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Journal of Technology and System Information
Published by Indonesian Journal Publisher
ISSN : -     EISSN : 30322081     DOI : https://doi.org/10.47134/jtsi
Core Subject : Science,
Computer Science & IT
The Journal of Technology and System Information is dedicated to publishing cutting-edge research and advancements in the broad and dynamic intersection of technology and information systems. The focus of the journal is to facilitate the exchange of knowledge and ideas in these interconnected domains, fostering a deeper understanding of the role of technology in shaping information systems and vice versa. The journal welcomes contributions that span theoretical, empirical, and practical aspects, with an emphasis on the transformative impact of technology on information systems and vice versa. The scope of JTSI is a Information Technology and Systems, Data Management and Analytics, Emerging Technologies, System Design and Optimization, Cybersecurity and Privacy, Networks and Communication Systems, Artificial Intelligence and Machine Learning, Human-Computer Interaction.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 13 Documents
 1   2 
Search results for , issue "Vol. 3 No. 1 (2026): January" : 13 Documents clear
Pengembangan Model Prediksi Penjualan dan Persediaan dengan Klasifikasi Produk untuk Efisiensi Manajemen Stok Toko Berkat Plastik Agustin, Evelyn; Setiawan, Ferdy; Wijaya, Andri
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5191

Abstract

Penelitian ini bertujuan untuk mengembangkan model prediksi penjualan dan persediaan pada Toko Berkat Plastik guna meningkatkan efisiensi manajemen stok dan mengurangi risiko overstock maupun stockout. Metode yang digunakan adalah analisis berbasis data mining dengan pendekatan Knowledge Discovery in Database (KDD), meliputi tahap seleksi data, pre-processing, transformasi, pemodelan, dan evaluasi. Algoritma Naïve Bayes digunakan untuk mengklasifikasikan tingkat penjualan dan kondisi stok ke dalam kategori Rendah, Sedang, Tinggi serta Aman, Menipis, dan Kritis. Hasil penelitian menunjukkan bahwa model prediksi stok mencapai akurasi 75%, sementara model penjualan memperoleh akurasi 68,42%. Model menunjukkan performa terbaik pada kategori stok Aman dan penjualan Tinggi, sedangkan kategori lain memiliki akurasi lebih rendah akibat karakteristik data yang fluktuatif. Kesimpulan penelitian ini menyatakan bahwa model Naïve Bayes efektif digunakan dalam memprediksi permintaan dan kondisi persediaan, serta dapat dimanfaatkan sebagai alat pendukung keputusan untuk mengoptimalkan perencanaan pembelian dan pengendalian inventori pada usaha retail skala menengah.
A Klasifikasi Mahasiswa Berprestasi Berdasarkan Nilai Mahasiswa Universitas X Dengan Algoritma C4.5 Marcello, Daniel; Ardika, Petra Putri; Wijaya, Andri
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5213

Abstract

Penelitian ini bertujuan mengklasifikasikan mahasiswa berprestasi berdasarkan nilai akademik dengan memanfaatkan algoritma C4.5 sebagai salah satu teknik klasifikasi yang populer dalam data mining. Data yang digunakan mencakup IPK semester 1–3, jumlah SKS yang telah ditempuh, status keaktifan dalam kegiatan, serta status prestasi mahasiswa. Tahapan penelitian meliputi proses data cleaning, seleksi atribut untuk menentukan variabel paling relevan, pembagian data menjadi training dan testing, hingga pembangunan model decision tree menggunakan aplikasi RapidMiner. Hasil penelitian menunjukkan bahwa atribut jumlah SKS dan IPK per semester merupakan faktor paling berpengaruh dalam menentukan kategori prestasi mahasiswa. Model C4.5 yang dihasilkan memperoleh tingkat akurasi sebesar 97%, dengan nilai precision untuk kelas “Ya” mencapai 100%, menunjukkan performa model yang sangat baik. Struktur pohon keputusan yang terbentuk mengindikasikan bahwa mahasiswa dengan jumlah SKS tinggi dan IPK yang stabil memiliki peluang lebih besar untuk diklasifikasikan sebagai mahasiswa berprestasi. Temuan ini membuktikan bahwa algoritma C4.5 efektif sebagai alat bantu dalam proses seleksi dan dapat mendukung pengambilan keputusan akademik secara objektif dan terukur.
Penerapan Metode K-Means Clustering dalam Pengelompokan Produk Kayu sebagai Sistem Pendukung Keputusan Produksi dan Penjualan (Studi kasus: CV Sutan Piko Mandiri) Seli; Nababan, Clara; Wijaya, Andri
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5215

