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All Journal Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika JUITA : Jurnal Informatika Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah Jurnal Tekno Insentif JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Komputer dan Informatika (JSON) Journal of Innovation Information Technology and Application (JINITA) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma Jurnal Informatika: Jurnal Pengembangan IT Journal of Informatics and Interactive Technology (JIITE) Journal of Digital Market and Digital Currency
Fandy Setyo Utomo
Universitas AMIKOM Purwokerto
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance Sekhudin, Sekhudin; Purwati, Yuli; Utomo, Fandy Setyo; Azmi, Mohd Sanusi; Subarkah, Pungkas
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.v15i2.1586.271-282

Abstract

A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.
A Comprehensive Evaluation of CatBoost and LightGBM Algorithms for Honorarium Prediction on Categorical Datasets with Class Imbalance Slamet Widodo; Fandy Setyo Utomo; Berlilana
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27363

Abstract

Determining income, including honoraria in the academic environment, is often done manually and subjectively, necessitating a predictive model to objectively determine the honorarium amount. However, the development of the prediction model faces challenges due to the dataset's characteristics, which include categorical data and an imbalanced class distribution. This research aims to evaluate the predictive performance and computational resource efficiency of the CatBoost and LightGBM algorithms in predicting honorariums. The dataset used includes 58,332 actual honorarium data of employees from higher education institution "A" in Purwokerto for the period from January 2024 to February 2025. The methods used include data preprocessing, dataset splitting using Stratified Splitting, modeling with CatBoost, LightGBM, Random Forest, Neural Network, and Linear Regression, as well as evaluation using MSE, RMSE, MAE, R² metrics, and computational resources (execution time, memory, CPU time). LightGBM achieved an RMSE of 665.960 and an R² of 0.54, while recording the lowest memory usage at only 2.67 MB. CatBoost produced an RMSE of 667.395 and an R² of 0.53, excelling in processing categorical features without one-hot encoding. Meanwhile, Linear Regression showed the lowest accuracy and high memory usage. These results confirm that LightGBM is the most optimal choice for fast, efficient, and accurate honorarium predictions. However, this research is limited to testing in a laboratory environment. Further research is recommended to implement direct integration with an active database and the integration of information retrieval methods to enhance the effectiveness and security of real-time honorarium predictions, as well as to integrate interpretability methods such as SHAP to improve decision-making transparency.
IMPROVING HANDWRITTEN DIGIT RECOGNITION USING CYCLEGAN-AUGMENTED DATA WITH CNN–BILSTM HYBRID MODEL Muhtyas Yugi; Fandy Setyo Utomo; Azhari Shouni Barkah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6982

Abstract

Handwritten digit recognition presents persistent challenges in computer vision due to the high variability in human handwriting styles, which necessitates robust generalization in classification models. This study proposes an advanced data augmentation strategy using Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve recognition accuracy on the MNIST dataset. Two architectures are evaluated: a standard Convolutional Neural Network (CNN) and a hybrid model combining CNN for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for sequential pattern modeling. The CycleGAN-based augmentation generates realistic synthetic images that enrich the training data distribution. Experimental results demonstrate that both models benefit from the augmentation, with the CNN-BiLSTM model achieving the highest accuracy of 99.22%, outperforming the CNN model’s 99.01%. The study’s novelty lies in the integration of CycleGAN-generated data with a CNN–BiLSTM architecture, which has been rarely explored in previous works. These findings contribute to the development of more generalized and accurate deep learning models for handwritten digit classification and similar pattern recognition tasks.
Evaluation of User Satisfaction in Web-based Library Information Systems: A Systematic Literature Review Faradina Faradina; Taqwa Hariguna; Fandy Setyo Utomo
SISTEMASI Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.6204

Abstract

The transformation of library management today is highly influenced by the acceleration of information and communication technology (ICT), particularly through the adoption of web-based information systems. While these systems can optimize productivity and service accessibility, their effectiveness ultimately depends on the level of user satisfaction. This study evaluates various user satisfaction assessment methodologies through a Systematic Literature Review (SLR) using the PRISMA protocol on 25 selected articles published between 2020 and 2024. The findings indicate a shift in the dominance of evaluation tools toward the Human-Organization-Technology Fit (HOT-Fit) model and the Net Promoter Score (NPS). Key determinants of satisfaction were identified in terms of information quality, system reliability, and responsiveness of technical support.
Prediksi Harga Bitcoin Menggunakan Model Hibrida LSTM–Transformer dengan Integrasi Indikator Teknikal dan Validasi Statistik Fajar Rohmattulloh; Fandy Setyo Utomo; Taqwa Hariguna
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3225

