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Anggai, Sajarwo
Universitas Pamulang
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Analisis Tren Dan Topik Populer Tugas Akhir Mahasiswa Teknik Informatika Menggunakan Bertopic Dan Lda Studi Kasus: Universitas Mercu Buana Suharyadi; Sajarwo Anggai; Abu Khalid Rivai
Jurnal Informatika dan Komputer Vol 16 No 1 (2026): April
Publisher : Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55794/jikom.v16i1.349

Abstract

In recent years, there has been little systematic mapping of trends and popular topics in student final projects, particularly in the field of Information Technology at Mercu Buana University. This is important because identifying trends can provide insight into the direction of scientific development and student research interests. This study aims to analyze the dominant trends and topics of the past few years in the final projects of Information Technology students at Mercu Buana University. The research object consists of a collection of titles and abstracts of student final projects from 2019 to 2024, totaling 1677 datasets. The data was collected through internal faculty documentation and compiled into a text-based dataset. The analysis process included the stages of preparation, text pre-processing, feature extraction, and the application of two topic modeling methods with BERTopic and LDA. BERTopic uses transformer-based representation and semantic clustering, while LDA utilizes a probabilistic distribution approach. The evaluation was conducted by comparing topic coherence and prediction accuracy to assess the quality and relevance of the results produced by each method. The results showed that there were 15 main topics. Model evaluation was conducted using a coherence score of 0.4 for LDA with an accuracy rate of 100%, while BERTopic had a coherence score of 0.39 with an accuracy rate of 86.01%.
Performance Evaluation of ARIMA, LSTM, and Hybrid ARIMA–LSTM Models for Daily Solar Energy Prediction in Bali Aslimah; Sajarwo Anggai; Tukiyat
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.v12i1.3283

Abstract

Solar energy is one of the most promising renewable energy sources in Indonesia, particularly in Bali, which has relatively high solar irradiance throughout the year. However, daily variability in solar radiation caused by weather conditions and atmospheric factors leads to fluctuations in solar energy production, making accurate forecasting essential for effective energy planning. This study aims to evaluate the performance of the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and hybrid ARIMA–LSTM models in forecasting daily solar energy at the Jembrana Climatological Station, Bali. The dataset consists of 10-minute solar radiation observations obtained from an Automatic Weather Station (AWS) for the period January 2023 to September 2025, which were aggregated into daily solar energy values expressed in kWh/m². Data preprocessing included missing value handling, outlier correction, normalization, and an 80:20 split between training and testing datasets. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–LSTM model achieved the best performance, with an RMSE of 0.960 kWh/m², MAE of 0.771 kWh/m², and MAPE of 22.245%, outperforming both the ARIMA and LSTM models. These findings indicate that the hybrid approach is more effective in capturing both linear and nonlinear characteristics of daily solar energy time series.
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.
Application of Latent Dirichlet Allocation (LDA) and BERTopic Algorithms for Headline and Topic Analysis of Palestine–Israel Conflict News in Indonesian Online Media Raden Gumilar Riyansyah; Sajarwo Anggai; Tukiyat Tukiyat
Jurnal Impresi Indonesia Vol. 5 No. 1 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i1.7395

Abstract

The news coverage of the Palestine–Israel conflict has become one of the most dominant international issues in Indonesian online media, necessitating a systematic analysis to understand the topic structures formed from the intensity and variation of the narratives presented. The main challenges arise from the high volume of text, differences in writing styles across media outlets, and the diversity of terminology, all of which hinder consistent and manual topic identification. To address these challenges, this study proposes a combined and comparative approach using two topic modeling algorithms, LDA and BERTopic, to obtain a more accurate, structured, and interpretable topic mapping. The modeling process begins with data collection through web scraping, followed by a preprocessing stage consisting of cleansing, case folding, tokenization, normalization, filtering, and stemming. The LDA model is developed by determining the optimal number of topics based on Coherence Score and Perplexity, whereas BERTopic leverages transformer-based embeddings, UMAP dimension reduction, and HDBSCAN clustering. Evaluation is conducted using Coherence Score, Perplexity, Silhouette Score, and visualizations such as Intertopic Distance Maps and Word Clouds to assess topic quality. The results show that BERTopic achieves the highest coherence score of 0.99 and lower perplexity, producing semantically cohesive topics. Meanwhile, LDA remains advantageous in providing a stable and measurable probabilistic structure. The combination of both models yields a mapping of five main topics: attacks in Gaza, Indonesian diplomacy, international support, humanitarian issues, and global political dynamics. These findings demonstrate that integrating LDA and BERTopic enhances the quality of topic analysis on complex issues
Co-Authors Abu Khalid Rivai Abu Khalid Rivai Agnia Bilqisti AGUNG BUDI SUSANTO Agung Budi Susanto Ali Imron Arya Adhyaksa Waskita Aslimah Brando, Charlo Chaerul Umam Cut Shifa Khoirunnisa Effendy, Rangga Febrian Hanto Sujatmiko Harasta Rahman Tri Putra Ismatullah Junedi, Beni Mahardika Paramarta Laia Masniari Samosir Maulana, Romdon Mizanul Hakim Muhamad, Faruk Muhammad Haikal Abdussalam Muhammad Najwah Muhammad Nana Muhammad Rafif Misbahuddin Murni Handayani musyafa, ahmad Nugraha Wahyu, Fajar Pahira, Wulan Permana, Surya Raden Gumilar Riyansyah Rafi Mahmud Zain Restu Putra, Catur Sabari Sabari Sabarrudin Slamet Wiyono Sudarno Sugiyo Suharyadi Supriatna, Dahlan Sutrisno, Sutrisno T., Thoyyibah Taswanda Taryo Taswanda Taryo Toyib, Wildan Tukiyat Tukiyat Tukiyat Tukiyat Tukiyat Tukiyat, Tukiyat Waskita, Arya Adhyaksa Wibowo, Rizki Septiyanto Wibowo, Satria Ardi Wily, Wily Arisandi Winarni Yudi Candra Zain, Rafi Mahmud