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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Bulletin of Electrical Engineering and Informatics Jurnal Penelitian Pendidikan Kreano, Jurnal Matematika Kreatif-Inovatif International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Buletin Ilmiah Sarjana Teknik Elektro Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Journal of Soft Computing Exploration Devotion: Journal of Research and Community Service Unnes Journal of Mathematics Education Unnes Journal of Mathematics Journal of Advances in Information Systems and Technology Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Innovative: Journal Of Social Science Research Recursive Journal of Informatics Scientific Journal of Informatics Hipotenusa: Journal of Mathematical Society Journal of Economic Resilience and Sustainable Development Jurnal Abdi Negeri Recursive Journal of Informatics Journal of Soft Computing Exploration
Endang Sugiharti, Endang
Unknown Affiliation
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Improving Brain Tumor Image Segmentation Accuracy Based on Residual Network (ResNet) Using Nearest Neighbor Upsampling Muhammad Afifudin; Endang Sugiharti
Recursive Journal of Informatics Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i1.12010

Abstract

Abstract. Brain tumors are a critical disease due to the abnormal growth of cells in the brain, which can damage surrounding normal cells and increasing the risk of death in patients. With advancements in technology, artificial intelligence can be developed to segment brain tumor areas, aiding medical professionals in identifying tumor characteristics and determining appropriate treatment plans. Convolutional Neural Network (CNN) models can be utilized for segmentation tasks because their ability to classify each pixel of an image, assign specific labels, and map them into homogeneous groups. To enhance the capability of CNNs against the possibility of vanishing gradients, the Residual Network (ResNet) architecture can be applied to the segmentation model. The use of ResNet provides additional capability for the network to choose between the training results in the current epoch or skip to the next network when the training results approach the identity value. However, ResNet also reduces the scale of images and feature maps during downsampling operations, sacrificing spatial resolution. This study proposes the implementation of the Nearest Neighbor Upsampling method on ResNet to improve the model's accuracy in the task of MRI brain tumor segmentation.  Purpose: This research proposes a method to increase the accuracy of brain tumor MRI image segmentation using the ResNet model by implementing the Nearest Neighbor Upsampling method. Methods/Study design/approach: The method used is Nearest Neighbor Upsampling on ResNet to enhance image dimensions and fill gaps in MRI brain tumor images during the learning process, preserving spatial information and context crucial for segmentation. Result/Findings: The optimization of the brain segmentation model for classifying brain tumor regions using ResNet and Nearest Neighbor Upsampling achieves an increase in accuracy from 96.94% to 98.44% and a decrease in loss value from 0.0881 to 0.0874. Novelty/Originality/Value: This research addresses the limitations of the Residual Network (ResNet) model, such as the drawback of reducing the scale of images and feature maps during downsampling, resulting in a loss of spatial resolution. To overcome this challenge, the study introduces the Nearest Neighbor Upsampling method applied to ResNet, demonstrating its effectiveness in representing spatial information and image context, thereby improving segmentation accuracy.
Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients Untsa Zaaidatunni'mah; Endang Sugiharti
Recursive Journal of Informatics Vol. 2 No. 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/vavw2205

Abstract

Abstract. Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique. Purpose: This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results. Methods/Study design/approach: To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases. Result/Findings: The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research. Novelty/Originality/Value: Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.
Hyperparameter Optimization Using Hyperband in Convolutional Neural Network for Image Classification of Indonesian Snacks Nuril Asyrofiyyah; Endang Sugiharti
Recursive Journal of Informatics Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/n9xhbf04

Abstract

Abstract. Indonesia is known for its traditional food both domestically and abroad. Several cakes are included in favorite traditional foods. Of the many types of cakes that exist, it is visually easy to recognize by humans, but computer vision requires special techniques in identifying image objects to types of cakes. Therefore, to recognize objects in the form of images of cakes as one of Indonesian specialties, a deep learning algorithm technique, namely the Convolutional Neural Network (CNN) can be used. Purpose: This study aims to find out how the Convolutional Neural Network (CNN) works by optimizing the hyperband hyperparameter in the classification process and knowing the accuracy value when hyperband is applied to the optimal hyperparameter selection process for classifying Indonesian snack images. Methods/Study design/approach: This study optimizes the hyperparameter Convolutional Neural Network (CNN) using Hyperband on the Indonesian cake dataset. The dataset is 1845 images of Indonesian snacks which consists of 1523 training data, 162 validation data and 160 testing data with 8 classes. In training data, the dataset is divided by 82% on training data, 9% validation, and 9% testing. Result/Findings: The best hyperparameter value produced is 480 for the number of dense neurons 2 and 0.0001 for the learning rate. The proposed method succeeded in achieving a training value of 87.53%, for the validation process it was obtained 66.8%, the testing process was obtained 79.37%. Results obtained from model training of 50 epochs. Novelty/Originality/Value: Previous research focused on the application and development of algorithms for the classification of Indonesian snacks. Therefore, optimizing hyperparameters in a Convolutional Neural Network (CNN) using Hyperband can be an alternative in selecting the optimal architecture and hyperparameters.
Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results Dany Pradana; Endang Sugiharti
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/hjs1fd70

