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All Journal The Indonesian Journal of Business Administration Jurnas Nasional Teknologi dan Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika Techne : Jurnal Ilmiah Elektroteknika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) ProBisnis : Jurnal Manajemen Jurnal Minfo Polgan (JMP) Community: Jurnal Pengabdian Pada Masyarakat
Adhitya Nugraha
Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
AN ENHANCED MULTI-LAYERED IMAGE ENCRYPTION SCHEME USING 2D HYPERCHAOTIC CROSS-SYSTEM AND LOGISTIC MAP WITH ROUTE TRANSPOSITION Fauzyah, Zahrah Asri Nur; Nugraha, Adhitya; Luthfiarta, Ardytha; Farandi, Muhammad Naufal Erza
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4007

Abstract

In the rapidly evolving digital era, image encryption has become a crucial technique to protect visual data from the threat of information leakage. However, the main challenge in image encryption is improving security against cryptanalysis attacks, such as brute-force and differential attacks, which can compromise the integrity of the encrypted image. Additionally, the creation of efficient and fast encryption schemes that do not degrade image quality remains a significant challenge. This research proposes a multi-layer image encryption scheme that integrates the Logistic Map algorithm, Cross 2D Hyperchaotic (C2HM) system, and Route Transposition techniques. The method aims to enhance the security of digital image encryption by combining chaotic and hyperchaotic systems. The Logistic Map is used to generate a sequence of random values with high chaotic properties, while C2HM contributes to increasing complexity and variability. The Route Transposition technique is applied to scramble pixel positions, further strengthening the encryption’s randomness. The encryption key is derived from a combination of the image hash and user key, which are then used to calculate the initial seed in the chaotic algorithm. Experiments were conducted using standard images with a resolution of 512×512 pixels. The security analysis includes evaluations of NPCR, UACI, histogram analysis, and information entropy. The experimental results show that NPCR consistently exceeds 99.5%, while UACI ranges between 33.23% and 33.56%, indicating high sensitivity to minor changes. Histogram analysis demonstrates an even intensity distribution, and the information entropy value of 7.999 reflects an exceptionally high level of randomness. Robustness tests also indicate that this method can maintain image integrity even when subjected to damage or data loss.
A TOPIC-BASED APPROACH FOR RECOMMENDING UNDERGRADUATE THESIS SUPERVISOR USING LDA WITH COSINE SIMILARITY Nisa, Laila Rahmatin; Luthfiarta, Ardytha; Nugraha, Adhitya; Hasan, Md. Mahadi; Wulandari, Kang, Andini; Huda, Alam Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4061

Abstract

The thesis is one of the critical factors in determining student graduation. While working on the thesis, students will be guided by a lecturer who has the role and responsibility to ensure that students can prepare the thesis well so that the thesis is ready to be tested and is of good quality. Therefore, selecting a supervisor with the same expertise as the thesis topic is essential in determining students' success in completing their thesis. So far, the selection of thesis supervisors at Dian Nuswantoro University still needs to be done manually by students, so the lack of information about the supervisor can hinder students in determining the supervisor. This study aims to model the topic of lecturer research publications taken from the ResearchGate and Google Scholar platforms so that it is easier for students to choose a thesis supervisor whose research topic is relevant to the student's thesis using the Latent Dirichlet Allocation method. The LDA method will mark each word in the topic in a semi-random distribution. It will calculate the probability of the topic in the dataset and the likelihood of the word against the topic for each iteration. The results of LDA modeling present six main topics of lecturer research with the highest coherence score of 0.764, and then the resulting topics and thesis titles will be compared using cosine similarity. Students can use The highest cosine value as a reference when determining the right thesis topic. Thus, the supervisor selection process will be more focused and in accordance with the student's research interests.
Single-Image Face Recognition For Student Identification Using Facenet512 And Yolov8 In Academic Environtment With Limited Dataset Imam Muttaqin, Almas Najiib; Luthfiarta, Ardytha; Nugraha, Adhitya; Salsabila, Pramesya Mutia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.3908

