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All Journal BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS YOUNGSTER PHYSICS JOURNAL Jurnal Sains dan Teknologi Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Journal of Physics and Its Applications Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics Jurnal Fisika Indonesia Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Jurnal Algoritma Scientific Journal of Informatics
Catur Edi Widodo
Departemen Fisika, Fakultas Sains Dan Matematika, Universitas Diponegoro, Semarang
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Global Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations: A Bibliometric Analysis Bachri, Otong Saeful; Widodo, Catur Edi; Nurhayati, Oky Dwi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1248

Abstract

Stunting and malnutrition continue to be significant public health challenges, particularly in low-income and rural populations. With the growing reliance on data-driven strategies in public health, machine learning (ML) has emerged as a promising tool for identifying, classifying, and predicting conditions related to undernutrition. This study presents a bibliometric analysis of global research from 2019 to 2025, focusing on the application of ML techniques—such as clustering, support vector machines (SVM), and random forest—in addressing malnutrition and stunting. A total of 417 Scopus-indexed publications were analyzed using Biblioshiny (R) to assess research trends, key themes, influential authors, prominent journals, and thematic evolution. The analysis reveals a consistent growth rate of 10.72% in publications, with notable contributions from China and other low- and middle-income countries. Keyword mapping highlights that “machine learning,” “spatial analysis,” and “stunting” are central to the research, although they remain areas for further development. Thematic evolution indicates a shift towards more integrated, context-aware approaches, with a growing focus on built environments and vulnerable populations. The study concludes that while ML holds significant promise for advancing decision-making in child health and nutrition, its impact will depend on continued methodological refinement and effective implementation within public health systems.
Topic Modelling Latent Dirichlet Allocation untuk Klasifikasi Komentar pada Layanan Streaming Platform Royani, Noorhanida; Widodo, Catur Edi; Warsito, Budi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 3 (2023): Oktober
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i3.68492

Abstract

Seiring dengan berkembangnya teknologi, memunculkan banyak platform online untuk streaming film. Streaming platform banyak digunakan masyarakat seperti netflix, disney+, hbo go, we tv, vidio. Banyaknya perbandingan antar streaming platform menjadi perbincangan dimedia sosial yaitu twitter. Opini yang disampaikan pengguna streaming platform berisi komentar positif dan komentar negatif yang mempengaruhi pengguna lainnya yang ingin menonton film. Penelitian ini dilakukan untuk mengkaji perbandingan antara komentar positif dan komentar negatif pengguna streaming platform pada media sosial Twitter. Metode Latent dirichlet allocation dapat digunakan sebagai topic modelling dan Support Vector Machine untuk klasifikasi. Pada tahapan pengambilan data dengan menggunakan tools framework scrapy dengan python, data diambil sebanyak 5.000 dan dilakukan preprocessing text. Metode LDA dapat mempresentasikan topik dan dokumen serta klasifikasi menggunakan Support Vector Machine (SVM) mendapatkan hasil komentar positif lebih banyak dari pada komentar negatif. Hasil evaluasi preforma didapatkan nilai akurasi 0,88, recall 0,88, F1score 0,87, precision 0,88. Topic Modelling Latent Dirichlet Allocation (LDA) untuk Klasifikasi Komentar pada Layanan Streaming Platform dengan menggunakan 5,000 data diambil dari sosial media yaitu twitter yang terbagi menjadi komentar positif dan komentar negatif. Hasil ini dipengaruhi dari jumlah komentar positif yang lebih dominan dari pada komentar negatif. Implikasi dari penelitian ini adalah pentingnya memperhatikan keseimbangan data dalam melakukan klasifikasi komentar pada platform streaming agar hasil prediksi klasifikasi dapat lebih akurat.
Perbandingan Metode Simple Additive Weighting dan Analytic Hierarchy Process Untuk Pemilihan Supplier pada Restoran Maratullatifah, Yulaikha; Widodo, Catur Edi; Adi, Kusworo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 1: Februari 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022914428

