cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andy Sapta
Contact Email
sapta@royal.ac.id
Phone
-
Journal Mail Official
lppm_stmik@royal.ac.id
Editorial Address
-
Location
Kab. asahan,
Sumatera utara
INDONESIA
JURTEKSI
Published by STMIK Royal Kisaran
ISSN : 24071811     EISSN : 25500201     DOI : -
Core Subject : Science,
Computer Science & IT
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) is a scientific journal which is published by STMIK Royal Kisaran. This journal published twice a year on December and June. This journal contains a collection of research in information technology and computer system.
Arjuna Subject : -
Articles 685 Documents
 45   46   47   48   49 
MAMDANI FUZZY LOGIC ANALYSIS FOR ANIMAL MEDICINE STOCK OPTIMIZATION Desmarini, Mutia; sriani
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4070

Abstract

Abstract: The management of veterinary drug stocks at the Veterinary Clinic Technical Implementation Unit (UPTD) of the North Sumatra Province Plantation and Livestock Service faces obstacles in the form of discrepancies between supply and demand, resulting in excess stock and budget waste. Uncertain demand for drugs is a factor that complicates decision-making in stock provision. This study aims to optimize drug stock management using the Mamdani fuzzy logic method, which is capable of handling data uncertainty and modeling information linguistically. Three input variables are used, namely initial stock, demand, and number of visits, with the output being the final stock. The process involves fuzzification, inference based on IF–THEN rules, and defuzzification using the centroid method. The results show that the developed system has a good accuracy level with a MAPE value of 17.52%, which means that this model is effective in providing optimal and efficient drug stock recommendations in a veterinary clinic environment. Keywords: fuzzy mamdani; optimization; animal drug stock.
VEGECHAIN: SMART CONTRACT MARKETPLACE FOR VEGETARIAN SUPPLY CHAIN OPTIMIZATION Febrianti, Eka Lia; Suryadi , Agus; Syafrinal , Ilwan; Andhika, Andhika
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4076

Abstract

Abstract: The global transition towards sustainable food systems faces significant challenges in vegetarian food supply chains, including transparency issues, distribution inefficiencies, and quality verification problems. This research proposes VegeChain development, a decentralized marketplace ecosystem based on smart contracts designed to transform vegetarian food supply chains and accelerate Meatless, Balanced, Green (MBG) program adoption. Using mixed-method methodology integrating blockchain system design, stakeholder analysis, and economic simulation, this research develops a comprehensive technology framework combining blockchain transparency, smart contract automation, and sustainable tokenomics with novel mathematical models. The system implements dynamic pricing algorithms based on Automated Market Maker (AMM) mechanisms, multi-objective optimization for supply chain efficiency, and reputation-based consensus protocols. Simulation results demonstrate that VegeChain implementation can improve supply chain efficiency by 35%, reduce food waste by 28%, and increase consumer trust by 42% measured through validated stakeholder satisfaction surveys (n=456) using 5-point Likert scales with statistical significance p<0.001. Technical innovations include Byzantine Fault Tolerant consensus with 99.9% reliability, gas optimization achieving 67% cost reduction, and real-time quality verification algorithms with 98.7% accuracy. Keywords: smart contracts; supply chain optimization; automated market makers; blockchain technology; sustainable tokenomics
EVALUATION OF HYBRID MOVIE RECOMMENDATION SYSTEM BASED ON NEURAL NETWORKS Widjaja, William; Robert; Johanes Terang Kita Perangin - Angin
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.4079

