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Yosep Septiana
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INDONESIA
Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,150 Documents
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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.
Pengembangan Sistem Informasi Verifikasi Data Balita pada Dinas Kesehatan Kota Batu Januar Muiz Triananda; Wildan Suharso
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.23-1.3233

Abstract

Manual management of toddler data at the local health department level is prone to data duplication and inconsistencies; however, information system solutions that integrate technical aspects and organizational capacity are still rarely studied. This study aims to design and evaluate a web-based infant data verification information system for the Batu City Health Department. The system was developed using the Waterfall SDLC method with the Laravel 10 framework and MySQL, and was evaluated through Black Box testing and the System Usability Scale (SUS) with 10 respondents. Test results showed zero data duplication, verification time reduced from approximately 2 days to approximately 4 hours (an efficiency gain of approximately 70%), and a SUS score of 82.5, which falls into the “Excellent” category. These findings indicate that a user-centered design approach effectively enhances system acceptance among non-technical users, and that the combination of the Waterfall SDLC with the SUS constitutes an applicable development framework for health information systems in local government.
Sistem Pengajuan Izin Kerja Karyawan Berbasis Web dengan Prediksi Pola Perizinan Menggunakan Algoritma Support Vector Machine (SVM) Ningsihati Halawa; Yoannes Romando Sipayung
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.23-1.3241

Abstract

The implementation of conventional work permit management systems often causes problems, such as delays in approval, the risk of document loss, and limitations in real-time monitoring of permit status. This study developed a web-based work permit management system that automates administrative processes and supports managerial decision-making through historical data analysis. The system was developed using the Waterfall method and implemented with a client–server architecture, while the Support Vector Machine (SVM) algorithm with Radial Basis Function (RBF) kernel was applied to classify permit patterns. Test results show that the SVM model is capable of achieving 99.33% accuracy in testing data classification. The findings of this study indicate that the integration of web-based systems with predictive algorithms can improve the efficiency of the licensing process while providing a scientific framework for analyzing labor licensing patterns, thereby contributing to the development of data-based human resource management methods.
Analisis dan Implementasi Sistem Pencatatan Produksi Berbasis Web dengan Modul Prediksi Menggunakan Metode ARIMA Dhamar Dhuha; Agung Wibowo
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.23-1.3242

Abstract

Small and Medium Enterprises (SMEs) still widely use manual production recording, which is prone to errors and does not support rapid performance analysis. This study developed a digital production recording system integrated with forecasting features to support data-driven decision making. The system was built using the Laravel framework and applied the Autoregressive Integrated Moving Average (ARIMA) algorithm to predict production volumes based on historical data from September 2024 to September 2025, with a one-month forecasting horizon. The evaluation was conducted by comparing the prediction results with actual production data. The test results showed that the ARIMA model performed well, with a Mean Absolute Error (MAE) of 21.6 and a Mean Absolute Percentage Error (MAPE) of 1.99%, indicating a low level of prediction error. The integration between the digital recording system and the forecasting model allows production managers to monitor production history in a structured manner, obtain estimates of production for the next period, and accelerate the preparation of operational reports. The scientific contribution of this research lies in the development of a system framework that combines automated recording and predictive analytics in an integrated manner, thus offering a new approach to improving operational efficiency and data-driven decision making in the SME sector.
Implementasi SMOTE pada Klasifikasi Email Spam Menggunakan Algoritma Machine Learning Berbasis TF-IDF Moh Ali Aljauhari; Fatchul Arifin
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.23-1.3246

Abstract

Class imbalance is a major challenge in email spam detection, causing classification models to be biased toward the majority class. This study examines the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) on six machine learning algorithms—Naive Bayes, SVM, KNN, Logistic Regression, Random Forest, and XGBoost—using TF-IDF feature representation. Test results show that synthetic data balancing successfully improved the sensitivity of linear models (SVM and Logistic Regression) with a recall value exceeding 49.1%, overcoming the failure to predict the minority class in the original dataset. KNN recorded the highest F1-score (0.436), while Random Forest provided the best class separation stability with an AUC-ROC of 0.601. The main contribution of this study is to demonstrate that although SMOTE improves minority class detection capabilities, its effectiveness on high-dimensional text data remains limited by feature sparsity constraints that trigger class overlap.
Analisis dan Evaluasi Search Engine yang Aman untuk Penggunaan Anak Usia Dini di Lingkungan Sekolah Dasar: Perspektif Rekayasa Komputasi Terapan dalam Meningkatkan Keamanan Online Rosi Ulibasa; Citra Fertia Anggraini; Mardi Hardjianto
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.23-1.3250

Abstract

The use of search engines as a learning resource in elementary schools increases the risk of exposure to inappropriate content, necessitating a fast and accurate filtering mechanism from the very beginning of the search. This study aims to design and evaluate a Bloom filter-based pre-query filtering system to detect risky keywords as early as the first 1–2 characters of the input. The evaluation was conducted using a query dataset representing the search patterns of elementary school students in grades 1–5 with variations in Bloom filter size (1024, 2048, 4096 bits) and the number of hash functions (7, 10, 15). Experimental results show that the 1024-bit configuration with 7 hash functions yields an average filtering time of 15 ms, a false positive rate (FPR) of 1%, and a false negative rate (FNR) of 0%, thereby meeting real-time response requirements. The 2048–4096-bit configurations reduce the FPR to 0% but increase latency to 18–25 ms. These findings demonstrate a measurable trade-off between latency and filtering accuracy. This study empirically contributes to showing that Bloom filters are effective as a low-latency initial filtering mechanism. The proposed system has the potential to serve as the foundation for developing safer and more responsive educational search engines for elementary school students
Pendekatan Deep Learning untuk Deteksi Kanker Payudara Menggunakan Concatenated ResNet50 dan ResNet152 Nur Nafiiyah; M. Lazuardi Elsony; Agus Harjoko; Achmad Nizar Hidayanto
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.23-1.3259

