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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics INDONESIAN JOURNAL OF APPLIED PHYSICS Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science SEMAR Jurnal Teknik Informatika (JUTIF)
Wiharto Wiharto
Sebelas Maret University
Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Social Sciences Other

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Squeeze-excitation half U-Net and synthetic minority oversampling technique oversampling for papilledema image classification Wiharto, Wiharto; Syaifuddin, Angga Exca Pradipta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1410-1419

Abstract

The emergence of various convolutional neural networks (CNN) architectures indicates progress in the computer vision field. However, most of the architectures have large parameters, which tends to increase the computational cost of the training process. Additionaly, imbalanced data sources are often encountered, causing the model to overfit. The aim of this study is to evaluate a new method to classify retinal fundus images from imbalanced data into the corresponding classes by using fewer parameters than the previous method. To achieve this, squeeze-excitation half U-Net (SEHUNET) architecture, a modification of half U-Net with squeeze-excite process to provide attention mechanism on each feature maps channel of the model, in combination with synthetic minority oversampling technique (SMOTE) is proposed. The test accuracy of SEHUNET is 98.52% with area under the curve of receiver operation characteristic (AUROC) of 0.999. This result outperforms the previous study that used CNN with Bayesian optimization, achieving accuracy of 95.89% and AUROC of 0.992. SEHUNET is also able to compete with the transfer learning methods used in previous research such as InceptionV3 with 96.35% accuracy, visual geometry group (VGG) with 96.8%, and ResNet with 98.63%. This performance can be achieved by SEHUNET with only 0.268 million parameters compared to the architecture parameters used in previous research ranging from 11 million to 33 million.
Detection of COVID-19 based on cough sound and accompanying symptom using LightGBM algorithm Wiharto, Wiharto; Abdurrahman, Annas; Salamah, Umi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp940-949

Abstract

Coronavirus disease 19 (COVID-19) is an infectious disease whose diagnosis is carried out using antigen-antibody tests and reverse transcription polymerase chain reaction (RT-PCR). Apart from these two methods, several alternative early detection methods using machine learning have been developed. However, it still has limitations in accessibility, is invasive, and its implementation involves many parties, which could potentially even increase the risk of spreading COVID-19. Therefore, this research aims to develop an alternative early detection method that is non-invasive by utilizing the LightGBM algorithm to detect COVID-19 based on the results of feature extraction from cough sounds and accompanying symptoms that can be identified independently. This research uses cough sound samples and symptom data from the Coswara dataset, and cough sound’s features were extracted using the log mel-spectrogram, mel frequency cepstrum coefficient (MFCC), chroma, zero crossing rate (ZCR), and root mean square (RMS) methods. Next, the cough sound features are combined with symptom data to train the LightGBM. The model trained using cough sound features and patient symptoms obtained the best performance with 95.61% accuracy, 93.33% area under curve (AUC), 88.74% sensitivity, 97.91% specificity, 93.17% positive prediction value (PPV), and 96.33% negative prediction value (NPV). It can be concluded that the trained model has excellent classification capabilities based on the AUC values obtained.
Model Group Decision Support System Based on Depression Anxiety Stress Scales Using Ordered Weighted Averaging Aggregation Method Wiharto, Wiharto; Putri, Della K.; Sihwi, Sari W.; Salamah, Umi; Suryani, Esti; Atina, Vihi; Utomo, Pradityo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.678

Abstract

Depression, anxiety, and stress are common psychological conditions often triggered by the pressures of daily life. Depression Anxiety Stress Scale (DASS), is a widely used tool for assessing the severity of these disorders, available in different versions such as the DASS-21 and DASS-42. In line with these findings, DASS-21 consists of 21 symptom items, categorized into three types of disorders, with seven items assigned to each. In contrast, the DASS-42 includes 42 symptom items, with 14 items allocated per disorder. Both versions serve as standardized tools for assessing the severity of depression, anxiety, and stress, and the different versions show that one item only affects one disorder. In practice, it can affect several disorders with different priorities. This condition increases the risk of subjective bias in a psychologist's decision-making, as personal experiences and perceptions may influence their assessments. Therefore, this study aims to develop a Group Decision Support System (GDSS) model that considers the preferences of several psychologists in determining the priority of disorders based on the DASS-42 and DASS-21 items. The model has been built using the psychologist's preference method for DASS-42 and DASS-21 in fuzzy form, then combined using the Ordered Weighted Averaging (OWA) method to produce one decision. The alignment of top-priority items between GDSS and DASS was assessed as part of the evaluation. The results show a high degree of similarity, with GDSS matching 16 out of 21 symptom items in DASS-21 and 35 out of 42 items in DASS-42. The GDSS model can accommodate the preferences of decision-makers in providing weighting of the influence on each item in the DASS-21 and DASS-42, thereby providing more objective decisions.
Breast Cancer Classification on Ultrasound Images Using DenseNet Framework with Attention Mechanism Azka, Hanina Nafisa; Wiharto, Wiharto; Suryani, Esti
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.779