Abstract

Abstract. The application of information technology in the field of education produces big data. It retains information that can be treated as useful. Having data mining, can be used to model highly useful student performance for educators performing corrective actions against weak students. Purpose: The study was to identify the application and accuracy algorithm Naive Bayes Classifier to predict students' study results. Methods: The prediction system for student learning outcomes was built using the Naive Bayes Classifier and Laplace Smoothing methods using a combination of two Information Gain and Chi Square feature selections. The experiment was carried out 2 times using different dataset comparisons. Result: In the first experiment using a dataset of 80:20, the accuracy Naive Bayes Classifier method with Laplace Smoothing and without Laplace Smoothing showed the same results as 94.937%. On the second experiment to equate dataset 60:40 results of the Naive Bayes Classifier accurate method without Laplace Smoothing only 86.076%, then score a 91.772% accuracy using the Laplace Smoothing. The improvement is caused by a probability of zero that can be worked out with Laplace Smoothing. Novelty: The selection feature process is very important in the classification process. Thus, in this study, information gain and chi square double selections of such features as information gain and so promote accuracy.
Effectiveness of Multisensory-Based Statistical E-Comic with Collaborative Learning to Enhance Deaf Students’ Mathematical Connection Sugiman; Najma Fatikasari; Emi Pujiastuti; Muh. Fajar Safaatullah; Endang Sugiharti
Hipotenusa: Journal of Mathematical Society Vol. 8 No. 1 (2026): Hipotenusa : Journal of Mathematical Society
Publisher : Program Studi Tadris Matematika Universitas Islam Negeri (UIN) Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/hipotenusa.v8i1.5326

Abstract

Deaf students face challenges in understanding abstract mathematical concepts, especially the material of data concentration measures, due to limited verbal communication and lack of learning media that suits their characteristics. This study aims to examine the effectiveness of statistical e-comic based on multisensory approach integrated with collaborative learning model in improving mathematical connection ability of deaf students. The research approach used is mixed-methods with Single Subject Research (SSR) design type A-B-A. The subjects consisted of three deaf students of class VIII-B in SLB Negeri Ungaran who were purposively selected. Data collection techniques included mathematical connection tests, learning observations, semi-structured interviews, and visual documentation. Quantitative data was analysed using visual inspection charts within and between conditions, while qualitative data was analysed through reduction, presentation, interpretation, and conclusion drawing. The results showed that statistical e-comic media was effective in significantly improving deaf students' mathematical connections, both from cognitive (concept understanding), affective (motivation and attitude), and psychomotor aspects (digital skills and participation). The conclusion shows that statistical e-comic with multisensory approach and collaborative learning is able to bridge the gap of mathematical understanding contextually and applicatively.
Tuberculosis classification on chest x-ray images using DenseNet-169 and convolutional block attention module Muhammad Agil Izzulhaq; Endang Sugiharti
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.14

Abstract

Tuberculosis remains a major global health challenge, and the manual interpretation of chest X-rays is often limited by the subjectivity and shortage of radiology experts. While deep learning approaches like DenseNet have shown promise in medical imaging, the integration of attention mechanisms such as the Convolutional Block Attention Module (CBAM) for tuberculosis detection has been less explored. This study aimed to develop a Convolutional Neural Network (CNN) model utilizing DenseNet-169 combined with CBAM to accurately classify chest X-ray images into normal and tuberculosis classes. A dataset of 7,000 chest X-ray images was preprocessed and partitioned into training, validation, and testing sets. DenseNet-169 served as the backbone architecture, while CBAM was applied to emphasize crucial spatial and channel features. Evaluated across standard metrics, the proposed model achieved an accuracy of 99.43%, a precision of 99.72%, a recall of 99.14%, and an F1-score of 99.43%, successfully outperforming the baseline DenseNet-169 model without CBAM. Ultimately, the integration of CBAM with DenseNet-169 demonstrates remarkable potential in improving tuberculosis detection, confirming that attention mechanisms can substantially enhance deep learning performance in medical imaging.
Potensi Guru Matematika SMP Mengembangkan LKS Terintegrasi Evaluasi Digitalnya untuk Meningkatkan Kreativitas Matematis Siswa Emi Pujiastuti; Sugiman; Endang Sugiharti; Nanang Sungkowo
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 1 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i1.90280

