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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Jurnal Neutrino : jurnal fisika dan aplikasinya JURNAL FISIKA JFA (Jurnal Fisika dan Aplikasinya) Jurnal Imejing Diagnostik Journal of Natural Sciences and Mathematics Research Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika Jurnal Inovasi dan Pembelajaran Fisika Knowledge Engineering and Data Science Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) Journal of Physics and Its Applications MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Teras Fisika : Teori, Modeling, dan Aplikasi Fisika Physics Education Research Journal Jurnal Fisika dan Terapannya Parta: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Jurnal Fisika Jurnal Teknik Elektro Journal of Holistic Medical Technologies Community Research and Application Journal (CRAJ)
Sumarti, Heni
Universitas Islam Negeri Walisongo Semarang
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Physics Social Sciences Other

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Pilot Study: Portable Non-Invasive Blood Sugar, Cholesterol, Uric Acid Monitoring System Heni Sumarti; Alvania Nabila Tasyakuranti; Qolby Sabrina
Jurnal Teknik Elektro Vol. 16 No. 1 (2024)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v16i1.8204

Abstract

Degenerative diseases commonly associated with abnormal blood sugar, cholesterol, and uric acid levels require regular monitoring. Remote health monitoring technology enables children to monitor their parents' health conditions from a distance. This research presents a prototype development through Research and Development (R&D) methodology. This study developed a portable, low-cost, non-invasive detection system for blood sugar, cholesterol, and uric acid levels using the TCRT5000 sensor with Telegram integration. The compact device offers real-time monitoring advantages without blood sampling. The development followed the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The research results show the prototype's coefficient of determination for blood sugar is 0.9733, cholesterol is 0.9411, and uric acid is 0.9610. The non-invasive prototype demonstrates measurement errors of 7.41% for blood sugar, 15.83% for cholesterol, and 14.69% for uric acid. These error rates currently exceed medical measurement standards. The system successfully integrates with the Telegram application for remote monitoring. Future research should incorporate artificial intelligence algorithms to minimize error values.
Comparison of Ultrasound Image Classification Methods for Benign and Malignant Breast Tumors Based on Texture and Shape Characteristics Nova Senandung; Nanda Firdayana; Dewi Anggun Puspita Septiani; Syahwa Ais Saputri; Heni Sumarti
Jurnal Fisika Vol. 16 No. 1 (2026): Jurnal Fisika 16 (1) 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v16i1.39242

Abstract

Early detection of breast tumors is important to  accurately distinguish benign and malignant lesions  . This study aims to compare the performance of Random Forest and Naïve Bayes algorithms in the classification of breast ultrasound images based on texture and shape features. The dataset comes from BUSI which consists of two classes: benign 891 images and malignant 421 images, with images through the pre-processing stage, histogram feature extraction, GLCM, as well as morphological features (circularity and elongation). Feature rankings using Relief show that GLCM Homogeneity has the greatest contribution in distinguishing the two classes. Performance evaluation was carried out using K-Fold Cross Validation with  variations of K=5, 10, 15, 20, and 25. The results showed that consistently placed Random Forest as the best-performing model. Random Forest achieved the highest accuracy at k-fold-5 at 74.18%, with stable AUC values at 0.771-0.777, sensitivity reaching 78.25%, and better specificity (62-63%) across the fold. In contrast, Naïve Bayes showed lower accuracy with a maximum value of 59% at k-fold-25, AUC in the range of 0.68, and low specificity (42-43%) despite the relatively high sensitivity. These findings confirm that across all k-fold validations, Random Forest remains the most balanced and reliable model for distinguishing benign and malignant in breast.
Comparative Analysis Of MRI Image Classification Methods Based On Texture Features For Brain Tumor Detection Using Random Forest Moch. Husain; Delia Okta Rahmadani; Muhammad Akmal K. H; Heni Sumarti
Jurnal Fisika Vol. 16 No. 1 (2026): Jurnal Fisika 16 (1) 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v16i1.39267

