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Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA)
Published by CV. Sinar Howuhowu
ISSN : -     EISSN : 31108245     DOI : https://doi.org/10.70134/jitifna
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering
Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA) didedikasikan untuk pengembangan dan penyebaran ilmu pengetahuan di bidang Teknologi Informasi di Indonesia. Jurnal ini memuat artikel berupa penelitian asli, ulasan literatur, maupun studi kasus yang membahas perkembangan dan penerapan teknologi informasi, baik di sektor akademik maupun industri. Topik yang menjadi fokus utama JITIFNA meliputi, tetapi tidak terbatas pada, rekayasa perangkat lunak, ilmu data (data science), kecerdasan buatan (artificial intelligence), jaringan komputer dan komunikasi, keamanan siber dan kriptografi, sistem informasi dan sistem pendukung keputusan, teknologi cloud dan komputasi terdistribusi, Internet of Things (IoT), teknologi mobile dan aplikasi cerdas, analitik big data dan machine learning, serta tren dan inovasi terbaru dalam teknologi informasi. JITIFNA bertujuan menjadi sarana publikasi ilmiah yang mendorong pertukaran ide dan solusi inovatif guna mendukung kemajuan teknologi informasi secara berkelanjutan di Indonesia.
Arjuna Subject : Teknik (semua kategori) - Teknologi Media
Articles 18 Documents
 1   2 
Pengembangan Aplikasi Mobile Berbasis Augmented Reality Untuk Pendidikan Interaktif Gidion
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 1 No. 1 (2025): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v1i1.740

Abstract

This study provides a comprehensive forensic analysis of a network-based ransomware attack using a digital forensics approach. Through a qualitative case study, we reconstructed a cyber incident that targeted corporate infrastructure, from the initial entry point to its final impact. The research methodology involved the acquisition of both volatile and static data, followed by in-depth analysis of various digital artifacts, including Windows Event Logs, the system registry, disk images, and memory dumps. Key findings indicate that the attack began with the exploitation of an RDP vulnerability, followed by lateral movement, the disabling of security features, and data exfiltration before the encryption process. The network forensics analysis confirmed the attackers' use of a double extortion tactic. This research underscores the critical importance of an integrated forensic approach (host, network, and memory) to obtain a complete picture of such a complex attack. The study's conclusions not only offer insights into the attackers' TTPs (Tactics, Techniques, and Procedures) but also provide strategic recommendations for strengthening an organization's cybersecurity posture in the future.
Penerapan Algoritma Deep Learning Dalam Pengenalan Wajah Untuk Sistem Keamanan Aldi Pebrian Simatupang
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 1 No. 1 (2025): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v1i1.741

Abstract

Modern security systems face complex challenges, especially in accurately and efficiently identifying individuals. Amidst rapid technological advancements, facial recognition systems have emerged as one of the most promising solutions. By leveraging deep learning algorithms, these systems can automatically identify and verify a person's identity from images or videos. However, the challenge lies in making these systems both accurate and fast under various environmental conditions, such as changes in lighting, viewing angles, and facial expressions. This research explores in depth the application of deep learning algorithms, specifically Convolutional Neural Networks (CNNs), in developing facial recognition systems for security applications. We test the performance of current models and analyze the effectiveness, challenges, and ethical implications of this technology. The results show that deep learning significantly improves the accuracy and robustness of facial recognition systems, making it a strong foundation for future security solutions. Nevertheless, issues such as algorithmic bias and high computational requirements remain important areas for further research.
Metodologi Agile Scrum Dalam Peningkatan Efisiensi Tim Pengembangan Aplikasi Mobile Ansari
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 1 No. 1 (2025): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v1i1.742

