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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) LINK Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi SISFOTENIKA Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining JRST (Jurnal Riset Sains dan Teknologi) Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) International Journal of Informatics and Computation Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Prosiding Seminar Nasional Multidisiplin Ilmu Jurnal Pengabdian Dharma Bakti Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Berita Kedokteran Masyarakat Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Marselina Endah Hiswati
Universitas Respati Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Other

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Enhancing Diabetes Classification Using a Relaxed Online Maximum Margin Algorithm Meliala, Dyan Avando; Sulistyawati, Arum Kurnia; Diqi, Mohammad; Hiswati, Marselina Endah; Kristian, Tadem Vergi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.267-278

Abstract

Diabetes mellitus is a growing global health concern that requires accurate and reliable classification models for early diagnosis and effective management. Traditional machine learning models often struggle with class imbalance, generalization limitations, and high false-positive rates, leading to misdiagnoses and delayed interventions. This study enhances the Relaxed Online Maximum Margin Algorithm (ROMMA) to improve the accuracy of diabetes classification. Using a publicly available dataset from Kaggle, which contains 768 medical records with nine health attributes, the model’s performance was evaluated through a confusion matrix and classification metrics. The Enhanced ROMMA achieved an accuracy of 92%, significantly improving upon the Standard ROMMA’s 85% accuracy. The recall for diabetes detection increased from 0.83 to 0.94, reducing false negatives and ensuring more accurate patient identification. While slight misclassification still exists, this improvement enhances the model’s reliability for clinical applications. Future research should incorporate larger datasets and advanced techniques to enhance robustness and generalizability. This study contributes to the development of more accurate machine learning models for diabetes prediction, ultimately supporting better healthcare decision-making.
PROTEGO: Improving Breast Cancer Diagnosis with Prototype-Contrastive Autoencoder and Conformal Prediction on the WDBC Dataset Hiswati, Marselina Endah; Diqi, Mohammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5294

Abstract

Breast cancer remains one of the leading causes of mortality among women, making accurate and trustworthy early detection a critical challenge in healthcare. To address this, we propose PROTEGO, a Prototype-Contrastive Autoencoder with integrated Conformal Prediction, designed to achieve both high diagnostic accuracy and reliable uncertainty quantification. The framework combines dual-head autoencoding, supervised contrastive learning, prototype-based regularization, and conformal calibration to generate discriminative yet interpretable representations. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, PROTEGO was trained and evaluated through stratified data splits, with performance measured by AUROC, AUPRC, F1-score, Balanced Accuracy, Brier score, calibration error, and conformal coverage metrics. The results show that PROTEGO achieves highly competitive performance with an AUROC of 0.992 and an AUPRC of 0.995, while uniquely providing conformal coverage guarantees with an average set size close to one and more than 92% decisive predictions. Ablation studies confirm the complementary role of each component in enhancing both accuracy and calibration. These findings demonstrate that integrating prototype-guided representation learning with conformal prediction establishes a clinically meaningful diagnostic framework. PROTEGO highlights the importance of unifying precision and reliability in medical AI, offering a step toward more interpretable, safe, and clinically trustworthy systems for breast cancer detection.
Android based mobile growth app “AmiGrow” to support early diagnosis of stunting and growth delays of toddlers Ngaisyah, Dewi; Hiswati, Marselina Endah; Mindarsih, Eko; Lestari, Nia Rizqi
BKM Public Health and Community Medicine PHS8 Accepted Abstracts
Publisher : Universitas Gadjah Mada

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

Abstract

Objective The prevalence of stunting toddlers in Indonesia is still in high rate at 27.6%, the largest prevalence compared to other nutritional problems. Moreover, stunting problems can have a direct impact on growth delays. One of the efforts to control these issues is monitoring toddler’s growth and progress, so the expert can do early diagnosis if stunting and growth delays are found. Utilization of android-based mobile growth application (AmiGrow) is an application that supports early diagnosis of stunting and growth delays of toddlers. Method: AmiGrow mobile application is developed with user-centered design (UCD) method using 8th Java development kit, android studio, visual studio code, and flutter framework Result: the app has run according to its function, with well received responses (35.6%) and very good responses (64.4%) by toddler’s mothers. Conclusion: mobile growth AmiGrow app is useful for supporting early diagnosis of stunting and developmental delays for toddlers in digital way.
Stacked Gated Recurrent Units and Indonesian Stock Predictions: A New Approach to Financial Forecasting DIQI, MOHAMMAD; HISWATI, MARSELINA ENDAH; WIJAYA, NURHADI
Jurnal IT UHB Vol 5 No 1 (2024): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v5i1.1106