Abstract

Medical personnel struggle to detect brain tumors including glioma and meningioma and pituitary tumors because these tumors display matching MRI features which produces different interpretation results between different observers. The research aims to identify three brain tumor types by analyzing MRI image textures and it assesses different classification methods. The Weka software analyzed 3,900 MRI images through Histogram and Gray Level Co-occurrence Matrix (GLCM) and GLRLMGray Level Run Length Matrix (GLRLM) feature extraction methods before Support Vector Machine (SVM)[SF1.1][A1.2] and Naive Bayes and Multilayer Perceptron and Multiclass Classifier and Random Forest algorithms conducted the classification tasks. The analysis revealed that each tumor pair possesses distinct dominant texture characteristics which consist of standard deviation for glioma–meningioma and energy for glioma–pituitary and homogeneity for meningioma–pituitary. The Random Forest algorithm achieved the best classification results in all experiments because it reached 88.62% accuracy for glioma–meningioma and 96.96% accuracy for glioma–pituitary and 95.92% accuracy for meningioma–pituitary while maintaining high sensitivity and specificity values. The research demonstrates that brain tumor MRI image identification becomes more accurate through the combination of texture features with Random Forest classification methods which leads to better medical diagnostic results.
EEG Classification while Listening to Murottal Al-Quran and Classical Music using Random Forest Method Sumarti, Heni; Septiani, Fahira; Sudarmanto, Agus; Caesarendra, Wahyu; Edison, Rizki Edmi
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music. The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification. The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal al-Quran stimulation were 45.32% delta, 31.60% alpha, 17.02 theta and 6.05% beta. Meanwhile, the average brain waves of teenagers when given classical music stimulation were 46.54% delta, 28.64% alpha, 19.21% theta and 5.50% beta. Classification is obtained with the best value that frequently appears (mode) from the prediction results for each sample using random forest methods. The accuracy, precision, and recall of classifying adolescent brain waves when given murottal and classical music stimuli using the Random Forest method with cross-validation technique (optimum at k-fold=5) were 65.38%, 76.92%, and 70.00%, respectively. The results of this study show that stimulation using murottal al-Quran and classical music effectively improves adolescent relaxation conditions.
Health Screening: Blood Glucose, Blood Pressure, and Body Composition Examination in RT 005 RW IV, Kelurahan Gondoriyo, Ngaliyan, Semarang Qisthi Fariyani; Irman Said Prastyo; Heni Sumarti; Sheilla Rully Anggita; Moch Husain; Delia Okta Rahmadani1; Munawarohthus sholikha
Community Research and Application Journal (CRAJ) Vol. 2 No. 2 (2026): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Non-communicable diseases (NCDs) such as diabetes mellitus, hypertension, and obesity remain a major public health burden in Indonesia. National data (Riskesdas, 2018) reported a hypertension prevalence of 34.1% among Indonesian adults and a diabetes mellitus prevalence of 10.9% among the population aged 15 years and above, with many cases undetected because these conditions are often asymptomatic in their early stages. This community health screening was carried out on Sunday, 14 June 2026, at Beringin Forest Park, Kelurahan Gondoriyo, Kecamatan Ngaliyan, Semarang, as part of the community service program of the Department of Physics, Universitas Islam Negeri Walisongo Semarang. The activity provided free examination of blood glucose (capillary method), blood pressure (digital tensimeter), and body composition (bioelectrical impedance analysis), together with education on the procedures and individual consultation for participants with abnormal findings. Of 16 participants screened for blood glucose, 81.3% (13 participants) fell within the normal range, while one participant (6.3%) recorded a value in the diabetes range requiring immediate medical evaluation. Of 17 participants screened for blood pressure, only 23.5% (4 participants) had normal blood pressure, whereas 70.6% (12 participants) fell into the Hypertension Stage 1 or Stage 2 category. Mean body fat percentage exceeded normal reference ranges for both female (34.3%, n = 11) and male (24.3%, n = 4) participants. These findings indicate a considerable burden of undetected cardiometabolic risk factors within this community and demonstrate the value of low-cost, university–community screening partnerships for early detection and referral. Given the limitations of single-visit measurement, confirmatory examination by authorized medical personnel is recommended for participants with abnormal results.
Co-Authors Adrial, Rico Affa Ardhi Saputri Agus Sudarmanto Alvania Nabila Tasyakuranti Alvania Nabila Tasyakuranti Alvania Nabila Tasyakuranti Alvnia Nabila Tasyakuranti Arifah Riana AYU WULANDARI Ayu Wulandari Azizah, Fitria Kholbi Azzahra, Jannatul Firdausa Baehaqi BELLA JULIA Cahyawati, Rina Susi Darma, Panji Nursetia Delia Okta Rahmadani Delia Okta Rahmadani1 Dewi Anggun Puspita Septiani Edi Daenuri Anwar, Edi Daenuri Fachrizal Rian Pratama Fahira Septiani Fahira Septiani Farah Alfiana Na’ila Fariyani, Qisthi Firman Hardianto Frida Agung Rakhmadi, Frida Agung Gideon, Samuel Hadi Kusuma, Hamdan Hani Nur Endah Hani Nur Endah Hardianto, Firman Hartono Hartono Huwaidah, Indah Rifdah Ice Uliya Sari Isnawati, Nurul Embun Istikomah Kholidah Kholidah, Kholidah Laelatul Munawaroh Lailiyatu Latifah Latifatul Istianah Maesyaroh, Uhty Marlin Ramadhan Baidillah Melany Puspa Damayanti Moch Husain Moch. Husain Mohammad Candra Malindo Muhammad Akmal K. H Muhammad Ghozali Muhammad Ghozali Muhammad Labib Muhammad Syafiul Huda Munawarohthus Sholikha Mushoffa, Fina Nanda Firdayana Nova Senandung Nuryani, Siska Permana, Riyan Prastyo, Irman Said Putri Diah Pitaloka Putri Zulfikah Putri, Diana Salsabila Qolby Sabrina Qolby Sabrina, Qolby Rahmani, Tara Puri Ducha Rahmawati, Aida Ramadhani, Bintang Rizal Krisdiyanto Rizki Edmi Edison Samuel Gideon Sari, Ice Uliya Septiani, Fahira Sheilla Rully Anggita Shofani, Maya Siska Nuryani Susilawati Susilawati Syahwa Ais Saputri Syntia Anggraeni Syukrotus Sa’diati Tasyakuranti, Alvania Nabila Tika Rahmawati Tria Nurmar’atin Triana, Devi Uhty Maesyaroh Wahyu Caesarendra Yuniati, Anis