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Jurnal Teknik Informatika dan Teknologi Informasi
Published by Lembaga Pengembangan Kinerja Dosen
ISSN : 28279379     EISSN : 28279387     DOI : 10.55606
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) adalah jurnal ilmiah peer review yang diterbitkan oleh Politeknik Pratama. Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) terbit dalam tiga edisi dalam setahun, yaitu edisi Februari, Juni dan Oktober. Kontributor Jurnal Teknik Informatika dan Teknik Informasi (JUTITI) berasal dari peneliti, akademisi (dosen dan mahasiswa) di bidang Teknik Informatika dan Teknik Informasi. Jurnal Teknik Informatika dan Teknik Informasi. memiliki fokus dan ruang lingkup yang terdiri dari: Computer Architecture Parallel and Distributed Computer Pervasive Computing Computer Network Embedded System Human—Computer Interaction Virtual/Augmented Reality Computer Security Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods) Programming (Programming Methodology and Paradigm) Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Network Traffic Modeling
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 197 Documents
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Klasifikasi Wajah untuk Rekomendasi Gaya Rambut Menggunakan SVM dan Random Forest Mochamad Rizky Ainur Ridho; Mahatma Mahesa; Bagus Adi Wibowo; Rachmat Adi Purnama; Veti Apriana; Rame Santoso
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6360

Abstract

The goal this project is to create a face-shape classification and hairstyle recommendation system by combining Support Vector Machine (SVM) and Random Forest (RF) algorithms with Histogram of Oriented Gradients (HOG) feature extraction. This study is motivated by the growing demand for individualized appearance support, as many users find it difficult to find haircuts that complement their face features. The method first preprocesses facial photos, uses HOG to extract key geometric and texture-based features, and then uses SVM and RF models to categorize the images. For training, validation, and testing, a dataset of five different face shapes is utilized. According to experimental results, the Random Forest model has an accuracy of about 89%, while the SVM model achieves an accuracy of about 95%. These findings suggest that SVM is better suited for managing high-dimensional feature spaces generated by HOG extraction. A recommendation system that offers hairstyle recommendations based on the anticipated face shape is then integrated with the trained model. The system is useful for real-time use since it can process pictures taken with the camera or uploaded from the gallery. Overall, this study shows that integrating HOG with SVM offers a dependable basis for creating customized hairdo recommendations as well as an efficient method for face-shape classification.  
Penerapan Algoritma Association Rule Mining (Apriori) untuk Analisis Pola Pembelian (Market Basket Analysis) pada Data Transaksi Ritel Jadiaman Parhusip; Tyara Rahmidasari; Erina Ekanova Safitri; Tety Citra Natha; Nur Haniatin Jannah
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6362

Abstract

Market Basket Analysis (MBA) is a data mining technique used to identify product combination patterns that frequently occur together within a single transaction. This study applies the Apriori Algorithm as the primary method for discovering Association Rules from a large-scale e-commerce retail transaction dataset consisting of approximately 500,000 records, focusing on consumer purchasing behavior in the United Kingdom market. The research follows a systematic data mining process that includes data integration, data cleaning to remove anomalies such as negative prices and quantities, and data transformation using One-Hot Encoding to convert transaction records into a suitable binary matrix format. The Apriori Algorithm is then used to generate frequent itemsets, which are evaluated using Support, Confidence, and Lift to determine their strength and significance. The results show several strong Association Rules with Lift values greater than 1.0, indicating positive correlations between specific product pairs. These findings offer useful insights into consumer purchasing tendencies and can support various retail strategies, such as improving product recommendation systems, optimizing store layout, enhancing promotional bundles, and strengthening targeted marketing efforts. Overall, this study demonstrates that Apriori-based MBA is capable of extracting actionable knowledge from large-scale retail datasets and contributes to more effective, data-driven decision-making in the retail sector.
Pengaplikasian Algoritma C4.5 untuk Menganalisis Hubungan Kebiasaan Harian Siswa terhadap Prestasi Akademik Muhammad Zikri Ansyari; Muhammad Yasin
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6364

Abstract

This study aims to analyze the relationship between students’ daily habits and their academic performance using the C4.5 algorithm. The daily habit variables examined include study duration and intensity, sleep quality and patterns, frequency and type of gadget use, attendance consistency, and students’ engagement in learning activities both inside and outside the classroom. The data were collected through questionnaires and combined with students’ academic grades as indicators of performance. The C4.5 algorithm was employed to construct a decision tree model capable of identifying the daily habit attributes that have the most significant influence on academic achievement. The findings reveal that study intensity, sleep quality, and attendance consistency serve as dominant factors in predicting students’ performance levels. Furthermore, the resulting decision tree model demonstrates a relatively high accuracy level, making it a useful tool for evaluation, student development planning, and decision-making processes by educators. These results confirm that the application of the C4.5 algorithm is effective in uncovering the relationship patterns between daily habits and academic achievement and has the potential to serve as a reference for efforts to improve students’ learning quality.
Klasifikasi Resiko Depresi Berdasarkan Durasi Layar dan Pola Hidup Digital Mengguanakan Metode K-Nearest Neighbor (KNN) Heri Setiawan; Helmi Fauzi Siregar
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6366

