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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Informasi dan Multimedia Jurnal Teknologi Informatika dan Komputer BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer International Journal Software Engineering and Computer Science (IJSECS) Abdi Jurnal Publikasi Jurnal PERKUSI: Pemasaran, Keuangan dan Sumber Daya Manusia JPNM : Jurnal Pustaka Nusantara Multidisiplin OKTAL : Jurnal Ilmu Komputer dan Sains Karimah Tauhid JUTIKA
Taswanda Taryo
Universitas Pamulang
Religion Humanities Astronomy Chemistry Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Other

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Comparison of Faster R-CNN and YOLO v12 on Passport Text Extraction Based on Optical Character Recognition Masniari Samosir; Sajarwo Anggai; Taswanda Taryo
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3307

Abstract

Current developments in information technology are driving the need for digitalization of official identity documents, including passports, to improve service efficiency and reduce reliance on manual processes. The digitalization of official identity documents such as passports still faces efficiency and accuracy challenges due to manual data entry processes. This study aims to compare the performance of Faster R-CNN and YOLO v12 in an automatic text extraction system based on Optical Character Recognition (OCR). The research employed an experimental method with a comparative approach using 31 preprocessed passport images. YOLO v12 was integrated with EasyOCR, while Faster R-CNN was combined with a PyTorch-based OCR module. The evaluation metrics included mAP, Character Accuracy Rate (CAR), Word Error Rate (WER), F1-score, and inference time. The results indicate that YOLO v12 outperforms Faster R-CNN in object detection, achieving an mAP@50 of 95.0% and mAP@50–95 of 90.0%, compared to 93.0% and 89.0%, respectively. In terms of text extraction accuracy, Faster R-CNN achieved a CAR of 50.01% and an F1-score of 55.75%, slightly higher than YOLO v12 with a CAR of 47.72% and an F1-score of 53.84%. However, YOLO v12 produced a lower WER and faster inference time of 2.4202 seconds (0.45 FPS). The findings suggest that YOLO v12 excels in efficiency and detection performance, while Faster R-CNN performs better in specific text extraction accuracy.
Sentiment Analysis of the Indonesian Megathrust Earthquake and Tsunami Issue Using BERT and Roberta Methods Hendra Rahman; Taswanda Taryo; Sudarno Wiharjo
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3563

Abstract

The megathrust earthquake in Indonesia is a major potential natural disaster capable of triggering high-magnitude earthquakes and tsunamis, thereby influencing public perception. This study aims to analyze public opinion and identify the main topics related to the megathrust earthquake issue using Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) models. The dataset consists of 16,592 comments collected from the X social media platform during the period 2012–2025, which were classified into three sentiment categories, positive, negative, and neutral. The research methodology included exploratory data analysis, text preprocessing, model training, and evaluation using four experimental scenarios. The results indicate that the best performance was achieved using an 80:10:10 train–validation test split with ten training epochs. The BERT model outperformed RoBERTa, achieving an accuracy of 92,4350%, precision of 92,4291%, recall of 92,4350%, and F1-score of 92,4292%. These findings demonstrate that BERT is more effective in capturing the linguistic context of the Indonesian language. Furthermore, this study contributes to the advancement of artificial intelligence-based sentiment analysis for monitoring public opinion on disaster-related issues and provides a valuable foundation for developing more effective risk communication strategies, disaster mitigation education, and evidence-based policymaking that is more responsive to public perception.
Advanced Persistent Threats Analysis and Intrusion Detection Systems Evaluation Dedy Wibowo; Taswanda Taryo; Ferhat Aziz
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5770

