Article Per Year (5 Year)
p-Index From 2021 - 2026
2.735
P-Index
This Author published in this journals
All Journal IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Transformasi: Jurnal Pengabdian Masyarakat JOIN (Jurnal Online Informatika) Edu Komputika Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal ULTIMATICS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) bit-Tech Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Abdi Teknoyasa Artificial Intelligence Systems and Its Applications (AISA)
Endang Wahyu Pamungkas
Universitas Muhammadiyah Surakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Model Metric untuk Mengukur Fleksibilitas Model Proses Bisnis Pamungkas, Endang Wahyu; Sinaga, Fernandes; Rochimah, Siti
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 1 No 2: Oktober 2014
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1028.471 KB) | DOI: 10.25126/jtiik.201412113

Abstract

Abstrak Organisasi bisnis dunia saat ini banyak memanfaatkan sistem informasi digital untuk memberikan pemahaman mengenai manajemen proses bisnis yang mereka jalani. Pemanfaatan sistem Enterprise Resource Planning (ERP) merupakan contoh teknologi dalam manajemen proses bisnis. Melalui sistem ini perusahaan dapat membangun dan mengembangkan proses bisnis. Selain itu, perusahaan juga dapat menyesuaikan proses bisnis secara cepat terhadap perubahan yang terjadi seiring bertambahnya kebutuhan dan informasi, berubahnya kondisi pasar, atau perubahan kebijakan. Sehubungan dengan perubahan proses bisnis yang sering terjadi, maka aspek fleksibilitas terhadap model proses yang dibangun harus ditingkatkan. Dalam mendukung peningkatan fleksibilitas tersebut tentunya dibutuhkan sebuah model untuk mengukur tingkat flesibelitas model proses bisnis. Model tersebut yang kemudian dapat digunakan oleh analis untuk melakukan perbandingan sehingga dapat diperoleh model proses bisnis yang paling fleksibel dan cocok dengan perusahaan. Hal ini dapat dianalisa dengan melibatkan aspek-aspek fleksibel yang telah diteliti pada penelitian-penelitian sebelumnya. Dalam paper ini akan dilakukan penelitian mengenai aspek fleksibitas dalam model proses bisnis untuk menghasilkan model metric yang dapat melakukan kuantifikasi tingkat fleksibilitas pada model proses bisnis. Model metric yang dihasilkan pada penelitian ini mampu melakukan perhitungan fleksibelitas pada model proses bisnis secara kuantitatif. Kata kunci: ERP, fleksibilitas, metadata, model metric, model proses bisnis, variasi Abstract Recently, business organizations in the world are making use of digital information systems to provide an understanding of the business process management in which they live. Utilization of Enterprise Resource Planning (ERP) system is an example of technology in business process management. Through this system, some companies can build and develop business process and can quickly adjust it to changes that occur with increasing needs and information, changing market conditions, or changes in policy towards the business process. According to changes in business process that frequently occur, then the flexibility aspect of the process models are built to be upgraded. This is because the process model can early describe the business process that run. So that the process model must have a high value of flexibility to deal with changes that happen. It can be analyzed with the involvement of the flexible aspects that have been investigated in previous studies. In this paper, we will do research on the flexibility of business process model to produce a model of metrics that can quantify the level of flexibility in business process models.Metric models in this study were able to perform calculations on the flexibility of business process models quantitatively. Keywords: business process model, ERP, flexibility, metadata, model metric, variation
DEVELOPMENT OF SCHEDULING SYSTEM WITH GENETIC ALGORITHM IN WEBSITE-BASED SMK NEGERI 1 SINE Saputra, Shafa Bani; Pamungkas, Endang Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.784

