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All Journal Jurnal Simetris Jurnal Teknologi dan Manajemen Informatika Bulletin of Electrical Engineering and Informatics Jurnal Teknik Elektro Jurnal technoscientia Jurnal Teknologi Informasi dan Ilmu Komputer WARTA Emitor: Jurnal Teknik Elektro Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Jurnal SOLMA Jurnal Ilmiah Matrik Jurnal Eksplora Informatika Jurnal Kebidanan JURNAL INSTEK (Informatika Sains dan Teknologi) KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Manajemen Informatika Jurnal Teknologi dan Informasi Jurnal Ilmiah Sinus JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Abdimas Mandiri Respati Jurnal Pesut : Pengabdian untuk Kesejahteraan Umat JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Indonesia Jurnal Abdi Masyarakat Indonesia Jurnal Penelitian Inovatif Jurnal Ilmu Komputer dan Informatika Proceeding ISETH (International Summit on Science, Technology, and Humanity) Prosiding University Research Colloquium Abdi Teknoyasa J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Politeknosain Jurnal INFOTEL Green Engineering: Journal of Engineering and Applied Science Global Science: Journal of Information Technology and Computer Science
Kurniawan, Yogiek Indra
Informatics, Engineering Faculty, Universitas Jenderal Soedirman, Indonesia
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Analisis Sentimen Kemungkinan Depresi dan Kecemasan pada Twitter Menggunakan Support Vector Machine Darmawan, Ferry; Joe, Michael; Kurniawan, Yogiek Indra; Afuan, Lasmedi
Eksplora Informatika Vol 13 No 1 (2023): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i1.854

Abstract

THALASSEMIA MINOR SCREENING APPLICATION USING THE C4.5 METHOD BASED ON LARAVEL Sohputro, Nicolas; Wijayanto, Bangun; Kurniawan, Yogiek Indra
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1672

Abstract

Thalassemia is an inherited blood disorder that causes anaemia and weak red blood cells. Thalassemia minor is a type of thalassemia where the patient is a carrier of thalassemia and only experiences mild anaemia. To prevent an increase in the number of thalassemia cases, a screening process is held for an individual to confirm whether there is a thalassemia carrier in the body. In providing screening in Banyumas Regency, the Unsoed Medical Faculty Thalassemia Research Team encountered several problems, namely that the screening results could only show whether an individual was a carrier of thalassemia minor or not. This causes a problem because a good screening result is a probability. The second problem is the absence of an integrated information system for thalassemia control in Banyumas Regency. The solution to these two problems is to build a thalassemia minor screening application. The application uses the C4.5 data mining method to calculate the likelihood of thalassemia minor in individuals. The application is made website-based using Laravel to speed up website development. The system also uses a web service to be able to access the created C4.5 algorithm.
WEB-BASED IMAGE CAPTIONING FOR IMAGES OF TOURIST ATTRACTIONS IN PURBALINGGA USING TRANSFORMER ARCHITECTURE AND TEXT-TO-SPEECH Muazam, Safa; Kurniawan, Yogiek Indra; Iskandar, Dadang
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2585

Abstract

Purbalingga is a region located in Central Java Province, offering interesting natural beauty and tourist destinations. Many tourists capture their moments in photos, which are then uploaded to social media. However, a picture can contain a lot of information, and each individual may interpret it differently. Without captions, people may struggle to extract this information. Image captioning addresses this challenge by automatically generating text descriptions for images. Additionally, text-to-speech is used to enhance accessibility for the visually impaired in understanding image descriptions. This research aims to develop an image captioning model for images of tourist attractions in Purbalingga using transformer architecture and ResNet50. The transformer architecture employs an attention mechanism to learn the context and relationships between inputs and outputs, while ResNet50 is a robust convolutional network for image feature extraction. Model evaluation using BLEU metrics, which compare generated sentences to reference sentences, shows the best results as BLEU-{1, 2, 3, 4} = {0.672, 0.559, 0.489, 0.437}. Experiments indicate that increasing embeddings and layers extends training time and lowers BLEU scores, while changing the number of heads has minimal impact on results. The best model is implemented in a web-based application using the SDLC waterfall method, Flask framework, and MySQL database. This application allows users to upload tourist attraction images, receive automatic descriptions in Indonesian, and listen to the captions read aloud using the Web Speech API-based text-to-speech feature. Blackbox testing results show valid outcomes for all tests, indicating that the application operates as required and is suitable for use.
Interest Patterns Measurement Application for Vocational High School Students Kumaidi, K; Kurniawan, Yogiek Indra; Farida, Rahayu
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2017: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2322

Abstract

Interest patterns are the characteristic of a student's interest in which the student wishes to engage in a particular field. The pattern of interest of students in vocational high school in Indonesia should be known to obtain an appropriate career based on the student's interest. To reveal the pattern of interest, an instrument based on the Holland theory has been developed in Indonesia. Nevertheless, the test is performed manually using pencil and paper thus there are some ineffectiveness during the implementation. Moreover, it requires longer time for data processing and is susceptible to error. Therefore, a computer-based application that can measure the interest patterns is built. With the existence of this computer-based application, data processing can be done quickly and accurately without any errors and can be accessed anywhere and anytime. This research found that the use of the application can support the effectiveness and efficiency of the test in recognizing the interest pattern of a student.
Decision Support System for Acceptance Scholarship with Simple Additive Weighting Method Kurniawan, Yogiek Indra
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2370