Abstract

Abstract: Recommendation systems are becoming increasingly important with the growth of streaming platforms. The purpose of this study is to compare the performance of Content-Based Filtering, Neural Collaborative Filtering, and a combination of both in a movie recommendation system. The method used in this study involves retrieving movie details from the TMDB API and ratings from the MovieLens 32M Dataset (2010-2023). Each model's performance is evaluated using evaluation metrics such as RMSE and MAE. The results of this study indicate that Neural Collaborative Filtering achieves the best prediction performance (RMSE = 0.785423, MAE = 0.581262), followed by the hybrid model (RMSE = 0.800863, MAE = 0.660872), while Content-Based Filtering produces low performance and limits the capabilities of the hybrid model. In conclusion, these findings highlight the superiority of latent feature-based models such as NCF that learn directly from user interaction patterns over content-based approaches in the context of modern recommendation systems. Keywords: content-based filtering; hybrid filtering; movie recommendation; neural collaborative filtering. Abstrak: Sistem rekomendasi menjadi semakin penting seiring berkembangnya platform streaming. Tujuan dari penelitian ini adalah membandingkan kinerja Content-Based Filtering, Neural Collaborative Filtering dan kombinasi keduanya dalam sistem rekomendasi film. Metode yang digunakan dalam penelitian ini melibatkan pengambilan detail film dari TMDB API dan rating dari dataset MovieLens 32M Dataset (2010-2023). Setiap peforma model dievaluasi dengan menggunakan metrik evaluasi seperti RMSE dan MAE. Hasil dari penelitian ini menunjukkan bahwa Neural Collaborative Filtering mencapai kinerja prediksi terbaik (RMSE = 0.785423, MAE = 0.581262), diikuti oleh model hybrid (RMSE = 0.800863, MAE = 0.660872), sementara Content-Based Filtering menghasilkankan peforma yang rendah dan membatasi kemampuan model hybrid. Kesimpulannya, penelitian ini menyoroti superiotas model berbasis latent feature seperti NCF yang belajar langsung dari pola interaksi pengguna dibandingkan pendekatan berbasis konten dalam konteks sistem rekomendasi modern. Kata kunci: content-based filtering; hybrid filtering; neural collaborative filtering; rekomendasi film.
ANALYSIS OF PSI METHOD IN DECISION SUPPORT SYSTEM TO SELECT THE FEASIBILITY OF COVID 19 PATIENT DATA SCANNER RESULTS Zulkarnain, Iskandar; Sri Wahyuni, Meri; Sonata, Fifin
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4081

Abstract

Abstract: Hospitals play an important role in examining the scan results of patient data infected with the Covid 19 virus. However, there are problems when processing the scan results, namely that sometimes errors occur in the scan data, causing many failures and delays in sending data to the Health Office. The purpose of this study is to build a Desktop-based decision support system application that can facilitate hospitals in selecting the eligibility of the scan results of Covid 19 patient data. The urgency in examining the scan results of Corona patient data is a very pressing public health issue, because the long-term impact is very significant for patients. Thus, a scientific discipline is needed that can support the decision-making process, namely the Decision Support System using the Preference Selection Index (PSI) method. PSI is a simple and easy calculation method, based on statistical concepts without having to determine attribute weights. The results of this method are clear and firm values ​​​​based on the level of strength of the rules applied. The results of the research conducted on the PSI process can be concluded that valid Covid 19 patient data is Recap File I with a value of 0.2042 which is declared valid and accepted. Keywords: covid-19; decision support system; PSI
CLOUD-DRIVEN OPTIMIZATION OF LECTURER PERFORMANCE DOCUMENT DIGITALIZATION USING AGILE UNIFIED PROCESS Irawan, Rio; Inayah Syar, Nur
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4106

Abstract

The development of digital technology encourages universities to improve effectiveness and efficiency in data management, particularly in recording and reporting faculty performance. Some lecturers still face difficulties in reporting their performance in the SISTER application due to challenges in locating documents scattered across various archives, which often leads to issues such as delays in reporting, low information accuracy, and lack of transparency of faculty performance documents for institutional needs. This study aims to optimize the digitalization of faculty performance documents based on cloud computing using the Agile Unified Process (AUP) approach, which is implemented in the development of a cloud-based system by utilizing Google Drive as the storage medium for digital faculty performance documents. The AUP methodology was chosen for its ability to combine flexible iterative and incremental principles, allowing the system to adapt quickly and continuously to user needs. Testing using Equivalence Partitioning, based on the functional and non-functional requirements of the system, has shown results in accordance with expectations.
CRITERIA ANALYSIS OF COURSE PARTICIPANTS USING K-MEANS: A CASE STUDY OF INET PALEMBANG Muhammad Rasuandi Akbar; Agramanisti Azdy, Rezania; Novaria Kunang, Yesi; Adha Oktarini Saputri , Nurul
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4112

Abstract

Abstract: INET Computer Palembang, as a computer training institution, faces difficulties in understanding participant characteristics due to variations in age, educational background, and chosen course packages. This study aims to analyze participant criteria and group them based on similarities using the K-Means Clustering algorithm. The data used were historical records of course participants from 2022 to 2025. The research process followed the CRISP-DM stages, starting from data cleaning and transformation, determining the optimal number of clusters using the Elbow Method, to evaluating cluster quality with the Davies-Bouldin Index. The implementation was carried out using Python and the scikit-learn library. The results show that the optimal number of clusters is k=5 with a Sum of Squared Errors (SSE) value of 1064.66 and a Davies-Bouldin Index (DBI) score of 0.820, indicating good cluster quality. The resulting clustering provides a structured profile of participants and demonstrates that K-Means is effective in segmenting course participants. These findings are expected to assist the institution in designing more targeted training programs. Keywords: clustering; data mining; elbow method; k-means; computer course
CNN-BASED ADAPTIVE IDS WITH FEDERATED LEARNING FOR IOT NETWORK SECURITY Sahren, Sahren; Dalimunthe, Ruri Ashari; Maulana, Cecep; Permana, Yogi Abimanyu
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4136

