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All Journal International Journal of Advances in Applied Sciences Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Visi Ilmu Pendidikan The Journal of Experimental Life Sciences (JELS) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Harmonia: Journal of Research and Education International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System ILKOM Jurnal Ilmiah JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Mobile and Forensics International Journal of Visual and Performing Arts Journal of Robotics and Control (JRC) ILKOMNIKA: Journal of Computer Science and Applied Informatics Sains, Aplikasi, Komputasi dan Teknologi Informasi Frontier Energy System and Power Engineering Indonesian Journal of Data and Science Science in Information Technology Letters International Journal of Robotics and Control Systems Jurnal Pengabdian Kepada Masyarakat Kaisa: Jurnal Pendidikan dan Pembelajaran ALINIER: Journal of Artificial Intelligence & Applications Fidelity : Jurnal Teknik Elektro SinarFe7 Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal INFOTEL Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Jurnal JEETech
Zaeni, Ilham Ari Elbaith
Universitas Negeri Malang
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Accuracy Enhancement of a Hybrid CNN–VGG16 Architecture through Dropout Regularization Strategy for Breast Cancer Histopathology Classification Fawaidul Badri Fawaid; Ilham Ari Elbaith Zaeni; Heru Wahyu Herwanto Heru; Muhammad Khusairi Osman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1356

Abstract

Breast cancer remains the leading cause of cancer-related mortality among women globally, necessitating accurate diagnosis through histopathological image analysis. However, manual examination of these images is time-consuming and susceptible to inter-observer variability, highlighting the critical need for reliable automated computer-aided diagnostic (CAD) systems. This study was conducted to systematically evaluate and optimize convolutional neural network (CNN) architectures for automated classification of breast cancer histopathology images, with a focus on mitigating overfitting and enhancing diagnostic accuracy through hybrid deep learning methodologies. The principal innovation is the development of a CNN-VGG16 hybrid architecture that strategically integrates pre-trained feature extraction with a customized CNN framework, hypothesized to substantially improve classification accuracy and model generalization. Three model configurations were developed and comparatively analyzed: (1) baseline CNN, (2) CNN with dropout regularization, and (3) hybrid CNN-VGG16 model. Input images underwent preprocessing, including resizing to 150×150 pixels, normalization, and data augmentation. All models were trained with identical hyperparameters: an Adam optimizer with a learning rate of 0.001, a batch size of 32, and 10 epochs. Dropout regularization with a fixed rate of 0.5 was applied to fully-connected layers to mitigate overfitting. Model evaluation was conducted utilizing standard performance metrics. The proposed CNN-VGG16 hybrid model achieved superior performance: accuracy of 85.19%, precision of 87.16%, recall of 92.75%, and F1-score of 88.37%. These metrics represent significant improvements of 4.2% relative to baseline CNN and 3.4% compared to the dropout-regularized variant, indicating substantially enhanced diagnostic capability and reduced false-negative rates. Strategic integration of pre-trained feature extraction with customizable CNN architectures significantly improves generalization and classification performance in histopathological image analysis. Future investigations should incorporate larger heterogeneous datasets, attention mechanisms, and explainable artificial intelligence (XAI) to enhance clinical interpretability and strengthen practitioner confidence in digital pathology systems
Comparative Evaluation of Machine Learning Models for Heavy Crude Oil Viscosity Prediction Using Repeated Nested Cross-Validation and Independent Holdout Testing Enggie Hendrawan Saputra; Ilham Ari Elbaith Zaeni; Didik Dwi Prasetya; Azlan Mohd Zain; Welly Antonius; I Made Wirawan
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.455