Abstract

Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Early detection of breast cancer being critical for increasing survival rates. Ultrasound image is commonly used for breast cancer screening due to its non-invasive, safe, and cost-effective. However, ultrasound images are often of low quality and have significant noise, which can hinder the effectiveness of classification models. This study proposes an enhanced breast cancer classification model that leverages transfer learning in combination with attention mechanisms to improve diagnostic performance. The main contribution of this research is the introduction of Dense-SASE, a novel architecture that combines DenseNet-121 with two powerful attention modules: Scaled-Dot Product Attention and Squeeze-and-Excitation (SE) Block. These mechanisms are integrated to improve feature representation and allow the model to focus on the most relevant regions of the ultrasound images. The proposed method was evaluated on a publicly available breast ultrasound image dataset, with classification performed across three categories: normal, benign, and malignant. Experimental results demonstrate that the Dense-SASE model achieves an accuracy of 98.29%, a precision of 97.97%, a recall of 98.98%, and an F1-score of 98.44%. Additionally, Grad-CAM visualizations demonstrated the model's capability to localize lesion areas effectively, avoiding non-informative regions, and confirming the model's interpretability. In conclusion, the Dense-SASE model significantly improves the accuracy and reliability of breast cancer classification in ultrasound images. By effectively learning and focusing on clinically relevant features, this approach offers a promising solution for computer-aided diagnosis (CAD) systems and has the potential to assist radiologists in early and accurate breast cancer detection.
MSME Entrepreneurship Promotion Training Through On-line Applications and Global Social Media: Pelatihan Promosi Wirausaha UMKM Melalui Aplikasi On-line dan Media Sosial Global Widiarto, Wisnu; Wiharto, Wiharto; Salamah, Umi; Suryani, Esti
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 3 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i3.15761

Abstract

Technological advances have had an impact on various fields. Likewise for UMKM entrepreneurs in Gadingan-Mojolaban-Sukoharjo, to promote karak and romeo businesses. This community service program has been implemented by means of socialization, training and mentoring to UMKM entrepreneurs in Gadingan Village, regarding on-line e-commerce applications, and social media. Preliminary analysis of partners has shown problems that occur in UMKM entrepreneurs, namely a lack of understanding of on-line applications and global social media. Implementation of socialization, training and mentoring for partners has been carried out to empower UMKM entrepreneurs. It is expected that partners can follow the flow of technology and be able to face competition in this digital technology era. With increased competition, UMKM entrepreneurs are required to be able to face all challenges. This can be done by increasing product innovation and expanding marketing networks, promotions and publications about their business. The method that has been used is to provide socialization-training- mentoring in promoting and registering businesses in on-line applications and global social media.
Stroke Risk Prediction using Winsorizing Interquartile Range and Tree-Based Classification with Explainable Artificial Intelligence Rahmadani, Fitria; Wiharto, Wiharto; Zuhdi, Shaifudin
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4760

Abstract

According to the Global Burden of Disease (GBD) Study, stroke is the third leading cause of death globally. Recognizing its signs early is crucial for both prevention and effective treatment. Although machine learning has made significant progress in predicting strokes, many current models operate like "black boxes", making them hard to interpret and often resulting in high error rates. This study aims to enhance prediction accuracy and interpretability in stroke risk detection by integrating Winsorizing Interquartile Range (IQR) for outlier management, a tree-based classification method, and Explainable Artificial Intelligence (XAI) techniques. The proposed approach applies Winsorizing Interquartile Range to handle extreme values while employing tree-based methods for prediction due to their superior performance in processing tabular data. Additionally, Explainable Artificial Intelligence techniques are utilized to improve model transparency and interpretability. Testing was conducted using the Cerebral Stroke Prediction-Imbalanced Dataset, comparing results with various existing models. The suggested approach demonstrated the lowest prediction error rates, achieving a False Positive Rate (FPR) of 15.74% and a False Negative Rate (FNR) of 8.56%. Additionally, it attained an accuracy of 84.39%, sensitivity of 91.43%, specificity of 84.26%, Area Under the Receiver Operating Characteristic Curve (AUROC) of 94.74%, and G-Mean of 87.76%, outperforming previous studies in stroke risk prediction. The combination of Winsorizing Interquartile Range, Random Under-Sampling, tree-based classification, and Explainable Artificial Intelligence techniques effectively enhances prediction accuracy and transparency, supporting early stroke detection with improved interpretability. This study contributes to medical informatics by integrating transparent predictive models suitable for decision support systems.
Implementation of CNN-SVM with Index Pattern-Based Feature Selection on PPG Signals for Cuffless Hypertension Detection Nazhifah, Dafina; Wiharto, Wiharto; Muslim, Fajar
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 15, No 2 (2025): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v15i2.102421

