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Peluang Kompetensi Clinical Engineering untuk Meningkatkan Prospek Kerja Lulusan Program Studi Teknik Biomedik di Indonesia Ahmad Fathir Rahman; Basari Basari
Jurnal Pendidikan Indonesia Vol. 6 No. 3 (2025): Jurnal Pendidikan Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i3.7209

In Indonesia, the clinical engineering sector is crucial for advancing healthcare services, yet the integration of university curricula with industry demands remains suboptimal. This mismatch between educational offerings and market needs has significant implications for graduate employability and the quality of healthcare services. This study aims to critically evaluate how well academic programs in clinical engineering align with the evolving needs of Indonesia’s healthcare industry and to enhance employment opportunities for graduates. Employing thematic analysis based on Clarke & Braun's model and drawing on purposive sampling, interviews were conducted with stakeholders across the biomedical field, including educators, industry professionals, and healthcare providers. This approach allowed for a deep dive into the existing gaps between educational content and industry expectations. The research highlights a substantial lack of practical training and insufficient collaboration between academic institutions and the healthcare industry. These factors contribute to the skill mismatch among graduates and underscore the need for curriculum adjustments. Key findings suggest that integrating practical skills training and enhancing industry-academic partnerships are critical steps towards improving graduate employability. The study suggests that strategic curriculum adjustments are needed to better prepare graduates for the workforce.

Grid-Aligned Patchification for Deep Learning-Based Macrophage Detection in Unstained Brightfield Haemocytometer Images Mohammad Ikhsan; Zino Ramdani Suharto; Rizal Azis; Basari Basari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.16005

Manual cell counting from haemocytometer images is slow, subjective, and operator-dependent, especially in unstained brightfield microscopy where cell boundaries and viability-related morphology are difficult to distinguish. Although prior cell detection models have mainly been evaluated on stained or fluorescence images, systematic comparisons between fine-tuned detectors and zero-shot cell segmentation models remain limited for unstained brightfield haemocytometer images. This study presents a controlled 2×2 factorial benchmark of patchification and augmentation across five detection approaches, with variance-decomposition analysis and comparison of fine-tuned versus zero-shot deployment modes. Using 24 unstained brightfield RAW 264.7 macrophage images with 6,307 polygon-level annotations, including 28.8% dead cells, we evaluated four preprocessing scenarios under six-fold stratified cross-validation. Faster R-CNN, Mask R-CNN, and YOLOv11n-Seg were fine-tuned within each fold, whereas Cellpose and StarDist were applied zero-shot. Grid-aligned patchification improved bounding-box mAP50 by 2.6–8.4× across all fine-tuned architectures (paired Wilcoxon p = 0.016, Cohen’s d > 3). A 2×2 ANOVA attributed 99.2–99.4% of explained variance to patchification, while augmentation and interaction effects each contributed less than 0.1%, suggesting that performance gains were driven mainly by scale rescaling rather than sample count. On patchified data, fine-tuned models converged to 85.5–86.4% mAP50. YOLOv11n-Seg achieved the highest mAP50-95 of 51.1%, with 6× faster inference and 17× fewer parameters. In contrast, zero-shot Cellpose and StarDist reached only 45.3–51.2% class-agnostic F1@0.5. These findings show that structure-aware patchification is critical for reliable cell detection in this modality.

Development of Proximity-Based COVID-19 Contact Tracing System Devices for Locally Virus Spread Prevention Ainul Fitriyah Lubis; Basari Basari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 1 (2022): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i1.23697

COVID-19 contact tracing is a preventive solution to slow the spread of the virus. Several countries have implemented manual contact tracing as well as digital tracking using smartphone applications. A proximity-based COVID-19 contact tracing system device using BLE (Bluetooth Low Energy) technology focuses on tracking and controlling the spread of the virus in local communities. The devices consist of a signal sending device (tag) and a signal receiving device (scanner). Suppose a system device is implemented in a factory. The tag will be used by employees by placing it in the front pocket of the factory employee's clothes or hooked on the shirt. The tag will continuously send a signal that will be read by the scanner. This received signal with the received signal strength indicator (RSSI) format will be used to calculate the distance between the scanner and the tag. Then the distance will be used to determine the coordinate point of the tag, with calculations using the trilateration algorithm. Therefore, the distance between tags can be obtained, while with signal fluctuation, the actual coordinate point cannot be obtained, yet proximity information can still be obtained by filtering distance data at a specified time interval that is less than the threshold value of the distance, 2 meters, then comparing the data with the overall data, resulting in a percentage value. A high percentage, above 80%, indicates the closeness between tags.

