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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Ilmu Komputer dan Informasi Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Kesehatan Lingkungan indonesia Media Statistika JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika WARTA Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JTP - Jurnal Teknologi Pendidikan Indonesian Journal of Community Services Jurnal Pustaka Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship J-KIP (Jurnal Keguruan dan Ilmu Pendidikan) Media Pustakawan STATISTIKA PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics Jurnal Informatika IJoICT (International Journal on Information and Communication Technology)
Budi Warsito
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering 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 Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Investigating the Profile of Digital Readiness and Sustainability Development: An Explainable Clustering Pamuji, Agus; Susanty, Aries; Warsito, Budi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.545

Abstract

The level of digital readiness within Islamic Higher Education Institutions (IHEIs) has emerged as a critical concern, drawing increasing scholarly and institutional attention over the past five years. This study aims to examine the empirical relationship between two key dimensions: digital readiness, as reflected by the National Readiness Index (NRI), and progress toward the Sustainable Development Goals (SDGs). Data were collected from more than 20 IHEIs between 2023 and 2024 to support a sequential analytical approach. Pearson’s correlation coefficient was employed to identify associations between NRI-based digital readiness and SDG performance within the IHEI context. Subsequently, cluster analysis was conducted using the Duda–Hart Index, while the Pseudo T² statistic was applied to validate the robustness of the clustering outcomes. A cartographic visualization was also generated to illustrate variations across readiness and sustainability clusters. The results indicate a considerable disparity between digital readiness and sustainability among IHEIs. Only a limited number of institutions demonstrate consistent performance in both areas, suggesting that effective leadership and strategic investment in digital infrastructure are essential prerequisites for achieving sustainable institutional transformation.
IMPELEMENTASI PBL BERBANTUAN E-LKPD (ELEKTRONIK LEMBAR KERJA PESERTA DIDIK) KONTEKSTUAL UNTUK MENGEKSPLOR KEMAMPUAN PEMAHAMAN KONSEP MATEMATIKA DITINJAU DARI SELF-EFFICACY Warsito, Budi; Suwardi, Dede; Ratnaningsih, Nani
J-KIP (Jurnal Keguruan dan Ilmu Pendidikan) Vol 7, No 1 (2026): FEBRUARI
Publisher : Faculty of Teacher Training and Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/j-kip.v7i1.22065

Abstract

Tantangan pada pembelajaran matematika yaitu berupa rendahnya kemampuan pemahaman konsep serta self-efficacy siswa terutama pada topik teorema phytagoras yang membutuhkan representasi dan penalaran matematika. Meskipun PBL berbantuan E-LKPD (Elektronik Lembar Kerja Peserta Didik) kontektual diyakini dapat memberikan pengalaman belajar yang autentik studi empiris yang memasukan varibel self-efficacy dalam menelaah capaian pemahaman konsep masih jarang dilakukan. penelitian ini memiliki tujuan yaitu untuk mengeksplor kemampuan pemahaman matematika ditinjau dari tingkat self-efficacy siswa dalam pembelajaran matematika melalui PBL berbantuan E-LKPD (Elektronik Lembar Kerja Pesrta Didik) Kontektual, metode yang digunakan adalah deskriptif kualitatif dengan sumber data angket, tes pemahaman konsep, dan wawancara untuk memperdalam temuan penelitian, hasilnya menunjukan adanya perbedaan yang jelas dalam capaian pemahaman konsep antar kategori self-efficacy, Dimana kelompok dengan self-efficacy tinggi memperlihatkan penguasaan konsep yang lebih baik dan konsisten dibandingkan dengan kelompok self-efficacy sedang maupun rendah, data wawancara menunjukan siswa dengan self-efficacy tinggi mampu menjelaskan serta menerapkan konsep, siswa dengan self-efficacy sedang memerlukan contoh sebagai panduan kemudian  siswa dengan self-efficacy rendah tergantung pada petunjuk E-LKPD. Temuan ini menegaskan bahwa implementasi PBL berbantuan E-LKPD kontekstual dapat dimanfaatkan oleh guru untuk memperkuat kemandirian belajar sekaligus mendorong peningkatan self-efficacy siswa pada pembelajaran matematika
Lightweight Brain Tumor Classification with Histogram Oriented Gradients (HOG) Features and Class-Weighted Support Vector Machine (SVM) Warsito, Budi; Fadhilah, Husni; Kartikasari, Puspita; Hakim, Arief Rachman
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1018