Abstract

Abstract: In the era of the Internet of Things (IoT), cyber threats are increasingly complex and dynamic, thus demanding an adaptive and intelligent network security system. This study proposes a Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) implemented through a Federated Learning (FL) approach in a Non-Independent and Identically Distributed (Non-IID) data environment. This approach allows the model to be trained in a distributed manner across multiple IoT devices without having to collect sensitive data to a central server, thereby maintaining data privacy while increasing the efficiency of the training process. The experiment used the CIC IoT 2023 dataset, which represents various modern IoT network traffic patterns. The results show that the proposed CNN–FL model achieves an overall accuracy of 0.99, with excellent performance in detecting various types of network traffic. The model obtains a perfect recall value (1.00) for normal traffic (Benign), as well as a very high F1-score for DDoS (0.99) and DoS (0.99) attacks. Stable and consistent performance across all five federation rounds demonstrates that this approach is a reliable, efficient, and accurate solution for detecting threats in distributed and privacy-preserving IoT networks. Keywords: cnn; federated_learning; ids; non-iid; ciciot2023 Abstrak: Dalam era Internet of Things (IoT), ancaman siber semakin kompleks dan dinamis, sehingga menuntut sistem keamanan jaringan yang adaptif dan cerdas. Penelitian ini mengusulkan Intrusion Detection System (IDS) berbasis Convolutional Neural Network (CNN) yang diterapkan melalui pendekatan Federated Learning (FL) pada lingkungan data yang bersifat Non-Independent and Identically Distributed (Non-IID). Pendekatan ini memungkinkan model dilatih secara terdistribusi di berbagai perangkat IoT tanpa harus mengumpulkan data sensitif ke server pusat, sehingga mampu menjaga privasi data sekaligus meningkatkan efisiensi proses pelatihan. Eksperimen menggunakan dataset CIC IoT 2023, yang merepresentasikan berbagai pola lalu lintas jaringan IoT modern. Hasil penelitian menunjukkan bahwa model CNN–FL yang diusulkan mencapai akurasi keseluruhan sebesar 0.99, dengan performa yang sangat baik dalam mendeteksi berbagai jenis lalu lintas jaringan. Model memperoleh nilai recall sempurna (1.00) untuk lalu lintas normal (Benign), serta nilai F1-score yang sangat tinggi untuk serangan DDoS (0.99) dan DoS (0.99). Kinerja yang stabil dan konsisten di seluruh lima putaran federasi membuktikan bahwa pendekatan ini merupakan solusi yang andal, efisien, dan akurat untuk mendeteksi ancaman pada jaringan IoT yang bersifat terdistribusi dan menjaga privasi (privacy-preserving). Kata kunci: cnn; federated_learning; ids; non-iid; ciciot2023
DEVELOPMENT OF A BLOCKCHAIN-BASED DECENTRALISED APPLICATION WITH NFT FOR LAND REGISTRATION Gesang, Rahmat Nugrohoning; Teduh Dirgahayu, Raden
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4147

Abstract

Abstract: Land registration in Indonesia often encounters challenges in transparency, data integrity, and centralized bureaucracy. Manual and semi-digital systems remain vulnerable to manipulation and delays. The National Land Agency has initiated digitalization, but several challenges remain, particularly in ensuring transparency, efficiency, and security of land ownership data. Blockchain technology offers a potential solution through its decentralized and immutable characteristics. This study adopted a design and development method consisting of system analysis, requirements identification, architecture design, implementation, and black-box testing. The developed decentralized application (DApp) integrates smart contracts, NFTs, and IPFS to manage land certificates. Core functions such as minting, transfer, splitting, and self-custody were implemented and successfully tested, with all scenarios producing expected results. The findings demonstrate that blockchain integration can enhance security, reduce duplication, and streamline land administration. The study contributes a functional prototype with practical implications for modernizing land registration in Indonesia while identifying scalability and regulatory adaptation as areas for further research. Keywords: blockchain; decentralized application; land registration; NFT; smart contract.
MACHINE LEARNING CONTENT-BASED FILTERING WOMEN EMPOWERING RECOMMENDATIONS ON YOUTUBE Yuliana, Yuliana; Mira, Mira; Hari Kristianto, Aloysius
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4154

