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All Journal International Journal of Electrical and Computer Engineering Tekno : Jurnal Teknologi Elektro dan Kejuruan JURNAL ECONOMIA CommIT (Communication & Information Technology) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Informatika Upgris Journal of Information Technology and Computer Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information System, Applied, Management, Accounting and Research Jurnal Mantik bit-Tech ILKOMNIKA: Journal of Computer Science and Applied Informatics CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi Nusantara Science and Technology Proceedings Jurnal Ilmiah Teknologi Informasi dan Robotika International Journal of Data Science, Engineering, and Analytics (IJDASEA) ALINIER: Journal of Artificial Intelligence & Applications Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat JUSIFOR : Jurnal Sistem Informasi dan Informatika JEECS (Journal of Electrical Engineering and Computer Sciences) Inspiration: Jurnal Teknologi Informasi dan Komunikasi STATISTIKA Journal of Technology and System Information Informasi interaktif : jurnal informatika dan teknologi informasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Andreas Nugroho Sihananto
Department Of Informatics, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences Other

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PENERAPAN HYBRID CRYPTOGRAPHY MENGGUNAKAN CAMELLIA DAN DUAL MODULUS RSA PADA PERTUKARAN FILE Ar Romandhon, Mitzaqon Gholizhan; Junaidi, Achmad; Sihananto, Andreas Nugroho
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5218

Abstract

Kebutuhan akan keamanan atas data merupakan hal yang sangat penting dalam era digital saat ini, terutama pada proses pertukaran data yang bersifat sensitif terhadap serangan siber. Selain keamanan data, ukuran data yang semakin besar juga menjadi permasalahan dalam proses pertukaran file karena waktu yang dibutuhkan dalam pemrosesan juga semakin lama. Penelitian ini melakukan implementasi skema hybrid cryptography menggunakan algoritma Camellia dan Dual Modulus RSA yang bertujuan untuk mengatasi permasalahan-permasalah tersebut. Pemilihan skema hybrid cryptography adalah untuk mendapatkan kekuatan dari masing-masing algoritma, sehingga keamanan dan kecepatan dari tiap algoritma dapat didapatkan. Tahapan yang dilakukan dalam penelitian ini adalah studi literatur, perancangan, implementasi dan pengujian. Hasil dari pengujian yang telah dilakukan, diperoleh bahwa untuk proses pembangkitan kunci skema hybrid memiliki waktu tempuh yang mirip dengan algoritma DM-RSA dengan perbedaan 9.3% lebih cepat dan pada proses enkripsi dan dekripsi memiliki waktu tempuh yang mirip dengan algoritma Camellia dengan perbedaan 2.3% lebih cepat. Untuk keseluruhan proses algoritma hybrid memiliki waktu tempuh yang mirip dengan algoritma Camellia untuk skenario ukuran data 600MB dan 1200MB dengan perbedaan 25.2% lebih lambat.
KLASIFIKASI CITRA PLANKTON DENGAN ALGORITMA HIBRIDA CONVOLUTIONAL NEURAL NETWORK DAN EXTREME LEARNING MACHINE Shahab, Muhammad Syaugi; Junaidi, Achmad; Sihananto, Andreas Nugroho
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5219

Abstract

Penelitian ini bertujuan untuk meningkatkan akurasi dalam klasifikasi plankton secara otomatis dengan pendekatan hibrida CNN-ELM. Menggunakan Convolutional Neural Network (CNN) untuk ekstraksi fitur dan Extreme Learning Machine (ELM) sebagai pengklasifikasi, model ini dirancang untuk mengatasi tantangan citra plankton yang buram, dataset kecil, dan ketidakseimbangan kelas. SMOTE digunakan untuk menangani ketidakseimbangan data. Dari implementasi SMOTE dengan metode interpolasi, permasalahan ketidakseimbangan kelas berhasil diatasi dengan menjadikan jumlah data latih sama rata untuk setiap kelas. Dari hasil pengujian, konfigurasi dengan 32 filter dan 2000 hidden node serta 64 filter dan 2000 hidden node memberikan performa terbaik dengan akurasi 97,78%. Sebaliknya, model dengan 64 filter dan 4000 hidden node menunjukkan performa terendah dengan akurasi 82,78% yang diakibatkan overfitting. Analisis confusion matrix mengungkapkan kinerja tinggi pada beberapa kelas plankton, namun masih kesalahan klasifikasi sering terjadi pada kelas seperti Alexandrium, Noctiluca, dan Nitzschia. Temuan ini menunjukkan bahwa konfigurasi dengan filter dan node yang lebih kompleks tidak selalu menghasilkan kinerja lebih baik. Penelitian ini diharapkan dapat mendukung pengambilan keputusan di bidang kelautan.
IMPLEMENTASI METODE RAPID APPLICATION DEVELOPMENT (RAD) DALAM PENGEMBANGAN SISTEM ENTERPRISE INDUSTRI TEKSTIL BERBASIS WEBSITE Ramadhan, Dimas Dharu; Mumpuni, Retno; Sihananto, Andreas Nugroho
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5222