Abstract

Early detection of brain tumors via MRI is crucial for improving patient outcomes. This study investigates a lightweight machine learning approach for multiclass brain tumor classification (glioma, meningioma, pituitary tumor, or no tumor) using Histogram of Oriented Gradients (HOG) for feature extraction and a Support Vector Machine (SVM) classifier. This study utilizes the public Brain Tumor Classification MRI Kaggle dataset, consisting of 2870 training and 394 testing MRI images across four classes. After converting the MRIs to grayscale and resizing them to 16×16 pixels, this study extracts HOG features and applies Principal Component Analysis (PCA) to retain 98% of the variance. An SVM is then trained with a GridSearchCV-optimized kernel and hyperparameters, and a custom class-weighted variant is compared. The best model, a polynomial-kernel SVM with custom class weights, achieved 91.8% test accuracy (95% CI (confidence interval): 90.9-92.7) with an F1-score of 0.919 ± 0.01, outperforming the best unweighted SVM (accuracy 86.0% ± 0.02, F1≈0.847). These results demonstrate that HOG+SVM, with proper weighting for class imbalance, can effectively classify brain tumors on small datasets at low computational cost. The novelty of this work lies in demonstrating that an optimized, class-weighted SVM leveraging compact HOG-PCA features can deliver over 91.8% accuracy with strong generalization on small-scale MRI data, providing a viable and interpretable alternative to complex Convolutional Neural Network (CNN) models. Future work can explore CNN and hybrid feature fusion to improve accuracy and generalization further.
Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application Eka Rahmawati; Adi Wibowo; Budi Warsito
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Supporting children’s early development requires consistent attention, ensuring their growth aligns with health standards. PrimaKu is one of the mobile applications developed by the Indonesian Pediatric Society. That application was created to assist parents in recording developmental milestones, monitoring immunization schedules, and accessing practical health information. This study investigates user perceptions of the application by analyzing publicly available reviews and ratings from the Google Play Store. Four supervised machine learning algorithms were applied to classify the sentiment expressed in the reviews: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. Among the models tested, SVM achieved the highest classification accuracy (81%), followed by Random Forest (77%), Decision Tree (74%), and Naive Bayes (73%). Precision, recall, and F1-score were also used to evaluate the performance of each model. The results highlight the relevance of machine learning in capturing and interpreting user sentiment toward digital health tools. Further exploration of deep learning architectures is encouraged to enhance classification accuracy and understanding of features.
Enhanced Robustness in Image Classification through DistortionMix: A Hybrid Distortion-Based Augmentation Technique Fadhilah, Husni; Warsito, Budi; Faridah, Hasna
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1558

Abstract

Deep neural networks perform well on clean image classification tasks but often fail under common corruptions and distribution shifts. This paper introduces DistortionMix, a lightweight hybrid distortion-based augmentation technique designed to improve model robustness. It randomly applies contrast variation, Gaussian noise, or impulse noise to training images, enhancing data diversity and encouraging resilient feature learning. We evaluate DistortionMix on CIFAR-10 (clean) and CIFAR-10-C (corrupted), which includes 19 corruption types at five severity levels. A variety of architectures e.g ResNet, DenseNet, EfficientNet, MobileNet, VGG, AlexNet, GoogleNet, and ViT are fine-tuned with and without DistortionMix. Experimental results show that DistortionMix improves corrupted accuracy by up to 13.8%, while maintaining or slightly improving clean accuracy. Among all models, ViT-Base (timm) achieves the highest robustness, reaching 89.4% on severe corruptions and 97.43% on clean data. These findings highlight DistortionMix as a simple yet effective strategy for enhancing out-of-distribution generalization. Future work includes extending distortion types, developing adaptive augmentation policies, and evaluating performance on real-world corrupted datasets. Source code: github.com/HusniFadhilah/DistortionMix.
Hybrid Stacking Model for Web Attack Classification Using LightGBM, Random Forest, and MLP Fadli Dony Pradana; Farikhin; Budi Warsito
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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