Abstract

Kedai susu Its Milk adalah restoran yang bergerak di bidang kuliner dengan ciri khas menu pengolahan susu, bahan utamanya menggunakan susu sapi murni lokasi berada di jalan Taman Siswa Unnes Gunungpati Semarang. Bahan baku susu sapi diperoleh dari supplier. Pemilihan supplier yang tepat adalah kunci kestabilan usaha diantaranya dapat mengurangi biaya, mengurangi resiko rantai pasokan, meningkatkan nilai barang dan membentuk strategi perusahaan. Permasalahan restoran yang terkait supplier adalah kualitas, kuantitas, harga, pelayanan dan garansi. Oleh karena itu, perlu dilakukannya pemilihan supplier yang tepat. Metode SAW dan AHP merupakan metode paling mendominasi diantara metode lainnya untuk sistem pemilihan supplier. Perhitungan sedehana dan mudah adalah keunggulan SAW, data yang terperinci adalah keunggulan AHP, dibandingkan juga dengan Euclidan Distance untuk penentuan metode yang paling baik yaitu memiliki nilai mendekati nol. Tujuan penelitian ini membandingkan metode SAW dan AHP dalam pemilihan supplier pada restoran. Data penelitian diperoleh dari pemilik dan managemen Its Milk berupa kuesioner dan wawancara. Hasil perbandingan metode diperoleh hasil alternatif  yang sama di dalam satu pengujian yaitu terpilihnya supplier A2 dengan nilai akurasi di SAW 0,86 dan akurasi di AHP 0,229. Berdasar euclidean distance metode AHP yang paling baik digunakan dalam penelitian ini dengan nilai rata-rata 0,19 sedangkan SAW nilai rata-rata 0,90. AbstractKedai Susu Its Milk is a restaurant in the culinary field with a characteristic menu of milk processing, the main ingredient of pure cow's milk is located on Jalan Taman Siswa Unnes Gunungpati Semarang. Raw material for cow's milk from suppliers. Selection of the right supplier is the key to business stability including reducing costs, reducing supply chain risk, increasing the value of goods and shaping company strategy. Restaurant problems related to suppliers are quality, quantity, price, service and warranty. Therefore, it is necessary to choose the right supplier. The SAW and AHP methods are the most dominating methods among other methods for supplier selection systems. Simple and easy calculations are the advantages of SAW, detailed data is the advantage of AHP, compared to Euclid and Distance for determining the best method, which has a value close to zero. The purpose of this study is to compare the SAW and AHP methods in the selection of suppliers in restaurants. Research data obtained from the owner and management of Its Milk in the form of questionnaires and interviews. The results of the comparison of methods obtained the same alternative results in one test, namely the selection of supplier A2 with an accuracy value of 0.86 in SAW and 0.229 accuracy in AHP. Based on the euclidean distance, the best AHP method used in this study is with an average value of 0.19, while SAW has an average value of 0.90.
Perencanaan Strategis Sistem Informasi Pada Lembaga Amil Zakat Menggunakan Analisis SWOT Berbasis Lima Faktor Seni Perang Sun Tzu Berdasarkan Anita Cassidy Novettralita, Ucky Pradestha; Isnanto, R. Rizal; Widodo, Catur Edi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107222

