Article Per Year (5 Year)
p-Index From 2021 - 2026
12.617
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) Informatics for Educators and Professional : Journal of Informatics Network Engineering Research Operation [NERO] KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Indonesian Journal of Applied Informatics Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology Jurnal Sistem Informasi Kaputama (JSIK) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Perangkat Lunak JSR : Jaringan Sistem Informasi Robotik JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika (JUTIF) Jurnal Mahasiswa Sistem Informasi (JMSI) International Journal of Social Science Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Janitra Informatika dan Sistem Informasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) INFORMATIKA Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Mahasiswa Ilmu Komputer TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan Manajemen Kreatif Jurnal JURSIMA Jurnal Ekonomi Manajemen Akuntansi BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat NERO (Networking Engineering Research Operation) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Sistem Informasi dan Manajemen INTERNAL (Information System Journal) Intechno Journal : Information Technology Journal
Yudhistira Arie Wijaya
STMIK IKMI Cirebon
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 105 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 105 Documents
Search

 7   8   9   10   11 
The Effectiveness of Dropout Layers in LSTM Architecture for Reducing Overfitting in Sony Stock Prediction Roni Saputra; Dian Ade Kurnia; Yudhistira Arie Wijaya
Intechno Journal : Information Technology Journal Vol. 7 No. 2 (2025): December
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i2.2369

Abstract

This study investigates the effectiveness of dropout layers in reducing overfitting within Long Short-Term Memory (LSTM) neural networks for Sony stock price prediction. Financial time series forecasting presents significant challenges due to market volatility and noise, often leading to models that overfit historical data while failing to generalize to unseen market conditions. We implemented two LSTM models: one without dropout layers and another with dropout layers (rate=0.2) applied after each LSTM layer. Using historical Sony stock data from 2015-2025, we evaluated both models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. The model with dropout demonstrated superior performance on testing data, achieving RMSE of 0.5971, MAE of 0.4411, and MAPE of 2.1502%, compared to the model without dropout which obtained RMSE of 0.7124, MAE of 0.5636, and MAPE of 2.6684%. Furthermore, the dropout model exhibited significantly reduced overfitting, with smaller performance gaps between training and testing datasets across all metrics, particularly in MAPE where the difference approached zero (0.0509%). This research provides empirical evidence that dropout regularization effectively enhances LSTM model generalization for stock prediction, offering practical value for developing more reliable financial forecasting models. Future research could explore optimal dropout rates for different market conditions and investigate combinations of dropout with other regularization techniques.
Implementation of IndoBERT for Sustainability Impact Assessment in University Collaboration Information Systems Hamonangan, Ryan; Danar Dana, Raditya; Arie Wijaya, Yudhistira; Nurdiawan, Odi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5330

Abstract

University collaboration plays a critical role in enhancing institutional quality and supporting global sustainability agendas. However, many higher education institutions face challenges in managing Memorandum of Understanding (MoU), Memorandum of Agreement (MoA), and Implementation Agreement (IA) documents, particularly in monitoring implementation and assessing their alignment with sustainability goals. This study introduces a University Collaboration Information System enhanced with IndoBERT-based Natural Language Processing (NLP) to automate sustainability impact assessment. A synthetic corpus of 30 annotated collaboration documents was developed, covering multi-label Sustainable Development Goals (SDG) classification and span-level Named Entity Recognition (NER). Two approaches were evaluated: (1) baseline TF-IDF + Support Vector Machine (SVM) for SDG classification and rule-based NER, and (2) fine-tuned IndoBERT for both tasks. Experimental results show that IndoBERT significantly outperforms the baselines, achieving an average F1-score of 0.93 for SDG classification (+16.3%) and 0.96 for NER (+18.5%). The system integrates these models to generate automated entity extraction, sustainability dashboards, and document monitoring features. This work contributes to the advancement of informatics by demonstrating the effectiveness of Transformer-based NLP in processing institutional documents and by providing an integrated information-system framework that strengthens the role of NLP within the field of computer science.
Optimalisasi Klasterisasi Tenaga Kesehatan Menggunakan K-Means dan Davies Bouldin Indexs Ayura Yufita; Rudi Kurniawan; Yudhistira Arie Wijaya; Tati Suprapti
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.96645