Abstract

Pengelolaan persediaan merupakan salah satu aspek krusial dalam industri pengolahan kayu karena berpengaruh langsung terhadap kelancaran produksi, efisiensi biaya, dan kemampuan perusahaan dalam memenuhi permintaan pasar. Ketidaktepatan dalam menentukan jumlah stok sering menimbulkan permasalahan berupa kelebihan atau kekurangan persediaan, yang pada akhirnya dapat berdampak pada kerugian operasional. Oleh karena itu, diperlukan pendekatan berbasis data untuk mendukung pengambilan keputusan yang lebih akurat. Penelitian ini bertujuan untuk menerapkan metode K-Means Clustering dalam mengelompokkan produk kayu pada CV Sutan Piko Mandiri berdasarkan pola penjualan sebagai dasar pendukung keputusan produksi dan pengelolaan persediaan. Data yang digunakan terdiri dari 1.000 transaksi penjualan tahun 2025 dengan atribut harga per unit, volume unit, dan jumlah terjual. Tahapan penelitian meliputi pengumpulan data, preprocessing untuk menangani data hilang dan pemilihan atribut, normalisasi data, serta penentuan jumlah cluster optimal menggunakan elbow method melalui analisis nilai Sum of Squared Error (SSE). Hasil analisis menunjukkan bahwa nilai K optimal adalah 4, sehingga produk kayu dapat dikelompokkan ke dalam empat cluster dengan karakteristik penjualan yang berbeda. Analisis hasil clustering menunjukkan bahwa jenis kayu Mahoni, Kamper, dan Durian termasuk dalam kategori penjualan tinggi sehingga direkomendasikan sebagai prioritas produksi dan perlu dijaga ketersediaan stoknya. Kayu Balam berada pada kategori penjualan menengah, sedangkan kayu Jati tergolong dalam kategori penjualan rendah sehingga memerlukan pengendalian produksi yang lebih ketat untuk menghindari penumpukan stok. Temuan ini membuktikan bahwa penerapan metode K-Means Clustering mampu memberikan wawasan yang bermanfaat bagi perusahaan dalam menyusun strategi produksi dan pengelolaan persediaan secara lebih efektif, efisien, dan berbasis data.
Clustering Performance on Heart Disease Data: Effects of Distance Metrics and Scaling Akbas, Ibrahim; Taspinar, Yavuz; Koklu, Murat
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5336

