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Heni Sumarti
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Journal of Holistic Medical Technologies
Published by Konsorsium Pengetahuan Innoscientia
ISSN : -     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Neuroscience
The Journal of Holistic Medical Technologies (JHMT) aims to advance the integration and application of diverse technological and scientific disciplines within the field of medical sciences. The journal aims to promote innovation and interdisciplinary research by publishing high-quality original research, reviews, and case studies that explore the intersection of medical physics, imaging technologies, electronics, herbal chemistry, and neuroscience. We aim to bridge the gap between theoretical and applied research, promoting holistic solutions to complex medical challenges. JHMT welcomes a broad range of submissions that contribute to these areas, aiming to integrate emerging technologies with traditional medical approaches to enhance patient care and expand scientific knowledge.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 16 Documents
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Classification of normal and relaxed conditions based on brain signal activity with Electroencephalography using k-Nearest Neighbor (kNN) Thooriq Nur Ali; Fadhillah
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 1 (2024): December
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Research on how to think, patterns that occur when humans act, human conditions observed through brain waves, and various studies related to the human nervous system and coordination are still extensive in scope for development. Electroencephalography (EEG) is an instrument commonly used to develop research related to the mechanism of brain wave activity. Changes in the brain's electrical potential can be exploited by carrying out specific analyses using various signal processing methods, which are grouped into various categories of waves, including delta (0 - 4 Hz), theta (4 - 7 Hz), alpha (8 - 12 Hz) and beta (12 – 30 Hz). This research has succeeded in building a classification model through several stages, namely preprocessing with filtration and data extraction, data processing consisting of clustering using the K-Means algorithm, and classification using the k-Nearest Neighbor (kNN) algorithm to calculate the accuracy value of the model. The classification process produces two categories of conditions: normal and relaxed. The results of testing the classification model using the k-Nearest Neighbor (kNN) produce an accuracy value of 88%.
Classification of gastric tissue images based on texture characteristics using the Random Forest method Hesti Windyasari; Putri Zulfikah; Hanin Aisya Fakihati; Nabila Triwahyuni Handayani; Fitria Kholbi Azizah; Wahyu Malda Sere
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 1 (2024): December
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Gastric cancer is a group of malignant diseases caused by many factors, including genetics, lifestyle, and environment. This study aims to create additional tools for distinguishing gastric cancer and normal in microphysical biopsy images from the Kaggle database; the dataset includes 98 gastric cancer and 95 normal. The method used in this research utilizes the coarse and delicate nature of the extracted image based on Histogram and Gray Level Co-occurrence Matrix (GLCM) texture features. Image classification uses the Random Forest method in WEKA software. The results showed that the highest accuracy was 94% in folds 15, 20, and 25, while the lowest accuracy was 93% in folds 5 and 10. This research can be an additional tool for differentiating microphysical biopsy images.
Analysis of Beta signal activity before and after smelling the scent of castor oil on Adolescent enthusiasm Amirul Ma’arif; Aenul Mutmainah
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 1 (2024): December
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Castor oil is an oil that Rasulloh favors. As in the Sahih Muslim Hadith, Rasulloh said, "the best fragrance is Castor oil," therefore this study aims to analyze the activity of beta signals in emotional adolescents. The research was conducted at the Modern Physics Laboratory of Universitas Islam Negeri Walisongo Semarang. This research is quantitative, with an experimental approach of as many as 20 respondents from the Faculty of Science and Technology of Universitas Islam Negeri Walisongo Semarang. Data were collected using the KT88 type EEG device for one minute without treatment and given the treatment of smelling the aroma of castor oil. In addition, respondents filled out an emotional intelligence questionnaire consisting of 5 questions with a scoring system using 3 Likert scales of normal, moderate, and high. Then, processing using Python software using the PSD (power spectral density) algorithm with the extraction method, in which the initial data is amplitude against time and then extracted into frequency against intensity. The results obtained in this study show that the frequency of beta waves before treatment and after treatment is 5.18% to 6.44%, respectively. The questionnaire score data before and after treatment is 7.75 to 9.15, respectively. It can be concluded that the aroma of Kasturi oil affects the increase in beta signals and enthusiasm of adolescents.
