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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 53 Documents
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Analysis of Different Sensor Data Using Machine Learning Methods for the Purpose of Determining Milk Quality Sevinç, Sinan; Taşpınar, Yavuz Selim
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.v3i1.5367

Abstract

Milk is a product with high nutritional value, but its quality may vary depending on factors from production to consumption. Milk is a food that can spoil over time and carries a disease risk due to microorganism growth. Therefore, continuous monitoring of milk quality is important. Quality loss can cause changes in milk components such as protein, fat, and lactose. In recent years, sensors have been used to evaluate milk quality by quickly measuring parameters such as chemical components, pH value, temperature, and fat content. These sensor data provide information not only about milk quality but also about the productivity and health of cows. This enables more efficient production processes and early detection of potential diseases. Sensor measurements help determine both milk quality and cow care needs. In this study, quality classification was performed using data from 1059 different milk samples. The dataset consists of 7 features and 1 class feature, and milk quality was classified into three classes: “high”, “medium”, and “low”. kNN (k-Nearest Neighbor), ANN (Artificial Neural Network), DT (Decision Tree), and RF (Random Forest) methods were used for classification. Model performance was evaluated using confusion matrix, accuracy, precision, recall, and F1 score, and detailed analysis was performed using the ROC curve. The kNN model achieved 99.8% accuracy, the ANN model 99.9%, the DT model 99.4%, and the RF model 100%. The RF model showed the highest success. Overall, the classification performances of all models were close to each other, and all can be used to determine milk quality.
Production Line Piston Position Control Based on Image Processing Ahmetserdar Çoban; Hakan Işık
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.v3i1.5410

Abstract

This study presents a real-time vision-based system for detecting the open and closed positions of pneumatic pistons in industrial production lines without using physical sensors. Conventional magnetic and inductive sensors are often affected by cable damage, environmental contamination, vibration, and temperature variations, which can cause unplanned downtime and increased maintenance costs. To address these limitations, a camera-based monitoring approach is proposed as a reliable and low-maintenance alternative.The main objective of this work is to develop a low-cost, robust, and easily integrable sensorless position-detection system using deep learning–based object detection. A dataset consisting of 250 RGB images was collected from a production-like test platform and annotated into two classes representing open and closed piston states. The dataset was split into training and testing sets with ratios of 80% and 20%, respectively.A YOLOv8 object detection model was fine-tuned using transfer learning and deployed on a Raspberry Pi 4B for real-time operation. To improve reliability, a high confidence threshold and a frame-based stability filter requiring consistent predictions across multiple frames were applied. Detected piston states were converted into digital control signals via GPIO outputs.Experimental results show that the proposed system achieves over 97% detection accuracy with a processing latency of 25–40 ms per frame on embedded hardware. The stability filter effectively reduces false state transitions, ensuring reliable output. The results indicate that the proposed approach provides a practical visual backup solution for sensor failures and a scalable alternative for new production line designs.
Pembangunan Sistem Informasi Kepegawaian Menggunakan Metode Extreme Programming (Studi Kasus: PT Surganya Motor Indonesia Cabang Surabaya) Subagyo, Goldy Praba Chandra
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.v3i1.5418

Abstract

Meningkatnya kompleksitas dalam manajemen kepegawaian menuntut solusi yang terotomatisasi untuk proses administratif. PT. Surganya Motor Indonesia saat ini menghadapi tantangan akibat pengolahan data yang masih manual, yang menyebabkan kesalahan dan keterlambatan dalam proses utama sumber daya manusia seperti pencatatan absensi, pengajuan cuti, pelaporan lembur, dan perhitungan gaji. Untuk mengatasi permasalahan ini, penelitian ini mengusulkan pengembangan sistem informasi kepegawaian berbasis web dengan menerapkan metodologi Extreme Programming (XP). Hasil penelitian telah mengembangkan sistem informasi kepegawaian berbasis web dengan fitur utama, modul absensi terintegrasi, pemrosesan pengajuan cuti dan izin sakit, pencatatan lembur, serta perhitungan gaji. Berdasarkan hasil penelitian, sistem informasi kepegawaian berbasis web yang dikembangkan melalui empat iterasi ini telah memenuhi kebutuhan pengguna. Sistem ini =menyediakan dua peran utama, yaitu user dan admin, dan dinyatakan layak digunakan berdasarkan hasil pengujian Black Box serta User Acceptance Test (UAT). Dengan diterapkannya sistem berbasis web ini, PT. Surganya Motor Indonesia dapat mempunyai sistem yang mendukung proses bisnis dalam bagian Human Capital Management.

Page 6 of 6 | Total Record : 53

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