Abstract

This study aims to classify depression risk levels based on screen time and digital lifestyle patterns using the K-Nearest Neighbor (KNN) method. The dataset used includes several important variables, such as daily screen time, frequency of social media use, and sleep duration and quality. These variables were chosen because they are considered to have a strong association with mental health, particularly depressive symptoms that often arise from excessive digital device use. A KNN model was then developed and tested to categorize individuals into three depression risk categories: low, medium, and high. The evaluation results showed that the model with a k value of 5 achieved a predictive accuracy of 85%, indicating that this method is quite effective as a data-driven classification tool. The findings of this study suggest that digital lifestyle patterns can be an early indicator in predicting depression risk, thus requiring more systematic monitoring. However, this model still needs to be combined with clinical assessment for a more comprehensive and accurate diagnosis.  
Sistem Informasi Penerbitan Surat Izin Berlayar Menggunakan Metode First In First Out (FIFO) di Kantor Kesyahbandaran dan Otoritas Pelabuhan Utama Belawan Muhammad Reynaldi; Ria Eka Sari
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6367

Abstract

The issuance of sailing permits at the Belawan Harbormaster and Main Port Authority Office (KSOP) still faces various obstacles, particularly related to the accumulation of application files, irregular queues, and long document processing times. These problems impact service efficiency and user satisfaction. This study aims to design and implement a web-based information system that applies the First In First Out (FIFO) method to improve the effectiveness of the sailing permit issuance process. The study uses a Waterfall approach, which includes the stages of needs analysis, system design, implementation, and functional testing using Blackbox Testing. The results show that the application of the FIFO method to the web-based system is able to organize the service flow more fairly based on the chronological order of submissions, thereby reducing the risk of service discrimination and minimizing input errors that previously often occurred in manual processes. The system is also able to accelerate the process of verification, data validation, and printing of permits, resulting in more efficient service times. In addition, transparency of the service process is increased through real-time monitoring of application status. Overall, the information system developed has been proven to improve service quality and public trust in the performance of KSOP Belawan
Pengaruh Perkembangan Teknologi AI terhadap Kenaikan Harga GPU STMIK PESAT NABIRE Kevin Herlambang; Sandy Mirongsenggo; Donianto Kusuma Rissing; Febbiyola Rumbrapuk
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6370

Abstract

The rapid evolution of artificial intelligence (AI) has triggered a transformative shift in global computing demand, particularly for graphics processing units (GPUs), which have become essential for handling large-scale parallel computations. This study explores how the accelerating development and adoption of AI contribute directly to rising GPU prices, emphasizing the interplay between technological growth, supply chain constraints, and semiconductor manufacturing limitations. By utilizing qualitative and literature-based approaches, the research examines academic publications, industry reports, and online datasets to evaluate pricing trends, production bottlenecks, and the broader economic implications of AI’s expansion. Findings indicate that AI-driven applications, such as generative models, predictive analytics, and automated decision-making systems, have significantly increased the demand for high-performance GPUs, placing additional pressure on semiconductor supply chains already limited by production capacity, geographical concentration, and fluctuating global market conditions. These constraints create persistent price inflation, highlighting a strong causal link between AI deployment and GPU market volatility. The study also identifies structural weaknesses in supply chain systems, including overdependence on a small number of manufacturers and delays in scaling fabrication technologies. The results offer a clearer understanding of how AI’s rapid growth reshapes hardware economics and complicates accessibility for education sectors, small businesses, and independent developers. Furthermore, the study underscores the need for sustainable strategies, such as diversifying semiconductor production, exploring alternative computing architectures, and improving forecasting models to support balanced technological advancement.
Sistem Deteksi Penyakit pada Tanaman Cabai Menggunakan RT-DETR dan YOLLOv8 Pedro Lucio Parera; Gregorius Bayu Listyoputro; Krisnavaro Raihananta; Rachmat Adi purnama; Rame Santoso; Veti Apriana
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6373