Abstract

Penelitian ini berfokus pada perancangan dan pengembangan aplikasi enterprise berbasis web yang khusus untuk industri konveksi tekstil di Bojonegoro, yang masih banyak menggunakan pencatatan manual atau aplikasi terpisah sehingga kurang efisien. Aplikasi ini dirancang untuk mengintegrasikan berbagai aspek operasional perusahaan, mulai dari manajemen, transaksi, hingga pengambilan keputusan, dengan tujuan meningkatkan efisiensi, produktivitas, dan koordinasi antar role dalam perusahaan. Pengembangan menggunakan metode Rapid Application Development (RAD), yang memungkinkan siklus pengembangan cepat dengan melibatkan klien secara intensif. Melalui prototyping yang berulang, klien dapat memberikan masukan langsung sehingga perangkat lunak dapat disesuaikan dengan kebutuhan hingga tercapai hasil yang optimal sesuai standar klien.
GWO-SVM: AN APPROACH TO IMPROVING SVM PERFORMANCE USING GREY WOLF OPTIMIZER IN INTELLECTUAL DISABILITY CLASSIFICATION Afifudin, Muhammad; Junaidi, Achmad; Sihananto, Andreas Nugroho; Fithriyah, Izzatul
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5359

Abstract

 Intellectual disability (ID) is a neurodevelopmental disorder that requires early and accurate diagnosis. This study aims to improve the efficiency of ID diagnosis using a machine learning approach. A Support Vector Machine (SVM) model optimized with Grey Wolf Optimizer (GWO) was developed and trained using data from questionnaires completed by 101 families/guardians of ID patients at RSUD Dr. Soetomo Surabaya. The features used include family history, cognitive abilities, and adaptive behaviors. The results showed that the GWO-SVM model achieved an accuracy of 95% in classifying ID patients, an improvement of 5% compared to the conventional SVM. The GWO algorithm successfully optimized the parameters in SVM, resulting in a model with the best performance. These findings indicate the potential of GWO-SVM as an effective and efficient tool for assisting in the diagnosis of ID.
Perancangan Mekanik Dialog pada Prototipe Game Edukasi Pengenalan Sejarah Peristiwa Puputan Margarana Putra, Raditya Lungguk Satya; Putra, Chrystia Aji; Sihananto, Andreas Nugroho
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 10, No 1 (2026): SEMNAS RISTEK 2026
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v10i1.8868

Abstract

Penerapan Arsitektur CNN-Dilated untuk Deteksi Manipulasi Citra Deepfake Taufiqurrahman, Rahmadany Fahreza; Anggraeny, Fetty Tri; Sihananto, Andreas Nugroho
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.821

