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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 18 Documents
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Search results for , issue "Vol. 3 No. 1 (2026): January" : 18 Documents clear
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.
Classification of Liquid Aroma Profiles Using Electronic Nose and Classical Machine Learning Methods Saycan, Binnur; Taspinar, 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.v3i1.5344

Abstract

The identification of aroma and quality profiles in liquids such as milk, coffee, tea, and vinegar is crucial for improving product quality. Since traditional methods are time-consuming and costly, the rapid detection of volatile organic compounds (VOCs) in such liquids using sensors has gained importance in recent times. Therefore, the AI Nose Dataset 250 data set obtained from the Electronic Nose (E-Nose) system was used in this study. This dataset contains 7 features consisting of 6 chemical and environmental sensors and 5 different classes: Perfume, Air, Coffee, Tea, and Vinegar. The Naive Bayes (NB) algorithm was used along with Random Forest (RF), k-Nearest Neighbor (kNN), AdaBoost, and Decision Tree (DT) methods to classify these data. To analyze the classification performance of the models, the Confusion Matrix was used along with the metrics Accuracy, Precision, Recall, and F1 Score. The ROC Curve was used for a detailed analysis of the classification performance of the models. As a result of the training and testing of the models, classification performance close to 100% was achieved with the RF and kNN models. The highest classification performance was achieved with the RF model. When the results were examined, it was seen that the classification performance of all Machine Learning models
Analisis Variasi Daya Tarik Konsumen Menggunakan Metode Repeated Measures Anova Jonathan Teguh Samuel Kaeng; Danu Satrio; Anggraini Puspita Sari
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.v3i1.5384

Abstract

  Penelitian ini bertujuan untuk menganalisis pengaruh variasi jenis kemasan terhadap tingkat ketertarikan konsumen pada produk kebab mini. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksperimen semu dan desain within-subjects (repeated measures design). Pengumpulan data melalui survei menggunakan Google Form, di mana setiap responden memberikan penilaian berupa rating terhadap tiga jenis kemasan kebab mini, yaitu mika, styrofoam, dan craft box, dengan asumsi harga produk yang sama. Data yang terkumpul dianalisis menggunakan metode Repeated Measures ANOVA pada taraf signifikansi 0,05. Hasil penelitian  menunjukkan terdapat perbedaan pada tingkat ketertarikan konsumen berdasarkan jenis kemasan (p < 0,05). Berdasarkan hasil penelitian ini didapatkan bahwa jenis kemasan berpengaruh terhadap daya tarik konsumen pada produk kebab mini
AI-Driven Energy Harvesting Communication Framework for Battery-less IoT Devices in 6G Networks Nabaa Alaa Abdulrazzaq; Huda Ali Mahdi; Sara Khairallah Mahdi; Raghad ALAA Kareem
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.v3i1.5488

Abstract

Batteryless IoT devices will be key in sixth-generation (6G) networks in the future because they allow massive, maintenance-free implementation of smart cities, agriculture, healthcare, and industrial monitoring. Even though the use of ambient energy sources of solar, thermal, vibration, and radio-frequency (RF) does not require using batteries, the randomized and unpredictable characteristics of harvested energy considerably decrease the communication reliability and performance. Current communication schemes on energy harvesting are mostly fixed and do not adjust themselves to the varying energy situations. The paper will suggest an AI-based, energy-conscious communication architecture of the battery-free IoT devices in 6G settings. The construction is a lightweight model using TinyML to predict the short-term forecasted energy, which was collected by using real-time and historical environmental data. Based on these predictions, a reinforcement learning (RL)-based scheduler would then trade-off spectral energy consumption and communication throughput by dynamically optimizing transmission power and data rate and duty cycle. The proposed method allows reliable and autonomous communication within severe and strict energy constraints through combined energy prediction and adaptive scheduling. Evaluation Simulation allows us to conclude that the given framework is much more effective in terms of throughput stability, packet delivery reliability, latency, as well as using energy in an efficient manner, when compared to traditional fixed-energy-harvesting-based communication approaches. The publication offers a long-lasting and smart background on self-enhanced IoT communications in the next-generation 6G networks.
Intelligent Search and Predictive Modeling Framework for Enhancing Software Reliability and Developer Productivity Faris Sattar Hadi
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.v3i1.5490