Beta signal activity analysis using Power Spectra Density (PSD) algorithm with murrotal surah al-Fatihah stimulation on adolescent anxiety Tika Rahmawati; Latifatul Istianah
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 1 (2024): December
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Adolescence is a period of changes within adolescents, both physically and emotionally. According to the results of the Basic Health Research (Riskesdas) from the data of the Indonesian Ministry of Health, people over the age of 15 experience indications of anxiety around 14 million people. This figure is equivalent to 6% of the total population of Indonesia. This study aims to determine changes in Beta signals and measure adolescent anxiety in anxious conditions and after listening to the murottal letter Al-Fatihah. This study uses quantitative methods with an experimental approach. Respondents were students of Walisongo State Islamic University aged 17-21 years. This study used a KT88-type EEG device to measure respondents' brain waves and used the Zung Self-Rating Anxiety Scale (SAS/SRAS) questionnaire to measure anxiety. Respondents filled out the questionnaire when they were anxious, and then they were fitted with an EEG and listened to the murottal of Surah Al-Fatihah. The results of this study showed that the average beta wave decreased from 5.83% to 5.08% after listening to the murottal surat Al-Fatihah, and measurement using the SRAS questionnaire obtained an average score when anxious was 55.85, while when listening to the murottal the average was 36.7. It shows that murottal surat Al-Fatihah can reduce anxiety in adolescents.
Classification of mammographic image based on texture features with Random Forest method for identification of breast tumors Rizal Krisdiyanto; Heni Sumarti
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 1 (2024): December
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Breast cancer is one of the most common sorts of cancer in ladies around the world. One method that can be used to detect breast cancer is to use medical imaging. Purpose: This study was conducted to identify the types of breast tumors by combining feature extraction and classification using the Random Forest method. The research data comes from the DDSM repository, which consists of 20 benign and 20 malignant images aged 40 to 50 years. The research stages consist of preprocessing, feature extraction, and classification. The preprocessing and feature extraction stages use MATLAB R2021a, while the classification process uses the WEKA application. Texture feature extraction methods include Histograms and GLCM (Gray-Level Co-Occurrence Matrix). The histogram feature extraction results show that the benign image has a higher level of brightness and contrast and is symmetrical compared to the malignant image. Meanwhile, the malignant image has a more random or irregular histogram than the benign image. Then, the average value of GLCM texture features resulting from benign images is higher than malignant images. The texture feature-based breast tumor classification process using the Random Forest method from the Training Set stage obtained an accuracy of 100%. Meanwhile, at the cross-validation stage with variations of 5-Folds, 10-Folds, and 15-Folds, the same value was obtained for an accuracy of 95%. This shows that the Random Forest classification method can be used to identify breast tumor types with more accurate results and does not depend on an individual's ability to read medical imaging results.
Classification of normal and cancerous mammogram images based on texture features using the Support Vector Machine (SVM) method Risma Eka Ashari Ashari; Hamdan Hadi Kusuma
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 1 (2024): December
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Breast cancer is the leading cause of death in older women, with more than one million women worldwide dying from this disease yearly. Mammography is a specialized radiological examination that uses low-dose X-rays to detect breast abnormalities, even before visible symptoms such as palpable lumps appear. This study aims to develop an effective mammogram image classification model using the SVM (Support Vector Machine) method with texture feature extraction analysis in histograms and GLCM (Gray-Level Co-Occurrence Matrix). The research involved 20 normal and 20 cancer images, starting with mammogram image preprocessing, then texture feature extraction using histograms and GLCM, and ending with data classification using the SVM method. Test results showed that SVM could classify images with an accuracy of 67.5%, a sensitivity of 33.3%, and a specificity of 70%. Therefore, this research could be a foundation for further developments to enhance mammogram image classification accuracy.
