kami mencoba membangun model estimasi regresi prediktif untuk memprediksi effort pada proses proyek pengembangan perangkat lunak, menggunakan beberapa algortima machine learning seperti Linier Regresion (LR), K-Nearest Neighbors (KNN), maupun Support Vector Machine (SVM) dan Random Forest (RF) serta Decision Tree (DT)
Information Technology Infrastructure Library Framework Implementation Using Machine Learning Method Kusuma, Muhammad Romadhona; Shidiq, Muchamad Bachram; Sudrajat, Agung
Journal of Innovation and Computer Science Vol. 1 No. 1 (2024): Journal of Innovation and Computer Science
Publisher : Yayasan Mitra Peduli Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57053/jics.v1i1.52

Abstract

A business company must be able to adapt to the rapid development of information technology. An organization's success depends on its users' ability and desire to use technology in business activities. If a company can provide technology but fails to achieve the expected results, this can be caused by less than optimal use of technology. This study aims to discuss the importance of ITIL implementation in maintaining and constantly monitoring technology in running business processes in the company. In addition, implementation evaluation can be assessed by looking at performance in handling IT problems based on historical data on the ticketing system application. It can be analyzed using machine learning methods to obtain the best analysis results for evaluation and input for future improvement of PT Wansya Indotama Jaya.
Transformasi Digital Pengelolaan Masjid Berbasis Inovasi Sistem Informasi dan Regulasi Nasional: Studi Evaluatif terhadap Aplikasi Menara Masjid BAZNAS Muhammad Romadhona Kusuma
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.447

Abstract

Effective and efficient mosque management is a key factor in ensuring operational continuity and enhancing the quality of services provided to congregants. In line with the advancement of the digital era, the adoption of information technology in mosque governance has become an urgent necessity to support transparency, accountability, and administrative efficiency. This study aims to evaluate the effectiveness, transparency, as well as the innovative and regulatory aspects of the Menara Masjid Application developed by BAZNAS RI. The research method employed is exploratory in nature, involving literature review, field observation, and primary data collection from mosque administrators using the application. The results indicate that the application represents a community-based information system innovation that facilitates the management of congregational data, reporting of zakat, infaq, and sadaqah (ZIS), and real-time dissemination of activity information. In addition to promoting internal efficiency and transparency, the application is supported by national regulations through BAZNAS Chairman Regulation No. 005 of 2024, which reinforces its broad legitimacy and adoption. These findings affirm that digitalization through innovative and regulation-based information systems plays a strategic role in establishing modern, participatory, and sustainable mosque governance.
Securing NFT Copyright with Robust DWT-Hessenberg-SVD Watermarking and RSA Signatures Muhammad Romadhona Kusuma; Efri Syamsul Bahri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6747

Abstract

In the digital era, protecting visual content from misuse and forgery is essential. This study proposes a robust image watermarking method by integrating Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HD), and Singular Value Decomposition (SVD), aiming to enhance watermark imperceptibility and resilience against common image attacks. Additionally, the system incorporates RSA digital signatures within the watermark metadata to ensure verifiable authenticity in NFT (Non-Fungible Token) applications. The method was implemented using Python and tested on multiple grayscale images across various attack scenarios, including noise addition and compression. Experimental results demonstrate high SSIM and PSNR values, confirming the method's effectiveness in maintaining both visual fidelity and embedded watermark integrity. These findings support the potential of this approach for secure and scalable NFT copyright protection.
Tinjauan Penerapan CNN dan YOLO pada Pengolahan Citra Agraria Medis Industri Cerdas Robby Saputra; Rianto, Yan; Kusuma, Muhammad Romadhona
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10988

Abstract

The Rapid growth of Artificial Intelligence (AI), particularly Deep Learning, is driving significant transformations in digital image processing in the argricultural, medical, and smart industrial sectors. Two approaches are most dominant in this research Convolutional Neural Network (CNN) for image classification and You Only Look Once (YOLO) for real-time object detection. The purpose of this reasearch is to systematically review the application, performance, and defense of CNN and YOLO in various domains with different data characteristics. The method used is a Systematic Literatur Review (SLR) of the latest relevant scientific publications, focusing on evaluation matrics such as accuracy, pression, recall, F1-score, and Mean Average Precision (mAP). The review results show that CNN excels in image classification tasks with a high level of accuracy, especially on data with relatively stabel visual patterns, while YOLO is more effective in applications that demand inference speed and direct object detection. However, several major limitations were found, including decreased performance in extreme lighting conditions, complex backgrounds, small objects, and visual similarity between classes. It is concluded that the choice of architecture must be adjusted to the characteristics of the data and application needs,
STEGAFLOW : END-TO-END DEEP NEURAL NETWORKS FOR IMAGE DATA HIDING AND STEGANOGRAPHY Muhammad Romadhona Kusuma; Muhammad Naufal Adnansyah Nuryadin; Rahmat Rahmat; Leo Ternado
International Journal of Computer Science and Information Technology Vol. 3 No. 1 (2026): IJCOMIT Vol 3 No 1
Publisher : Computer Science Department, Malang National Institute of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/ijcomit.v3i1.18022

Abstract

Deep neural networks are highly sensitive to subtle pixel-level variations, a property that is commonly discussed in the context of adversarial learning. Rather than treating this characteristic solely as a vulnerability, this study explores its potential for image data hiding. This paper proposes StegaFlow, an end-to-end deep learning-based framework for image steganography and blind watermarking, in which encoder and decoder networks are jointly optimized to embed binary messages into digital images while preserving visual quality and enabling reliable message recovery. To improve robustness against real-world image degradations, the training process incorporates multiple distortion models, including Gaussian blur, pixel-wise dropout, image cropping, and JPEG compression. Because JPEG compression is inherently non-differentiable, differentiable approximations are introduced during training to enable effective gradient-based optimization. Experimental results evaluate the proposed framework in terms of capacity, secrecy, and robustness, and show that it achieves strong performance under common image distortions. In particular, the framework demonstrates improved robustness to several spatial-domain degradations, while adversarial training contributes to better perceptual quality by reducing visible embedding artifacts. These findings indicate that end-to-end neural optimization provides a flexible and effective approach for robust image data hiding
Co-Authors Aco Linggato Agung Sudrajat, Agung Ahmad Baihaqi Ali Ahmad Binti Husna, Modesta Dedi Dwi Saputra Dedi Dwi Saputra Dina Ramadhania Dwi Daryanto Dwiza Riana Efri Syamsul Bahri Ferda Ernawan Fikri Ismaya Frieyadie Gata, Windu Jupriyanto . Kurniawan, Triadi Laela Kurniawati Laela Kurniawati, Laela Leo Ternado M. Rezky Revansyah Suprihono Marta Dinata, Riadi Muhammad Abdul Karim Luthfi Muhammad Abdul Karim Luthfi Muhammad Naufal Adnansyah Nuryadin Muhammad Reno Fathur Rahman Muhammad Sholeh Muhammad Suhandar Sukma Suhandar Mulia Ningsih Niko Purnomo Noor Azis Noor Azis Purnomo, Niko Rahmat Rahmat Rengga Gumilar Riadi Marta Dinata Rianto, Yan rivan, almay faiz Robby Saputra Rojulan Fayadul Mujahid Ropi, Muhamad Saeful Mu'minin Sepdiyanto Sepdiyanto Shidiq, Muchamad Bachram Sigit Kurniawan Sigit Kurniawan Supriadi Panggabean Supriadi Panggabean Tri Widianto Wildan Hakim Windu Gata Windu Gata Yudhiarma Yudhiarma