Abstract

Breast cancer is one of the leading causes of death in women, so accurate early detection is key to improving patient survival rates. Although mammography is the standard method for breast cancer screening, manual interpretation of mammogram images still depends on the expertise of radiologists and has the potential to lead to misdiagnosis. This study proposes a concatenate transfer learning-based deep learning approach by combining two Residual Network architectures, namely ResNet50 and ResNet152, to improve feature representation capabilities in mammogram image classification. The dataset used is a combination of MIAS and CBIS-DDSM with two classes, namely benign and malignant. The evaluation was carried out using original test data without augmentation to ensure the objectivity of the results. The experimental results show that the proposed model achieves an average accuracy of 97.09% and outperforms several individual transfer learning models. The main contribution of this study lies in demonstrating that combining deep features from similar but different depth CNN architectures can improve classification stability and accuracy. These findings provide a conceptual basis for the development of more reliable deep learning-based medical decision support systems for early breast cancer detection.
Rancang Bangun Kerangka Kerja X-UEBA dengan Fusi Skor Risiko Dinamis untuk Deteksi Ancaman Insider Fatih Ahmad Zakaria; Erik IH Ujianto; Rianto
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.23-1.3268

Abstract

Insider threat detection faces major challenges in the form of high false positive rates and limited interpretability in single machine learning models. This study proposes the Explainable User and Entity Behavior Analytics (X-UEBA) framework, which integrates Isolation Forest for static anomaly detection and Stacked BiLSTM for temporal patterns, enhanced by a domain-knowledge-based Logic Injection mechanism. Unlike conventional hybrid approaches, this system employs dynamic risk score fusion with threshold optimization (F1-Score Optimized Thresholding) to address extreme class imbalance. Experimental results on the CERT r4.2 dataset show that the model achieves an AUC of 0.68 with a sensitivity (Recall) of 43% against valid attacks. The system proved effective in reducing operational overhead by filtering out 261,967 normal activities (significantly reduced search space), while SHAP integration provides transparency into detection decisions. This research contributes to delivering a security solution that balances adaptive detection coverage with operational validity that analysts can trust.
Perancangan Video Animasi 3D Menggunakan Metode MDLC untuk Meningkatkan Pemahaman Materi IPAS Wasihatun Hasanah; Rujianto Eko Saputro; Dinar Mustofa
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.23-1.3277

Abstract

Pembelajaran Ilmu Pengetahuan Alam dan Sosial (IPAS) pada materi Tata Surya memiliki karakteristik abstrak sehingga sering menimbulkan kesulitan pemahaman bagi siswa tingkat sekolah dasar apabila disampaikan melalui media konvensional seperti buku teks dan Lembar Kerja Siswa (LKS). Penelitian ini bertujuan untuk merancang media pembelajaran berupa video animasi tiga dimensi (3D) menggunakan metode Multimedia Development Life Cycle (MDLC) sebagai upaya meningkatkan pemahaman siswa terhadap bentuk dan susunan Tata Surya. Pengembangan media dilakukan melalui tahap MDLC yang menghasilkan animasi 3D yang menggambarkan Matahari serta delapan planet beserta ciri-ciri masing-masing. Efektivitas media dievaluasi dengan menggunakan desain pretest–posttest pada siswa kelas VI di MI Nurul Iman Glempang. Hasil pengujian menunjukkan peningkatan nilai rata-rata kelas dari 52,7 pada pretest menjadi 80,9 pada posttest, dengan kenaikan sekitar 28 poin. Temuan ini mengindikasikan bahwa penggunaan video animasi 3D yang didasarkan pada MDLC dapat secara efektif meningkatkan pemahaman konseptual siswa pada materi IPAS yang memiliki karakteristik abstrak. Penelitian ini memberikan kontribusi sebagai acuan untuk pengembangan media pembelajaran digital yang berbasis visualisasi ruang dan dapat dijadikan pilihan alternatif untuk bahan ajar yang inovatif dalam pembelajaran IPAS di jenjang Madrasah Ibtidaiyah.
Kombinasi Metode Design Thinking Dan User-Centered Design Pada Perancangan UI/UX Aplikasi Bimbingan Konseling Dina Juliarti; Angga Bayu Santoso
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.23-1.3293

Abstract

Counseling Guidance (BK) services at SMA Global Madani have been constrained by an inefficient manual system for teachers and psychological barriers experienced by students. This study designs a mobile-based user interface and user experience prototype to optimize counseling guidance services. The methods used combine Design Thinking during the discovery stage and User-Centered Design (UCD) during the technical validation stage. Data were collected from 13 participants through interviews and questionnaires, which were then analyzed using Empathy Maps and User Personas. The testing results using the System Usability Scale (SUS) showed an average score of 84.80 (Grade B/Excellent), outperforming previous studies that used a single-method approach, which achieved a score of 74.37. Conceptually, this integration demonstrates that users’ emotional comfort is a crucial parameter in the effectiveness of sensitive digital services. This study contributes to the development model of educational information systems that position students as the primary subjects within the digital ecosystem.

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