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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JTERA (Jurnal Teknologi Rekayasa) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JSI (Jurnal sistem Informasi) Universitas Suryadarma INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Jurnal Teknik Mesin, Industri, Elektro dan Informatika Gaster JAMU : Jurnal Abdi Masyarakat UMUS Scientica: Jurnal Ilmiah Sains dan Teknologi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Indrawan Ady Saputro
STMIK AMIKOM SURAKARTA
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Optimizing Rice Plant Disease Classification Using Data Augmentation with GANs on Convolutional Neural Networks Agustin, Tinuk; Saputro, Indrawan Ady; Rahmadi, Mochammad Luthfi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23834

Abstract

Background: Rice disease classification using CNN models faces challenges due to limited data, particularly in minority classes, and inconsistent image quality, which affect model performance. Data augmentation techniques can potentially enhance classification accuracy by improving data diversity and quality. Objective: This study evaluates the effectiveness of data augmentation techniques, specifically classical augmentation and Deep Convolutional Generative Adversarial Networks (DCGAN), in improving CNN performance for rice disease classification. Methods: A quantitative study was conducted using four CNN training scenarios: no augmentation, classical augmentation, DCGAN augmentation, and a combination of both. Model accuracy was analyzed to determine the impact of each augmentation technique. Results: The baseline CNN model achieved an accuracy of 91.88%. Classical augmentation improved accuracy by 2.56%, while DCGAN augmentation led to a 5.44% increase. The combination of classical augmentation and DCGAN yielded the highest accuracy of 98.13%. Conclusion: Data augmentation significantly enhances CNN performance in rice disease classification, with the combined approach of classical augmentation and DCGAN proving to be the most effective. These findings highlight the importance of augmentation techniques in addressing data limitations and improving classification accuracy. Future research should explore additional augmentation strategies and test the model across different datasets to further validate its effectiveness.
Rancang Bangun Sistem Invoice Digital CV Putra Alami Sejahtera Menggunakan Metode Rapid Application Development Rosyidi, Afnan; Ady Saputro , Indrawan; Surya Nugraha , Febrianta
JSI (Jurnal sistem Informasi) Universitas Suryadarma Vol 12 No 2 (2025): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Universitas Dirgantara Marsekal Suryadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v12i2.1542

Abstract

Perkembangan teknologi informasi mendorong digitalisasi berbagai proses bisnis, termasuk dalam pengelolaan invoice. Banyak perusahaan masih menggunakan pencatatan manual yang rentan terhadap kesalahan, keterlambatan, dan ketidakefisienan. CV Putra Alami Sejahtera menghadapi permasalahan serupa, sehingga dibutuhkan sistem terintegrasi yang dapat meningkatkan akurasi dan efisiensi pencatatan transaksi keuangan. Penelitian ini menggunakan metode Rapid Application Development (RAD) untuk mempercepat pengembangan sistem melalui iterasi yang melibatkan pengguna. Sistem dirancang berbasis web menggunakan framework Laravel dan arsitektur Model-View-Controller (MVC). Pengujian dilakukan dengan metode black-box testing berdasarkan skenario nyata. Hasil menunjukkan bahwa 100% fungsi sistem berjalan dengan baik tanpa kendala berarti. Sistem dinyatakan layak untuk diimplementasikan, serta diharapkan dapat menggantikan pencatatan manual, meminimalkan kesalahan input, dan meningkatkan pengambilan keputusan berbasis data yang cepat dan terstruktur.
Pengembangan Aplikasi Berbasis Web untuk Pemetaan Penerima Zakat Menggunakan Metode K-Means Clustering Ady Saputro, Indrawan; Ady Prabowo, Iwan; Abdul Aziz, Riyan
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 2 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i2.721

Abstract

Zakat is an asset collected from muzaki and then distributed to those who are entitled to receive, namely 8 asnaf consisting of very poor people, people who are lacking, zakat management officers, people who have just converted to Islam, people who have a lot of debt, slaves who want to be free, travelers, and those who fight in the way of Allah. In Al-Huda Mosque, the data of zakat recipients currently only displays names, addresses, the number of family dependents, and the amount of zakat distributed. Takmir does not yet know the recipients who are entitled to zakat in large or small amounts. Therefore, an algorithm is required to streamline the grouping of data on zakat recipients in Al-Huda Mosque based on data ratios such as the number of family dependents, work, home conditions, and transportation. This study applies the K-Means Clustering method approach to group zakat recipient data. Web-based applications are developed using PHP to make it easier to manage data and grouping effectively. As a result, 7 recipients received high priority recipients, 30 medium priority recipients, and 16 low priority recipients. Testing of data clusters with the Silhouette Coefficient method showed that 4 clusters have strong structural values. For future research, additional variables can be extracted and analyzed to maximize more accurate and comprehensive grouping results
Sosialisasi Penggunaan Media Sosial yang Aman dari Bahaya Phising di Masjid Al Huda Pandeyan Saputro, Indrawan Ady; Sugiarto, Lilik; Nugraha, Febrianta Surya; Nurhidayanto, Nurhidayanto
Abditeknika Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abditeknika.v4i1.3146