Abstract

A large number of scholarships have been extensively distributed in the educational institutions including college and university. It is, however, vulnerable to subjectivity. In general, students applying for the scholarship will be selected by the committee that may be subjective in the assessment process. In consequence, it can affect the result of scholarship recipients. Decision Support System (DSS) is a computer-based information system that supports the decision activities to be more. One method of the application of decision support systems is Simple Additive Weighting (SAW). This study was exploring the application of SAW in the case study of scholarship recipient selection process by weighting some predetermined criteria.
Adaptive Graph Based Intelligence Models for Cross Domain Knowledge Discovery in Large Scale Heterogeneous Information Systems Winny Purbaratri; Krisna Widi Nugraha; Rian Ardianto; Rosyid Ridlo Al-Hakim; Yogiek Indra Kurniawan; Ribut Julianto
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.193

Abstract

The rapid growth of heterogeneous information systems across multiple domains has introduced complex challenges in data analysis, particularly when dealing with diverse data types such as text, images, and sensor data. Traditional machine learning (ML) methods often struggle to capture the intricate relationships inherent in these large scale datasets, as they typically rely on linear models and feature vectors that fail to represent the full complexity of the data. This study aims to develop an adaptive graph based intelligence model that addresses these challenges by leveraging the power of graph structures to represent heterogeneous data and capture both structural dependencies and semantic connections. The proposed model integrates Graph Neural Networks (GNNs) with adaptive learning mechanisms, allowing for continuous knowledge extraction, pattern discovery, and cross domain inference. By representing diverse data sources as interconnected graphs, the model enables the transfer of knowledge across different domains, improving its ability to make accurate predictions and generate insights in dynamic environments. The results demonstrate that the graph based model outperforms traditional machine learning techniques in terms of accuracy, efficiency, and scalability, especially when applied to real world applications involving large and complex datasets. This paper also discusses the advantages of the adaptive learning mechanisms, which personalize the model’s training process and improve its robustness over time. Furthermore, the findings highlight the model’s potential for cross domain knowledge discovery, with applications in fields such as healthcare, marketing, and industrial automation. Finally, the paper offers recommendations for future research, including refining adaptive learning mechanisms and exploring new graph based techniques to enhance the representational power of the model. The study contributes to the ongoing development of intelligent systems capable of handling heterogeneous data across multiple domains and offers a foundation for future advancements in cross domain knowledge discovery.
Carbon Neutral Industrial Process Optimization through Hybrid Machine Learning and Real Time Energy Efficiency Monitoring Framework Suyahman Suyahman; Ardy Wicaksono; Dwi Utari Iswavigra; Yogiek Indra Kurniawan; Very Dwi Setiawan; Dedi Setiadi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.285

Abstract

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.
APLIKASI PREDIKSI USIA KELAHIRAN DENGAN METODE K-NEAREST NEIGHBOR Desy Kartika Indahsari; Yogiek Indra Kurniawan
Jurnal Kebidanan VOLUME 11. No.01, JUNI 2019
Publisher : Sekolah Tinggi Ilmu Kesehatan Estu Utomo Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35872/jurkeb.v11i01.335