Abstract

Introduction: Accurate prediction of heavy crude oil viscosity is important for reservoir engineering, production planning, and flow assurance because viscosity strongly affects fluid mobility and transport behavior. This study comparatively evaluates established machine learning models under a rigorous validation protocol rather than proposing a new predictive framework. Method: A published Middle Eastern heavy crude-oil dataset containing 196 development measurements and 47 independent holdout measurements was used. Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, and the Beggs–Robinson correlation were evaluated using repeated nested cross-validation with five outer folds repeated twice and five inner folds. Preprocessing and hyperparameter selection were embedded within the validation pipeline, while the untouched holdout set was used only for final evaluation. Results and Discussion: Gradient Boosting achieved the best internal performance with R² = 0.99313 and RMSE = 11.41 cP. On the independent holdout set, it achieved R² = 0.99308, RMSE = 8.43 cP, MAE = 6.64 cP, and MAPE = 0.78%, outperforming Random Forest and Support Vector Regression. Residual diagnostics showed no detectable heteroscedasticity, while permutation importance identified temperature and C7+ as the dominant predictors. Conclusion: Gradient Boosting provides highly accurate viscosity predictions within the sampled domain; however, the absence of row-level oil identifiers and external reservoir data limits conclusions regarding oil-disjoint and field-level generalization.
Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm Sendari, Siti; Zaeni, Ilham Ari Elbaith; Lestari, Dian Candra; Hariyadi, Hanny Prasetya
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Opinion Analysis is a research study needed to social media, since the content could become a trending topic and has a significant impact on social life. One of the social media that have a big contribution to cyberspace and information development is Twitter. In the Twitter application, users can insert images that represent emotions, facial expressions, or icons. Emoji is a graphic symbol in the form of an image to express a thing, with the Emoji, a text can be read and understood according to its meaning because the image represents it. Of the several things that have been mentioned then, the researchers conducted research on the classification of tweet content based on the use of Emojis. This study aims to determine the emotional uses of Twitter in one period. Every tweet on the Twitter timeline, which contains both text and Emojis, will be classified according to several categories. The algorithm used was Naïve Bayes. It calculated the probability of Emoji tweet to obtain the text classification with Emojis. The results of the classification of emotions are grouped with three categories, namely "angry," "joy," and "sad," it showed that the category "joy" had become the emotional trend of Twitter users where Emojis (x1f60a) dominate the most. Meanwhile, the accuracy of the algorithm used to reach 90% with a 70:30 holdout technique.
Generating Javanese Stopwords List using K-means Clustering Algorithm Wibawa, Aji Prasetya; Fithri, Hidayah Kariima; Zaeni, Ilham Ari Elbaith; Nafalski, Andrew
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable.
Multiple Imputation of White Blood Cell Measurements for Early Emergency Assessment Using Tree-Based MICEforest Prasetya, Renaldi Primaswara; Ashar, Muhammad; Zaeni, Ilham Ari Elbaith
The Journal of Experimental Life Science Vol. 16 No. 2 (2026)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2026.016.02.03

Abstract

Early clinical assessment in Emergency Departments (EDs) frequently relies on laboratory indicators that are often unavailable at the time of patient arrival. The White Blood Cell (WBC) count is a key marker for infection, inflammation, and acute physiological stress, yet missing or delayed WBC measurements are common in emergency care workflows. Conventional imputation methods typically replace missing values with single-point estimates, implicitly assuming full reliability and ignoring the uncertainty inherent in the imputation process. This paper investigates the use of multiple imputation strategies to address missing WBC measurements in emergency care data. A multivariate, tree-based imputation approach is employed to generate multiple plausible WBC values for each missing observation, capturing the inherent variability of laboratory uncertainty. Instead of focusing solely on point estimates, this study emphasizes the role of imputation variability as an indicator of confidence in reconstructed WBC values. Experiments are conducted on a synthetic emergency care dataset designed to mimic early ED scenarios, using three multiple imputations (M = 3) to compare single imputation and tree-based multiple imputation in terms of variability and uncertainty representation. Through analytical comparison and illustrative experiments on emergency care data with realistic missingness patterns, the proposed approach demonstrates that multiple imputation provides more informative and robust representations of missing WBC measurements compared to traditional single-imputation techniques. The results highlight how uncertainty-aware WBC reconstruction can better support early emergency assessment, particularly in high-risk and time-sensitive clinical scenarios. By focusing on WBC as a representative and clinically critical laboratory variable, this work underscores the importance of treating missing laboratory data as uncertain rather than deterministic. The proposed perspective offers practical insights for improving the reliability of data-driven decision support systems in emergency medicine and lays the groundwork for future integration with predictive modeling frameworks.
OPTIMALISASI PRODUKTIVITAS DAN KUALITAS BASRENG BERBASIS AUTOMATIC OIL DRAINER MACHINE TECHNOLOGY PADA UMKM DELLA MUDA Soenar Soekopitojo; Sujito Sujito; Ilham Ari Elbaith Zaeni; Dyah Lestari; Revanza Akiella Jihan Putra
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 6 No 1 (2026): Mei 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v6i1.5268