Abstract

Lembaga Amil Zakat (LAZ) memanfaatkan strategi Sistem Informasi/Teknologi Informasi (SI/TI) untuk meningkatkan daya saing. Seni Perang Sun Tzu telah banyak digunakan dalam penelitian untuk menyusun strategi bisnis dan strategi penjualan. Sayangnya, belum ada penelitian dengan menggunakan Seni Perang Sun Tzu untuk perencanaan strategis Sistem Informasi (SI). Kontribusi penelitian adalah penyusunan analisis SWOT berbasis lima faktor Seni Perang Sun Tzu sehingga dapat menjadi dasar untuk penelitian selanjutnya. Tujuan dari penelitian ini adalah untuk mengidentifikasikan kondisi lingkungan internal bisnis dan eksternal bisnis sehingga memberikan rekomendasi strategi kunci kepada LAZ dalam domain strategi bisnis, strategi SI/TI, dan strategi infrastruktur SI/TI berdasarkan analisis SWOT berbasis lima faktor Seni Perang Sun Tzu yang disusun berdasarkan metode Anita Cassidy. Beberapa strategi kunci yang dihasilkan dari peneitian ini adalah promosi dan edukasi zakat melalui media sosial dan media daring lainnya; menyediakan teknologi untuk memudahkan masyarakat membayar zakat dengan membuat aplikasi seperti Mobile Zakat, Customer Relationship System (CRS); dan mengembangkan kemampuan dalam memanfaatkan teknologi 5G dan teknologi baru.   Abstract Amil Zakat Institution (LAZ) uses Information System/Information Technology (IS/TI) strategy to improve competitiveness. Sun Tzu's Art of War has been widely used in research to develop business strategies and sales strategies. Unfortunately, there has been no research using Sun Tzu's Art of War for Information System (IS) strategic planning. The contribution of the research is the preparation of a SWOT analysis based on the five factors of Sun Tzu's Art of War so that it can be the basis for future research. This research aims to identify the condition of the internal business and external business environment to provide key strategy recommendations to LAZ in the domains of business strategy, SI/TI strategy, and SI/TI infrastructure strategy based on SWOT analysis based on five factors Sun Tzu's Art of War compiled based on the Anita Cassidy method. Some of the key strategies obtained from this research are the promotion and education of zakat through social media and other online media; providing technology to make it easier for people to pay zakat by creating applications like Mobile Zakat application, Customer Relationship System (CRS); and developing capabilities in utilizing 5G technology and new technologies.
Development of GWIDO: An Augmented Reality-based Mobile Application for Historical Tourism Faisal Akbar; Hadiyanto, Hadiyanto; Catur Edi Widodo
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3439

Abstract

This research aimed to design and reconstruct a business model for an augmented reality (AR) camera mobile application for historical tourism at Keraton Kasepuhan Cirebon. The goal was to utilize AR technology to provide an immersive and informative experience for tourists. The research addressed several main problems, including navigation and historical information through object tracking, by implementing an online application with features such as Indonesian and English language instructions to better serve domestic and foreign tourists. The research also aimed to investigate the benefits of using AR technology for object tracking and navigation and to explore how these aspects could be related to creating a formula that supports each other in addressing the formulated problems. Through the development of the GWIDO application, a positive impact on the development of historical tourist attractions was observed. This can be seen from the usefulness of its features such as AR navigation, which can be used as a virtual guide. The data collected was used to design and reconstruct the business model, which was implemented and tested to collect additional data for analysis. The final results of the research showed that the AR camera mobile application was effective in providing an immersive and informative experience for tourists. The redesigned business model improved the utilization of AR technology in the tourism industry. Based on the test results, the average response time for object distance between 0.1 meters to 0.5 meters was between 1.45 to 2.07 seconds, and the average time for object distance from visitors was between 3.15 to 4.71 seconds with a confidence level of 95%. Meanwhile, testing for navigation features using augmented reality is very dependent on the internet signal used on the user's device. The level of accuracy of objects that have been placed at certain coordinates is determined by how well the internet network performs, allowing objects to appear precisely according to their coordinates.
Prediksi Perubahan Hemodinamik Pasien setelah Pemberian Premedikasi menggunakan Machine Learning Neural Network Guna Meningkatkan Kinerja Penanganan Medis Aryasa, Jiyestha Aji Dharma; Widodo, Aris Puji; Widodo, Catur Edi
Jurnal Sistem Informasi Bisnis Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss3pp256-266

Abstract

This research presents the development process of a machine learning neural network model for predicting hemodynamic changes in patients after premedication, aiming to enhance the performance of medical interventions. The model was constructed using 3055 patients’ data who underwent premedication processes. The developed neural network model has an architecture consisting of 10 nodes in the input layer, 10 nodes in the hidden layer, and 3 nodes in the output layer. The evaluation results of the model indicate an overall accuracy of 85%. The precision values are high for normal class predictions at 0.85 and for hypertension class predictions at 0.81 with corresponding recalls of 1 (high) and 0.6 (moderate), respectively. However, predictions for the hypotension class still have a low precision of 0.6 and a recall of 0.04 (very low) due to the significantly lower number of samples in the hypotension class compared to the normal and hypertension classes. While testing with new data, the model has successfully predicted whether patients will experience hemodynamic pressure changes. It is expected that this model can contribute to improving the performance of medical interventions, thereby minimizing undesirable hemodynamic pressure changes.
Image-Based Fish Freshness Classification Using Two-Phase Transfer Learning with Deep Learning Fusion Model Helmud, Ellya; Edi Widodo, Catur; Dwi Nurhayati, Oky
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.988