Electrical Capacitance Volume Tomography (ECVT) for real-time brain activity monitoring: a comparative frequency analysis study Hanin Aisya Fakihati; Seftina Diyah Miasary; Marlin Ramadhan Baidillah
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 2 (2025): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Current brain imaging modalities such as CT scan and MRI, while providing excellent anatomical detail, have limitations in real-time functional brain activity monitoring. Electrical Capacitance Volume Tomography (ECVT) emerges as a promising non-invasive, cost-effective alternative for dynamic brain activity assessment. This study aims to evaluate the sensitivity of ECVT technology in detecting brain motor activity variations across different frequencies and determine the optimal frequency for brain wave fluctuation measurement. A 16-electrode ECVT helmet system was employed to monitor brain activity in subjects performing motor stimulation tasks including hand gripping, imagined movement, and control conditions (water and empty space). Measurements were conducted at three frequency variations: 500 kHz, 1 MHz, and 5 MHz. Data acquisition involved multiple channel combinations (C14-16, C14-15, C14-13, C14-12, C16-15, C16-9, C16-8, C16-10) with voltage peak-to-peak (Vpp) measurements recorded via oscilloscope. The 500 kHz frequency demonstrated the highest sensitivity in detecting brain activity variations. Distinct Vpp patterns were observed across different motor tasks, with imagined movement producing the highest values, indicating increased neural activity. The ECVT system successfully differentiated between active motor tasks and resting states. ECVT at 500 kHz frequency shows superior sensitivity for brain activity monitoring, offering a portable, low-cost alternative to conventional neuroimaging modalities for real-time functional brain assessment.
Classification of diabetic retinopathy and normal fundus images based on texture features using Multilayer Perceptron (MLP) Ayu Wulandari; Heni Sumarti
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 2 (2025): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Diabetic retinopathy is a disease caused by uncontrolled blood sugar levels and occurs continuously. Funduscopic examination with an ophthalmoscope tool to determine diabetic retinopathy. This study aims to classify funduscopy images in distinguishing normal eyes and diabetic retinopathy based on texture characteristics using the multilayer perceptron (MLP) method. Texture feature extraction as a class recognition process that aims to produce characteristics based on the texture of each image. The texture features used are histogram and GLCM with 10 parameters. Research data is sourced from the Zenodo website and the National Library of Medicine. Based on the results of the study, it shows that the multilayer perceptron method with the help of Weka machine learning in classifying eye fundus images to distinguish normal eye cases and diabetic retinopathy produces an accuracy value of 83.75% at k-folds 20 cross validation with sensitivity and specificity values of 49.20% and 95.09%.
Analysis of urine pH measurement using Arduino UNO-based pH Sensor Achmad Safii; Muhammad Ghozali
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 2 (2025): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Naturally, humans possess excretory organs that function to eliminate metabolic waste products. Urine is one of the fluids resulting from metabolic waste in the body. Urine can serve as an indicator of actual body condition. Urine pH measurement is one of the easily accessible methods for determining the body's acid-base balance. The pH meter system for urine pH detection was constructed using a pH V1.1 sensor as hardware with Arduino Uno assistance as both hardware and software for program processing. This research focuses on designing a device to determine urine pH levels using the Research and Development (R&D) pH meter method. Device calibration using buffer solutions with pH values of 4.01, 6.86, and 9.18 yielded a device error value of 0.11% and standard deviation (S) = 0.0091. Testing was also conducted using 30 human urine samples from subjects aged 16 to 40 years. The tested urine samples were 100 ml each, with results showing pH values ranging from 4.86 to 6.97. The standard deviation (S) for urine sample testing was 0.081. The difference in standard deviation values between calibration and urine samples was attributed to probe cleanliness on the pH meter, emphasizing the importance of probe cleanliness when switching between samples.
Identification of istihadhah blood color using TCS3200 color sensor with Multilayer Perceptron (MLP) method Syukrotus Sa’diati; Heni Sumarti
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 2 (2025): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

One of the natures of women, including menstruation, namely the discharge of blood from the vagina periodically and has a certain cycle. However, this cycle can experience disturbances, in Islam it is referred to as istihadloh. Women who are experiencing istihadloh are called mustahadloh. One of the mustahadloh categories is Mu'tadah Ghoiru Mumayyizah, namely women who have recently bled but cannot distinguish between strong and weak blood. This study aims to distinguish between menstrual blood and istihadloh based on its color. The research method was carried out by designing and manufacturing the TCS3200 color sensor tool based on Arduino Uno with RGB value output results. The average RGB value of istihadloh blood is R 71.6, G 83.7 and B 55.2. While the average RGB values for menstrual blood color are R 143.3, G 176.6 and B 79.3. The results of the classification accuracy using the MLP method in differentiating menstrual blood and istihadloh based on color using the TCS3200 color sensor are 96.7%.

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