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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Innovation Information Technology and Application (JINITA) Journal of Applied Data Sciences
Thinakaran, Rajermani
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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Efficient Fruit Grading and Selection System Leveraging Computer Vision and Machine Learning Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Thinakaran, Rajermani; Batumalay, Malathy; Habib, Shabana; Islam, Muhammad
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.443

Abstract

Automated fruit grading is crucial to overcoming the time and accuracy challenges posed by manual methods, which are often limited by subjective human judgment. This study introduces an intelligent grading system leveraging computer vision and AI to improve speed and consistency in assessing fruit quality. Using high-resolution imaging and advanced feature extraction, including grayscale processing, binarization, and enhancement, the system achieves non-destructive, efficient sorting for fruits like apples, bananas, and oranges. Grayscale processing reduces image complexity while preserving essential details, binarization isolates the fruit from its background, and enhancement highlights critical features. Notably, the Edge Pixel method proved most effective, achieving 79.20% accuracy in grading, while the Grayscale Pixel method reached 93.94% accuracy for fruit types. Edge Pixel also achieved 80.32% in differentiating grading types, showcasing its ability to capture essential shapes and edges. Fruits are classified into four grades: Grade_01 (highest quality), Grade_02 (minor imperfections), Grade_03 (notable defects but consumable), and Grade_04 (unfit for consumption). A specialized dataset supports model training, ensuring practical real-world application. The study concludes that this automated system offers significant improvements over traditional grading, providing a scalable, objective, and reliable solution for the agricultural sector, ultimately enhancing productivity and quality assurance.
Enhanced Precision Control of a 4-DOF Robotic Arm Using Numerical Code Recognition for Automated Object Handling Sukri, Hanifudin; Ibadillah, Achmad Fiqhi; Thinakaran, Rajermani; Umam, Faikul; Dafid, Ach.; Kurniawan, Adi; Morshed, Md. Monzur; Kurniawan, Denni
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research develops a 4-DOF robotic arm system that utilizes numerical codes for accurate, automated object handling, supporting advancements in sustainable industrial automation aligned with the UN Sustainable Development Goals (SDGs), particularly Industry, Innovation, and Infrastructure (SDG 9). Key contributions include the integration of EasyOCR for reliable code recognition and a control mechanism that enables precise positioning. The robotic system combines a webcam for visual sensing, servo motors for movement, and a gripper for object manipulation. EasyOCR effectively recognizes numerical codes on randomly positioned objects against a uniform background while the microcontroller calculates servo angles to guide the arm accurately to target positions. Testing results show a success rate exceeding 94% for detecting codes 1 to 4, with minor servo angle errors requiring adjustments in arm extension by 30 mm to 50 mm. Positional error analysis reveals an average error of less than 1.5 degrees. Although environmental factors like lighting can influence code visibility, this approach outperforms traditional methods in adaptability and precision. Future research will focus on enhancing code recognition under variable lighting and expanding the system's adaptability for diverse object types, broadening its applications in industries demanding high efficiency.
Exploring the landscape of approximate subtraction methods in ASIC platform Priyadharshni, M.; Thinakaran, Rajermani; Naga Jyothi, Grande; Varadarajan, Vijayakumar; Murthy, C. Srinivasa
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp388-397

Abstract

Approximate computing has emerged as a crucial technique in modern computing, offering significant benefits for error-resilient applications. Error resilient applications include signal, image, audio processing, and multimedia. These applications will accept the errored results with some degree of tolerance. This approach allows these applications to process and embrace data that may deviate slightly from perfect accuracy. The utility of approximate computing extends to both hardware and software domains. In hardware, arithmetic units are particularly important, among that approximate subtractors have gained attention for their role in these units. A comparative study was conducted on various approximate subtractors from existing literature, considering structural analysis in all scenarios. These approximate subtractors are coded in Verilog hardware description language (HDL) and synthesized in Synopsys electronic design automation (EDA) Tool using Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm technology. Out of the available choices, approximate subtractor 3 is particularly well-suited for processing higher bit data due to its reduced hardware complexity and minimal error. Notably, it outperforms exact subtractors by achieving a notable reduction of 20% in the area delay product (ADP) and 15% in the power delay product (PDP) as process innovation. These improvements highlight the efficiency and effectiveness of approximate subtractor 3, making it a compelling option for various computing applications which accept the inaccurate results.
Optimizing K-Nearest Neighbors with Particle Swarm Optimization for Improved Classification Accuracy Dafid, Ach.; Sudianto, Achmad Imam; Thinakaran, Rajermani; Umam, Faikul; Adiputra, Firmansyah; Izzuddin; Sitepu Debora , Ribka
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30775

