Publication Search

This study aims to examine the role of Computer-Assisted Audit Techniques (CAATs) in evaluating internal control within accounting information systems (AIS) to detect fraud in the expenditure cycle. The research employs a literature review method by analyzing five relevant studies selected based on publication criteria within the last ten years and a focus on technology-based auditing, internal control, and fraud. The findings indicate that CAATs, through features such as test data and parallel simulation, are effective in identifying system weaknesses, detecting transaction anomalies, and strengthening controls in the expenditure cycle. Fraud in this cycle is commonly caused by weak authorization, incomplete documentation, and expenditures conducted without proper procedures. CAATs address these challenges through data-driven and automated audit approaches. In conclusion, CAATs represent a strategic solution for enhancing monitoring accuracy, preventing fraud, and supporting organizational transparency and accountability in the digital era.

The rapid development of Artificial Intelligence (AI) technology has opened significant opportunities to support maritime monitoring systems, particularly in detecting anomalies in ship movements that may indicate illegal or abnormal activities. However, the understanding and utilization of this technology among the general public and maritime stakeholders remain limited. This Community Service Program aims to conduct socialization and dissemination of knowledge on AI-based ship anomaly detection through the development and utilization of an interactive and informative web-based socialization platform. This activity is the result of collaboration between a team of lecturers from the Faculty of Communication and Informatics Technology (FTKI) and the National Research and Innovation Agency (BRIN). The implementation methods include the design of web-based educational content, presentation of fundamental AI concepts and ship anomaly detection, as well as visual simulations of ship movement data analysis results. The web-based socialization platform serves as an educational medium to enhance users’ understanding of the benefits, working mechanisms, and potential applications of AI technology in maritime surveillance. The results indicate an improvement in participants’ understanding of ship anomaly detection concepts and the role of artificial intelligence in supporting maritime security and safety. This PKM activity is expected to promote technological literacy, strengthen synergy between academia and research institutions, and serve as a model for practical and sustainable web-based technology socialization

Penelitian ini mengembangkan sistem pemantauan berbasis Internet of Things (IoT) untuk mengoptimalkan kinerja Mini PC dan pemeliharaan real-time di CV Permata Gemilang Jaya. Metodologi waterfall diterapkan menggunakanNodeMCU sebagai mikrokontroler utama, dilengkapi dengan sensor DHT22, DS18B20, dan INA219 untuk memantau parameter suhu, CPU, dan memori. Arsitektur sistem mengintegrasikan kerangka kerja Laravel dengan database MySQL, menghasilkan aplikasi web responsif dengan kontrol akses berbasisperan untuk Admin Pusat, Admin Regional, dan Teknisi Cabang. Infrastrukturserver cloud dengan konektivitas GSM cadangan memfasilitasi pemantauanterpusat di wilayah Ciayumajakuning. Desain sistem menggunakan Unified Modeling Language (UML) dengan diagram kasus penggunaan dan diagram aktivitas yang komprehensif. Penerapan sistem pemberitahuan otomatisdengan mekanisme peringatan berbasis ambang batas memungkinkan deteksidini anomali perangkat. Antarmuka yang dioptimalkan untuk selulermeningkatkan aksesibilitas teknisi untuk operasi lapangan. Validasi sistemmenunjukkan strategi pemeliharaan preventif yang sukses dalam mengurangiwaktu henti perangkat dan mengoptimalkan efisiensi operasional infrastrukturteknologi informasi.

Bridge infrastructure is a vital component of transportation systems that is vulnerable to structural damage caused by dynamic loads, environmental factors, and aging. Early crack detection is crucial to prevent structural failures that may lead to catastrophic consequences. This study aims to develop a non-destructive detection method based on acoustic sensors to identify cracks in bridge structures with higher sensitivity and accuracy compared to conventional visual inspections. The research was conducted through laboratory experiments and field tests using acoustic sensors, data acquisition devices, and signal analysis software. The procedure included sensor installation on a bridge model, simulation of artificial cracks with varying sizes and positions, recording of acoustic wave signals, and data analysis using frequency spectrum, amplitude, and waveform pattern approaches. The results show significant differences between normal and cracked conditions in the frequency spectrum, where cracks produced amplitude anomalies at specific frequencies. Amplitude analysis revealed a positive correlation between crack size and acoustic signal intensity, while waveform pattern analysis demonstrated the influence of crack position on distortion levels. Cracks located at the center generated the highest distortion, followed by joints and edges. These findings confirm that acoustic sensors, particularly fiber-optic-based ones, offer advantages such as high sensitivity, reliability under complex environmental conditions, and the ability to detect subsurface cracks. The implications of this research highlight the potential development of an acoustic sensor-based structural health monitoring system integrated with real-time analysis software, thereby supporting preventive maintenance, extending infrastructure lifespan, and enhancing transportation safety.