Abstract

This study aims to develop an accurate, stable, and adaptive Bitcoin price prediction model by integrating Long Short-Term Memory (LSTM) and Transformer Encoder architectures with technical indicators as additional features. Four deep learning architectures were comparatively evaluated: LSTM, Bidirectional LSTM (BiLSTM), Convolutional Neural Network–LSTM (CNN–LSTM), and a hybrid LSTM–Transformer model, using historical Bitcoin to US Dollar (BTC/USD) price data from 2014 to 2025 obtained from Yahoo Finance. The technical indicators incorporated include Moving Average (MA), Exponential Moving Average (EMA), Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD). Model performance was assessed using three primary metrics—Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²)—along with paired t-tests to evaluate the statistical significance of performance differences among models. Experimental results indicate that the hybrid LSTM–Transformer model achieves the most competitive performance, with an RMSE of 0.0412, a MAPE of 4.36%, and an R² of 0.9617. The paired t-test results confirm that the performance differences among the models are statistically significant (p-value < 0.05), thereby providing empirical support for the superiority of the hybrid approach. The integration of technical indicators enhances the model’s ability to capture price trends and volatility patterns in Bitcoin markets. However, further analysis—such as ablation studies or explicit before-and-after comparisons—is required to isolate and quantify the individual contributions of these indicators. From a scientific perspective, this research reinforces the effectiveness of attention mechanisms in capturing long-term temporal dependencies and demonstrates that combining technical indicators with hybrid deep learning architectures can improve both the stability and validity of cryptocurrency price predictions. The main contribution of this study lies in proposing a cryptocurrency price prediction framework that emphasizes not only predictive accuracy but also reliability and statistical significance, making it a promising approach for digital financial market analytics.
Systematic Literature Review Penerapan Physics-Informed Machine Learning untuk Analisis Seismisitas dan Bahaya Gempa Bumi Abdul Hakim Prima Yuniarto; Fandy Setyo Utomo; Giat Karyono
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9830

Abstract

Pendekatan machine learning konvensional yang murni berbasis data dalam bidang seismologi sering kali menghasilkan model kotak hitam yang melanggar hukum fisika dasar serta memerlukan data pelabelan masif. Sebagai solusi, paradigma Physics-Informed Machine Learning (PIML) hadir mengintegrasikan pengetahuan domain fisis ke dalam arsitektur kecerdasan buatan. Penelitian ini bertujuan melakukan tinjauan literatur sistematis (Systematic Literature Review/SLR) untuk memetakan mekanisme integrasi, klaster parameter, dampak komputasi, serta kesenjangan riset dari penerapan PIML pada analisis kegempaan. Melalui evaluasi terstruktur terhadap 36 studi utama rentang 2021–2026, hasil analisis menunjukkan bahwa integrasi hukum fisika dominan dilakukan melalui modifikasi fungsi kerugian memanfaatkan residu persamaan diferensial parsial. Penerapan paradigma ini pada sejumlah studi terbukti mampu meningkatkan efisiensi komputasi serta mempercepat waktu simulasi numerik dibandingkan dengan metode konvensional, serta menyajikan pemodelan tanpa jaring yang stabil pada frekuensi tinggi. Namun, analisis kesenjangan mengungkap adanya ketergantungan pada skema terpandu deterministik. Riset ini merekomendasikan arah pengembangan masa depan pada integrasi metode unsupervised dan konstrain statistik seismologi guna menjamin kemasukakalan fisis model.
Impact of Stopword Variation on Qur'anic Text Classification using Support Vector Machine and Backpropagation Afit Ajis Solihin; Fandy Setyo Utomo; Azhari Shouni Barkah
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3069