Abstract

Scheduling is an information that has limited conditions that must be met. Preparation of the schedule will take quite a long time if it is done using conventional media such as writing on paper or books. Scheduling optimization is needed to provide effectiveness and efficiency so that the implementation of learning activities can run more optimally. The genetic algorithm approach method is used to get the optimum schedule. This algorithm produces the best combination for subject pairs and teaching teachers as a whole by determining the initial population and initializing the chromosomes, determining the fitness value, then carrying out crossover selection, and carrying out mutations to produce the best fitness value which will be used to determine the final value of scheduling. The results of the entire algorithm process are consistent with the original prediction data, and the same teacher is not scheduled to teach more than once at the same time. The results of the subject scheduling process using the genetic algorithm obtain a fairly good optimization in subject scheduling.
DESIGNING UI/UX FOR A MEDITERRANEAN DIET APP TO MINIMIZE TYPE 2 DIABETES RISK Salam, Farah Danisha; Pamungkas, Endang Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1932

Abstract

The industrial revolution has had a significant impact in health field, where the crucial disease in Indonesia is Type 2 Diabetes Mellitus (T2DM). One of the effective ways to minimize the increase in T2DM is by developing a diet application using Mediterranean diet Method. The Mediterranean diet involves controlling certain types of foods and beverages, characterized by high intake of fruits, vegetables, whole grains, nuts, legumes, moderate consumption of fish, low intake of red/processed meat, and low intake of high-fat dairy products. In this research and design, the Author applied User Centered Design (UCD) method, which encompasses User Interface (UI) and User Experience (UX). The author also applied the UX law (Hick’s Law, Law of Proximity, dan Aesthetic-usability Effect) on designing the user interface. This article included a A/B Testing and System Usability Scale (SUS) result that showed an interesting findings, where the design that achieved a higher success rate had a lower user satisfaction score and vice versa. However, both designs have an acceptable score and C grade.
Analysis of A Deep Learning Algorithm for Fracture Detection In X-Ray Images Zebada, Alana Mulya; Pamungkas, Endang Wahyu
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1451

Abstract

Identifying bone fractures in X-ray images is a complex task that requires special expertise from radiologists and can be time-consuming in clinical workflows. Deep learning offers a significant automated diagnostic solution to improve accuracy and efficiency. This study aims to analyze the performance of three Convolutional Neural Network (CNN) architectures namely, ResNet50, DenseNet169, and EfficientNet-B3 and specifically compare the performance of models trained using augmented data with that of models trained without augmentation. The research method utilizes a local dataset, which is divided equally between the fractured and non-fractured classes. Preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) were applied, and the models were evaluated on a separate test set (hold-out test set). Model evaluation was conducted using accuracy, precision, recall, F1-score, ROC-AUC metrics, as well as analysis through confusion matrix, classification report, sensitivity, specificity, and calibration curve to assess overall performance. The experimental results show that the application of data augmentation consistently improves the accuracy and robustness of all three models. In the augmentation scenario, EfficientNet-B3 showed the best performance, achieving an accuracy of 93.33%. This study concludes that the combination of the EfficientNet-B3 architecture with the data augmentation strategy is the most optimal and recommended approach for developing a reliable automatic detection system on local X-ray image datasets.
Sentiment Analysis of Public Feedback on Universitas Muhammadiyah Surakarta Through Google Maps Reviews: Insights and Implications Agus Ardiansyah Nh; Endang Wahyu Pamungkas; Sohail Akhtar
Artificial Intelligence Systems and Its Applications Vol. 1 No. 1 (2025): Vol. 1, No. 1, June 2025
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v1i1.12

Abstract

In the era of digital feedback, higher education institutions face growing challenges in making sense of large volumes of user-generated reviews, especially those written in multiple languages. This study analyzes 1,717 Google Maps reviews related to Universitas Muhammadiyah Surakarta (UMS), collected over five years in Bahasa Indonesia and English. To overcome limitations of manual and monolingual sentiment analysis, we employed a pre-trained multilingual transformer model—lxyuan/distilbert-base-multilingual-cased-sentiments-student—without additional fine-tuning. The analysis revealed that 88% of reviews were classified as positive, with most praise directed at campus facilities, while criticism often targeted administrative services. Beyond sentiment classification, this study explored text length, confidence scores, and user engagement patterns to uncover deeper behavioral insights. We also developed SentiMu, an interactive dashboard that visualizes sentiment trends, recent reviews, word clouds, and key metrics, enabling university stakeholders to monitor feedback in real time. The dashboard was built using Next.js and FastAPI for optimal performance and scalability. By automating the analysis and visualization of multilingual online reviews, this study provides a practical and scalable framework for institutions to understand student and visitor experiences, supporting data-driven decisions to enhance campus services and reputation.
Pengembangan Platform Berbasis Web untuk Edukasi Gizi dan Deteksi Dini Stunting Aldhyno Yoghatama; Endang Wahyu Pamungkas
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2559