Fraud in digital transactions has become a serious issue threatening the security and integrity of the fintech and e-commerce sectors. To address this problem, machine learning technology has emerged as an effective solution for automatically detecting anomalies and fraudulent transactions. This study aims to analyze the application of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Ensemble Learning, in detecting fraud in digital transactions. The research adopts a quantitative approach with experimentation, testing the effectiveness of the three algorithms using a digital transaction dataset consisting of both fraudulent and non-fraudulent transactions. The results show that the Random Forest algorithm performs the best in terms of accuracy and recall, followed by Ensemble Learning, which enhances fraud detection performance by combining multiple prediction models. Meanwhile, SVM demonstrates satisfactory performance but is prone to overfitting issues when handling large and complex datasets. The study also finds that the problem of imbalanced data can affect model accuracy, and data balancing techniques such as oversampling are required to improve fraud detection performance. Overall, the findings suggest that machine learning, particularly Random Forest and Ensemble Learning algorithms, can be relied upon to improve fraud detection in digital transactions. However, challenges such as model interpretability and the need for periodic algorithm updates still need to be addressed to enhance the effectiveness of fraud prevention systems in countering the ever-evolving nature of fraud.

Adequate provision of drinking water in quantity, quality, and continuity is needed to realize a healthy and productive society. A well-managed Drinking Water Supply System (SPAM) is essential to meet this need. Based on Government Regulation Number 122 of 2015, the implementation of SPAM involves the development and management of drinking water which is the responsibility of the local government and PUDAM as the implementer. The main challenges faced by PUDAM include the high level of water loss or Non-Revenue Water (NRW), which reaches 40% in Indonesia. One of the efforts to reduce the NRW level at PUDAM Banyuwangi Regency in the Kalipuro District area is to detect abnormal consumption in customer drinking water consumption. This study uses the Deep Q Network and Local Outlier Factor algorithms to detect anomalies in drinking water consumption, with the aim of comparing the performance of the two algorithms in identifying abnormal consumption patterns at PUDAM Banyuwangi Regency. The results of the study indicate that the Local Outlier Factor algorithm is more suitable for anomaly detection as evidenced by the absence of detection errors and an F1-Score value of 36%.

Abstract.Network anomaly detection is a situation that occurs in network traffic that causes conditions to become abnormal. This research aims to analyze the performance of various machine learning algorithms in network anomaly detection and compare the performance of single classifier algorithms with ensemble learning. This ensemble learning technique has advantages such as increased accuracy and performance, can reduce the risk of overfitting and underfitting by using different subsets and features of data, and can turn weak learning into strong learning. However, on the other hand, this ensemble learning technique also has disadvantages in its use, namely that this ensemble method may not work well with high variance models, as the ensemble method may not be optimized for anomaly detection and that this method can be computationally expensive and time consuming due to the need to train and store multiple models. Some of the techniques used are deep learning, eager learning, lazy learning, bagging, feature selection, boosting, and stacking. In addition to this, this machine learning algorithm has weaknesses, including if any of the data used is incomplete, it will result in inaccurate completion data, making the programming process quite time-consuming. This research can help develop a more effective and efficient network anomaly detection system. The results of this research show that using ensemble learning and feature selection techniques can improve anomaly detection performance by reducing the processing time of redundant data and classification, as well as increasing precision values.    

Tujuan Utama: Tujuan dari penelitian ini adalah untuk mengembangkan algoritma deteksi anomali yang lebih efektif dan akurat menggunakan Extended Isolation Forest (EIF) dan mengimplementasikannya ke dalam platform sumber terbuka Machine Learning (ML) H2O-3. Background problem: Algoritma Isolation Forest (IF) asli menghadirkan bentuk deteksi baru, meskipun algoritme mengalami bias yang berasal dari percabangan pohon. Perpanjangan algoritme menghilangkan bias dengan menyesuaikan percabangan, dan algoritme asli hanya menjadi kasus khusus. EIF diimplementasikan ke dalam platform sumber terbuka ML H2O-3. kebaharuan: Kebaruan dari penelitian ini adalah penggunaan algoritma EIF dalam deteksi anomali. Selain itu, penelitian ini juga mengimplementasikan EIF ke dalam platform sumber terbuka ML H2O-3 untuk dijalankan pada sistem komputasi terdistribusi dengan pustaka Map/Reduce. Research Method: Penelitian ini menggunakan metode deteksi anomali dengan fokus pada algoritma EIF.  temuan: Hasil pengujian menunjukkan bahwa Extended Isolation Model perlu disesuaikan. Tes kinerja deteksi anomali mengungkapkan sedikit ketidaksempurnaan dalam deteksi struktur data jika dibandingkan dengan satu-satunya implementasi algoritma Python yang tersedia. Hasil ujian untuk tahap evaluasi dinyatakan lulus dan waktu komputasi secara logaritmik lebih kecil dengan jumlah utas.  Kesimpulan: pada penelitian selanjutnya, algoritma dapat ditingkatkan lebih lanjut dengan menskalakan anomali deteksi untuk data dimensi tinggi. Ini dapat diimplementasikan dengan menambahkan parameter lain yang memungkinkan metode pemilihan fitur dalam perhitungan..