Abstract

This study introduces a novel deep learning approach for automated fish freshness classification using image analysis. The objective is to design and validate a Deep Learning Fusion Model that combines the strengths of EfficientNetB0 and InceptionV3 architectures to improve accuracy and robustness in classifying fresh and non-fresh fish. Input images were subjected to extensive augmentation, including RandomFlip, RandomRotation, RandomZoom, RandomContrast, RandomBrightness, and RandomTranslation, applied exclusively to the training dataset to enhance generalization, followed by backbone-specific pre-processing. Extracted features were fused via global average pooling and forwarded to a newly designed classification head with dropout and L2 regularization to mitigate overfitting. A two-phase transfer learning strategy was employed: initially training the classification head with frozen backbones, followed by fine-tuning the backbone layers using the Adam optimizer with a reduced learning rate. To highlight the contribution of the fusion strategy, ablation studies were conducted with single-backbone models. The EfficientNetB0 model achieved 89.17% validation accuracy, 85.83% test accuracy, and an F1-score of 85.69%, while the InceptionV3 model achieved 86.67% validation accuracy, 81.67% test accuracy, and an F1-score of 81.59%. In contrast, the proposed Fusion Model achieved 93.33% validation accuracy, 95.00% test accuracy, and an F1-score of 94.95%. Additional evaluations with confusion matrices, ROC curves, AUC, and precision-recall curves confirmed the model’s superiority. The findings demonstrate that integrating features from diverse CNN architectures enables the model to learn richer representations, resulting in significantly improved classification performance. The novelty of this work lies in the effective fusion of complementary backbones through global average pooling and fine-tuned transfer learning, establishing a human-centric computational approach that offers a reliable solution for practical fish freshness assessment in food safety and market scenarios.
CT Radiomics and Ensemble Learning for 5-Year Survival Prediction in Colorectal Liver Metastases Widya Astuti; Catur Edi Widodo; Qidir Maulana Binu Soesanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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

Abstract

Colorectal liver metastases (CRLM) significantly impact patient survival with high recurrence rates. Traditional prognostic models often overlook tumor heterogeneity, leading to suboptimal risk stratification. To address this, radiomics was employed to quantify sub-visual tumor phenotypes, while ensemble learning was selected to robustly handle high-dimensional feature complexity and improve generalization capability. This retrospective study analyzed 145 CRLM patients from The Cancer Imaging Archive, extracting 1130 radiomics features from preoperative CT scans alongside clinical variables. Data were split into training (n=101) and testing (n=44) sets, with feature selection reducing the input to 16 key features. Three ensemble models (XGBoost, LightGBM, Random Forest) were optimized using Optuna, incorporating SMOTE and isotonic calibration. On the test set, XGBoost achieved ROC-AUC 0.918, sensitivity 0.739, and specificity 0.952. LightGBM yielded ROC-AUC 0.916, sensitivity 0.782, and specificity 0.904. Random Forest recorded ROC-AUC 0.888, sensitivity 0.826, and specificity 0.667. Key features included "progression or recurrence" and wavelet-based texture metrics reflecting tumor heterogeneity. These findings demonstrate the effectiveness of combining CT radiomics with gradient boosting models to capture complex prognostic patterns. This integration enhances 5-year survival prediction in CRLM, offering a non-invasive tool for personalized risk stratification and improved clinical decision-making compared to the currently utilized traditional prognostic models.
Machine Learning for Post-Disaster Building Damage Classification and Rehabilitation Recommendation: A Review Eka Rahmawati; Catur Edi Widodo; Sorja Koesuma
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 1 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2532