Abstract

This study examines the implementation of the Agile Scrum Methodology in improving the efficiency of a mobile application development team. The research focuses on analyzing how the Scrum framework, with its iterative cycles called Sprints, impacts both quantitative aspects such as development velocity and qualitative aspects like team collaboration and product quality. The research method used is a qualitative approach with a case study, involving observation, interviews, and document analysis of Scrum artifacts from a mobile application development team. The findings indicate that the adoption of Scrum significantly enhanced the team's efficiency. Dividing the project into Sprints allowed the team to maintain focus, reduce delays, and consistently deliver new features faster. Daily Scrum was proven effective in facilitating communication and problem-solving, while Sprint Review and Retrospective played a crucial role in ensuring user feedback was integrated early and fostering continuous improvement. Although initial challenges in adapting to new roles and processes were found, the benefits gained from improved collaboration, product quality, and development speed far outweighed them. This study concludes that Scrum is a highly relevant and effective methodology for mobile application development teams in facing the dynamics of a fast-paced market.
Perancangan Dashboard Interaktif Untuk Visualisasi Data Penjualan Menggunakan Teknologi Big Data Achsan Noorsalam
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 1 No. 1 (2025): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v1i1.743

Abstract

This study aims to design and develop an effective interactive dashboard for sales data visualization by leveraging big data technology. The ever-increasing volume of data necessitates more sophisticated solutions for business analysis, where static data visualization is no longer adequate. Using a descriptive-evaluative approach, this research designs a system that integrates Apache Spark for large-volume data processing and modern web technologies for the user interface. The result is a dashboard that not only displays key sales metrics in real-time through various charts but also empowers users with interactive features such as filtering and drill-down capabilities. Functionality and performance testing showed that the system could process millions of data rows with low latency. User evaluation confirmed a high level of satisfaction with the ease of use and clarity of the visualizations. This study thus proves that combining big data technology with a user-centered dashboard design can transform complex sales data into valuable business insights, accelerating decision-making and improving operational efficiency.
Analisis Pola Transaksi Pengguna Menggunakan Algoritma Asosiasi Pada Data E-Commerce Hendri
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 1 No. 1 (2025): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v1i1.744

Abstract

This study analyzes user transaction patterns in e-commerce data using association rule mining. With the increasing volume of data, understanding consumer behavior is key to gaining a competitive advantage. Market basket analysis is employed to discover relationships between items that are frequently purchased together. The method involves several steps: data preprocessing of transaction records, followed by the application of an association algorithm like Apriori or FP-Growth to generate association rules. The strength of these rules is evaluated using metrics such as support, confidence, and lift. The results successfully identify significant purchasing patterns that can be used to improve business strategies. The insights gained from this analysis can be applied to personalize product recommendations, optimize website layouts, and design more effective product bundling promotions. Overall, this study demonstrates that association rule mining is a powerful tool for transforming transactional data into actionable business intelligence, ultimately increasing profitability and customer satisfaction in the e-commerce industry.
Prediksi Curah Hujan Bulanan Di Medan Menggunakan Metode Long Short-Term Memory (LSTM) Dedek; Lailan Sofinah Harahap; Muhammad Rayhans Adrian
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 1 (2026): JITIFNA - Januari
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i1.969

Abstract

This study aims to predict monthly rainfall in Medan City using the Long Short-Term Memory (LSTM) method. The data utilized in this research comprises monthly rainfall figures and the number of rainy days for the 2015–2023 period, obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) Region I Medan via official publications of the Central Statistics Agency (BPS) of North Sumatra Province. The pre-processing stage involves data cleaning, normalization, and the construction of a time series dataset using a sliding window structure. The LSTM model was developed with two hidden layers and optimized using the Adam algorithm. Evaluation results indicate that the LSTM model effectively captures seasonal patterns and rainfall trends, as evidenced by a low Root Mean Square Error (RMSE) value. This study is expected to serve as a reference for hydrometeorological disaster mitigation in the Medan region.
Penerapan Aplikasi Marketplace Untuk Mendukung Digitalisasi Terhadap Peningkatan Penjualan UMKM Di Grosir Samingan Pria Mitra Purba
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 1 (2026): JITIFNA - Januari
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i1.975