Abstract

Abstract: YouTube is one of the most popular video streaming platforms, but it has constraints that can cause problems when clients have difficulty finding content according to their wishes. The main objective of this study is to increase user capacity in viewing content specifically in the field of women's empowerment. By using content-based filtering techniques, the system will analyze user preferences and interests through recommendations for women's empowerment content. The data source is via the YouTube API and is analyzed using PHP programming content-based filtering techniques. The system's recommendations provide a list of women's empowerment content with a user request display. The results of the research evaluation obtained a precision value of 62%, meaning that the recommendations match the topic being searched for, namely women's empowerment. The recall value of 84% indicates that the system has succeeded in finding relations from the database. The f1-score value of 72% indicates that there is a balance between precision and recall, meaning that a system is needed that is not only accurate but also complete. While the cosine value shows a score of 0.7071 approaching the maximum value (1.0). The recommendation of the content-based filtering method produces quite effective women's empowerment content. Keywords: content-based filtering, recommendations, women Empowerment, youtube
DEVELOPMENT RICE PLANT DISEASE CLASSIFICATION USING CNN WITH TRANSFER LEARNING Fitrony, Fachri Ayudi; Utami, Ema
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4159

Abstract

Abstract: The rice plant, Oryza sativa, is a major food source in Indonesia. This plant is processed into rice, a staple food for the Indonesian people. Rice growth is crucial to ensure the rice produced is of good quality. One part of the rice plant that is susceptible to disease is the leaves, which can inhibit growth and reduce rice quality. Therefore, early detection and accurate classification of rice diseases are crucial to minimize these negative impacts. This has driven the development of a Deep Learning model capable of high-performance automatic classification. This study aims to create a rice leaf classification model using the CNN algorithm and several transfer learning architectures such as ResNet101, VGG16, and Xception. A dataset of 859 rice leaf images collected from the Kaggle website was then processed using augmentation techniques to a total of 2,439 images, plus 215 smartphone photos for external data validation. Thus, the total dataset increased to 2,656 images, covering four categories: leafblast, brownspot, healthy, and hispa. The model was processed in two stages: on the initial dataset (Non-Augmented Dataset) and the Augmented Dataset. The best experimental results were obtained using the ResNet architecture, with a training accuracy of 96.17% and a validation accuracy of 95.22%. Based on the research results, the rice plant disease classification model using deep learning demonstrated good performance. Keywords: convolutional neural network; deep learning; fine-tuning; image classification; resnet; rice plant

Page 47 of 69 | Total Record : 685

 45   46   47   48   49 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol. 12 No. 1 (2025): Desember 2025 Vol. 11 No. 4 (2025): September 2025 Vol. 11 No. 3 (2025): Juni 2025 Vol 11, No 3 (2025): Juni 2025 Vol 11, No 2 (2025): Maret 2025 Vol. 11 No. 2 (2025): Maret 2025 Vol 11, No 1 (2024): Desember 2024 Vol. 11 No. 1 (2024): Desember 2024 Vol. 10 No. 4 (2024): September 2024 Vol 10, No 4 (2024): September 2024 Vol. 10 No. 3 (2024): Juni 2024 Vol 10, No 3 (2024): Juni 2024 Vol 10, No 2 (2024): Maret 2024 Vol. 10 No. 2 (2024): Maret 2024 Vol. 10 No. 1 (2023): Desember 2023 Vol 10, No 1 (2023): Desember 2023 Vol 9, No 4 (2023): September 2023 Vol. 9 No. 4 (2023): September 2023 Vol 9, No 3 (2023): Juni 2023 Vol 9, No 2 (2023): Maret 2023 Vol 9, No 1 (2022): Desember 2022 Vol 8, No 3 (2022): Agustus 2022 Vol 8, No 2 (2022): April 2022 Vol 8, No 1 (2021): Desember 2021 Vol 7, No 3 (2021): Agustus 2021 Vol 7, No 2 (2021): April 2021 Vol 7, No 1 (2020): Desember 2020 Vol 6, No 3 (2020): August 2020 Vol 6, No 2 (2020): April 2020 Vol 6, No 1 (2019): December 2019 Vol 5, No 2 (2019): Juni 2019 Vol 5, No 1 (2018): Desember 2018 Vol 4, No 2 (2018): Juni 2018 Vol 4, No 1 (2017): Desember 2017 More Issue