Abstract

- Advanced Persistent Threats are significant cybersecurity threats that employ covert and strategically planned operations to achieve long-term unauthorized access and data exfiltration. PT XYZ, a logistics company with considerable operational and customer data, is more susceptible to APTs, which is why the company decided to implement Wazuh as an open-source SIEM platform for improved intrusion detection capabilities. We assessed how effectively this IDS-SIEM implementation could detect and respond to APT scenarios by analyzing multi-source logs from Wazuh, Sysmon, and endpoint telemetry across PT XYZ’s PC infrastructure between June 3-30, 2025—capturing 35,333 records in total. Simulated APT attacks were carried out using Atomic Red Team with detection mapping based on MITRE ATT&CK tactics. Most of the early stages of attack phases were identified by Wazuh particularly Initial Access and Execution phases where the system logged 1,060 true positives; 8,537 true negatives; 563 false positives; and 440 false negatives at an accuracy rate of 91%. Normal traffic detection results were good with a precision of 0.95, recall of 0.94 F1-score at the same value whereas attack detection had a precision value of 0.65 with a recall of 0.71 giving it an F1 score of 0.68 making macro-averaged metrics fall at values such as 0.80 for precision and 0.82 for recall which further brought the F1 score up to 0.81 while weighted averages peaked at 0.91.Our results indicate that an open-source SIEM like Wazuh can be used effectively for the detection of APTs in logistics operations when configured appropriately using MITRE ATT&CK-based threat simulations – hence having real-world applicability towards improving cybersecurity defenses within this sector.
Pendampingan Pengembangan Kualitas Sumber Daya Manusia Bagi Pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) Ali Zaenal Abidin; Kamsidik; Taswanda Taryo
Karimah Tauhid Vol. 5 No. 7 (2026): Karimah Tauhid
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/karimahtauhid.v5i7.26199

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) merupakan salah satu pilar utama perekonomian nasional yang memiliki kontribusi signifikan terhadap pertumbuhan ekonomi, penyerapan tenaga kerja, dan peningkatan kesejahteraan masyarakat. Namun, masih banyak pelaku UMKM yang menghadapi berbagai tantangan, terutama dalam aspek kualitas sumber daya manusia (SDM), seperti rendahnya kompetensi manajerial, keterampilan digital, literasi keuangan, serta kemampuan dalam menghadapi perubahan teknologi dan dinamika pasar. Kondisi tersebut mengakibatkan daya saing UMKM belum optimal sehingga diperlukan upaya pengembangan kapasitas SDM secara berkelanjutan. Kegiatan pengembangan kualitas SDM UMKM bertujuan untuk meningkatkan pengetahuan, keterampilan, dan kompetensi pelaku usaha melalui pelatihan, pendampingan, serta penguatan kapasitas dalam bidang kewirausahaan, manajemen usaha, pemasaran digital, pengelolaan keuangan, dan pemanfaatan teknologi informasi. Metode yang digunakan meliputi penyuluhan, pelatihan interaktif, diskusi kelompok, praktik langsung, serta evaluasi terhadap tingkat pemahaman peserta sebelum dan sesudah kegiatan. Hasil pelaksanaan menunjukkan adanya peningkatan pemahaman pelaku UMKM mengenai pentingnya pengelolaan usaha yang profesional, penerapan strategi pemasaran berbasis digital, penyusunan laporan keuangan sederhana, serta pemanfaatan teknologi digital untuk mendukung produktivitas dan daya saing usaha. Selain itu, kegiatan ini juga meningkatkan motivasi peserta dalam mengembangkan inovasi produk, memperluas jaringan pemasaran, serta membangun usaha yang adaptif terhadap perubahan lingkungan bisnis. Dengan demikian, pengembangan kualitas sumber daya manusia merupakan faktor strategis dalam meningkatkan daya saing dan keberlanjutan UMKM. Program peningkatan kapasitas SDM yang dilaksanakan secara berkesinambungan diharapkan mampu mendorong terciptanya UMKM yang lebih profesional, inovatif, mandiri, dan berdaya saing tinggi sehingga dapat memberikan kontribusi yang lebih besar terhadap pembangunan ekonomi daerah maupun nasional.
Weighted Ensemble of GRU, LSTM and XGBoost for Multi-Horizon Temperature Forecasting at a Tropical Highland Station Kalimi Kalimi; Ahmad Musyafa; Taswanda Taryo; Marzuki Sinambela; Tonny Wahyu Aji
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13593