Abstract

Abstrak : Optimalisasi model pengelompokan data tenaga kesehatan adalah langkah strategis untuk memahami pola dan karakteristik kelompok data tertentu. Tujuan dari  penelitian ini adalah untuk  mendapatkan nilai K optimal menurut Davies Bouldin Indeks (DBI), mendapatkan nilai iterasi yang diperlukan oleh algoritma K-Means Clustering untuk mencapai hasil yang optimal, dan menentukan jenis metrik apa yang akan menghasilkan nilai (DBI) yang paling kecil. Hal ini  penting karena penelitian ini membantu perencanaan distribusi tenaga kesehatan yang lebih efisien di wilayah Jawa Barat denfan menghasilkan klaster optimal berbasis K-Means dan Optimize Parameter Grid. Penggunaan metode Knowledge Discovery in Database (KDD), yang mencakup proses pemilihan, praproses, transformasi, data mining, dan interpretasi/ evaluasi hasil. Hasil penelitian ditunjukkan pada iterasi 1-10 menggunakan K=2 dengan nilai DBI terendah sebesar 0,377.====================================================Abstract : Optimisation of health worker data clustering model is a strategic step to understand the patterns and characteristics of certain data groups. The objectives of this study are to obtain the optimal K value according to the Davies Bouldin Index (DBI), obtain the iteration value required by the K-Means Clustering algorithm to achieve optimal results, and determine what type of metric will produce the smallest (DBI) value. This is important because this research helps to plan a more efficient distribution of health workers in the West Java region by producing optimal clusters based on K-Means and Optimise Parameter Grid. The use of Knowledge Discovery in Database (KDD) method, which includes the process of selection, preprocessing, transformation, data mining, and interpretation/evaluation of results. The results showed in iterations 1-10 using K=2 with the lowest DBI value of 0.377.
Application of Weighted Loss Function in Convolutional Neural Network for Acne Image Classification Abubakar Sidik; Ade Irma Purnamasari; Denni Pratama; Puji Pramudya Marta; Yudhistira Arie Wijaya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1885

Abstract

Automated acne image classification using Convolutional Neural Networks (CNN) holds significant potential in dermatological diagnosis but faces a fundamental challenge of class imbalance. This phenomenon causes standard models to be biased towards majority classes and fail to recognize clinically important minority classes. This study aims to address this bias by applying a Weighted Loss Function to the EfficientNetB1 architecture. The research method employs a comparative experimental approach between two scenarios: the Baseline model (Standard Cross-Entropy) and the Proposed model (Weighted Cross-Entropy). The dataset consists of 5 acne classes with an imbalanced distribution. The results show that the Weighted Loss model significantly outperforms the Baseline model. Overall accuracy increased from 80% to 86%. The most significant improvement occurred in the minority class 'Papules', where the F1-Score surged by 0.10 points (from 0.71 to 0.81). It is concluded that the application of Weighted Loss Function effectively overcomes bias due to imbalanced data without the need for synthetic data augmentation, resulting in a fairer and more reliable model for clinical implementation.
Association Analysis of Printing and Photocopying Sales Data in Adzmi Art Shop Cirebon Uses the FP-Growth Algorithm Suteja; Rudi Kurniawan; Yudhistira Arie Wijaya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.830