Abstract

Cardiovascular diseases (CVD) are one of the leading causes of morbidity and mortality worldwide, requiring advanced analytical approaches to identify early-stage risk groups and classify patient profiles in greater detail. The aim of this study is to reveal latent patient subgroups associated with CVD using unsupervised machine learning methods on clinical data. In this context, a dataset consisting of 11 clinical variables from 303 patients who visited the VA Medical Center in Long Beach, California, was analyzed. During the preprocessing stage, missing observations were eliminated, only numerical variables were used, and both z-score standardization and min–max normalization were applied to the data. Subsequently, hierarchical clustering analyses were performed using single, complete, and average linkage approaches based on Euclidean and cosine distance measures) (the number of possible clusters for different distance–scaling combinations was evaluated using the Elbow and Silhouette measures. The results obtained showed that the 4-cluster solution, particularly under the complete and average linkage methods, represented the data structure in the most clinically explanatory manner. The similarity between the clustering results obtained using the k-means algorithm with Euclidean distance in standardized data and cosine distance in normalized data was calculated as the Rand Index (RI) = 0.8179) (this value demonstrated that the cluster structure was largely preserved despite different distance metrics and scaling strategies.  The findings demonstrate that unsupervised learning-based clustering approaches provide a useful tool for defining meaningful risk classes within heterogeneous patient populations based on clinical datasets and for conducting comparative clinical evaluations between these classes.
Classification and Analysis of Real and Fake Aerial Vehicle Images Using Machine Learning Aksoy, Hasan; Ozcelik, Ziya; Taspinar, Yavuz
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5345

Abstract

Aircraft are widely used in both military and civilian fields today. Detecting aircraft in the airspace is of great strategic and societal importance. In recent years, distinguishing images generated by artificial intelligence from real images has become increasingly difficult. This article presents a study on the classification of real aircraft images and AI-generated aircraft images by machine learning algorithms. Six classifications were obtained from 300 images in the dataset. These classifications are: fake commercial aircraft AI, fake military aircraft AI, fake private aircraft AI, real commercial aircraft, real military aircraft, and real private aircraft. These data were classified using common machine learning models such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). Accuracy, Precision, Recall, and F1 Score metrics were used to analyze the classification success of these models. ROC was used for a detailed analysis of the classification success of the models. According to the results obtained, the ANN model achieved a classification success rate of 96.6%, the KNN model 90.4%, the SVM model 96.7%, and the LR model 96.5%. The highest classification success rate was obtained from the SVM model. These results show that all models achieved similar classification success rates, with the KNN model achieving a lower classification success rate than the others. In conclusion, it can be said that all models can be used in the classification of aircraft images.
Classification of Sleep Disorders Using Machine Learning Algorithms Ekim, Ufuk; Koklu, Murat
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5346

Abstract

This study aims to analyse the relationship between individuals' sleep health and lifestyle using machine learning algorithms. The Sleep Health and Lifestyle dataset used in the study includes variables such as age, gender, occupation, physical activity, stress level, and sleep duration. The data has been cleaned during the pre-processing stage and normalisation procedures have been applied. Subsequently, the classification of individuals' sleep quality was performed using the K-Nearest Neighbour (KNN), Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) algorithms. Model performance has been evaluated using metrics such as accuracy, F1-score, precision and sensitivity. In this study, the 5-fold cross-validation method was preferred to evaluate the model's performance in a more reliable and generalisable manner. The results show that ANN and Random Forest models achieve a higher accuracy rate compared to other algorithms. These findings reveal that lifestyle factors have a strong influence on predicting sleep quality.
AI Transformations Data Networking and Cybersecurity through Advanced Innovative Alrikabi, Israa
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5347