Abstract

Perkembangan teknologi kecerdasan buatan telah menghasilkan berbagai inovasi multimedia, salah satunya adalah deepfake. Teknologi ini memanfaatkan algoritma deep learning untuk memanipulasi citra dan video secara realistis, sehingga sulit dibedakan dengan konten asli. Meskipun memiliki manfaat di bidang hiburan, deepfake juga menimbulkan ancaman serius terhadap keamanan digital. Penelitian ini bertujuan mendeteksi citra deepfake menggunakan arsitektur Convolutional Neural Network (CNN) dengan integrasi Dilated convolution. Integrasi ini memperluas receptive field tanpa meningkatkan jumlah parameter, sehingga model dapat menangkap informasi global dan detail lokal secara bersamaan. Dataset yang digunakan adalah Kaggle Deepfake Dataset berisi 8.000 citra (4.000 asli dan 4.000 palsu). Model diuji dengan lima rasio pembagian data (50:50 hingga 90:10) dan dibandingkan dengan CNN konvensional. Evaluasi menggunakan Confusion matrix dengan metrik akurasi, presisi, recall, dan F1-score. Hasil terbaik diperoleh pada rasio 80:20 dengan akurasi 85,69%, presisi 85,82%, dan recall 85,90%. Model CNN-Dilated secara konsisten mengungguli CNN standar dengan peningkatan akurasi 1–3% pada berbagai skenario. Hasil ini membuktikan efektivitas Dilated convolution dalam meningkatkan performa deteksi citra deepfake, yang berpotensi diterapkan dalam bidang keamanan digital dan forensik media.
An Adaptive DTN Routing Protocol Using a Q-Learning Framework for Archipelagic Emergency Networks Agussalim Agussalim; Henni Endah Wahanani; Andreas Nugroho Sihananto
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Natural disasters in archipelagic regions often disrupt communication networks, particularly in geographically isolated islands where terrestrial infrastructure is limited and highly vulnerable. Hence, adaptive, infrastructure-independent solutions are required to maintain connectivity during emergencies. The research proposes an adaptive routing protocol for Delay Tolerant Network (DTN), named Q-learning-based Forwarding Routing (QFR), designed to enhance data delivery performance in disaster scenarios characterized by intermittent connectivity and constrained resources. QFR employs a lightweight, tabular Q-learning framework to make intelligent forwarding decisions based on real-time state information, including buffer occupancy, encounter history, and local node density. The protocol further integrates adaptive replica control and prioritybased scheduling mechanisms to regulate congestion and optimize bandwidth and buffer utilization. Performance evaluation is conducted using the ONE Simulator with realistic maritime mobility traces derived from vessel movement patterns around Madura Island, Indonesia, representing inter-island emergency communication conditions. The results indicate that QFR consistently outperforms benchmark protocols such as Epidemic and PRoPHETv2, particularly in maintaining a high delivery ratio under heavy traffic loads while keeping routing overhead moderate and latency stable. Time-series analysis further demonstrates QFR’s ability to improve its performance over time as the agent learns. The key finding is that a lightweight, adaptive algorithm based on a tabular Q-learning framework provides a practical and effective solution for reliable communication in resource-constrained emergency networks, avoiding the computational complexity of deep reinforcement learning approaches.
Validating a Delay Tolerant Network Architecture for Urban Opportunistic Contact Using Long Range Bisma Putra Sulung; Agussalim Agussalim; Andreas Nugroho Sihananto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3248

Abstract

Traditional communication infrastructures are vulnerable to failure during natural disasters in dense urban areas, hindering early warning dissemination. Delay Tolerant Network (DTN) offers a resilience, yet most studies relies on simulations, overlooking physical layer constraints affecting on opportunistic routing in real-world urban scenarios. This study addresses that specific gap by empirically validating a DTN-LoRa prototype-based communication system for flood data monitoring in Surabaya. A 4-node field test simulated the Store-Carry-Forward mechanism. A fixed node in the Dukuhpakis flood zone (TMP) transmitted bundles to mobile nodes on public transport routes (FD9, FD4). These data mules relayed the bundles via an opportunistic contact at Marmoyo Shelter, delivering them to a gateway node at the BPBD Command Post (PMI Surabaya). Performance was evaluated by Packet Delivery Ratio (PDR) and Latency under varying LoRa parameters (Spreading Factor, Coding Rate). The experiment validated the functional DTN architecture, achieving 100% PDR in the optimal configuration (SF7, CR4/7). The key finding was a "Contact Window Bottleneck" as a critical failure factor. LoRa configurations with high Time on Air (ToA) failed to transfer the entire data bundle within the narrow opportunistic contact window between mobile nodes, causing PDR to drop as low as 60%. Implementation success depends on the physical layer throughput that must be high enough to complete data transfer during brief opportunistic contacts, rather than merely maximizing signal range. These findings provide a critical performance baseline for disaster management agencies, demonstrating a feasible, low-cost architecture that can enhance the reliability of urban disaster response communication.
Development of a 2D Educational RPG on the 10th November Battle Alif Wisam Desanta Fitrianto; Chrystia Aji Putra; Andreas Nugroho Sihananto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3344

Abstract

The advancement of digital technology provides opportunities to innovate learning methods, especially for subjects like history that are often considered less engaging. This study develops a 2D Adventure RPG educational game themed on the November 10 Battle, integrating branching narrative and lane tower defense minigames to enhance students’ historical understanding. This integration addresses a research gap, as few studies have combined branching narrative and lane tower defense in a historical educational RPG. The game allows players to make choices affecting the storyline while facing strategic challenges through interactive mechanics. The study employed a single-group pre-test–post-test design involving 23 students from SMP Negeri 6 Surabaya. Results showed a significant improvement in learning outcomes, with the average score increasing from 53.48 to 94.78 and standard deviation decreasing from 19.21 to 5.93. The Wilcoxon Signed Rank test yielded p = 0.000 (< 0.05), confirming statistical significance. User experience evaluation using GUESS-18 indicated positive responses (mean scores 3.78–4.02), with branching narrative (4.06) and lane tower defense (3.94) receiving favorable feedback. Validity tests confirmed all items were valid (r > 0.413; p < 0.05), and reliability was high (Cronbach’s Alpha = 0.867). However, the small sample size (23 students) may limit generalizability of the findings. These findings suggest the November 10th educational game effectively improves historical understanding, cognitive and affective engagement, learning motivation, and strategic thinking. The study highlights gamified learning as an engaging alternative for history education in the digital era.
Medan Area Battle Educational Game Using Branching Narrative and Turn-Based Tactical Combat Maulana Fauzan; Chrystia Aji Putra; Andreas Nugroho Sihananto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3345