Abstract

ABSTRAKKehamilan dan proses persalinan merupakan suatu proses kehidupan yang terjadi secara alamiah pada setiap makhluk hidup. Usia kelahiran bayi dengan berat badan rendah merupakan salah satu hal yang berpengaruh besar terhadap proses persalinan premature. Dasar tersebut berhubungan dengan banyaknya angka kematian pada kelahiran bayi. Selain itu bayi yang lahir melebihi batas waktu berpengaruh pada proses kelahiran postdate. Aplikasi yang dibuat bertujuan untuk meminimalisir terjadinya hal yang tidak diinginkan dengan mengetahui prediksi pada proses persalinan. Penelitian ini merupakan jenis penelitian klasifikasi dengan metode K-Nearest Neighbor (pendekatan tetangga). Variable yang digunakan merupakan bagian dari faktor penting yang dialami seorang ibu pada saat proses kehamilan, diantaranya: usia ibu hamil, tekanan darah, jumlah bayi, riwayat persalinan, riwayat abortus/kuretase, malnutrisi, penyakit bawaan sebelum hamil dan masalah saat kehamilan. Hasil dari penelitian ini berupa aplikasi prediksi usia kelahiran dengan nilai precicion tertinggi 100 %, nilai recall tertinggi 84.905660377358 % dan nilai accuracy tertinggi 96 %.Kata Kunci : Aplikasi, Kelahiran, K-Nearest Neighbor , Prediksi. BIRTH AGE PREDICTION APPLICATION USING K-NEAREST NEIGHBOR METHOD ABSTRACTPregnancy and giving birth is a natural life process that happen to every human being. Baby’s giving birth age with low-weight is one that gave big impact to premature giving birth. These statement relate with many cases of baby’s death birth. Beside that, baby birth that exceed normal time give impact to postdate giving birth process.the application made to anticipate and decrease bad thing by knowing the prediction of giving birth process. This research is a type of classification research with the K-Nearest Neighbor method. The variable that used is a part of important factors that happen to a mom in pregnancy process, such as : pregnant mom’s age, blood pressure, baby’s number,giving birth’s story, abortion / curettage’s history, malnutrition, congenital disease before pregnant and other problems in a pregnancy. Results of this study is application that can prediction the age of birth with highest precicion value of this application on number 100%, highest recall value on number 84.905660377358% and highest accuracy value on number 96%.Keywords : Application, Birth, K-Nearest Neighbor, Prediction
Co-Authors Abdul Kemal Nasa’i Wibowo ABIDIN, ANIDA ZULAIFA Aditama, Maulana Rizki Affandi, Syihabuddin Aflit Nuryulia Ahmad Mardalis Aini Hanifa Ajib Rosyadi Aldi Farhan Razak Alfarizi, Rizki Zakaria Andika Putra Pratama Andika Rustam Andri Lukman Nurjaman Anin Ammbya Soulani Aniq Hudiyah Bil Haq, Aniq Hudiyah Bil Anisah Tri Setyowinarti Annas, Rifai Annastalia Fatikasari Antaristi, Monika Ardy Wicaksono Arfandi Ahmad Arief Kelik Nugroho Arkham Zahri Rakhman Avifah Hasna Nur Fadila Ayu Putri Wardhani Aziz Abdul Rahman Aziz Prasuci Priambadha Bagas Ario Dewanto Bangun Wijayanto Bangun Wijayanto Bangun Wijayanto Bangun Wijayanto Budi Santoso Chrismawan, Stephen Prasetya Dadang Iskandar Dadang Iskandar, Dadang Daffa Ammar Muaafii Daffa Naufaldi Al Rasyid Dedi Gunawan Dedi Setiadi Deny Febriyanto Desy Kartika Indahsari Desy Puspitassari Dhenok Prastyaningtyas Paramesvari Diky Alfian Kurniawan Diva Kurnia Achmadi Dwi Utari Iswavigra Dzulfikar, Muhammad Zaki Eddy Maryanto Eddy Maryanto Eddy Maryanto Efrina Fitriani Fakhrur Razi Farida Angguntina Farida, Rahayu Faris Akbar Abimanyu Fatah Yasin Al Irsyadi Febri Sutmo Febri Sutomo Ferry Darmawan Ihsan Puntadewa Indra Permana Jati Indraswari, Naisha Rahma Ipung Permadi Ipung Permadi Irfan Agus Tiawan Ivan Darmawan Jati Hiliamsyah Husen Joe, Michael Julianto, Ribut Krisna Widi Nugraha Kumaidi, K Kusuma, Agung Fajar Surya Laksono, FX Anjar Tri Lasmedi Afuan Lena Rosmayani Lia Dewi Susanti Mahendra, Galuh Raka Majid Narendra Maria Ulfa Chasanah Marpid, Nuravifah Novembriana Meilisa Ayu Susantiva Mochammad Muslih Maruzi Mohamad Waluyo Monika Herliana Muazam, Safa Muhamad Taufik Hidayat MUHAMMAD ABDUL GHOFAR Muhammad Adam Mulyadi Mucoffa Muhammad Bahrul Ashfiya Muhammad Fikri Rivaldi Muhammad Hikal Muhammad Luthfi Muhammad Luthfi Hidayat Muhammad Naufal Faza Muhammad Thoriq Aziz Muhammad Zein Albalki Naisha Rahma Indraswari Nofiyati Nofiyati Nofiyati Nofiyati Nofiyati Nofiyati, Nofiyati Nofiyati, Nofiyati Novanto, Adi Nur Chasanah Nur Chasanah Octaviano, Atha Narentha Priandika Ratmadani Anugrah Puput Muliana Putri Rahayu, Swahesti Puspita Rahman, Ahmada Auliya Ramadhan, Muhammad Rivai Putra Ramadhani, Faza Abdillah Rian Ardianto Riski Agung Putro Laksono Rochmat Mulyo Sugihono Rosyid Ridlo Al-Hakim Setyowinarti, Anisah Tri Singgih Rama Pradana Sohputro, Nicolas Sri Murwanti Sugih Ahmad Fauzan Sunan, Huzaely Latief Susi Setianingsih Suyahman Suyahman Swahesti Puspita Rahayu Teguh Cahyono Tiyssa Indah Barokah Uki Hares Yulianti Very Dwi Setiawan Widhiatmoko Herry Purnomo Widiyarti Endang Saputri Windiasani, Pungki Arina Winny Purbaratri Yulianita, Nadia Gitya