Abstract

This study investigates the performance of two state-of-the-art object detection models, YOLOv8 and RT-DETR, in identifying diseases in chili plants, which represent a major challenge affecting horticultural productivity. Diseases such as anthracnose and Cercospora leaf spot often cause significant yield losses, and traditional manual identification tends to be inefficient, subjective, and error-prone due to the visual similarities found among disease symptoms. The objective of this research is to evaluate and compare the capabilities of both models using the Chili dataset from Roboflow Universe consisting of four classes: Anthracnose, Cercospora Leaf Spot, Healthy Fruit, and Healthy Leaf. The methodology includes data preprocessing, training using identical hyperparameters, and performance evaluation through accuracy and model behavior analysis during real-world testing. The findings indicate that RT-DETR achieves higher accuracy in controlled testing, reaching 90% for Anthracnose, 95% for Healthy Leaf, 100% for Healthy Fruit, and 85% for Cercospora Leaf Spot, supported by its transformer-based architecture that enhances spatial understanding. However, YOLOv8 demonstrates superior stability and consistency in real-world scenarios involving varying lighting, leaf orientations, and natural texture variations. The model also produces fewer misclassification errors, making it more reliable for practical field deployment. The implications of these results show that YOLOv8 is the most suitable model for integration into a Streamlit-based application due to its fast, responsive, and accurate inference, supporting early disease detection for chili farmers.
Sistem Pendukung Keputusan Penerimaan Anak Asuh Baru di Panti Asuhan Al-Washliyah Pulo Brayan Menggunakan Metode Multi-Attribute Utility Theory (Maut) Widya Adelina; Fhery Fhery
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6376

Abstract

This study discusses the development of a Decision Support System (DSS) to assist the process of accepting new foster children at the Al-Washliyah Pulo Brayan Orphanage. The previous foster child selection process was conducted manually and subjectively, making it vulnerable to unfairness and assessment errors. This study applies the Multi-Attribute Utility Theory (MAUT) method to objectively assess foster children based on eight criteria: age, family circumstances, prospective economic conditions, health, education, legal status, independence, and behavioral and social well-being. This study uses a web-based software engineering approach with the PHP programming language and a MySQL database. The results show that the application of the MAUT method can accelerate the decision-making process, improve fairness in selection, and minimize subjective bias from decision-makers. The developed system is expected to serve as a reference for other orphanages in developing more efficient and transparent digital-based admission systems.
Perancangan Sistem Informasi Pengadaan Barang Berbasis Web pada PT XYZ Kota Depok Muhammad Rizky Perdana; Muhammad Raulfie Al Adzani; Yohanes Beharato Lase; Anak Agung Gede Adwitya Yudaya Aryuntra
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6377

Abstract

The procurement process is a critical activity that supports the operational continuity of a company. However, manual procurement procedures often lead to issues such as delayed documentation, data duplication, inaccurate information, and difficulty in tracking records. This study aims to design a web-based procurement information system for PT XYZ in Depok City to ensure that procurement activities are managed in an integrated, efficient, and accurate manner. The development method used in this research is the Prototype model, which allows direct interaction between developers and users, enabling iterative validation of system requirements. Data were collected through observation, interviews, and literature review to obtain a comprehensive understanding of user needs. The system design includes process modeling using UML diagrams, database modeling with ERD and LRS, and the creation of a system interface prototype. Based on black-box testing, essential functions such as user authentication performed as expected. The resulting system design is considered capable of improving data recording effectiveness, accelerating ordering processes, and simplifying procurement monitoring. This research serves as a foundation for future system development and full implementation within the company.
Comprehensive Cybersecurity Framework for Digital Governance: Threat Assessment, Risk Mitigation, and Regulatory Compliance in Indonesia Wildan Maulana Assani Mualim; Fitri Yul Dewi Marta; Ira Meiyenti
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6379

Abstract

Digital transformation of government administration brings significant benefits in improving public service efficiency and citizen access to information. However, digitalization also opens opportunities for increasingly complex and organized cyber threats. This journal explores a comprehensive cybersecurity framework for digital governance through an extensive literature review that includes threat assessment, risk mitigation strategies, and regulatory compliance analysis. This research analyzes international frameworks (NIST CSF 2.0, ISO/IEC 27001:2022, COBIT 2019), Indonesian national standards (Law No. 1 of 2024 on Information and Electronic Transactions, SPBE, BSSN), and best practices in incident response and Zero Trust Architecture. Results demonstrate that government cybersecurity requires a holistic approach integrating technical aspects, policy, human resources, and governance. This journal recommends implementing a comprehensive cybersecurity framework, enhancing human capital capacity, adopting cutting-edge technology, and fostering inter-institutional coordination to build sustainable cybersecurity resilience for government entities.

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