Abstract

Face recognition has become one of the most significant research areas in image processing and computer vision, mainly due to its wide applications in security, identity verification, and human and machine interaction. In this study, FaceNet512 and YOLOv8 models are used to overcome the challenges in face recognition with a limited dataset, which is only one formal photo per individual. The application of image augmentation to the model achieved 90% accuracy and ROC curve of 0.82, while the model without augmentation achieved 89% accuracy and ROC curve of 0.79. FaceNet512 showed superiority in producing more accurate and detailed facial representations compared to other models, such as ArcFace and FaceNet, especially in handling minimal facial variations. Meanwhile, YOLOv8 provides efficient face detection across various lighting conditions and viewing angles. The main challenge in this research is the low quality of the original image, which can reduce the accuracy of face recognition. These results show the great potential of using deep learning-based face recognition systems in the real world, especially for automatic attendance applications in academic environments.
Comparison of IndoNanoT5 and IndoGPT for Advancing Indonesian Text Formalization in Low-Resource Settings Firdausillah, Fahri; Luthfiarta, Ardytha; Nugraha, Adhitya; Dewi, Ika Novita; Hafiizhudin, Lutfi Azis; Mumtaz, Najma Amira; Syarifah, Ulima Muna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4935

Abstract

The rapid growth of digital communication in Indonesia has led to a distinct informal linguistic style that poses significant challenges for Natural Language Processing (NLP) systems trained on formal text. This discrepancy often degrades the performance of downstream tasks like machine translation and sentiment analysis. This study aims to provide the first systematic comparison of IndoNanoT5 (encoder-decoder) and IndoGPT (decoder-only) architectures for Indonesian informal-to-formal text style transfer. We conduct comprehensive experiments using the STIF-INDONESIA dataset through rigorous hyperparameter optimization, multiple evaluation metrics, and statistical significance testing. The results demonstrate clear superiority of the encoder-decoder architecture, with IndoNanoT5-base achieving a peak BLEU score of 55.99, significantly outperforming IndoGPT's highest score of 51.13 by 4.86 points—a statistically significant improvement (p<0.001) with large effect size (Cohen's d = 0.847). This establishes new performance benchmarks with 28.49 BLEU points improvement over previous methods, representing a 103.6% relative gain. Architectural analysis reveals that bidirectional context processing, explicit input-output separation, and cross-attention mechanisms provide critical advantages for handling Indonesian morphological complexity. Computational efficiency analysis shows important trade-offs between inference speed and output quality. This research advances Indonesian text normalization capabilities and provides empirical evidence for architectural selection in sequence-to-sequence tasks for morphologically rich, low-resource languages.
Co-Authors Abu Salam Amanda, Nabila Rizka Ardytha Luthfiarta Ariel Immanuel Prayogo Astuti, Yani Parti Cahya, Leno Dwi Dheo Rimbano Dwi Larso Egia Rosi Subhiyakto, Egia Rosi Erick Irawadi Alwi, Erick Irawadi Erwin Yudi Hidayat Fahrezi, Sahrul Fahrezi Fahrezi, Sahrul Yudha Fahri Firdausillah Farandi, Muhammad Naufal Erza Fauzyah, Zahrah Asri Nur Firmansyah, Gustian Angga Gaffar, Andi Widya Mufila Ganiswari, Syuhra Putri Gustian, Dinda Aulia Hafiizhudin, Lutfi Azis Harun Al Azies Huda, Alam Muhammad Idayati, Irma Ika Novita Dewi Imam Muttaqin, Almas Najiib Joshua Calvin Kurniawan Krisna, Julius Immanuel Theo Kurniawan, Joshua Calvin Md. Mahadi Hasan, Md. Mahadi Miftahussilmi, Ray Ikhsandri Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nisa, Laila Rahmatin Novanto , Fandy Novanto , The, Fandy Octaviani, Dhita Aulia Paramita, Cinantya Prayogo , Ariel Immanuel Primadya, Nauval Dwi Rachmad Hidayatullah, Rachmad Rumita, Dea Salsabila, Pramesya Mutia Setiawan, Dicky Sri Winarno Sundari Sundari Syarifah, Ulima Muna The, Fandy Novanto Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine