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Journal of Practical Computer Science (JPCS)
Published by Universitas Pelita Bangsa
ISSN : -     EISSN : 28098137     DOI : https://doi.org/10.37366/jpcs
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal of Practical Computer Science (JPCS) sebagai media kajian ilmiah dari hasil penelitian, pemikiran dan kajian dan implementasi berkaitan dengan bidang Ilmu Komputer Praktis. Fokus dan ruang lingkup Journal of Practical Computer Science (JPCS) meliputi: - Rekayasa Perangkat Lunak - Kecerdasan Buatan - Data Mining - Machine Learning - Internet of Things - Jaringan Komputer - Keamanan Informasi - Topik kajian lain yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol. 5 No. 2 (2025): November 2025" : 6 Documents clear
Analisis RIsiko Keamanan Teknologi Infromasi Pada Instansi Pemerintahan Purbalingga Aprilia, Kharisma; Maghfira, Rahajeng Sasi; Aji, Ranggi Praharaningtyas; Saputra, Dhanar Intan Surya
Journal of Practical Computer Science Vol. 5 No. 2 (2025): November 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v5i2.5960

Abstract

Digital transformation within the governmental sector has accelerated the wide use of information systems for the support of public services and administrative efficiency. However, this development has also introduced serious challenges of information security that are often overlooked by local governments. The purpose of this research is to identify and assess the risk of IT security at Kabupaten Purbalingga’s Department of Communication and Information . The research is to be conducted using the qualitative descriptive approach based on the OCTAVE-S method. Data collection will involve direct observation of IT infrastructure and a thorough interview with the technical personnel responsible for information systems. From the analysis conducted, it can be concluded that DINKOMINFO has serious threats such as defacement attacks, DDoS, and internal vulnerabilities due to the use of weak credentials. Without a strong security policy, these weaknesses will only become more widespread, not much different from the current resource limitations. Therefore, the best solution that can be implemented in the long term is the implementation of a Security Operation Center or SOC, adaptive security policies, and cybersecurity awareness and training. Keyword: Information Security, OCTAVE-S, Local Government, DINKOMINFO, Cyber ​​Risk.
Human Development Index Classification in Central Java Using the K-Nearest Neighbors Method for Data-Driven Policy Making Vulandari, Retno Tri; Harjanto, Sri
Journal of Practical Computer Science Vol. 5 No. 2 (2025): November 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v5i2.5996

Abstract

Human development in Central Java continues to show positive progress, as reflected in the consistent increase of the Human Development Index (HDI) across the province. The HDI serves as a key indicator for assessing the success of initiatives aimed at improving the overall quality of life. It measures how well residents are able to access the benefits of development, including long and healthy lives, education, knowledge, and a decent standard of living. The HDI is influenced by four primary components: life expectancy, expected years of schooling, mean years of schooling, and per capita expenditure. Currently, the Central Bureau of Statistics determines HDI values for each regency and city in Central Java using a specific calculation formula. In this study, we aim to classify these regions into three categories based on their HDI levels: very high, high, and moderate estimate areas. To perform this classification, we applied the K-Nearest Neighbors (KNN) algorithm—an effective, non-parametric method that classifies data points based on the majority class among their nearest neighbors in the feature space. KNN is well-suited for classification tasks involving complex, real-world data, offering both accuracy and interpretability. The classification of the 2024 HDI data using KNN resulted in three distinct groups: Cluster 1 (moderate estimate) includes 18 regions: Cilacap, Banyumas, Purbalingga, Banjarnegara, Kebumen, Wonosobo, Magelang, Wonogiri, Grobogan, Blora, Rembang, Temanggung, Kendal, Batang, Pekalongan, Pemalang, Tegal, and Brebes. Cluster 2 (high estimate) consists of 13 regions: Purworejo, Boyolali, Klaten, Sukoharjo, Karanganyar, Sragen, Pati, Kudus, Jepara, Demak, Semarang Regency, Kota Pekalongan, and Kota Tegal. Cluster 3 (very high estimate) comprises 4 urban areas: Kota Magelang, Kota Surakarta, Kota Salatiga, and Kota Semarang. This classification provides valuable insights into regional development disparities and can support evidence-based planning and policy-making.
Analisis Big Data Kesiapan Digital Global Menggunakan PCA dan FASTCLUS Setiawan, Ariyono; Yudianto, Prima Yudha; Ika, Ni Made; Masdiyas, I Gede Susrama
Journal of Practical Computer Science Vol. 5 No. 2 (2025): November 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v5i2.6075