Abstract

Stunting remains a major public health issue in Indonesia with a prevalence of 21.5%, negatively affecting children’s growth and development. Limited access to interactive and integrated nutrition education poses a barrier to early stunting prevention efforts. This study aims to develop an interactive web-based platform called GiziMu Mijen that integrates evidence-based nutrition education, a nutritional status calculator for children aged 0–60 months based on WHO standards, and an online nutrition consultation service via WhatsApp to support family-level stunting prevention. The system was developed using the Agile Software Development methodology and the Laravel 11 PHP framework, with system design modeled through Unified Modeling Language (UML) for use case, activity, and data structure visualization. Testing was conducted using black-box and acceptance testing involving nutritionists to validate the nutritional calculator and consultation features. Results indicate that the platform successfully provides nutrition education content, complementary feeding recommendations, automated nutritional status calculations, and functional interactive consultation services. The system facilitates parents’ independent monitoring of their children’s nutritional status and quick access to professional advice. In conclusion, GiziMu Mijen offers a comprehensive and effective digital solution to improve nutrition literacy and early stunting detection at the family level. The Agile and Laravel-based development approach ensures flexibility and usability, positioning the platform as a strategic tool in national stunting reduction programs, with recommendations for broader testing and integration into national health information systems for data validation.
Detecting Indonesian Online Gambling Promotions in Digital Images: An OCR-LSTM Pipeline Ariefin Nur Hidayat; Endang Wahyu Pamungkas
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.68

Abstract

The rapid spread of online gambling promotions via digital images on social media renders manual identification processes highly inefficient, necessitating an automated detection system. This study aims to implement a system to identify online gambling promotions in digital images using a combination of Optical Character Recognition (OCR) and Long Short-Term Memory (LSTM) methods. The research utilizes a dataset of 2,200 images, evenly balanced between gambling and non-gambling categories. The proposed system involves several key stages: image preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve contrast, text extraction via PaddleOCR, text and vocabulary correction using FastText, and finally, contextual classification using the LSTM architecture. The testing results on 330 test data demonstrated excellent and stable model performance in recognizing text patterns. The model successfully achieved an impressive accuracy rate of 94%, with a precision of 0.96, a recall of 0.92, and an F1-score of 0.94. In conclusion, the combination of OCR and LSTM technologies is proven to be highly effective for automatically detecting online gambling promotional content.
Emotionally-Aware Chatbots: A Survey Endang Wahyu Pamungkas; Ikhlasul Amal
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.71

Abstract

Textual conversational agents, also known as chatbot technology, have garnered significant attention in both academia and industry in recent times. Chatbots have found wide application as agents for human communication in various services, such as booking assistance, customer service, and even personal companionship. The primary challenge in chatbot development lies in creating a machine capable of human-like interaction to enhance user engagement. Several studies have highlighted the importance of incorporating emotion in humanizing machines, including chatbots. However, to the best of our knowledge, there has been no focused work in this specific area. Therefore, this paper aims to provide a systematic review of approaches to building emotionally-aware chatbots (EACs). We propose three key focuses for studying EACs. Firstly, we explore the history and evolution of EACs. Next, we review the approaches adopted by previous studies in building EACs. Lastly, we delve into available resources for constructing EACs, with a particular emphasis on datasets and affective resources. Our investigation reveals that early EAC development predominantly relied on a rule-based approach, while more recent works have embraced neural-based approaches. We observe that most EACs incorporate emotion classifiers within their architecture, leveraging various existing affective resources. We argue that the field of EAC development will continue to attract increasing attention from researchers, as evidenced by recent studies proposing new datasets for building EACs in diverse languages.
ANALISIS SENTIMEN TERHADAP TIMNAS INDONESIA DI PIALA ASIA 2023 DENGAN MODEL TRANSFORMER BERBAHASA INDONESIA Muhammad Irfan Abidin; Endang Wahyu Pamungkas
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6142

