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All Journal JUITA : Jurnal Informatika Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal on Software Engineering (IJSE) Jurnal Penelitian Pendidikan IPA (JPPIPA) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika Infotekmesin Journal of Information Systems and Informatics Computer Science and Information Technologies International Journal of Engineering, Science and Information Technology Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM)
Taqwa Hariguna
Universitas Amikom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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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.
ANALISIS PROFILING KINERJA DOSEN BERBASIS ALGORITMA CLUSTERING: STUDI KASUS DATA EVALUASI MAHASISWA (EDOM) Heri Subangkit; Taqwa Hariguna; Dhanar Intan Surya Saputra
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7455

Abstract

Lecturer quality is a key factor determining the success of a higher education institution. Although EDOM assessment is crucial for performance, large data processing often does not provide sufficient strategic information. This study utilizes a no-learning method to profile lecturer performance. K-Means and Fuzzy C-Means (FCM) are two clustering algorithms that are compared with four competency variables. These variables are pedagogical, professional, social, and personality. The results show that there are three ideal clusters (k = 3) which are categorized as "Very Good", "Good", and "Fair" performance groups, respectively. The K-Means algorithm produces a Silhouette Score of 0.507. However, FCM is more flexible in determining the number of data transition members required. The profiling results show that pedagogical competence is the variable with the lowest score in the "Fair" cluster. The findings of this study suggest that, to improve the quality of educational services, lecturers in the cluster need to undergo training related to teaching methods.
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.
Otomatisasi Pelabelan Korpus Sarkasme pada Komentar YouTube Berbahasa Indonesia Menggunakan Model Bahasa RoBERTa Rizky Agil Singgih Susanto; Taqwa Hariguna; Azhari Shouni Barkah
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

Sarcasm detection in Indonesian YouTube comments remains challenging due to contextual ambiguity, limited labeled data, and class imbalance. This study proposes a fine-tuned RoBERTa-based auto-labeling pipeline to generate high-confidence pseudo-labels for unlabeled comments. The scientific contribution lies in integrating Back-Translation augmentation, confidence-threshold filtering at P ≥ 0.85, and comparative evaluation against baseline models on a YouTube sarcasm corpus. The data were collected from 10 public Indonesian YouTube videos covering public service, political, social, and entertainment topics during January-March 2025. From 1,000 raw comments, 493 clean comments, 200 manually labeled instances, and 536 final instances were obtained after augmentation and pseudo-label filtering. The 5-fold cross-validation results show that RoBERTa achieved an accuracy of 0.89 and a macro F1-Score of 0.87, with class-wise precision/recall of 0.81/0.81 for sarcasm and 0.92/0.92 for non-sarcasm. Compared with TF-IDF + SVM, BiLSTM, and IndoBERT, RoBERTa improved the F1-Score by 24.29%, 12.99%, and 3.57%, respectively. These findings indicate that RoBERTa-based auto-labeling can support a more controlled expansion of sarcasm corpora while reducing reliance on fully manual annotation.
Deep Reinforcement Learning-Based Control Architectures for Autonomous Maritime Renewable Energy Platforms Sura Sabah; Refat Taleb Hussain; Ismail Abdulaziz Mohammed; Haider Mahmood Jawad; Intesar Abbas; Taqwa Hariguna
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1209

Abstract

Autonomous vessels driven by renewable energy are increasingly envisioned as vital for sustainable ocean?operations such as environmental monitoring, offshore power generation, and long-haul unmanned surface vehicles. Implementing fine-scale control of these systems has proven challenging however,?due to time-varying sea-state dynamics, sporadic energy inputs, the possibility of failure at the component level, and the requirement for coordination between multiple agents. In the article, an end-to-end deep reinforcement learning-based hierarchical control solution with real-time navigation and?its synthesis for energy optimization is proposed. It combines high-level energy regulation with low-level actuator scheduling so as to react to the variations of?the environment and internal perturbations. Simulations using actual wave realizations, sensor failures, actuator outages, and network communication variation were used?to demonstrate the performance of the control system in the following 5 performance aspects: energy saving, navigation accuracy, communication reliability, fault tolerant and multi-agent coordination. Results indicate that the architecture sustained over 80% of the performance and achieved energy efficiencies up to 54.5% in the?best case under failure scenarios. Performance-measures demonstrated reasonable scalability?up to 5–7 agents without significant communication overhead. The findings support the applicability of deep reinforcement learning for real-time maritime control under uncertainty, offering a viable alternative to conventional rule-based or predictive control strategies. The framework’s modular design allows for future integration with federated learning, hybrid control models, or autonomous deployment. The article contributes to the growing field of intelligent marine systems by providing a robust and adaptable control strategy for sustainable and scalable operations in autonomous maritime environments.
COMPARISON OF THE PERFORMANCE OF SVM, RANDOM FOREST, AND NEURAL NETWORK ALGORITHMS IN SENTIMENT ANALYSIS OF OPENAI APPLICATION REVIEWS ON THE GOOGLE PLAY STORE Ahmad Latif; Muhtyas Yugi; Fandy Setyo Utomo; Taqwa Hariguna
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7793

Abstract

This study compares the performance of three machine learning algo-rithms—Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN)—in sentiment analysis of user reviews for the OpenAI application on the Google Play Store. The primary objective of this study is to evaluate the effectiveness of each algorithm in clas-sifying user reviews into three sentiment categories: positive, negative, and neutral. The dataset used consists of user reviews of the OpenAI application, collected directly from the Google Play Store. Model per-formance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the Neural Network algorithm achieved the best overall performance in terms of accuracy and F1-score. SVM demonstrated competitive performance, particularly in classifying positive and neutral sentiments, while Random Forest showed an advantage in terms of precision but performed lower over-all, especially in classifying negative sentiments. Therefore, the Neural Network is considered the most effective algorithm for sentiment analysis tasks in this study
Co-Authors Abednego Dwi Septiadi Ade Fatahillah Agung Purwo Wicaksono Ahmad Latif Andhika Rafi Hananto Azhari Shouni Barkah Dewi Fortuna Dhanar Intan Surya Saputra Dwi Puji Lestari Eka Tripustikasari Fajar Rohmattulloh Fandy Setyo Utomo Faradina Faradina Faridatun Nida Fiby Nur Afiana Haider Mahmood Jawad Heri Subangkit Intesar Abbas Ismail Abdulaziz Mohammed Lusi Dwi Oktaviana Muhammad Abdul Aziz Muhtyas Yugi Nurul Hani Nurwita Widyastuti Prayoga Pribadi Qurotul Aini Refat Taleb Hussain Retno Waluyo Rizky Agil Singgih Susanto Sarmini - Sarmini Seli Saputri Silvia Anggun Rahayu Sura Sabah Untung Rahardja