Abstract

This research evaluates multi-horizon forecasting of daily mean temperature (TAVG) and maximum temperature (TMAX) at the Malang/Karangploso Climatology Station WMO 96943 using daily meteorological observations from 2014–2023. The predictors include humidity, rainfall, atmospheric pressure, wind variables, weather conditions, and sunshine duration. Forecasts are generated for H+1, H+3, H+7, and H+14 using a chronological train, validation, and test split. The study compares GRU, LSTM, Hybrid Gated LSTM-GRU, XGBoost, and Weighted Ensemble models against Persistence and Climatology baselines. To prevent data leakage, preprocessing includes missing-date handling, placeholder correction, training-set-based imputation, lag and rolling feature construction, and input normalization. Optuna is used to tune recurrent models, while ensemble weights are optimized on the validation set. Model performance is assessed using MAE, RMSE, and R², with the lowest test RMSE as the main selection criterion. Results show that the Weighted Ensemble achieves the best aggregate performance, with mean MAE of 0.796, mean RMSE of 1.007, and mean R² of 0.472. GRU is the strongest individual model, with mean RMSE of 1.022. However, the best model varies by target and horizon. Weighted Ensemble leads in five of eight scenarios for TAVG H+1, TAVG H+3, TAVG H+14, TMAX H+1 and TMAX H+14, Hybrid Gated performs best for TAVG H+7, and GRU is superior for TMAX H+3 and H+7.  
Pengembangan Model Hibrida CNN-Transformer untuk Meningkatkan Akurasi Prediksi Curah Hujan Jangka Pendek di Kota Bogor sebagai Peringatan Dini Bencana Banjir Ary Setyoko; Agung Budi Santoso; Taswanda Taryo
JPNM Jurnal Pustaka Nusantara Multidisiplin Vol. 4 No. 4 (2026): October : Jurnal Pustaka Nusantara Multidisiplin (ACCEPTED)
Publisher : SM Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59945/jpnm.v4i4.1598

Abstract

Prediksi curah hujan jangka pendek yang presisi diperlukan untuk memperkuat mitigasi banjir di Kota Bogor sebagai wilayah hulu Daerah Aliran Sungai Ciliwung. Penelitian ini mengembangkan model hybrid Convolutional Neural Network (CNN)-Transformer untuk memprediksi curah hujan harian pada horizon H+1 hingga H+3 menggunakan deret waktu meteorologi multivariat periode 2018-2023. Variabel masukan mencakup curah hujan, suhu, kelembaban, tekanan udara, serta arah dan kecepatan angin. Pra-pemrosesan meliputi penanganan nilai hilang, penyaringan pencilan tidak valid, normalisasi, pembentukan sliding window tujuh hari, dan pembagian data kronologis 80:10:10. CNN digunakan untuk mengekstraksi pola lokal, sedangkan Transformer memodelkan dependensi temporal melalui self-attention. Hasil pengujian menunjukkan koefisien determinasi model hybrid sebesar 0,93-0,97, lebih tinggi daripada CNN tunggal sebesar 0,62-0,69 dan Transformer tunggal sebesar 0,67-0,70. Meskipun demikian, diagnostik memperlihatkan underestimation pada hujan sangat lebat karena kejadian ekstrem jarang terwakili dalam data historis. Model ini berpotensi memperkuat prediksi kuantitatif curah hujan sebagai masukan sistem peringatan dini banjir, tetapi kalibrasi khusus untuk kejadian ekstrem tetap diperlukan sebelum penerapan operasional. Analisis feature importance juga menempatkan kelembaban sebagai prediktor dominan, sehingga temuan model tetap selaras dengan proses fisik pembentukan presipitasi tropis lokal Bogor.
Digital Forensic Analysis of Signature Images Using Error Level Analysis, Image Hashing, and Support Vector Machine Within the DFRWS Framework Amelia Yahya; Taswanda Taryo; Kahfi Heryandi Suradiradja
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7224