Abstract

Perkembangan teknologi pendidikan mendorong perlunya inovasi dalam pembelajaran matematika, salah satunya melalui digitalisasi Lembar Kerja Siswa (LKS) yang terintegrasi dengan evaluasinya untuk mendukung Kompetensi Kreativitas Matematis. Penelitian ini bertujuan memperoleh gambaran kemampuan guru matematika SMP di wilayah eks-Karesidenan Semarang dalam melakukan digitalisasi LKS tersebut, sekaligus menilai kompetensi kreativitas matematis siswa selama implementasinya. Penelitian menggunakan metode campuran dengan pendekatan kuantitatif dan kualitatif. Sampel terdiri atas 20 guru matematika, enam di antaranya diwawancarai untuk pendalaman kualitatif. Analisis kuantitatif dilakukan menggunakan uji-t berbantuan SPSS, sedangkan analisis kualitatif melalui reduksi data, penyajian data, interpretasi, dan penarikan kesimpulan. Hasil menunjukkan rata-rata kemampuan guru sebesar 87,98 dan penerimaan positif terhadap digitalisasi LKS sebagai inovasi pembelajaran. Kompetensi kreativitas matematis siswa dari dua SMP menunjukkan nilai rata-rata 85,44 dan 83,00 setelah implementasi. Kendala utama mencakup keterbatasan jaringan Wi-Fi dan kebutuhan kuota internet. Temuan ini menegaskan bahwa digitalisasi LKS efektif mendukung kreativitas matematis siswa serta layak diterapkan lebih luas, dengan dukungan pelatihan guru dan penguatan infrastruktur teknologi.
INKUBASI BISNIS BERBASIS ZERO WASTE STRATEGI EKONOMI HIJAU PADA KOMUNITAS ANAK TERLANTAR SEMARANG Yahya Nur Ifriza; Alamsyah Alamsyah; Endang Sugiharti; Abas Setiawan; Lola Kurnia Pitaloka; Nur Fateah; Widodo Widodo; Sri Prastiti Kusuma Anggraeni
Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia. Vol 5, No 2 (2026): Oktober 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jpm.v5i2.2839

Abstract

Komunitas anak terlantar di Al-Idris Kota Semarang menghadapi tantangan struktural berupa keterbatasan akses pendidikan formal dan ketiadaan keterampilan praktis untuk kemandirian ekonomi. Fenomena ini berisiko memperpanjang siklus kemiskinan pada kelompok rentan. Program pengabdian ini bertujuan mentransformasi keterbatasan ekonomi mitra melalui inkubasi bisnis Edu-Ecoprint berbasis Ethnogreen. Pendekatan ini mengintegrasikan teknik pewarnaan alami dengan penguatan nilai kearifan lokal sebagai identitas produk unik. Melalui metode pendampingan komprehensif mulai dari pelatihan teknis produksi hingga manajemen pemasaran digital program ini membekali mereka dengan life skills bernilai jual tinggi di green market. Secara strategis, permasalahan ini selaras dengan urgensi Astacita Presiden RI, khususnya pada Poin 4 Memperkuat pembangunan manusia, sains, teknologi, dan pendidikan serta Poin 6 Membangun dari bawah untuk pemerataan ekonomi dan pemberantasan kemiskinan. Program ini juga merespons kebijakan strategis mengenai 8 Industri Strategis, terutama dalam pengembangan Sektor Industri Kreatif dan Pengolahan berbasis sumber daya lokal yang ramah lingkungan. Di tengah meningkatnya tren pasar green product, inisiatif ini hadir untuk menjembatani potensi pasar dengan pemberdayaan kelompok marginal. Secara global, langkah ini merupakan manifestasi nyata dari pencapaian SDGs, terutama dalam upaya penghapusan kemiskinan dan penyediaan akses pendidikan bagi semua lapisan masyarakat.
Optimization of Mango Plant Leaf Disease Classification Using Concatenation Method of MobileNetV2 and DenseNet201 CNN Architectures Ahmad Ramadhan Auni; Endang Sugiharti
Scientific Journal of Informatics Vol. 11 No. 4: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15169