Abstract

The research presents a stacking-based hybrid intrusion detection framework for web application attacks, addressing the persistent limitation that minority classes, including Brute Force, Cross-Site Scripting (XSS), and Structured Query Language (SQL) Injection, are frequently underdetected in conventional Intrusion Detection Systems (IDS) due to severe class imbalance. The proposed architecture combines LightGBM and Random Forest as base learners, while a Multi-Layer Perceptron (MLP) functions as the meta-learner. The framework is supported by rigorous preprocessing, ANOVA F-testbased feature selection, and domain-informed augmentation of critical traffic features, such as Flow Inter-Arrival Time (IAT) Min, Init Win bytes forward, and Backward (Bwd) Packets/s, through optimized weighting strategies. Evaluation on the CICIDS-2017 web attack subset using 10-fold stratified cross-validation shows that the proposed model improves the macro F1-Score from 0.62 ± 0.004 to 0.76 ± 0.003 and achieves a binary accuracy of 99.67% with a macro F1 of 0.94. The observed performance gains are statistically significant (p < 0.001), confirming the robustness of the framework. These findings indicate that targeted feature engineering and heterogeneous stacking substantially improve minority-attack detection while preserving majority-class performance. In addition, the framework demonstrates sub-millisecond inference time, highlighting its practical suitability for real-time IDS deployment in resource-constrained and high-throughput operational cybersecurity environments. The proposed design also offers methodological generalizability for broader anomaly detection tasks in dynamic network environments, where reliable recognition of low-frequency but high-impact attack patterns remains increasingly critically important.
Diabetes Mellitus Early Detection Simulation using The K-Nearest Neighbors Algorithm with Cloud-Based Runtime (COLAB) Jamil, Mohamad; Warsito, Budi; Wibowo, Adi; Kiswanto, Kiswanto
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1510.215-221

Abstract

Diabetes Mellitus is a genetically and clinically heterogeneous metabolic disorder with manifestations of loss of carbohydrate tolerance characterized by high blood glucose levels as a result of insulin insufficiency. Public knowledge of diabetes mellitus 39.30% is influenced by public health education and information about diabetes mellitus that the public has ever received. Early detection of diabetes mellitus can prevent the development of chronic complications and allow timely and rapid treatment. The aim of this study is to simulate the early detection of diabetes mellitus with the K-Nearest Neighbors (K-NN) algorithm using Cloud-Base Runtime (COLAB). The highest accuracy is 76% in K=3, the highest precision is 68% in K=3 and the highest recall is 60% in K=3.  The researchers used K-NN as a method to classify data from the Pima Indians Diabetes Database and obtained a fairly good accuracy value of 76% with a value of k = 3.
Dropout Prediction Using KNN, Decision Tree, Naive Bayes, and Ensemble Learning: A Comparative Performance Analysis with Synthetic Data Validation Puspitasari, Norma; Wibowo, Mochammad Agung; Warsito, Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2591

Abstract

Student dropout is a critical issue in higher education because it affects institutional performance, resource allocation, and student success. Early identification of students with a high risk of dropout enables institutions to design timely academic and non-academic interventions. However, predicting dropout is challenging due to the complexity of influencing factors and class imbalance in educational data. This study presents a comparative performance analysis of four machine learning algorithms—K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and an Ensemble Weighted Voting classifier—to support the development of an effective dropout prediction model. Due to restricted access to complete non-dropout student records, this study integrates real institutional withdrawal data from 2023–2024 to calibrate dropout characteristics and employs a transparently generated synthetic dataset for methodological validation. The dataset consists of 300 instances and is processed using the SMOTE technique to address class imbalance. Model performance is evaluated using accuracy, precision, recall, F1-score, and AUC. The experimental results obtained from synthetic validation indicate that the ensemble model outperforms individual classifiers, achieving an accuracy of 0.97, precision of 1.00, recall of 0.86, F1-score of 0.92, and AUC of 0.93. These findings highlight the potential of ensemble learning as a robust approach for early-warning systems in higher education while providing a transparent framework for predictive modeling under data-access constraints.
LATENT DIRICHLET ALLOCATION DALAM IDENTIFIKASI RESPON MASYARAKAT INDONESIA TERHADAP PROFESI PEGAWAI NEGERI SIPIL Nurul Fajrin Aghentika; Sugito Sugito; Budi Warsito
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.57-66

Abstract

Data on social media comments can be extracted to produce hidden information that is useful as a guide for evaluation and decision making. YouTube has a comments feature as a forum for expressing opinions, experiences and questions. Civil Servants are known as one of the job choices of Indonesian people, the government announced that there were resignations of Civil Servant Candidates in 2022. Responses written in the comment column are difficult to understand, topic modeling can be applied as a text analysis process to find descriptions from unstructured data. Latent Dirichlet Allocation method is able to find out hidden topics in a document as well as the words that make up a topic so that the application of this method will help in identifying responses discussed by the audience. The data used is textual data in the form of comments from YouTube scrapping during 2022. The results of topic modeling form eight topics, namely retirement life, parents hopes, dream jobs, civil servants, job differences, characteristics of generation Z, salary and benefits, and reasons for resignation. The RStudio GUI program can make it easier for users to analyze topic modeling with similar methods.
A BIMAS-Based Assessment Framework of Digital Readiness: Evidence and Institutional Patterns Agus Pamuji; Aries Susanty; Budi Warsito
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