Abstract

Hypertension is one of the leading causes of death worldwide and often goes undetected due to its minimal symptoms. Early detection is crucial, and one non-invasive method involves the use of photoplethysmogram (PPG) signals. However, PPG signals contain a large number of features, which can lead to information redundancy and decreased model performance. This study proposes a hypertension detection system based on a CNN-SVM combination, preceded by feature selection using position-based indices (odd, even, specific multiples) to reduce data dimensionality and accelerate computation. The PPG signal dataset was obtained from 216 patients at UNS Hospital. After preprocessing and feature selection, feature extraction was performed using a Convolutional Neural Network (CNN), followed by classification using a Support Vector Machine (SVM). The model was evaluated under three classification scenarios: binary classification (normal vs. prehypertensive-hypertension and normal-prehypertension vs. hypertension) and three-class classification (normal, prehypertension, hypertension). The best classification accuracy achieved was 93.10% for the normal vs. prehypertension-hypertension scenario, 88.38% for normal-prehypertension vs. hypertension, and 82.79% for the three-class classification. This approach demonstrates that the combination of CNN-SVM with simple feature selection can improve both accuracy and efficiency in PPG-based hypertension detection.
Pengembangan Sistem Manajemen Donasi pada Griya PMI Surakarta untuk Meningkatkan Efektifitas Pemenuhan Kebutuhan Warga Griya Salamah, Umi; Saptono, Ristu; Wiharto, Wiharto; Suryani, Esti; Widiarto, Wisnu; Zuhdi, Shaifudin
SEMAR (Jurnal Ilmu Pengetahuan, Teknologi, dan Seni bagi Masyarakat) Vol 15, No 1 (2026): Mei
Publisher : LPPM UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/semar.v15i1.110962

Abstract

Griya PMI merupakan salah satu wujud kepedulian PMI Surakarta terhadap permasalahan sosial dalam masyarakat, khususnya terkait keberadaan orang-orang terlantar. Pemeliharaan orang-orang terlantar tersebut dipenuhi dengan donasi yang diberikan masyarakat. Agar pemberian donasi sesuai dengan kebutuhan suatu secara real time, diperlukan suatu sistem yang dapat memberikan informasi yang akurat terkait dengan prioritas kebutuhan saat ini. Sistem yang dibangun berbasis web dan mobile di Griya PMI Surakarta sebagai solusi terhadap permasalahan pengelolaan donasi yang selama ini dilakukan secara manual. Proses pengembangan dilakukan menggunakan metode Software Development Life Cycle (SDLC) model Waterfall, mencakup analisis kebutuhan, desain sistem, implementasi, pengujian, hingga pelatihan pengguna. Sistem ini memungkinkan pencatatan donasi secara real-time, pengelolaan stok barang, notifikasi kebutuhan prioritas, dan pelaporan transaksi. Dengan integrasi sistem web dan mobile, kegiatan ini berhasil meningkatkan efisiensi, transparansi, dan akurasi dalam pengelolaan donasi. Selain itu, dokumentasi teknis dan pelatihan bagi mitra telah disiapkan guna memastikan keberlanjutan sistem. Hasil dari kegiatan ini menunjukkan bahwa pemanfaatan teknologi informasi mampu memberikan dampak signifikan dalam mendukung operasional lembaga sosial. Diharapkan, sistem ini dapat terus dikembangkan lebih lanjut untuk menjawab kebutuhan strategis Griya PMI Surakarta di masa depan. Kata kunci: Donasi, Griya PMI, pengelolaan stok, SDLC Waterfall, sistem informasi
Pendampingan Pembuatan Video Pembelajaran untuk Menunjang Penyelenggaraan Kelas Virtual di SMAN 1 Kemusu Boyolali Salamah, Umi; Wiharto, Wiharto; Suryani, Esti; Prakisya, Nurcahya PT; Setyawan, Sigit
SEMAR (Jurnal Ilmu Pengetahuan, Teknologi, dan Seni bagi Masyarakat) Vol 11, No 1 (2022): Mei
Publisher : LPPM UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/semar.v11i1.55968