Abstract

The rapid advancement of digital technology has created new opportunities for integrating interactive media into history education, a subject often perceived as passive and less engaging. Addressing the lack of educational games that combine narrative with strategic simulation, this study develops and evaluates Medan Area Battle, a 2D action role-playing game that introduces a dual-pedagogical mechanism through branching narrative and turn-based tactical combat. This integration is designed to allow students to experience historically grounded decision-making while simultaneously engaging in strategic reasoning with the events of the Medan Area Battle. A design-based research framework guided the development process through requirement analysis, design, implementation, and user testing. The evaluation involved twenty-five junior high school students, providing insight into the game's effectiveness. Learning performance was measured through pre-test and post-test assessments, while user experience was examined using Game User Experience Satisfaction Scale-18 (GUESS-18). Two additional dimensions Branching Narrative and Tactical Combat were incorporated to capture the unique interaction patterns introduced by the game’s narrative and strategic systems. Statistical analysis employed the Shapiro–Wilk test, Wilcoxon Signed Rank test, and Cronbach’s Alpha reliability testing. Results indicated a significant improvement in learning outcomes, with mean scores rising from 60.80 to 85.60 (p = 0.000767). The overall GUESS-18 rating of 4.15 (“Very Good”), alongside high scores in Educational (4.30) and Tactical Combat (4.27), suggests strong user engagement. These findings demonstrate that integrating narrative choice with tactical gameplay offers effective and theoretically grounded approach enhancing students’ cognitive understanding and motivation in history learning.
Co-Authors Abdul Rezha Efrat Najaf Abdurrahman, Nizar Achmad Junaidi Aditya Primayudha Aditya Rizqi Ardhana Afifudin, Muhammad Afriani, Regita Agung Mustika Rizki, Agung Mustika Agussalim Agussalim Agussalim Agussalim Agussalim, Agussalim Alif Wisam Desanta Fitrianto Alifah, Nurul Aini Amalia, Nadhia Rizqy Amri Muhaimin Anggraini PS Anggraini Puspita Sari Ani Dijah, Rahajoe Ar Romandhon, Mitzaqon Gholizhan Ardiansyah, Muhammad Dafa Arif Widiasan Subagio Basuki Rahmat Masdi Siduppa Bisma Putra Sulung Christianty, Theressa Marry Dwi Arman Prasetya Edi Sugiyanto Edi Sugiyanto Eristya Maya Safitri Fakhruddin, Fikri Farkhan Fauzi, Zaky Ahmad Fetty Tri Anggraeny Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri Henni Endah Wahanani Himawan, Rantau Izzatul Fithriyah Kartini Kartini Kartini Lesmana, Benedictus Rafael Lumangkun, Mordekhai Gerin M Shochibul Burhan, M Shochibul M. Arif Mardhavi M. Shochibul Burhan Made Hanindia Prami Swari Mardhavi, Arif Marselina, Anif Fitria Dewi Maulana Fauzan Maulana, Hendra Maulana, Yoga Mohammad, Farrel Adel Muhammad Afifudin Muhammad Dafa Ardiansyah Muhammad Muharrom Al Haromainy Naila, Amelia Maslaqun Nandaru, Laudy Nurdibya Nurhaliza, Risma Nurlaili, Afina Lina Octaviani, Vincentia Indri Pangestu, Arif Fajar Parlika, Rizky Pradana, Ilham Akbar Prami, Made Hanindia Putra, Chrystia Aji Putra, Gredy Christian Hendrawan Putra, Raditya Lungguk Satya Ramadhan, Dimas Dharu Rasjid, Azka Avicenna Ratna Yulistiani Retno Mumpuni Reza, Reno Alfa Safitri, Erista Maya Sankalla, Sabda Santosa, Mochammad Kevin Saputra, Dewa Raka Krisna Saputri, Asih Sebrina, Aida Fitriya Shahab, Muhammad Syaugi Suryandari, Sabrina Heryanti Taufiqurrahman, Rahmadany Fahreza Tirana Noor Fatyanosa, Tirana Noor Trianingsih, Arini Trimono, Trimono Wayan Firdaus Mahmudy Wiwik Handayani Yisti Vita Via Yudistira, Mochammad Ervinda Yulianto, Rusman