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Jurnal Publikasi Teknik Informatika
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 28089367     EISSN : 28088972     DOI : 10.55606
Core Subject : Science,
Computer Science & IT
Jurnal Publikasi Teknik Informatika diterbitkan oleh Universitas Sains dan Teknologi Komputer Semarang. Jurnal Publikasi Teknik Informatika memuat naskah hasil-hasil penelitian di bidang Teknik Informatika, Teknik Komputer, Teknik Elektro. JUPTI berkomitmen untuk memuat artikel berbahasa Indonesia yang berkualitas dan dapat menjadi rujukan utama para peneliti dalam bidang ilmu Teknik Informatika, Teknik Komputer, Teknik Elektro
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 141 Documents
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KEPEMIMPINAN TRANSFORMASIONAL DALAM TATA KELOLA PEMERINTAHAN DAN INOVASI SEKTOR PUBLIK: KAJIAN LITERATUR: Studi Kasus: Dinas Koperasi dan UKM Provinsi Kepulauan Riau
Jurnal Publikasi Teknik Informatika Vol. 4 No. 3 (2025): September : Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v4i3.5622

Abstract

The submission of the Certificate of Feasibility and Propriety (SK UKK) for cooperatives in the Riau Islands Province is still carried out manually, which hinders administrative processes, especially for cooperatives located far from the Office of Cooperatives and SMEs of the Riau Islands Province. To address this issue, this internship project aims to design and develop the Feasibility and Propriety Test Application (ALPUKAT), a web-based platform that facilitates online SK UKK submissions. The system was developed using the Laravel 12 framework, the PHP programming language, and MySQL database. The application provides account registration and document upload features for cooperatives, while the Cooperative Office, acting as the administrator, can verify documents, schedule interviews, and upload meeting minutes and SK UKK certificates. The result of this internship is a functional application that can be utilized by both cooperatives and the Cooperative Office within the Riau Islands Province. The implementation of this system is expected to make the SK UKK submission process more efficient, transparent, accurate, and centralized, while also supporting the government’s efforts to promote the digitalization of public services.
Kerangka Kerja Penambangan Data yang Skalabel untuk Analisis Hukum Komputasional: Implementasi Pipeline Python dan Selenium pada Putusan Perkara Perdata Mahkamah Agung Indonesia
Jurnal Publikasi Teknik Informatika Vol. 4 No. 3 (2025): September : Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v4i3.5634

Abstract

The digitization of judicial records has introduced challenges in handling large-scale data, which traditional legal research methods cannot adequately address. This paper outlines the development and evaluation of an automated data mining framework designed to collect judicial decisions from the Indonesian Supreme Court's public directory. The aim is to create a data pipeline for analyzing civil litigation trends. The approach involves a multi-stage data acquisition process using a custom Python script and a headless Selenium WebDriver to navigate complex, JavaScript-rendered websites and handle asynchronous pagination. The BeautifulSoup library is used for efficient HTML parsing and metadata extraction. Data is structured and stored in a CSV file, ensuring data integrity during interruptions. The system successfully mined 21,780 civil case records from the 2024 period, achieving an extraction rate of 12 decisions per minute with a 75% success rate. This success rate was influenced by the website's responsiveness, requiring a 120-second Read Timeout and persistent retries. Descriptive analysis using the Pandas library identified unlawful acts, breach of contract, and land disputes as the most prevalent civil litigation categories. This research provides a scalable model for legal informatics and offers foundational data for future analyses, such as Natural Language Processing (NLP) on judicial texts.
Klasifikasi Tingkat Kematangan Buah Kelapa Sawit Menggunakan Metode Convolutional Neural Network
Jurnal Publikasi Teknik Informatika Vol. 4 No. 3 (2025): September : Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v4i3.5841