Abstract

Accurate classification of building damage following disasters plays a critical role in facilitating efficient rehabilitation and reconstruction. Traditional field-based assessment methods, however, present significant limitations—including time inefficiencies, susceptibility to subjective interpretation, and potential safety risks for survey personnel. Recent advancements in machine learning (ML) have significantly improved the efficiency and objectivity of post-disaster damage assessment by leveraging diverse data sources such as satellite imagery, unmanned aerial vehicles (UAVs), and even crowdsourced social media content. This study conducts a narrative literature review of 78 peer-reviewed articles published from 2020 to 2024, focusing on ML-driven methodologies for classifying building damage and generating rehabilitation recommendations. The literature review reveals a prevailing reliance on deep learning models—especially convolutional neural networks (CNNs) and transformer-based architectures—due to their robust accuracy and adaptability across varied disaster scenarios. Furthermore, novel approaches like self-supervised learning, ensemble methods, and few-shot learning show promising potential in addressing challenges posed by sparse or unevenly distributed datasets. Despite rapid advancements in ML-based post-disaster building damage classification, real-world implementation remains constrained. This review synthesizes current trends, persistent challenges, and critical research gaps to inform the development of a robust ML framework for post-disaster recovery efforts. This study uniquely highlights the integration of ML-based classification with rehabilitation planning frameworks, providing practical guidance for disaster management agencies to optimize post-disaster recovery strategies.
Integrated Maturity Assessment of Information Security for Land and Building Tax Management System Using National Institute of Standards and Technology Cybersecurity Framework 2.0, International Organization for Standardization/International Electrotechnical Commission 27002:2022, and Cybersecurity Capability Maturity Model 2.1. Paramesvari, Dhenok Prastyaningtyas; Suseno, Jatmiko Endro; Widodo, Catur Edi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5551

Abstract

Regional tax information systems such as the Sistem Informasi Manajemen Objek Pajak (SISMIOP) are vulnerable to cybersecurity threats due to the sensitivity of taxpayer data and the persistence of ad-hoc security management practices. These conditions pose risks to data confidentiality, integrity, and service availability, potentially undermining public trust and the effectiveness of local government services. This study aims to assess the information security maturity of SISMIOP operated by the Badan Pengelolaan Pendapatan, Keuangan, dan Aset Daerah (BPPKAD) through an integrated application of the NIST Cybersecurity Framework (CSF) 2.0, ISO/IEC 27002:2022, and the Cybersecurity Capability Maturity Model (C2M2) 2.1. A qualitative case study approach was employed. An organizational profile was developed using interviews, observations, and document analysis, followed by mapping 38 relevant NIST CSF subcategories to ISO/IEC 27002 controls and C2M2 capability domains. Security maturity was evaluated using questionnaires and interviews based on the C2M2 Maturity Indicator Levels (MIL0-MIL3), and a gap analysis was conducted against the target maturity level of MIL2. The results show that most cybersecurity functions, Govern, Identify, Detect, Respond, and Recover, remain at MIL1, indicating that practices are performed but not yet formalized or consistently implemented. The Protect function partially achieved MIL2. The largest gaps were identified in governance and risk management domains. Based on these findings, 38 prioritized strategic recommendations were formulated to improve policy formalization, risk management, technical controls, monitoring, and incident handling. This study contributes a practical and replicable multi-framework maturity assessment model to strengthen information security governance in public-sector tax information systems.
Co-Authors Agusta Rakhmat Taufani Aprilia Ayu Andarinny Aris Puji Widodo Aryasa, Jiyestha Aji Dharma Budi Warsito Choirul Anam AM Diponegoro Eka Rahmawati Eko Sediyono ellya helmud, ellya Faisal Akbar Fatkhur Rohman Figur Humani Fitra Nur Asri, Muh Hadiyanto Hadiyanto Hadyan Arifianto Husen Nasrullah Armin I Gusti Bagus Wiksuana Irwan Agus Saputro Isnain Gunadi Iswanti, Arie Jatmiko Endro Suseno K. Sofjan Firdausi Kusworo Adi Kuworo Adi Labib Mustofa, Refo Labib Mustofa, Tarno Maratullatifah, Yulaikha Muchammad Azam, Muchammad Mustafid Mustafid Novettralita, Ucky Pradestha Oky Dwi Nurhayati Oky Dwi Nurhayati Otong Saeful Bachri Paramesvari, Dhenok Prastyaningtyas Qidir Maulana Binu Soesanto R. Rizal Isnanto Rachmat Gernowo Rahmatika, Yuni Ramanda, Febri Retno Hapsari, Angga Ayu Rizky Ayomi Syifa Romadhon, Zainur Royani, Noorhanida Saitem Saitem Salsabila Naqiyah Siswahyudianto Sorja Koesuma Sri Mulyani Tri Retnaningsih Soeprobowati Tutur Urip Wahyu Adi Wibowo Widya Astuti Zainal Bachrudin