Abstract

Purpose: Mango production can be severely impacted by diseases affecting mango plants. By leveraging artificial intelligence, the agricultural sector can automate the analysis of mango leaves to monitor plant health. The goal of this research is to improve the early detection of diseases in mango leaves to allow early treatment to minimize damage to the crops. Methods: This study employs an approach of combining two pre-trained CNN architectures, namely MobileNetV2 and DenseNet201 through concatenation method. To enhance the model’s generalization ability, various image augmentation techniques were applied during the training phase. Result: The model developed in this study achieved great performance in classifying mango leaf diseases with a testing accuracy of 99.25%. This result indicates the effectiveness of the concatenation method by outperforming the accuracy of either MobileNetV2 or DenseNet201 when implemented separately. Novelty: This research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification, a method not previously explored in this context. The model developed from this study has the potential to serve as a tool for the early detection and treatment of mango leaf diseases.
Co-Authors Abas Setiawan Abas Setiawan Adha, Nugraha Saputra Adi, Pungky Tri Kisworo Adi, Pungky Tri Kisworo Afifah, Eka Nur Afifah, Eka Nur Ahmad Ramadhan Auni Akbar Lintang Aji Alamsyah Alamsyah - Amin Suyitno Anggara, Dian Christopher Anggyi Trisnawan Putra Arief Broto Susilo Astuti, Winda Try Astuti, Winda Try Asyrofiyyah, Nuril Atikah Ari Pramesti, Atikah Ari Budi Prasetiyo, Budi Bunardi, Gunawan Choirunnisa, Rizkiyanti Clarissa Amanda Josaputri, Clarissa Amanda Dany Pradana Devi, Feroza Rosalina Devi, Feroza Rosalina Dian Tri Wiyanti Dwijanto Dwijanto, Dwijanto Dwika Ananda Agustina Pertiwi Emi Pujiastuti Fabian Dominggus Eka Boro Fauzan, Riantama Sulthana Fitriana, Erma Nurul Florentina Yuni Arini Hakim, M. Faris Al Hani'ah, Ulfatun Hariyanto, Abdul Hernowo, Mutiara Heryadi, Muhammad Heri Imam Sonny, Imam Indah Urwatin Wusqo Isa Akhlis Juliater Simamarta Jumanto Jumanto, Jumanto Jumanto Unjung Khoirunnisa, Oktaria Gina Korzhakin, Dian Alya Krida Singgih Kuncoro Kuncoro, Rizki Danang Kartiko Kurniawati, Putri Aida Nur Lestari, Dewi Indah Listiana, Eka Malisan, Johny Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Much Aziz Muslim Muh. Fajar Safaatullah Muhammad Afifudin Muhammad Agil Izzulhaq Muhammad Kharis Mulyono Mulyono Muzayanah, Rini Najma Fatikasari Nanang Sungkowo Nofrisel, Nofrisel Nugroho, Prisma Bayu Nur Fateah Nuril Asyrofiyyah Perbawawati, Anna Adi Perbawawati, Anna Adi Pipit Riski Setyorini Pitaloka, Lola Kurnia Pradana, Dany Pradhana, Fajar Eska Purnamasari, Ratnaningtyas Widyani Raharjo, Ahmad Solikhin Gayuh Ratri Rahayu Riza Arifudin Rofik Rofik, Rofik Rupiah, Siti S.Pd. M Kes I Ketut Sudiana . Sampurno, Global Ilham Sampurno, Global Ilham Sari, Firar Anitya Sekartaji, Novanka Agnes Sekarwati Ariadi, Tiara Sri Prastiti Kusuma Anggraeni Subarkah, Agus Sugiman Sugiman Sugiman Sukestiyarno Sukestiyarno Sukmadewanti, Irahayu Sukmadewanti, Irahayu Sulis Eli Triliani, Sulis Eli Supriyono Supriyono Susanti, Eka Lia Sutarti, Sri Sutarti, Sri Umi Latifah Untsa Zaaidatunni'mah Vedayoko, Lucky Gagah Vedayoko, Lucky Gagah Whisnu Ulinnuha Setiabudi, Whisnu Ulinnuha Widodo Widodo Wijaya, Henry Putra Imam Yahya Nur Ifriza Zaaidatunni'mah, Untsa Zaenal Abidin