Abstract

This study aims to identify global digital readiness patterns using an innovative big data approach by integrating Principal Component Analysis (PCA) and the FASTCLUS clustering algorithm. By analyzing 1,076 observations from 2000 to 2023, this study focuses on four key indicators: secure server deployment and ICT infrastructure index. Referring to the Digital Readiness Framework, this study positions digital infrastructure and cybersecurity as the foundation of national digital capacity. PCA results show that two main components explain 56.35% of the total variance, and the clustering process produces strong segmentation with Pseudo F = 1275.85 and R² = 70.4%. Three digital readiness clusters were successfully identified: advanced (4 countries), moderate (64 countries), and transitional (1008 countries), each exhibiting distinct infrastructure and digital index characteristics. This study contributes a replicable, globally scalable classification framework for comparing digital readiness across countries. Its originality lies in the integration of big data techniques and unsupervised learning into national digital readiness assessments. These findings are important for supporting data-driven digital policy formulation, particularly for developing countries in designing more precise and contextual ICT resource allocation strategies.
Prediksi Risiko Hipertensi Dini di Puskesmas Sukabumi Menggunakan Algoritma Support Vector Machine (SVM) Mubharak, Gilang Fauzul; Asriyanik, Asriyanik; Prajoko, Prajoko
Journal of Practical Computer Science Vol. 5 No. 2 (2025): November 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v5i2.6109

Abstract

Perancangan dan Analisis Optimasi Metode Akuisisi Disk Paralel Menggunakan Python Thread dan Multiprocess Pada Odroid H4 Plus Nugroho, Catur Adi; Sabila, Fadlilah Izzatus; Fauziah, Fauziah; Sholohati, Ira Diana
Journal of Practical Computer Science Vol. 5 No. 2 (2025): November 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v5i2.6382

Abstract

Digital data acquisition in a forensic context requires high efficiency, especially when handling large-capacity storage media with limited power consumption devices such as Single Board Computers (SBCs). This study aims to design and evaluate a parallel disk acquisition tool based on the Odroid H4 Plus SBC with a multiprocess and thread approach. Three methods are compared: Serial DC3DD, Parallel DC3DD, and the Designed Method (parallel multiprocess and thread). Evaluations are conducted on various disk sizes (4GB, 16GB, 240GB, and 500GB) and two media types (USB and SATA), with key metrics including execution time, throughput, memory (RAM) usage, and hash validity. The test results show that the design method has the most efficient execution time, with significant time savings especially on large disks. The highest throughput is also achieved by this method, especially on SATA media. In terms of memory consumption, this method shows an increase in RAM usage of up to 32 MB, which is still within the light and efficient limits for SBC devices. All methods produce hash values identical to the reference hash, proving that data integrity is maintained. These findings demonstrate that a multiprocess and thread-based parallel approach effectively improves digital forensic acquisition performance significantly without sacrificing accuracy or resource efficiency. Future research can focus on optimizing buffer management, supporting a higher number of disks simultaneously, and integrating automation into a web-based interface to improve portability and ease of use
Pengembangan AI Generatif ChatBot berbasis Mobile App dengan MIT App Inventor Suharto, Agus; Hartono, Rahmat
Journal of Practical Computer Science Vol. 5 No. 2 (2025): November 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v5i2.6395

Abstract

This research explores the integration of generative artificial intelligence (AI) into mobile app development using MIT App Inventor, a visual programming platform widely adopted in educational settings. As generative AI technologies such as Large Language Models and image synthesis tools become more accessible, the potential for significantly enhancing creativity, personalization, and automation in mobile apps becomes clear. The research presents a prototype chatbot application with AI to a dynamic story generator built using Web App Inventor components to interact with external AI APIs. Through usability testing with high school students to evaluate technical feasibility, user experience, and pedagogical value, the research shows that generative AI can enrich learning outcomes and application functionality, although challenges remain in managing latency, API complexity, and ethical considerations. . The results of the study were evaluated using EUCS and Likert Scale as measurements, namely: Content 75% “Satisfied”, Accuracy 76% “Satisfied”, Ease Of Use 87% “Very Satisfied”, Format 69% “Satisfied”, Timeliness 67% “Satisfied”, so that the final result of the average index is 74.8%. It is hoped that it can recommend platform improvements, and be used for future research to support the development of AI-powered applications in a block-based environment.

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