Abstract

The increasing use of digital documents in administrative and legal activities has expanded the use of image-based signatures for authentication and verification. However, signature images are vulnerable to manipulation using image-editing software, potentially resulting in document forgery and disputes over authenticity. This study examined the use of Error Level Analysis (ELA), perceptual hashing (pHash), and the Gray Level Co-occurrence Matrix (GLCM) to detect manipulation in signature images. It also evaluated the performance of a Support Vector Machine (SVM) in classifying genuine and forged signatures within the Digital Forensic Research Workshop (DFRWS) framework. The dataset comprised 720 signature images obtained from the Starter Handwritten Signatures Dataset. The research process involved image preprocessing, feature extraction, model training, and performance evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The model achieved an accuracy of 80.56% on previously unseen test data. The developed system also produced visual analysis outputs and generated digital investigation reports based on the DFRWS framework. These results indicate that the combination of ELA, pHash, GLCM, and SVM can support a structured digital forensic process for distinguishing between genuine and forged signature images.
Predicting Peak Ground Acceleration Using RNN and LSTM Model on MCGuire’s Empirical Calculation Irna Purwanti; Taswanda Taryo; Ferhat Aziz
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7268

Abstract

Peak ground acceleration (PGA) is an important parameter in seismic hazard assessment because it represents the maximum ground acceleration generated by an earthquake and informs structural design and disaster mitigation. Predicting PGA remains challenging because seismic-wave propagation is nonlinear and affected by geological heterogeneity. This study developed, evaluated, and spatially mapped recurrent neural network (RNN) and long short-term memory (LSTM) models for predicting PGA values calculated using the McGuire empirical equation in BMKG Regional II, Indonesia, which extends from South Sumatra to West Java. A historical earthquake catalog covering 1971–2025 was used to generate a spatial dataset comprising 3,727 grid points based on surface-wave magnitude and hypocentral distance. The dataset was divided sequentially into 70% training data and 30% testing data. On the test set, the LSTM produced an RMSE of 55.1559, an MAE of 31.0443, and an R² of 0.7112, whereas the RNN produced an RMSE of 56.7773, an MAE of 36.5842, and an R² of 0.6940. The spatial results also showed that the LSTM reproduced high PGA values more closely than the RNN, which produced smoother estimates in areas with abrupt PGA variations. Within the dataset and model configuration used in this study, the LSTM therefore provided better predictive performance than the RNN for reconstructing the spatial distribution of McGuire-based PGA. The resulting model may support regional seismic hazard assessment, but validation against observed ground-motion records and the inclusion of local site parameters remain necessary before its use in engineering or regulatory applications.
Co-Authors Achmad Hindasyah Agung Budi Santoso Ahmad Musyafa Ali Zaenal Abidin Amelia Yahya Anang Tri Wahyudi Anggai, Sajarwo Ary Setyoko Dedy Wibowo Endang Subarna Erni Nila Sari Fajar Yoga Pradana Ferhat Aziz Hendra Rahman Irna Purwanti Ishlah Ramadhanti Kahfi Heryandi Suradiradja Kalimi Kalimi Kamsidik Makhsun Marzuki Sinambela Masniari Samosir Moh. Triyana Abbas Mohammad Bahtiar Murni Handayani Nilovar Asyiah Novan Nurviyaddin Risma Apriyani Saripudin Saripudin Sindy Saputri Sudarno Wiharjo Sukma Hadi Sulastri Sulastri Tanti Cahya Herdiyani Taryari Taryari Tonny Wahyu Aji Ubaidillah Ubaidillah Yansen Paseru Zafira Salsabilah