Abstract

This research paper introduces a novel approach to predicting stock prices using a Stacked Gated Recurrent Unit (GRU) model. The model was trained on historical data from the top 10 companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. The performance of the model was evaluated using key metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results demonstrated promising performance, with average RMSE, MAE, and MAPE values of 0.00592, 0.00529, and 0.01654, respectively, indicating a high level of accuracy in the model's predictions. The average R2 value of 0.97808 further suggests a high degree of predictive power, with the model able to explain a significant proportion of the variance in the stock prices. These findings highlight the effectiveness of the Stacked GRU model in capturing stock price patterns and making accurate predictions. The practical implications of this research are significant, as the model provides a powerful tool for forecasting future stock price trends, which can be utilized in investment decision-making, financial analysis, and risk management. Future research could explore other deep learning architectures, incorporate additional features, or consider different evaluation metrics to enhance the model's performance further.
INOVASI KAMPUNG KOMPLEMENTER BERBASIS TEHNOLOGI SEBAGAI UPAYA MENINGKATKAN KETAHANAN KELUARGA PADA MASA PANDEMI COVID-19 Widaryanti, Rahayu; Muflih, Muflih; Hiswati, Marselina Endah
Jurnal LINK Vol 18 No 2 (2022): NOVEMBER 2022
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat, Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/link.v18i2.9119

Abstract

Ketahanan keluarga dapat dioptimalkan salah satunya dengan pemafaatan terapi komplementer, namun terapi ini kurang diketahui oleh masyarakat. Selain itu melihat kemajuan tehnologi dan mudahnya akses internet di Desa Tirtomartani namun diperlukan informasi secara menyeluruh pada media edukasi tentang terapi komplementer. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk membentuk kampung komplementer dengan konsep pengembangan terapi komplementer berbasis komunitas atau wilayah sehingga diharapkan dapat lebih dekat dengan masyarakat agar dapat dimanfaatkan secara optimal. Metode kegiatan berupa pelatihan kader komplementer sebagai motor penggerak kampung komplementer serta melakukan inovasi dengan mengoptimalkan teknologi digital untuk media edukasi terapi komplementer, selain itu dilakukan pendampingan dan monitoring serta evaluasi yang dilakukan secara berkala untuk keberlangsungan dari program kampung komplementer. Hasil dari kegiatan ini adalah terbentuknya kampung komplementer serta peningkatan pengetahuan, wawasan dan keterampilan masyarakat mengenai terapi komplementer untuk meningkatkan derajat kesehatan yang dapat diterapkan pada individu, keluarga maupun masyarakat. Mitra memperoleh peningkatan pengetahuan pada kategori baik dengan rata-rata 50,66% dan penurunan pengetahuan dengan kategori kurang dari pengetahuan sebelumnya yaitu 17,71%.
Enhancing Hepatitis Patient Survival Detection: A Comparative Study of CNN and Traditional Machine Learning Algorithms DIQI, MOHAMMAD; HISWATI, MARSELINA ENDAH; DAMAYANTI, EKA
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.28241

Abstract

Hepatitis patient survival prediction is a critical medical task impacting timely interventions and healthcare resource allocation. This study addresses this issue by exploring the application of a Convolutional Neural Network (CNN) and comparing it with traditional machine learning algorithms, including Support Vector Machine (SVM), Decision Tree, k-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost). The research objectives include evaluating the algorithms' performance regarding confusion matrix metrics and classification reports, aiming to achieve accurate predictions for both "Live" and "Die" categories. The dataset of 155 instances with 20 features underwent preprocessing, including data cleansing, feature conversion, and normalization. The CNN model achieved perfect accuracy in hepatitis patient survival prediction, outperforming the baseline algorithms, which exhibited varying accuracy and sensitivity. These findings underscore the potential of advanced machine learning techniques, particularly CNNs, in improving diagnostic accuracy in hepatology.
Leveraging Linear Discriminant Analysis for Early Mental Health Disorder Identification Deden Iwan Setiawan; Marselina Endah Hiswati; Sriwidodo Sriwidodo; Mohammad Diqi; Luh Putu Erikawati; Rahayu Cahya Ariani
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23053