Abstract

This study aims to analyze public sentiment towards the Indonesian National Team during the 2023 Asian Cup through Instagram comments, using the Indonesian-language Transformer model. A total of 21,045 comments were collected from the official @timnasindonesia account and filtered into 17,829 comments worthy of analysis after going through preprocessing processes such as text cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Comments were then automatically classified into three sentiment classes, namely positive, negative, and neutral, using the Indonesian Sentiment Lexicon (InSet). Three Transformer models were used, namely IndoBERT, IndoRoBERTa, and DistilBERT Multilingual, and compared to the baseline SVM + TF-IDF. The evaluation used accuracy, precision, recall, and F1-score metrics. The results showed that IndoBERT (learning rate 5e-5) gave the best performance with an accuracy of 0.8897 and an F1-score of 0.8859, outperforming other models. The analysis was conducted by considering the typical challenges of Instagram comments such as slang, abbreviations, emojis, and mixed use of Indonesian-English. These findings validate the effectiveness of the monolingual Transformer model for Indonesian, which is still rarely compared systematically in the context of social media. These findings can also be used by the management of the Indonesian National Team or sports policy makers to evaluate public responses in real-time, as well as being a reference in developing an adaptive and contextual public opinion analysis system.
PERBANDINGAN ALGORITMA MACHINE LEARNING: SVM, RANDOM FOREST, DAN XGBOOST UNTUK PREDIKSI STROKE: COMPARISON OF MACHINE LEARNING ALGORITHMS: SVM, RANDOM FOREST, AND XGBOOST FOR STROKE PREDICTION Hanifah Afkar Nabila; Endang Wahyu Pamungkas
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6444

Abstract

Stroke is a leading cause of death and disability worldwide, making early detection essential. This study compares three machine learning algorithms Support Vector Machine (SVM), Random Forest, and XGBoost for stroke prediction. The dataset includes Kaggle data for training and clinical data from Indonesian primary healthcare (Puskesmas) for external validation. Pre-processing involved handling missing values, encoding categorical features, normalization, and balancing using SMOTE. Performance was evaluated using accuracy, precision, recall, F1-score, AUC-ROC and AUC-PRC. Unlike most prior studies, this research incorporates clinical data to assess generalizability in real-world settings. Results show that Random Forest and XGBoost outperform SVM, especially with clinical data. This study contributes a practical perspective by validating models using local datasets and emphasizes the importance of robust algorithms and external validation in medical prediction systems.
Co-Authors ABDUL MUNIF Agus Ardiansyah Nh Al Isyadi, Fatah Yasin Aldhyno Yoghatama Ariefin Nur Hidayat Ary Okta Sulistyo Diah Priyawati Dian Purworini Divi Galih Prasetyo Putri Divi Galih Prasetyo Putri Fatah Yasin Al Irsyadi Fernandes Sinaga Galih Wiratmoko Hanifah Afkar Nabila Hepy Adityarini Husni Thamrin Ikhlasul Amal Jan Wantoro Lelita Azaria Rahmadiva Maryam Muhammad Fahmi Johan Syah Muhammad Irfan Abidin Naidoo, Gedala Mulliah Putra Weka Pratama Riyanarto Sarno Rona Rizkhy Bunga Chasana Salam, Farah Danisha Saputra, Shafa Bani Sidiq Setyawan Siti Rochimah Sohail Akhtar Sohail Akhtar Wili Astuti Yanti Haryanti Yusuf Sulistyo Nugroho Zebada, Alana Mulya ‘Ammar, Mohammad Faqih Eza