Abstract

This study aims to analyze the impact of varying stopword sets on the performance of Qur'anic text classification models in Indonesian translations, using two machine learning algorithms: Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN). The research involved six stopword variants: Sastrawi, Damian Doyle, Fadillah Z. Tala, Natural Language Toolkit (NLTK) Indonesian, Yudi Wibisono, and a combination of all these lists. The preprocessing steps included cleaning, case folding, tokenization, stopword removal, and stemming, followed by TF-IDF (Term Frequency-Inverse Document Frequency) text representation. Feature selection was performed using the Chi-Square method to select the top 1,000 features. The evaluation results showed that SVM consistently outperformed BPNN across all metrics, including accuracy, precision, recall, and F1-score. The Sastrawi stopword variant delivered the best performance with an F1-score of 0.6697, followed by Fadillah Z. Tala and Damian Doyle. In contrast, BPNN showed lower performance, with the highest F1-score of 0.4607 achieved using the NLTK stopword variant. These findings highlight that selecting relevant, contextually appropriate stopwords is critical to classification Effectiveness. SVMs proved more reliable at handling high-dimensional text data while preserving the semantic meaning of Qur'anic verses.
Explainable Transfer Learning for Breast Cancer Histopathology Classification Using Grad-CAM Ade Fatahillah; Fandy Setyo Utomo; Taqwa Hariguna
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.34993

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, highlighting the need for diagnostic systems that are both accurate and interpretable. Although transfer learning has shown promising results in histopathological image classification, studies simultaneously examining predictive performance, statistical reliability, and interpretability remain limited. This study proposes an explainable transfer learning framework for breast cancer histopathology classification and investigates the relationship between classification performance and visual interpretability. Experiments were conducted using 2,013 histopathological images from the BreakHis dataset at 200× magnification. Three pretrained architectures, ResNet50, DenseNet121, and EfficientNetB0, were trained and evaluated under identical preprocessing, augmentation, and training settings. Performance was assessed using accuracy, precision, recall, F1-score, AUC, confidence intervals, McNemar testing, confusion matrix analysis, and Grad-CAM visualization. Results showed that DenseNet121 achieved the most balanced classification performance and the highest discriminative capability among the evaluated models. Statistical analysis confirmed significant performance differences, while Grad-CAM visualizations demonstrated more focused and diagnostically relevant activation regions. These findings suggest that models learning more discriminative histopathological representations tend to generate more meaningful visual explanations. The study emphasizes integrating predictive performance, statistical validation, and explainability to support reliable and transparent artificial intelligence systems for breast cancer diagnosis.
Digitalisasi Pengelolaan PAM Desa: Optimalisasi Pencatatan Meteran Air dengan Barcode Scanning di BUMDES Candi Mulya Fandy Setyo Utomo; Chyntia Raras Ajeng Widiawati; Anies Indah Hariyanti; Yuli Purwati
JURPIKAT Vol 7 No 1 (2026): 7.1 2026
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v7i1.2721