Abstract

Mental health disorders pose a significant global challenge, with early identification playing a crucial role in effective intervention and treatment. However, existing diagnostic methods often rely on subjective assessments, leading to potential misdiagnosis and delayed treatment. This study aims to address these limitations by exploring the application of Linear Discriminant Analysis (LDA) for early identification of mental health disorders, specifically focusing on Bipolar Type-1, Bipolar Type-2, Depression, and Normal conditions. Utilizing a publicly available dataset from Kaggle comprising 120 records and 17 attributes, this study applies LDA to classify mental health conditions. The preprocessing steps included handling missing values, encoding categorical data, and normalizing the dataset to enhance model performance. The classification performance was evaluated using a confusion matrix and classification report metrics, demonstrating high accuracy, precision, recall, and F1-scores, particularly for Bipolar Type-1 and Depression, while slightly lower for Bipolar Type-2 and Normal conditions. The novelty of this research lies in the application of LDA to a nuanced mental health dataset, emphasizing its potential as a computational diagnostic tool to complement traditional assessment methods. However, findings suggest that larger, more diverse datasets and the incorporation of objective clinical assessments are necessary to further improve classification accuracy. This study underscores the potential of LDA as a practical and interpretable approach for early mental health diagnosis, providing a foundation for future research to enhance its robustness and clinical applicability.
Adaptive Kernel Probability Model (AKPM) for Interpretable and Reliable Diabetes Prediction using Clinical Diagnostic Data Marselina Endah Hiswati; Izattul Azijah; Yeyen Subandi; Mohammad Diqi
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3689

Abstract

Diabetes mellitus poses a growing global health concern, particularly in low- and middle-income countries where early detection remains limited, demanding classification models that balance accuracy, interpretability, and adaptability to heterogeneous clinical data. This study proposes and evaluates the Adaptive Kernel Probability Model (AKPM), a novel nonparametric probabilistic classifier designed to enhance diabetes prediction by performing localized kernel density estimation with adaptive bandwidth selection via k-nearest neighbors. Implemented and tested on the Pima Indians Diabetes Dataset, AKPM outperformed conventional classifiers—Naïve Bayes and Gaussian Mixture Models (GMM)—across all evaluation metrics, achieving 87.5% accuracy, 83.3% precision, 76.9% recall, and an F1-score of 80.0% for the diabetic class, alongside 89.3% precision and 92.6% recall for the normal class. These results surpassed GMM (83.0% accuracy, 71.6% F1-score) and Naïve Bayes (80.0% accuracy, 66.6% F1-score), confirming AKPM’s superior capability to detect diabetic cases while minimizing false negatives. Offering transparent posterior inference and a modular design, AKPM emerges as a reliable and interpretable solution for clinical decision support systems and real-world healthcare applications.
Log-Scale Correlation Classifier for Mushroom Identification in Agricultural Internet of Things Systems I Wayan Ordiyasa; Mohammad Diqi; Marselina Endah Hiswati; Dian Rhesa Rahmayanti; Umar Basuki; Ida Hafizah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6841

Abstract

Classifying edible and poisonous mushrooms is crucial to food safety, as misidentification can pose severe toxicological risks. Conventional probabilistic classifiers, such as Naïve Bayes and Logistic Regression, often underperform on categorical datasets with correlated attributes and skewed distributions. This study introduces the Log-Scale Feature Correlation Classifier, a novel probabilistic framework that integrates logarithmic transformation and correlation-weighted probability estimation to address these challenges. Using the UCI Mushroom dataset and a 10-fold cross-validation scheme, LSFCC was benchmarked against standard models. The results demonstrate that LSFCC achieved consistently superior accuracy (0.99), precision, and recall, significantly outperforming both Logistic Regression and Naïve Bayes, as confirmed by statistical tests (p<0.01). Its lightweight design and interpretability make it highly suitable for real-time deployment on resource-constrained IoT devices, particularly within Agricultural IoT systems for autonomous mushroom identification. Future research will explore LSFCC’s adaptability to noisy, multimodal data and hybrid architectures, ensuring broader applicability in real-world bioinformatics and food safety domains.
Co-Authors ADNAN ADNAN ABIDIN Ahmad Wakhid Arum Kurnia Sulistyawati Azijah, Izattul Basuki, Umar Dian Rhesa Rahmayanti Dian Rhesa Rahmayanti Elisabeth Deta Lustiyati Erizal Erizal Fajar Y Zebua FAUZIYYAH, ANNUR HAFIZAH, IDA Hamzah Hamzah Hamzah Hamzah Huang J. Jie I Wayan Ordiyasa Ida Hafizah Kristian, Tadem Vergi Lestari, Nia Rizqi Lestariningsih Lestariningsih Lila Retnani Utami Luh Putu Erikawati Lutfi Nur Wicaksono Meliala, Dyan Avando Mohammad Diqi Muflih Muflih Muflih, Muflih Ngaisyah, Rr Dewi Noverianus, Noverianus Ordiyasa, I Wayan Putra Wanda Putra Wanda Putra Wanda Putra Wanda, Putra Rahayu Cahya Ariani Rahayu Widaryanti Rainbow Tambunan Romana Herlinda Rufi'i Salsabela, Marcella Sapariyah, Sapariyah Setiawan, Deden Iwan Sri Hasta Mulyani Sriwidodo Sriwidodo Sriwidodo Sriwidodo Subandi, Yeyen Sudiyatmi, Sudiyatmi Supardi RS Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Umar Basuki Wijaya, Nurhadi Wiratma, Harits Dwi