Abstract

In the digital era, transaction data analysis plays a crucial role in strategic decision-making, especially for SMEs such as Toko Adzmi Art in Cirebon Regency. This study aims to develop a sales data association model using the FP-Growth algorithm to identify product association patterns. Daily transaction data over a year were collected, processed through data cleaning, standardization, and transformation, and analyzed using RapidMiner software. Minimum support and confidence parameters were applied to evaluate the frequency and strength of product relationships. The results show that the combination of "Photocopy" and "Passport Photo" services has a confidence of 0.491 and a support of 0.061, with "Photocopy" as the most in-demand product (support 0.497). These findings open opportunities for bundling strategies and inventory optimization to enhance operational efficiency. This model provides an empirical foundation for SMEs to leverage data mining technology to improve competitiveness and customer satisfaction.
Co-Authors Abubakar Sidik Ade Irma Purnama Sari Ade Irma Purnamasari Ade Irma Purnamasari Adi Hermawan Aditiya Arif Firmansyah Adiyanto, Alfian Adjie Setyadj, Mochammad Agesty Kusmiyaty Agni, Vega Putra Dwi Ahmad Faqih Ahmad Jamalul Noor Ahmad Rifai Ikhsanudin AKBAR, MUHAMAD DENI Akhmad Taukhid Alfirda Sofyan, Zahra Aliya Anisa Rahma Alwan Azhar Alya Fadia An-naziz Safaat, Wafik Andi Ardiansyah Andriyani, Wini Anggara, Doni Anjar Permadi Aprianto, Wili Arya Hadi Wicaksana ASEP SAEFUDDIN Asmana, Asmana Astri Amelia Athaullah Abrar Bayan Ayura Yufita Beby Maryam Cintia Putri Prasetia Dadang Sudrajat Danar Dana, Raditya Darma Irawan, Bobi Darussalam, Luthvi Nurfauzi Denni Pratama Denni Pratama Denni Pratama Dermawan, Hibrizi Dzaky Dian Ade Kurnia Dian Ade Kurnia Dodi Solihudin Edi Tohidi Edi Wahyudin Falih, Alfi Rizqi Falih FANDI ACHMAD Fauzan, Muhamad Nur Fianita Rusadi Fianita Rusadi Firmansyach, Wildan Attariq Hajijin Amri Hamonangan, Ryan Hayati, Umi Hegarmanah Muhabatin Heliyanti Susana Heliyanti Susana Herman Hermawan, Adi Hidayat, Zaids Syarif Ibnu Ubaedila Ikhwan Fahruddin, Yusuf Inawati, Windi Intan Wangi Nur Qibti Irfan Ali Irfan Ali Irma Agustina Jaelani Sidik Jayawarsa, A.A. Ketut Jurnal Konsera Khaerul Anam Khoeri, Yajid Komala, Wulan Kurniawan , Rudi Laela Laela Leli Oktaviani Lukmanul Hakim Manzis, Zian Marta, Puji Pramudya Martanto Martanto . Martanto Martanto Masjunedi, Masjunedi Maulana, Tedy Mifta Almaripat Mita Amelia Moh Nurdayat Dayat MUHAMAD DENI AKBAR Muhamad Fahrurozi Muhamad Nur Fauzan Muhammad Aditya Rabbani Adit Mulyawan Nabila, Aynun Nana Suarna Nana Suarna Narasati, Riri Narasati Nashir, Mukhtar Nining Rahaningsih Nisa Dieanwati Nuris Nisa Dienwati Nuris Nur Amalia, Yustika Nurazijah, Wulan Nurdiawa, Odi Nurholipah, Titin Nurrahman, Rizki Odi Nurdiawa Odi Nurdiawan Pebriyanto, Ramdhan Pratama, Denni Puji Pramudya Marta Puji Pramudya Marta Purnamasari, Ade Irma Restu Normalasari Rini Astuti Rini Astuti Rini Astuti Rio Febriyan Rizal Rizal Roni Saputra Rubangiya Rubangiya Rudi Kurniawan Rudi Kurniawan Rudi Kurniawan Rudi Kurniawan Saeful Anwar Saeful Anwar, Saeful Satria Turangga Septian Nugraha, Titan Septiani Gumilar, Tia Shifa Dwi Oktaviani Siti Sopiyah Suarna, Nana Sugianto, Nanda Putri Sulaeman, Muhammad Suteja Syach Putra, Yanuar Tati Suprapti Taufik Hidayat Tegar Lazuardi, Muhammad Thomas Agam Tiana Dewi Tri Anelia Trian Nurmansyah Triswanto, Triswanto Tuti Hartati Tuti Hartati Tuti Hartati Umi Hayati Wahyudi Wahyudi Wartumi Wartumi Willy Prihartono Winayah, Winayah Windy Astuti Witriyani Witriyani Yudis Firmansyah yulani, Yulani - Yulia, Yuli