Development of Simple Control System for a Negative Pressure Wound Therapy Device Angga Davida; Basari Basari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27152

Diabetic ulcers are wounds found on the legs of diabetic patients. Improper treatment opens the risk of complications like sepsis and osteomyelitis. A notable method of treatment is through a Negative Pressure Wound Therapy (NPWT) device. This device helps ulcer recovery by removing exudate, increasing blood flow, and promoting cellular proliferation via negative pressure. The objective of this study is to increase the local content of an affordable and effective method of diabetic ulcer therapy by developing a simple, low-cost NPWT prototype. This was achieved by using an Arduino UNO microcontroller, which included PID controls, an MPXV4115VC6U sensor reading function, an in-built timer, two modes, and an alarm system. The resulting prototype was calibrated before testing to reduce error rates. Testing was conducted using a Gas Flow Analyzer and an ulcer wound phantom. Negative pressure settings of 75, 85, and 125 mmHg were used for testing and were conducted on both modes for 30 minutes each. From these tests, it was found that the prototype could reach the negative pressure thresholds with minimal average error of at most -1.81%. With a wound phantom, the average error was -0.56% and -0.20% for the continuous and intermittent modes respectively. This small variance is negligible because NPWT therapy has a wide range of acceptable negative pressure, namely 60-80 mmHg and 80-125 mmHg, depending on wound type. In conclusion, a simple Arduino UNO-based system can function as an NPWT therapy device to aid diabetic ulcer recovery with minimal error.

Measuring on Physiological Parameters and Its Applications: A Review Hazzie Zati Bayani; Basari Basari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28767

In providing patient care, it is essential to know the patient’s status to avoid incorrect treatment. Patient status includes various physiological parameters such as heart rate, blood oxygen saturation, blood pressure, body temperature, and respiratory rate. Measuring each physiological parameter requires data collection and analysis. Data acquisition in measuring physiological parameters can be categorized into contact methods, non-contact methods, invasive methods, and non-invasive methods. After data collection, it is crucial to analyze the collected data to ensure accurate and reliable measurements. This analysis can utilize RF signals, PPG signals, machine learning, and deep learning, depending on the specific needs and objectives of the study. This paper aims to identify studies based on types of data acquisition and analysis methods developed. These studies will be reviewed to understand the limitations of the data acquisition methods and analysis methods used. Additionally, this paper will discuss and classify the types of applications developed in these studies over the last five years, focusing on functionality, device design, and body-to-device connectivity. This review will identify whether the studies developed wearable or portable, wired or wireless devices, and their purpose whether for diagnosis, monitoring, or both. This review will also highlight the limitations and provide a brief perspective on future developments.

CNN-Based Transfer Learning Models for Histopathological Detection of Non-Hodgkin Lymphoma on Histopathological Images Aghnia Hasya Affan; Basari Basari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15207

More than 85.720 new cases and 21.000 fatalities from lymphoma were reported globally in 2021. This type of cancer can spread through the body using the lymphatic system and then enter the blood. Since lymphoma affects the lymphatic system, it can be hard to diagnose correctly because there are many different subtypes, such as Mantle Cell Lymphoma (MCL), Follicular Lymphoma (FL), and Chronic Lymphocytic Leukemia (CLL). The diagnostic complexity of lymphoma highlights the need for more accurate and reliable automated diagnostic methods. This research proposes a transfer learning approach employing pre-trained Convolutional Neural Network (CNN) models using DenseNet-201, Xception, and ResNet-50, for lymphoma subtype classification. The dataset consists of microscopic histopathology images from three lymphoma classes (MCL, FL, and CLL). Each image was resized and segmented into 24 non-overlapping patches, followed by Macenko stain normalization and data augmentation. Model performance was evaluated using a random sampling with a fixed random seed train–validation–test split, and validated using cross-validation method. The proposed approach achieved classification accuracies of 96.7% for DenseNet-201, 97.15% for Xception, and 96.3% for ResNet-50. These results indicate that deeper architectures with efficient feature reuse and depthwise separable convolutions improve the detection of subtle morphological differences among lymphoma subtypes. Despite limitations related to dataset size and external validation, the findings demonstrate the potential of transfer learning-based CNN models as decision-support tools for lymphoma diagnosis.

Peluang Kompetensi Clinical Engineering untuk Meningkatkan Prospek Kerja Lulusan Program Studi Teknik Biomedik di Indonesia Ahmad Fathir Rahman; Basari Basari
Jurnal Pendidikan Indonesia Vol. 6 No. 3 (2025): Jurnal Pendidikan Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i3.7209

In Indonesia, the clinical engineering sector is crucial for advancing healthcare services, yet the integration of university curricula with industry demands remains suboptimal. This mismatch between educational offerings and market needs has significant implications for graduate employability and the quality of healthcare services. This study aims to critically evaluate how well academic programs in clinical engineering align with the evolving needs of Indonesia’s healthcare industry and to enhance employment opportunities for graduates. Employing thematic analysis based on Clarke & Braun's model and drawing on purposive sampling, interviews were conducted with stakeholders across the biomedical field, including educators, industry professionals, and healthcare providers. This approach allowed for a deep dive into the existing gaps between educational content and industry expectations. The research highlights a substantial lack of practical training and insufficient collaboration between academic institutions and the healthcare industry. These factors contribute to the skill mismatch among graduates and underscore the need for curriculum adjustments. Key findings suggest that integrating practical skills training and enhancing industry-academic partnerships are critical steps towards improving graduate employability. The study suggests that strategic curriculum adjustments are needed to better prepare graduates for the workforce.

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