Abstract

Classifying the ripeness level of oil palm fruits represents a critical aspect of the oil palm industry that dominates Indonesia's economy. This research aims to develop and evaluate a Convolutional Neural Network (CNN) model to automatically, objectively, and accurately classify the ripeness level of oil palm fruits based on digital image analysis. The underlying problem of this research is the manual harvesting practice that relies on subjective assessment by harvesters, resulting in inconsistency and substantial economic losses. The research approach employs a quantitative experimental methodology with a dataset of 1,840 digital images of oil palm fruits balanced across four ripeness categories (unripe, semi-ripe, ripe, overripe). Image preprocessing was performed to standardize input with a data split of 80% training and 20% testing. The implemented CNN model achieved an average accuracy of 76.52% with optimal accuracy of 82.61%, precision of 0.77, recall of 0.77, and F1-score of 0.76 from five independent test runs. RGB profile analysis revealed a significant correlation between color pigment values and ripeness level, with extreme categories (unripe and ripe) achieving accuracy >95%, while transitional categories (semi-ripe) demonstrated higher challenges. Per-category results showed excellent F1-scores (0.946–0.983) for all classes, indicating that the model learned meaningful ripeness indicators based on biological pigment physiology. System implementation was complemented with a user-friendly Graphical User Interface (GUI) based on MATLAB, enabling non-technical operators to use the model directly. Functional black-box testing demonstrated a 100% pass rate, validating the system's readiness for operational deployment. In conclusion, CNN can be implemented as a practical solution for harvest automation that enhances objectivity, consistency, and efficiency in harvest timing determination, with positive implications for product quality, industry sustainability, and economic value added for Indonesian oil palm farmers. Keywords: Ripeness classification, oil palm fruit, Convolutional Neural Network, digital image processing, harvest automation, machine learning, RGB analysis
Peran Website dalam Meningkatkan Citra dan Branding Sekolah
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6031

Abstract

This study explores the role of school websites in shaping institutional image and branding in the digital era. Using a descriptive qualitative approach through a structured literature review, the research synthesizes national and international publications from 2020–2025 related to educational websites, institutional reputation, and school branding. Data were obtained through systematic database searches and document selection using purposive and snowball techniques. Thematic analysis identified three core insights: (1) website design and quality serve as representations of institutional identity, (2) authentic and communicative digital content enhances public engagement, and (3) stakeholder trust is shaped by the transparency, completeness, and timeliness of website information. These findings affirm the strategic importance of school websites in branding practices and emphasize the need for professional digital information management. Theoretically, the study integrates brand identity and website quality into the discourse on educational branding, while practically offering guidance for schools to develop credible, user-centered websites. Further research is recommended to examine user experiences directly.
Pemanfaatan Sistem Informasi Manajemen dalam Meningkatkan Efisiensi Operasional UMKM: Studi Kualitatif di Kantin Universitas Pamulang Viktor
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6185

Abstract

This study investigates the effectiveness of Management Information System (MIS) implementation in enhancing operational efficiency among Micro, Small, and Medium Enterprises (MSMEs) at Kantin Universitas Pamulang Viktor. Employing a qualitative approach with descriptive analysis, this research collected primary data from 50 MSME respondents through structured interviews and observation. The findings reveal that 58% of MSMEs have adopted various forms of MIS, including Digital POS systems (34.5%), accounting applications (24.1%), spreadsheet tools (24.1%), and QRIS payment systems (17.2%). Quantitative analysis demonstrates that MSMEs utilizing MIS achieved significantly higher daily revenue (Rp1,047,931) compared to non-adopters (Rp784,762), representing a 33.5% increase. Furthermore, MIS-adopting businesses recorded higher daily customer counts (42.3 customers) versus non-adopters (29.8 customers), indicating improved service efficiency. The study identifies key benefits including streamlined transaction processing, accurate financial recording, and enhanced customer service quality. However, challenges such as limited digital literacy, initial investment costs, and technological adaptation remain significant barriers. This research contributes to the understanding of digital transformation in traditional food service MSMEs and provides practical recommendations for policy makers and MSME practitioners in accelerating digital adoption within campus-based economic ecosystems.
Model Perancangan Sistem Informasi Pengelolaan Perpustakaan di SMAN 1 Maniis Menggunakan Metode UML
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6187

Abstract

This research aims to design and build a stevedoring workforce information system (TKBM) application using the Extreme Programming method. Currently, the process of recording attendance, scheduling, and assigning tasks is still done manually, which often results in errors and administrative delays. The application was developed using the Flutter framework using the Dart language and a MySQL database, to replace conventional paper-based and spreadsheetbased methods that are inefficient. This system makes it easier for officers to record attendance, manage payslips, and convey information about ships that will dock. Using the Extreme Programming approach, development was carried out iteratively and responsive to user needs. Test results show that the application improves work efficiency, reduces recording errors, and speeds up the TKBM administrative process. It is expected that this system can improve port operations more organized and encourage a significant increase in workforce productivity.
PENERAPAN METODE PCA DAN APRIORI DALAM DETEKSI POLA PENGGUNAAN INTERNET DIKALANGAN MAHASISWA
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6207