Abstract

Peluncurkan Rintisan Kelas Virtual di Jawa Tengah adalah inovasi di tengah pandemi Covid-19, dimana tujuannya memberi pendidikan bagi yang miskin dan putus sekolah. Karena dilaksanakan virtual, para guru di SMAN 1 Kemusu Boyolali sebagai salah satu pilot project kegiatan ini, dituntut untuk kreatif dan inovatif dalam memanfaatkan media pembelajaran sebagai penunjang proses belajar mengajar. Penggunaan Teknologi Informasi sebagai media pembelajaran sudah merupakan suatu tuntutan. Video menjadi media pilihan karena mengutamakan kekuatan suara dan gambar. Dengan video diharapkan siswa lebih mudah mengingat dan memahami apa yang diajarkan guru. Namun, penggunan video dalam pembelajaran terkendala kurangnya kemampuan sebagian besar guru dalam mengolah video untuk membuat materi bahan ajar. Untuk itu perlu dilakukan pendampingan pembuatan video pembelajaran di SMAN 1 Kemusu. Tahapan dalam kegiatan ini: (1) persiapan, membuat konsep video dengan matang dan penyiapan materi, (2) pelatihan dan pendampingan pembuatan video pembelajaran, (3) implementasi dan evaluasi. Hasil kuisioner peserta pendampingan menunjukkan bahwa terjadi peningkatan secara signifikan penggunaan video pada proses pembelajaran virtual dan juga peningkatan kualitas pembelajaran yang dilakukan. Bisa disimpulkan, tujuan pendampingan yaitu meningkatkan kompetensi para guru dalam membuat video pembelajaran dan meningkatkan kualitas pembelajaran untuk menunjang keberhasilan proses kegiatan belajar mengajar pada kelas virtual dapat tercapai dengan baik.
Pendampingan Pembuatan Karya Ilmiah dalam Rangka Peningkatan Profesionalitas Guru di SMK Negeri 1 Gantiwarno Klaten Salamah, Umi; Wiharto, Wiharto; Suryani, Esti; Prakisya, Nurcahya PT
SEMAR (Jurnal Ilmu Pengetahuan, Teknologi, dan Seni bagi Masyarakat) Vol 11, No 1 (2022): Mei
Publisher : LPPM UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/semar.v11i1.56008

Abstract

Aturan kenaikan pangkat saat ini yang mensyaratkan adanya publikasi ilmiah banyak menjadi hambatan bagi para guru. Hal ini disebabkan karena guru mengalami kesulitan memulai menulis artikel yang disebabkan, antara lain kesulitan menentukan dan mengembangkan ide, kurangnya pemahaman tentang teknik penulisan, dan gagap teknologi. Akibatnya, guru kesulitan dalam menaikkan jabatan fungsionalnya. Untuk itulah dilakukan Pendampingan Pembuatan Karya Ilmiah bagi Guru SMK dalam Rangka Peningkatan Profesionalitas Guru di SMK Negeri 1 Gantiwarno Klaten. Metode pendampingan terdiri dari 5 tahapan yaitu (1) survey dan studi analisis situasi, (2) introduksi sosialisasi kegiatan dan konsep umum pembuatan artikel ilmiah, (3) edukasi dan pendampingan penggunaan Zotero dan SPSS, (4) konsultasi dan pendampingan, dan (5) evaluasi. Dari pendampingan ini dihasilkan 70% dari peserta menghasilkan draft artikel ilmiah/PTK. Hasil kuisioner yang diberikan pada peserta menunjukkan dampak positif bagi guru untuk bisa membuat karya ilmiah, terbukti dengan peningkatan minat, prioritas, dan target dalam membuat karya ilmiah. Bisa disimpulkan bahwa tujuan dari pendampingan ini yaitu meningkatkan kompetensi guru dalam menulis artikel, publikasi ilmiah, dan pengolahan data hasil penelitian berhasil dilaksanakan dengan baik
Co-Authors Abdurrahman, Annas Anita Indrasari Atina, Vihi Azka, Hanina Nafisa Esti Suryani Katarina Novi Wahyudiani, Katarina Novi Meiyanto ES Mufidah, Yasmin Muslim, Fajar Nazhifah, Dafina Pamundi, Dias Lanjar Pradityo Utomo Prakisya, Nurcahya PT Pratama, Fadil Putri, Della K. Rahmadani, Fitria Ristu Saptono Sihwi, Sari W. Syaifuddin, Angga Exca Pradipta Syamsul Arifin Umi Salamah Wina Isti Retnani Wisnu Widiarto Zuhdi, Shaifudin