Abstract

Basreng (fried meatballs) is one of popular Sundanese snacks that is highly favored by the Indonesian people. Its savory flavor, crunchy texture, and long shelf life make it a favorite snack choice among various groups. In Madiredo Village, Pujon District, Malang Regency, there is a micro, small, and medium enterprise (MSME) called Della Muda, led by Atik Muda. This MSME has a unique innovation in producing basreng made from vegetables, with a production capacity of up to 30 kilograms per batch. However, in practice, the business faces challenges during the oil-draining stage, which is still done manually. This process not only takes a considerable amount of time but can also affect the final quality of the product, such as its texture and shelf life.As a solution to this problem, appropriate technology in the form of an Automatic Oil Drainer Machine is applied. This technology works on the principle of centrifugal force, which can speed up and optimize the oil-draining process from fried products. The machine is designed using food-grade stainless steel material, which is rust-resistant, easy to clean, and safe for use in both household and small-scale industries. The implementation of this machine is expected to make the production process more efficient and improve product quality. This will certainly have a positive impact on increasing the productivity of Della Muda MSME, maintaining the consistency of taste and texture of basreng, and indirectly supporting the economic growth of the surrounding community through the strengthening of local businesses based on technological innovation.
Co-Authors A.N. Afandi Adam Rachmawan Adib Nur Sasongko Adika Prana Ihsanuddin Aditama Yudha Atmanegara Adjie Rosyidin Afifah Salim Afnan Habibi, M. Afrian, Ronny Agung Bella Putra Utama Aji Prasetya Wibawa Aji Wibawa Akhmad Afrizal Rizqi Amalia Sufa Andrew Nafalski Andy Hermawan Anggraeni Budiarti Anik N. Handayani Anik Nur Handayani Arengga Wibowo, Danang Arifin, Samsul Aripriharta - Aripriharta Aripriharta Arya Kusuma Wardhana Arya Tandy Hermawan Ashar, Muhammad Atmaja, Nimas Hadi Azlan Mohd Zain Danang Arengga Wibowo Dessy Rif’a Anzani Dian Candra Lestari Didik Dwi Prasetya Dony Setiawan Dwiyanto, Felix Andika Dyah Lestari Eko Pambagyo Setyobudi Elmusyah, Hakkun Enggie Hendrawan Saputra Erinda, Hayyu Fahreza Al Rafi, Muhammad Alif Fanani, Erianto Faozan Fauzi, Rochmad Fawaidul Badri Fawaidul Badri Fawaid Febi Elvara Aprilia Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdiansyah, Dodik Septian Ferdinand, Miftakhul Anggita Bima Fithri, Hidayah Kariima Fitriana Kurniawati Gunawan Gunawan Gunawan Gwinny Tirza Rarastri Hakkun Elmunsyah Hanny Prasetya Hariyadi Hari Putranto Harits Ar Rosyid Hariyadi, Hanny Prasetya Hartono, Nickolas Hendrawan, William Hartanto Heru Wahyu Herwanto Heru Hidayah Kariima Fithri Hsien-I Lin I Made Wirawan Irvan, Mhd Ismail, Amelia Ritahani Ivatus Sunaifah Kartika Kirana Kevin Raihan Khafit Zaman Kotaro Hirasawa Lestari, Dian Candra Liliek Rahayu M. Adib Nursasongko M. Afnan Habibi Maftuh Ahnan Mahisha Laila Moh. Iqbal Ardiansyah Mohamad Iqbal Mokh Sholihul Hadi Muhammad Arrazy Muhammad Firmansyah Muhammad Hafiizh Muhammad Iqbal Akbar Muhammad Khusairi Osman Muhammad Khusairi Osman Muhammad Rifai Muhammad Syauqi Muhammad Usman Mursyit, Mohammad Nafalski, Andrew Ningtyas, Yana Nurfadila, Piska Dwi Nusantar, Alrizal Akbar Nusantar Akbar Prana Ihsanuddin, Adika Puji Santoso Pundhi Yuliawati Ramadhan, Aslan Poetra Rasidy, Ahmad Himawari Renaldi Primaswara Prasetya Retno Indah Rokhmawati Revanza Akiella Jihan Putra Ridwan Shalahuddin Rina Dewi Indahsari Riris Andriani Rizal Kholif Nurrohman Ronny Afrian Samsul Arifin Setumin, Samsul Setyorini Setyorini Shandy Darmawan Simbolon, Triyanti Siti Sendari Soenar Soekopitojo Sugiono, Bhima Satria Rizki Sujito Sujito Suyono Suyono Syaad Patmanthara Syafaat, Mokhammad Tri Atmadji Sutikno Utama, Agung Bella Putra Welly Antonius Wibisono, M. Nurwiseso Yandhika Surya Akbar Gumilang Yogi Dwi Mahandi Yosi Kristian Zafifatuz Zuhriyah