Abstract

University students generate extensive digital footprints that form complex, high-dimensional datasets reflecting diverse patterns of online behavior. Understanding these patterns requires analytical methods capable of handling large and interrelated variables. This study aims to map internet usage trends among university students using an integrated approach that combines Principal Component Analysis (PCA) and the Apriori Algorithm. PCA is employed to reduce data dimensionality by identifying the most influential components and eliminating redundant information, thereby simplifying the dataset without losing essential characteristics. Subsequently, the Apriori Algorithm is applied to uncover association rules that describe relationships between different types of digital activities. Data were collected through structured questionnaires distributed to active university students, capturing various aspects of their internet usage behavior. Through this combined methodology, the study seeks to identify the main factors that shape students’ digital habits and to reveal hidden behavioral patterns that may not be evident through conventional analysis. The expected results will provide a clearer understanding of how students interact with digital platforms and online resources. Ultimately, the findings are intended to serve as an empirical basis for designing more effective, data-driven digital literacy programs and strategies in higher education environments.
PENGARUH DINAMIKA HARGA ITEM DIGITAL TERHADAP STABILITAS EKONOMI DI MARKETPLACE GAME ONLINE
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6236

Abstract

The digital economy has transformed online games into complex virtual markets, where digital items hold real monetary value. This price volatility raises concerns about the stability of the virtual economy. This study analyzes the impact of digital goods price dynamics on economic stability in online game markets and identifies key factors contributing to instability. A quantitative approach with an explanatory design was used, collecting data through a survey of 150 active online game players who transact digital items. Multiple linear regression was employed to assess the effects of price volatility, asset scarcity, and transaction volume on economic stability. The results indicate that price volatility negatively affects virtual economic stability (β = −0.47; p < 0.05), while asset scarcity (β = 0.32; p < 0.05) and transaction volume (β = 0.41; p < 0.01) positively impact stability. These findings suggest that uncontrolled price fluctuations reduce purchasing power and player confidence, while proper scarcity management and high transaction liquidity help stabilize the virtual economy. The study recommends that game marketplace developers implement price stabilization mechanisms, such as price floors and ceilings, regulate digital goods supply, and enhance transaction transparency and frequency to foster a stable, fair, and sustainable virtual economy.
Perancangan Website Profil Dan Layanan Informasi Inspektorat Provinsi Sumatera Utara
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6302

Abstract

The Inspectorate of North Sumatra Province plays a strategic role in ensuring internal government supervision, strengthening accountability, and supporting the implementation of good governance. However, limitations in integrated digital platforms have caused public information delivery to be less effective and difficult for users to access. This study aims to design and develop a Profile and Information Service Website for the Inspectorate of North Sumatra Province as a digital solution that provides institutional profiles, organizational structure, employee data, news updates, and public information request services in a structured and user-friendly manner. The system was developed using the Waterfall method, which includes requirement analysis, system design, implementation, and testing phases. The implemented system successfully delivers an informative interface that aligns with user needs and supports easy access to all main features. Blackbox testing results confirm that the system behaves as expected, with all functionalities—such as accessing profiles, viewing information centers, and submitting information requests—operating without errors. This website is expected to enhance transparency, improve public information services, and strengthen the Inspectorate’s communication with stakeholders. Future development may include integrating public complaint services, enhancing data security, and improving mobile responsiveness to support a broader and more efficient user experience.
Optimasi Artificial Neural Network Untuk Klasifikasi Gaya Belajar Mahasiswa Menggunakan Model Visual, Auditori, dan Kinestetik (VAK)
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Universitas Sains dan Teknologi Komputer Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6311

Abstract

This study proposes an optimized Artificial Neural Network (ANN) framework for the automatic classification of student learning styles based on the Visual, Auditory, and Kinesthetic (VAK) model to support educational personalization. The system utilizes data from a 36-item questionnaire, which are preprocessed using z-score standardization and encoded into numerical features. Several ANN configurations were evaluated to examine the influence of network depth, activation functions, regularization strength (λ ranging from 1 × 10⁻⁴ to 1 × 10⁻²), and the number of training iterations between 500 and 2000. Model training employed the Adam optimizer combined with early stopping to ensure stable and efficient convergence. The results demonstrate that proper data standardization and suitable regularization significantly enhance model generalization and training stability. The optimal model consists of two hidden layers with 64 and 32 neurons using ReLU activation and λ = 1 × 10⁻², achieving an accuracy of 91.9% and a macro-F1 score of 0.908. Overall, systematic hyperparameter optimization improves the robustness and reliability of ANN-based learning style classification for adaptive learning systems.

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