Abstract

The swift pace of the market transformation of the infrastructure of the data networking has introduced the necessity of having a more sophisticated security system to fight a high sophisticated cyber threat. The digital sphere is being developed, networks are working more than ever, and the Internet of Things (IoT), 5 G networks, and cloud technologies are ever-expanding. Though this expansion amount to more connectivity, it is a massive challenge on the security front with the consideration of sharing sensitive information in regard to the changing cyber-attacks. At the same time, the artificial intelligence (AI) has also been presented as one of the technologies that can revolutionize the data networking and cybersecurity. The possibility to process a large amount of data, predict kernels and make decisions in real time, AI is a valuable asset in the direction of solving the arising issues of network security in the new environment. Whether it is the possibility to be more efficient when it comes to utilizing the bandwidth of the network with intelligent resource distribution, or increase the level of information protection against cyber-attacks, AI is changing the way businesses make their web space safer. Within the framework of this research paper, the empirical research of AI in data networking and cybersecurity has been introduced on the basis of information gathered by the network operators, cybersecurity agencies, and government organizations. The paper will focus on some of the core areas, that is, predictive detection of threats, anomaly detection, and incident response, which is automated. The article relying on statistical modeling, visual data analysis, and case study analysis proves the point that AI proves beneficial in terms of identifying the cyber threats and enhancing the network performance, and is more efficient in coping with the challenges than the classical security solutions. Such results indicate that the efficiency of the operations and threat reduction was raised considerably, which confirms the possibility of the application of AI-based solutions. Such a shift toward proactive and reactive AI-based security is going to be the majority as more complicated network topologies and more advanced cybers threat activities are refined. Since the aim of the paper is to respond to the existing issues and elucidate the way the data networking and cybersecurity will evolve in the future, the paper may be used to display useful information about the way AI would transform the data networking and cybersecurity.
A Two-Stage Framework for Object Detection in Low-Light Images Using Image Enhancement and Deep Learning Models Sabea, Asmaa
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5349

Abstract

In low-lighting scenarios in object detection, a major challenge exists owing to reduced lighting, greater noise, lower contrast, and lighting changes. Thus, such scenarios have a significant effect on vision-based systems used in surveillance, path detection for autonomous vehicles, and security surveillance. A two-tier method using classical image processing and a deep learning platform for object detection in images is proposed and implemented in this work. The first stage uses a dedicated image processing chain aimed at increasing image brightness, contrast, and clarity while eliminating image noise. These processed images are then subjected to evaluation by two separate object detection models: YOLOv9 and Faster R-CNN. From ExDark dataset testing, the effectiveness of the method implemented has a mean Average Precision value of 96% at IOU= 0.50 for YOLOv9 and 88% mAP@50 for Faster R-CNN.
Sum Rate Maximization for Irregular Reconfigurable Intelligent Surface (RIS) Using Reinforcement Learning Alsharify, Thimar
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5363

Abstract

Reconfigurable Intelligent Surfaces (RIS) is an innovative technology in telecommunications systems that use artificial and programmable surfaces to manage radio waves and optimize communication environments. This technology is particularly relevant in 5G and 6G systems as a tool to improve signal quality, reduce interference, and increase the capacity of communication systems. The signal-to-noise ratio (SNR) is very important in radar target detection. In this research, Reinforcement Learning Method used to optimize irregular reconfigurable intelligent surface. The reward function is optimized by adjusting the phase parameters of the arrays and pre-coding vectors. The reconfigurable intelligent surface is considered as irregular arrays. The method presented in this study is based on Sum rate Maximization. Reinforcement learning is used to find the optimal location of antennas. The results indicate the superiority of reinforcement learning over Tabu Optimization and greedy search methods.
Pengembangan Website Sebagai Platform Desain Interior Subagiyono, Yegar; Calvinno, Steven; Irwanto, Kevin; Tito, Anita
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5365

Abstract

Penelitian ini bertujuan merancang dan mengembangkan Luxora, sebuah platform digital berbasis website untuk layanan desain interior online, guna mengatasi hambatan aksesibilitas, biaya tinggi, dan waktu pengerjaan yang lama. Metode penelitian deskriptif kualitatif digunakan dengan pendekatan Business Model Canvas, analisis SWOT, Five Porter’s Forces, serta proyeksi keuangan (ROI, IRR, Payback Period). Hasil penelitian menunjukkan bahwa Luxora layak secara teknis dan operasional, serta sangat menguntungkan secara finansial dengan ROI tahun pertama 270%, IRR 447,6%, dan payback period 3,1 bulan. Dengan fitur seperti estimasi biaya otomatis, pemilihan desainer berdasarkan portofolio, preview 3D, dan konsultasi online, Luxora diusulkan sebagai alternatif layanan desain interior yang inklusif dan adaptif terhadap kebutuhan pasar digital.

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