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Prajanto Wahyu Adi
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Jurnal Masyarakat Informatika
Published by Universitas Diponegoro
ISSN : 20864930     EISSN : 27770648     DOI : https://doi.org/10.14710/jmasif.crossmark
Core Subject : Science,
Computer Science & IT
JURNAL MASYARAKAT INFORMATIKA - JMASIF is a Journal published by the Department of Informatics, Universitas Diponegoro invites lecturers, researchers, students (Bachelor, Master, and Doctoral) as well as practitioners in the field of computer science and informatics to contribute to JMASIF in the form of research articles and review articles. We accept articles in English and Bahasa. Detailed information about the submission process can be read HERE. Authors can also download Templates at HERE. JMASIF Topics include, but are not limited to Applied Computer Science, Artificial Intelligence, Text and Natural Language Processing, Image Processing and Pattern Recognition, Computer Vision, Data Mining, Cryptography, Cybersecurity, Computer Network, Computational Theory and Mathematics, Game Technology, Human and Computer Interaction or UI/UX, Information System, Software Engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 196 Documents
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Regionprops Segmentation in Convolutional Neural Network for Identification of Lung Cancer Disease and Position Syafira, Zahra Ghina; Sari, Christy Atika; Mulyono, Ibnu Utomo Wahyu; Agustina, Feri; Suprayogi, Suprayogi; Doheir, Mohamed
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.73967

Abstract

Lung cancer is one of the leading causes of death in the world, so early detection is very important to increase the chances of patient recovery. This study aims to develop a method for identifying lung cancer types using Convolutional Neural Network (CNN) combined with Regionprops segmentation technique to determine the position of cancer in CT scan images. The dataset used consists of 1,294 CT scan images classified into three classes, namely Benign, Malignant, and Normal, with variations in the ratio of training and testing data: 80:20, 70:30, 60:40, 50:50, and 40:60. The CNN method is used to perform classification, while the Regionprops segmentation technique is applied to determine the position of the cancer. The results showed that the model with a data ratio of 80:20 achieved the highest accuracy of 99.54%, indicating a very good generalization ability of the model. The Regionprops segmentation technique successfully separated the nodule area in the CT scan image clearly, thus providing more detailed information regarding the position of the cancer. The conclusion of this study shows that the combination of CNN and Regionprops segmentation methods is effective in detecting and analyzing lung cancer and has the potential to be used as a diagnostic tool in the medical field. This study recommends further testing with a larger dataset and optimization of model parameters to improve classification and segmentation performance.
An Efficient Bidirectional Gated Recurrent Unit Approach for Student Study Duration Modeling and Timely Graduation Forecasting Purnama, Satriawan Rasyid; Tantyoko, Henri; Vianita, Etna
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.73275

Abstract

Delays in student graduation remain a persistent challenge in higher education, with approximately 28% of students requiring more than four years to complete their studies, exceeding the standard duration. This study addresses the issue by proposing a predictive model to estimate students’ graduation year using a Bidirectional Gated Recurrent Unit (BiGRU) neural network. The model is trained on a combination of academic and financial indicators, including Grade Point (GP) scores from the first to the fifth semester, cumulative Grade Point Average (GPA), and the single tuition fee tier (UKT). The integration of these features allows the model to learn temporal patterns in students’ academic progression and financial capacity. Empirical analysis reveals that students in the UKT 8 group consistently demonstrate superior academic performance, as evidenced by their higher average GPA across semesters, compared to students in lower UKT groups. The BiGRU model achieves a Mean Absolute Percentage Error (MAPE) of 9.5%, indicating high predictive accuracy. These findings highlight the potential of deep learning models, particularly BiGRU, in forecasting academic outcomes. Furthermore, the insights generated from this model can serve as a valuable tool for universities in formulating targeted academic interventions and policies aimed at promoting timely graduation and reducing dropout rates.
Performance Analysis Cryptography Using AES-128 and Key Encryption Based on MD5 Pratama, Reza Arista; Rachmawanto, Eko Hari; Irawan, Candra; Erawan, Lalang; Laksana, Deddy Award Widya; Ali, Rabei Raad
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.75091

Abstract

The rampant misuse of data theft has created data security techniques in cryptography. Cryptography has several algorithms that are very strong and difficult to crack, including the AES (Advanced Encryption Standard) algorithm consisting of 128 bits, 192 bits, and 256 bits which have been proven resistant to conventional linear analysis attacks and differential attacks, then there is the MD-5 algorithm (Message-Digest algorithm 5) which is a one-way hash function by changing data with a long size and inserting certain data in it to be recovered. If the two are combined, it becomes more difficult to crack; therefore, to determine its performance, this study conducted a combination experiment of AES-128 with a key encrypted by MD-5, including avalanche effect tests, encryption and decryption execution times, and entropy values of encryption. The types of documents for testing are files with the extensions .docx, .txt, .pptx, .pdf, and .xlsx. After conducting tests on document files obtained from the processing time test, it shows that .txt and .pptx documents dominate with a fast process, while the longest process is obtained by .xlsx and .docx documents for all test files, then the avalanche effect test with an average of 98% and the entropy test is classified as good between values 3 - 7 which are close to value 8. This proves that the combination of the AES-128 algorithm with the MD-5 key can be used as an alternative for securing documents with stronger security, while maintaining standard processing times
Regional Stability and Dynamics of Rice Production in West Java through Spatiotemporal Clustering Hidayat, Restu Puji
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.76056