Abstract

This study aims to examine the implementation of a marketplace application as a means of digitalization to improve sales for micro, small, and medium enterprises (MSMEs), particularly at Grosir SAMINGAN, which still relies on conventional methods such as cash transactions and direct marketing. A qualitative case study approach was used, with data collected through interviews with business owners, direct observation, and literature review. The findings reveal that limited understanding of digital technology and concerns about transaction security are the main obstacles to digital adoption. However, respondents showed strong interest in online shopping, and the store owner expressed willingness to use a marketplace if guided properly. The implemented marketplace system functioned effectively, improved operational efficiency, and expanded market reach for the business. Therefore, the use of a marketplace is considered an effective solution to support digital transformation and enhance the competitiveness of MSMEs in the digital era.
Pengembangan Aplikasi Mobile Berbasis Augmented Reality Untuk Pendidikan Interaktif Gidion
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 1 (2026): JITIFNA - Januari
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i1.991

Abstract

This study provides a comprehensive forensic analysis of a network-based ransomware attack using a digital forensics approach. Through a qualitative case study, we reconstructed a cyber incident that targeted corporate infrastructure, from the initial entry point to its final impact. The research methodology involved the acquisition of both volatile and static data, followed by in-depth analysis of various digital artifacts, including Windows Event Logs, the system registry, disk images, and memory dumps. Key findings indicate that the attack began with the exploitation of an RDP vulnerability, followed by lateral movement, the disabling of security features, and data exfiltration before the encryption process. The network forensics analysis confirmed the attackers' use of a double extortion tactic. This research underscores the critical importance of an integrated forensic approach (host, network, and memory) to obtain a complete picture of such a complex attack. The study's conclusions not only offer insights into the attackers' TTPs (Tactics, Techniques, and Procedures) but also provide strategic recommendations for strengthening an organization's cybersecurity posture in the future.                                                                      
Peran Edukasi Etika Digital Dalam Mengurangi Risiko Serangan Keamanan Informasi Alfina Elsa Putri; Nazori Suhandi
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 1 (2026): JITIFNA - Januari
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i1.1010

Abstract

Digital ethics education is becoming increasingly important with the increasing threat of cybercrime and the need to protect personal data in the digital age. This study aims to analyze the role of digital ethics education and digital literacy in reducing the risk of security attacks. Using a literature review method, the study gathered information from journals, books, and reports related to digital ethics, data security, and user behavior. The results show that digital ethics education, which includes an understanding of the safe, legal, and responsible use of technology, can increase public awareness of cyber threats and strengthen their ability to maintain data integrity and privacy. The use of information technology in the educational process has also proven effective in encouraging ethical digital behavior, reducing the opportunity for attacks such as phishing, social engineering, and data misuse. Therefore, digital ethics education plays a crucial role in shaping a society that is intelligent, critical, and safe in its digital activities, thereby significantly reducing the risk of information security incidents.
Penerapan Machine Learning Untuk Prediksi Produktivitas Pertanian Berbasis Data Cuaca Di Indonesia Anuarman Hura
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 1 (2026): JITIFNA - Januari
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i1.1018

Abstract

The agricultural sector plays a vital role in ensuring food security and economic sustainability in Indonesia. However, agricultural productivity is highly vulnerable to weather fluctuations and climate change, which significantly affect crop yields. This study aims to develop a machine learning-based predictive model for estimating agricultural productivity using meteorological data such as rainfall, temperature, humidity, and solar radiation. Historical data from 2013 to 2023 were collected from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) and the Central Bureau of Statistics (BPS). Three machine learning algorithms—Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR)—were implemented and compared using Python. Model performance was evaluated through Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The results show that the Random Forest model achieved the best performance, with R² = 0.912, MAE = 0.318, and RMSE = 0.445, indicating a strong predictive capability. Rainfall and temperature were identified as the most influential variables, contributing over 60% of yield variation. The findings suggest that machine learning can effectively support data-driven decision-making in Indonesia’s agricultural sector, enabling more accurate crop planning and climate adaptation strategies to enhance national food resilience.

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