Abstract

  Ancaman penyebaran tindak kejahatan phishing melalui platform media sosial semakin menjadi-jadi oleh pihak-pihak yang tidak bertanggung jawab. Dampak dari kejahatan phishing dapat mencakup kerugian baik secara materiil, finansial, maupun psikologis. Oleh karena itu, sangat penting untuk mengambil langkah-langkah pencegahan guna melindungi diri dari potensi serangan phishing yang dapat terjadi. Salah satu metode yang dapat diterapkan adalah melalui kegiatan sosialisasi tentang penggunaan media sosial di Masjid Al Huda Pandeyan, bertujuan untuk memberikan perlindungan kepada jamaah dari risiko phishing. Tujuan utama dari kegiatan ini adalah menciptakan lingkungan di mana para jamaah dapat menggunakan media sosial dengan bijak dan aman, serta terhindar dari potensi bahaya kejahatan siber, termasuk phishing. Jumlah peserta dalam kegiatan sosialisasi sebanyak 25 peserta. Pendekatan yang diadopsi dalam sosialisasi ini melibatkan metode ceramah dan diskusi. Dari hasil kegiatan sosialisasi ini, bahwa sosialisasi yang telah dilakukan pada jamaah Masjid Al Huda Pandeyan mendapatkan respons positif. Dari hasil kuisioner 32% peserta menilai bahwa kegiatan tersebut berjalan dengan "baik" dan 68% menilai kegiatan tersebut "Sangat baik". Mayoritas peserta, yang sebagian besar adalah pengguna media sosial, merespon kegiatan tersebut dengan baik.     The threat of spreading phishing crimes through social media platforms is increasingly being carried out by irresponsible parties. The impact of phishing crimes can include material, financial, or psychological losses. Therefore, it is very important to take precautionary steps to protect yourself from potential phishing attacks that can occur. One method that can be applied is through socialization activities about the use of social media at Masjid Al Huda Pandeyan, aimed at providing protection to worshippers from the risk of phishing. The main goal of this activity is to create an environment where pilgrims can use social media wisely and safely, and avoid the potential dangers of cybercrime, including phishing. The number of participants in the socialization activity was 25 participants. The approach adopted in this socialization involves the method of lectures and discussions. From the results of this socialization activity, that the socialization that has been carried out at the Al Huda Pandeyan Mosque congregation received a positive response. From the results of the questionnaire, 32% of participants rated the activity "good" and 68% rated it "very good". The majority of participants, most of whom were social media users, responded well to the event.
Evaluating Steganography Detection in JPEG Images Using Gaussian Mixture Model and Cryptographic Keys Saputro, Indrawan Ady; Nugraha, Febrianta Surya; Sugiarto, Lilik; Prabowo, Iwan Ady
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.v9i6.6084

Abstract

This study introduces a novel approach that integrates Gaussian Mixture Models (GMM) with MD5 hash-based verification to detect hidden messages embedded via Least Significant Bit (LSB) steganography in JPEG images. Unlike previous methods, the proposed dual-layer technique combines probabilistic modeling with data integrity verification. The model was trained and evaluated using a dataset comprising both original and stego-JPEG images. The experimental results achieved an accuracy of 78.67% and a precision of 89.15%, indicating good class separation between stego and non-stego images (AUC-ROC = 0.8659). However, the recall rate of 69.70% suggests that there is room for improvement in detecting all stego instances. Although MD5 is a hash function rather than an encryption algorithm, it effectively aids in identifying data anomalies resulting from message embedding. Overall, this lightweight approach offers a practical solution for steganalysis and can be further enhanced through the integration of hybrid deep learning techniques in future research.
Co-Authors Abdul Aziz, Riyan Ady Prabowo, Iwan Afnan Rosyidi Febrianta Surya Nugraha Hadis Turmudi Iwan Ady Prabowo Latifah Nurul Habibah Maulina, Rufidah Nugroho Ridho Harjanto Nurhidayanto, Nurhidayanto Pangesti , Oktafian Dyah Prasetiya, Bayu Ade Purwidiantoro, Moch. Hari Rahmadi, Mochammad Luthfi Riyan Abdul Aziz Siska Ningtyas Prabasari Sugiarto, Lilik Suryawati, Liling Daru Susanti, Anggi Tinuk Agustin Wibowo, Alfian Nur Widianti, Ina Sholihah Widiyanti, Sri Yudam, Zaky Zulfahmi