Abstract

The demand for smart automation to improve code quality, error fixing and developer efficiency has grown with the faster growth and complexity of today’s software. This work introduces the Intelligent Search and Predictive Modeling Framework (ISPMF) - an integrated data-driven framework that leverages neural predictive modeling, adaptive human-in-the-loop feedback, and semantic code retrieval to enhance software development. In order to model both syntactic and semantic relations in code, our approach adopts a hybrid Transformer–BiLSTM architecture equipped with retrieval-augmented generation (RAG), which leverages structural information brought from Abstract Syntax Trees (ASTs) and Graph Neural Networks (GNNs). ISPMF significantly improves from state-of-the-art baselines (SequenceR, CoCoNut, and GraphCodeBERT-Repair) on all the crucial metrics based on extensive experimental results conducted on real-world datasets such as Defects4J, QuixBugs and ManySStuBs4J. Our proposed approach decreased mean debugging time by 68%, and had an 83% acceptance rate from developers; it also achieved a Top-1 retrieval accuracy of 0.61, fix correctness of 89%, and compilation pass rate of 94%. This evidence confirms that the framework is scalable, robust and applicable in realistic settings. In addition, ISPMF advances explainable and human-centered AI in software engineering by combining data-driven automation with transparent, adaptive feedback along the development process. This work opens the door to future directions including multi-language repair, reinforcement learning-based adaptability, and next-generation intelligent development environments (IDEs) that seamlessly integrate predictive analytics with developer cognition.
Visual Artifact Detection and Correction for Digital Images Via Deep Neural Networks (Real-ESRGAN) Jabir, Ruaa Kadhim; Kareem, Malath Sabri
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

Abstract

This work pursues to the improvement of digital images quality and the correction of visual artifacts, in particular noise, by means of deep neural networks, through an application-oriented investigation based on pre-trained models which are ready to be used in the execution on a resource-limited platform. The study is inspired by a typical problem in processing digital images, on one hand traditional image enhancement algorithms do not work well on real world low-quality images because they cannot well trade off between over smoothing noise and preserving visual details and image structural information, on the other hand, high quality ground truth images corresponding to low quality real images are not available. The experimental part of this work was performed on a real noisy digital image from an open-source platform. The image was processed using a specialized processing pipeline developed in Python. The PyTorch library was used to run the DL model and some dedicated libraries are also included such as Real-ESRGAN as the default model to enhance the image quality, BasicSR as the generic framework to manage the process workflow, and the installation of GFPGAN and FaceXLib to facilitate optional facial restoration. Prior to all other testing were done on the CPU, these experiments were conducted using only the Central Processing Unit(DCPU) and not any high end Graphical Processing Units(GPUs) The processing approach was to use the Real-ESRGAN model only in the inference stage without any training process or modification on the model architecture. The discussion of results was grounded on qualitative visual inspection aided with a descriptive quantitative analysis of result indicators that can be obtained from the image itself before and after processing, such as the image size, the total number of pixels, and the spatial upscaling factor. The result indicates a 16 times enlargement in pixel number after processing (under the upscaling factor of ×4), as well as an intuitive improvement on clearness of details and the visual noise decreasing, which means the enhancement on perceptual quality of the image. Results show that exploiting pre-trained DNN models is a realistic and time-efficient strategy for improving quality of noisy images of the real world, even if computational power is scarce at the time of acquisition of images. In addition, the work demonstrates the feasibility of leveraging open source software in the domain of digital imaging and thus paves the way for potential future investigation incorporating larger datasets or employing standardized quantitative evaluation metrics when appropriate evaluation conditions are obtainable.

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