Jazi Eko Istiyanto

Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky et al.

Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings. To address this critical safety vulnerability, we propose DeepIPCv3, a novel multi-modal autonomous navigation framework that synergizes the dense 3D spatial geometry of LiDAR point clouds with the microsecond-level asynchronous event streams of a Dynamic Vision Sensor (DVS). We introduce a Transformer-inspired cross-modal attention mechanism to dynamically correlate these distinct modalities, allowing the network to instantaneously prioritize high-speed dynamic updates without sacrificing structural scene awareness. The fused latent representations are then mapped to safe local waypoints and executable control commands via a hybrid policy network that blends heuristic trajectory tracking with direct neural predictions. Due to the severe physical risks associated with live testing of these sudden crossing scenarios, the framework is rigorously evaluated offline using a custom multi-modal dataset collected across both well-illuminated noon and challenging evening conditions. Extensive comparative and ablation studies demonstrate that DeepIPCv3 achieves state-of-the-art predictive performance. By effectively eliminating exposure failures and motion blur, the proposed LiDAR and DVS fusion yields the lowest trajectory and control command errors, enabling highly reactive, mathematically bounded evasive maneuvers regardless of ambient illumination. To support future research, we will release the codes to our GitHub repo at https://github.com/oskarnatan/DeepIPCv3.

Andi Wahju Rahardjo Emanuel, Retantyo Wardoyo, Jazi Eko Istiyanto et al.

Open Source Software (OSS) Projects are gaining popularity these days, and they become alternatives in building software system. Despite many failures in these projects, there are some success stories with one of the identified success factors is modularity. This paper presents the first quantitative software metrics to measure modularity level of Java-based OSS Projects called Modularity Index. This software metrics is formulated by analyzing modularity traits such as size, complexity, cohesion, and coupling of 59 Java-based OSS Projects from sourceforge.net using SONAR tool. These OSS Projects are selected since they have been downloaded more than 100K times and believed to have the required modularity trait to be successful. The software metrics related to modularity in class, package and system level of these projects are extracted and analyzed. The similarities found are then analyzed to determine the class quality, package quality, and then combined with system architecture measure to formulate the Modularity Index. The case study of measuring Modularity Index during the evolution of JFreeChart project has shown that this software metrics is able to identify strengths and potential problems of the project.

Roy Rudolf Huizen, Jazi Eko Istiyanto, Agfianto Eko Putra

This research was conducted to develop a method to identify voice utterance. For voice utterance that encounters change caused by aging factor, with the interval of 10 to 25 years. The change of voice utterance influenced by aging factor might be extracted by MFCC (Mel Frequency Cepstrum Coefficient). However, the level of the compatibility of the feature may be dropped down to 55%. While the ones which do not encounter it may reach 95%. To improve the compatibility of the changing voice feature influenced by aging factor, then the method of the more specific feature extraction is developed: which is by separating the voice into several channels, suggested as MFCC multichannel, consisting of multichannel 5 filterbank (M5FB), multichannel 2 filterbank (M2FB) and multichannel 1 filterbank (M1FB). The result of the test shows that for model M5FB and M2FB have the highest score in the level of compatibility with 85% and 82% with 25 years interval. While model M5FB gets the highest score of 86% for 10 years time interval.

Imam Riadi, Jazi Eko Istiyanto, Ahmad Ashari et al.

Internet crimes are now increasing. In a row with many crimes using information technology, in particular those using Internet, some crimes are often carried out in the form of attacks that occur within a particular agency or institution. To be able to find and identify the types of attacks, requires a long process that requires time, human resources and utilization of information technology to solve these problems. The process of identifying attacks that happened also needs the support of both hardware and software as well. The attack happened in the Internet network can generally be stored in a log file that has a specific data format. Clustering technique is one of methods that can be used to facilitate the identification process. Having grouped the data log file using K-means clustering technique, then the data is grouped into three categories of attack, and will be continued with the forensic process that can later be known to the source and target of attacks that exist in the network. It is concluded that the framework proposed can help the investigator in the trial process.