Abstract

The classification of 23 regencies/cities in West Java from 2008 to 2024 was executed using the K-Means algorithm on a dataset spanning five variables: production, harvested area, productivity, population, and agricultural workforce. K-Means was chosen for its efficiency and ease of interpretability when analyzing large-scale multivariate data across time. Optimal cluster determination involved evaluating the Elbow Method, Silhouette Score, and the Davies-Bouldin Index (DBI). Although K=5 was suggested by the Elbow Method, K=6 was selected because it demonstrated a more stable and representative regional separation, supported by the lowest DBI of 0.8221 and a relatively high Silhouette Score of 0.4531. Cluster boundaries were further validated through PCA and GIS visualization. The analysis revealed precise regional segmentation. Key findings indicate that Indramayu, Karawang, and Subang regencies are stable, high-production centers, suitable for intensification and modernization. Conversely, regions like Bandung and Garut regencies exhibited dynamic cluster shifts driven by urbanization and climate variability. This segmentation has crucial policy implications: stable areas are suitable for intensification, dynamic areas require adaptive risk-mitigation policies, and urban-influenced regions (Bandung, Bekasi, and Depok cities) must focus on diversification and agricultural innovation. Despite the limitations of K-Means’ inability to capture complex, non-linear clusters, this research highlights the value of integrating spatiotemporal clustering for policy insights. Future research should incorporate climate and land-use data with advanced clustering methods, such as DBSCAN and HDBSCAN. HDBSCAN is more suitable for modeling clusters with varying densities, and time-series approaches should also be integrated. Overall, these results provide an essential, evidence-based framework for targeted agricultural planning.
Multi-Level Secure Image Cryptosystem Using Logistic Map Chaos: Entropy, Correlation, and 3D Histogram Validation Latifa, Anidya Nur; Sari, Christy Atika; Rachmawanto, Eko Hari; Sarker, Md Kamruzzaman
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.74537

Abstract

This study presents a multi-level image encryption framework that combines password dependent SHA-256 key generation with a Logistic Map-based chaotic mechanism, supporting three operational modes: Speed, Balanced, and Security. The system is designed for scalability and robustness across diverse image sizes, achieving up to 27 percent faster encryption than AES on 1024×1024 images while maintaining high cryptographic strength. Experimental results show strong randomness with entropy reaching up to 7.98 bits per pixel, reduced adjacent pixel correlation below 0.01, and high resistance to differential attacks with NPCR above 99.6 percent and UACI around 33.4 percent. Structural integrity after decryption is also preserved with SSIM scores above 0.98. Compared to existing chaos based methods such as those proposed by Arif et al. and Riaz et al., the proposed system offers superior entropy performance, enhanced flexibility through multi-mode encryption, and broader resolution support up to 2048×2048 pixels. Comprehensive evaluations using entropy, correlation, PSNR, SSIM, XOR, and 3D histogram analysis confirm the method’s effectiveness. These findings highlight the system’s suitability for real-time, secure image transmission in environments such as IoT, medical imaging, and embedded applications.
A Critical Review of Agentic AI: Core Technologies, Applications, Ethical Implications, and Future Research Directions Goyal, Sumit
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.77084

Abstract

Artificial intelligence (AI) is progressing toward the Agentic AI paradigm, which involves intelligent systems capable of autonomous, proactive, and goal-focused behavior through adaptive interactions with their environment. This article provides a critical review of the development of Agentic AI, examining its technological foundations, application areas, and the associated technical, ethical, and policy challenges. The review employs a narrative approach, examining primary literature from the IEEE, Scopus, and ScienceDirect databases for the period 2019–2025, using keywords such as agentic AI, multi-agent systems, human–AI collaboration, and autonomous decision systems. The findings are organized into a three-layer conceptual framework that links core technologies, such as Reinforcement Learning, Multi-Agent Systems, and Natural Language Processing, with various application domains and cross-cutting challenges. The analysis indicates that despite the significant potential of Agentic AI, gaps remain in areas such as agent interoperability, autonomy assessment metrics, and field implementation limitations. This article proposes a structured research agenda aimed at developing Agentic AI that is more transparent, trustworthy, and aligned with human values.

Page 20 of 20 | Total Record : 196

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