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

Abstract

This study examines the digital readiness of Islamic higher education institutions (IHEIs) in Indonesia in response to the intensifying demands of digital transformation, which have increasingly exposed structural constraints related to limited investment capacity, persistently low levels of digital equity, and the absence of consistent and comparable empirical evidence. Although digitalization has been widely promoted across higher education, existing assessments remain fragmented and insufficiently contextualized, thereby creating a research gap concerning how institutional readiness can be systematically evaluated within value-based educational systems. To address this gap, the study adopts the BIMAS framework as a comprehensive analytical model and applies a cross-sectional research design using a survey-based data collection approach. Methodologically, digital readiness is measured through the calculation of the Digital Readiness Index (DRI), the aggregation of the Net Promoter Score (NPS), and the qualitative evaluation of readiness levels across BIMAS dimensions. The findings, which are interpreted across seven distinct readiness levels, reveal that the Business Model, Infrastructure and Technology, and Audit and Quality Control dimensions demonstrate relatively significant developmental progress, particularly where technological adoption and procedural formalization have been prioritized.
Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Pamuji Agus Pamuji Agus Rusgiyono Agus Winarno, Agus Ahmad Lubis Ghozali Ahmed, Kamil Alan Prahutama Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arief Rachman Hakim Aries Susanty Aries Susanty Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Bagus Dwi Saputra Bayastura, Shahnilna Fitrasha Bayu Surarso Bimastyaji Surya Ramadhan Budiyono Budiyono Calvin, Esagu John Catur Edi Widodo Chrisna Suhendi Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Dyna Marisa Khairina Eka Rahmawati eka rahmawati Ekky Rosita Singgih Wigati Endang Fatmawati Endang Fatmawati Fachry Abda El Rahman Fadhilah, Husni Fadli Dony Pradana Faisal Fikri Utama Faliha Muthmainah Faridah, Hasna Fath Ezzati Kavabilla Fatiya Nur Umma Ferry Hermawan Fiqria Devi Ariyani Firdonsyah, Arizona Gayuh Kresnawati Gertrude, Akello Ghifar Rahman Gregorius Anung Hanindito Handayani, Sri Hanif Kusumasasmita Haritsa, Rifda Tsaqifarani Harjum Muharam Hasbi Yasin Hendri Setyawan Henny Widayanti, Henny Heriyanto Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Jafron Wasiq Hidayat Junta Zeniarja Juwanda, Farikhin Kadarrisman, Vincensius Gunawan Slamet Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maharani, Chintya Ayu Mahrus Ali Maori, Nadia Annisa Maryono Maryono Maryono Maryono Masruroh, Fitriana Maulida Najwa, Maulida Mifta Ardianti Moch. Abdul Mukid Mochamad Arief Budihardjo Mochammad Agung Wibowo Moh Ali Fikri mohamad jamil muhammad shodiq Muliyadi Muliyadi Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nani Ratnaningsih Nisa Afida Izati Noor Azizah Nur Fitriyah Nurcahyanti, Tri Meida Nurul Fajrin Aghentika Nurul Hidayati Oktavia, Cintika Oky Dwi Nurhayati Pandu Anggara Paul, Gudoyi M Perdana, Ery Purwanto Purwanto Puspita Kartikasari Puspita Kartikasari Puspitasari, Norma Putri, Nitami Lestari R Rizal Isnanto R. Rizal Isnanto RACHMAN HAKIM, ARIEF Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmatul Akbar Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riva Amrulloh Riza Rizqi Robbi Arisandi Royani, Noorhanida Rukun Santoso Rully Rahadian Safitri, Adila Salma Farah Aliyah Sang Nur Cahya Widiutama Sari, Juwita Dwinda Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sudarno utomo Sugito Sugito Sulardjaka Sulardjaka Suparti Suparti Suwardi, Dede Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Whisnumurti Adhiwibowo Wibowo, Catur Edi Widiyatmoko, Carolus Borromeus Winahyu Handayani Winarno, Bowo Yanuar Yoga Prasetyawan Yundari, Yundari