Abstract

This community service program aims to enhance the efficiency and transparency of clean water service management in Candinegara Village by implementing a barcode scanning-based water meter recording system. Problems faced by the Candi Mulya Village-Owned Enterprise (BUMDES) include manual recording errors, late billing, and a lack of transparency in water usage data. To address these issues, a Progressive Web App (PWA) for customers and a monitoring dashboard for administrators were developed, enabling real-time data monitoring. Implementation methods included outreach, application usage training, and the implementation of a barcode scanning system for meter recording. Evaluation results through a questionnaire distributed to 28 respondents showed a Perceived Usefulness (PU) score of 3.75, Perceived Ease of Use (PEOU) of 3.44, Attitude Toward Using (ATU) of 3.85, and Behavioral Intention to Use (BIU) of 3.63, reflecting positive acceptance of the application. In conclusion, the implementation of a barcode scanning-based digital system has successfully increased the accuracy, efficiency, and transparency of Village Water Supply (PAM) management and received a positive response from the community and BUMDES managers.
Audit Data Leakage dan Evaluasi Model Machine Learning untuk Prediksi Capaian Pembelajaran Lulusan dalam Outcome-Based Education Priaji Januardi; Rujianto Eko Saputro; Fandy Setyo Utomo
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Outcome-Based Education (OBE) implementation requires continuous evaluation of graduate learning outcomes (CPL). While machine learning models are widely used for predicting academic performance, most studies overlook fundamental issues like data leakage and class imbalance. This study evaluates the impact of data leakage audits on the performance of four models (Logistic Regression, Random Forest, XGBoost, and Deep Neural Network) in predicting CPL. The novelty lies in applying a structured audit framework prior to model comparison. Experiments utilized 27,262 academic records with stratified cross-validation. Audit results proved that proxy features caused unrealistic performance (Accuracy 0.999). After removing leaked features, Logistic Regression achieved the highest discrimination stability (AUC 0.772), while DNN recorded the highest F1-score. Wilcoxon tests confirmed no statistically significant performance difference among the models (α=0.05). In conclusion, on leakage-free OBE data, simple linear models remain highly competitive and suitable as the foundation for early warning systems.
Co-Authors Abdul Hakim Prima Yuniarto Ade Fatahillah Adiya, Az Zahra Dwi Nur Afit Ajis Solihin Ahmad Latif Aisha Hukama Setyowati Aji Saeful Aji Septa, Adrian Ajis Solihin, Afit Amar Al Farizi Anas Nur Khafid Anggini, Melisa Anggraeni, Mutia Dwi Anggraini, Nova Anggriani, Epri Anies Indah Hariyanti Azhari Shouni Barkah Azmi, Mohd Sanusi Bagus Adhi Kusuma Baihaqi, Wiga Maulana Balit, Muhamad Naufal Burhanuddin Berlilana Berlilana Berlilana Burhanuddin Balit, Muhamad Naufal Churil Aeni, Agustina Chyntia Raras Ajeng Widiawati Darmono Dedi Purwanto, Dedi Didi Prasetyo Dwi Krisbiantoro, Dwi Dwi Putriana Nuramanah Kinding Dzaky Candy Fahrezy Fadhilah, Siti Nur Fajar Rohmattulloh Faradina Faradina Febriansyah Husni Adiatma Giat Karyono Giat Karyono Giat Karyono Gilang Miftkahul Fahmi Fahmi Hanif Hidayatulloh Hendra Marcos, Hendra hidayatulloh, hanif Ilham, Rifqi Arifin Imam Tahyudin Imam Tahyudin Indriyani, Ria Iqbaluddin Syam Had Jamie Mayliana Alyza Kafilla, Princess Iqlima Kusuma, Bagus Adhi Kusuma, Velizha Sandy Lasmedi Afuan Latif, Ahmad Linda Perdana Wanti Lubna, Zuhriyatul Lukita, Dita Maulana Baihaqi, Wiga Mohd Fairuz Iskandar Othman Mohd Nazrin Muhammad Mohd Sanusi Azmi Muaziz, Imam Muhamad Naufal Burhanuddin Balit Muhtyas Yugi Muhtyas Yugi Muhtyas Yugi Murtiyoso Murtiyoso Nandang Hermanto Nanna Suryana Nikmah Trinarsih Nugroho, Khabib Adi Nugroho, Lustiyono Prasetyo Nur Cholis Romadhon Octavia, Annisa Suci Prayoga, Fandhi Dhuga Priaji Januardi Pungkas Subarkah Purbo, Yevi Septiray Purwidiantoro, Moch. Hari Pyawai, Hero Galuh R. Vitto Mahendra Putranto Ramadhan, Aziz Ramadhan, Rio Fadly Rifqi Arifin Ilham RR. Ella Evrita Hestiandari Rujianto Eko Saputro Sagita, Selvi Samsul Arifin Sarmini - Sarmini Sarmini Sarmini Sekhudin, Sekhudin Setiabudi, Rizki Setiawan, Ito Shafira, Lulu Shendy Filanzi Slamet Widodo Slamet Widodo Sofa, Nur Sri Hartini Suryana, Nanna Taqwa Hariguna Titi Safitri Maharani Trinarsih, Nikmah Turino, Turino Utomo, Dadang Wahyu Wahid, Arif Mu'amar Wibisono, Arif Cahyo Wiga Maulana Baihaqi Yugi, Muhtyas Yuli Purwat Yuli Purwati Yulianto, Koko Edy