Abstract

This study aims to improve the performance of the K-Nearest Neighbors (KNN) algorithm in classifying public reviews of Batik Madura through optimizing the K value using the Particle Swarm Optimization (PSO) algorithm. Public reviews collected from the Google Maps platform are used as a dataset, with positive, negative, and neutral sentiment categories. Optimization of the K value is carried out to overcome the constraints of KNN performance, which is highly dependent on the K parameter, with PSO providing a more efficient approach than the grid search method. However, PSO also presents challenges such as sensitivity to parameter tuning and potential computational overhead. This study has succeeded in developing a web-based system using the Python Streamlit framework, which makes it easy for users to access sentiment analysis results. Testing shows that optimizing the K value with PSO increases the accuracy of KNN to 88.5% with an optimal K value of 19. However, this accuracy is not compared to other optimization techniques, leaving its relative advantage unverified. The results are expected to help Batik Madura entrepreneurs in evaluating public perception and guiding strategic innovations. Research outputs include a prototype, intellectual property registration, and journal publication, although the role of deep learning models is only briefly noted without further development.
A Novel Principles+ Framework for Improving User Experience of Augmented Reality Wahyuningrum, Tenia; Isdiarto, Aditya Tama; Yudianto, Robert Hendra; Thinakaran, Rajermani
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2728

Abstract

This study introduces a framework that aligns analysis and synthesis approaches, PRInCiPleS+, to enhance the user experience (UX) for Mooi Indie paintings through Augmented Reality (AR). By integrating cultural depth, emotional engagement, and technical usability, this study aims to revive this traditional art form and engage younger audiences. The customized PRInCiPleS+ design method combines empathy mapping and sustainability considerations to develop an AR application for Instagram filters. This study highlights the role of AR in preserving traditional arts while aligning it with the Sustainable Development Goals. The results of the Mooi Indie AR test using the independent sample T Test method showed no significant difference between users who had never used AR and those who had used AR in terms of user experience. In other words, users who are using AR for the first time and those who have used AR before feel almost the same user experience. The user experience score measured using the UMUX-Lite questionnaire showed that the group of users who had never used AR had a user experience score of 78.15, while the group who had ever used AR was 71.10, which means good. Analytically, Mooi Indie AR still needs some improvement, especially in terms of filter loading time, time required to explore filters, the number of users who use filters, and then save and share them with other users to increase engagement.
Smart Cold Storage Based on Photovoltaic with Adaptive Fuzzy Control Approach for Guard Quality of Fish Catch on Fishing Vessels Findiastuti, Weny; Umam, Faikul; Sulaiman, Yoga Aulia; Thinakaran, Rajermani; Dafid, Ach.; Andriansyah, Adi; Yusuf, Ahcmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14508

Abstract

This research is motivated by the importance of maintaining the quality of fish catches on fishing vessels, which generally experience a decline in quality due to suboptimal conventional fish storage systems and limited energy supplies at sea. To address these challenges, the development of renewable energy-based cold storage technology through a Solar Power Plant (PLTS) or Photovoltaic system is needed. This research aims to design a PLTS-based smart cold storage system capable of optimally maintaining temperature stability using the Adaptive Fuzzy Control method. It is hoped that fish quality can be maintained and the economic value of fishermen's catches can be increased. This research uses an experimental approach through the design, implementation, and testing of a fuzzy logic-based adaptive control system in real-time. The performance results are then evaluated in maintaining the temperature stability of the cooling room and the efficiency of electrical energy sourced from solar panels. It is hoped that this research can provide real solutions for fishermen, support the economic independence of the fisheries sector, and support the achievement of sustainable development targets (SDGs) 7, 9 12, and 14. During the 24-hour test, the Adaptive Fuzzy Control system in a solar-based refrigerator demonstrated consistent performance in maintaining temperature stability (standard deviation σ = 3.28 °C–3.45 °C). The average refrigerator temperature was recorded at -5.44 °C with a range of -0.9 °C to -12 °C, which remains acceptable for marine fish preservation under superchilling and mild freezing conditions. The battery capacity was at an average of 89.95%, decreasing when there was no power supply and then increasing again during charging, thus reflecting adaptive energy management. The average charging speed was 3.14 A, with a peak of up to 15.6 A at 7–8 hours, then decreasing gradually as the battery was full to prevent overcharging. These findings confirm that the proposed system effectively balances cooling performance and renewable energy utilization. The use of solar photovoltaic energy directly supports SDG 7 (Affordable and Clean Energy), while system innovation and energy optimization align with SDG 9 (Industry, Innovation, and Infrastructure). The prototype demonstrates stable and efficient operation, and the design concept is scalable for practical implementation on small to medium-sized fishing vessels. A preliminary cost analysis indicates up to 50% lower operating costs compared to conventional diesel refrigeration systems.
Co-Authors Achmad Fiqhi Ibadillah Adi Kurniawan Aditya Tama Isdiarto Andriansyah, Adi Batumalay, Malathy Dafid, Ach Dewi, Deshinta Arrova Faikul Umam Firmansyah Adiputra Habib, Shabana Izzuddin Kurniawan, Denni Kurniawan, Tri Basuki Morshed, Md. Monzur Muhammad Islam, Muhammad Murthy, C. Srinivasa Naga Jyothi, Grande Priyadharshni, M. Robert Hendra Yudianto Sitepu Debora , Ribka Sudianto, Achmad Imam Sukri, Hanifudin Sulaiman, Yoga Aulia Tenia Wahyuningrum Varadarajan, Vijayakumar WENY FINDIASTUTI, WENY Yusuf, Ahcmad