The Utilization of Ethanol as Antisolvent to Enhance the Glucomannan Content of Tuber of Amorphophallus muelleri Blume
Orchidea Rachmaniah, Hazira Larasati Novida Putri, Cindy Shofya Maharany, Nuniek Hendrianie, Sri Rachmania Juliastuti, Raden Darmawan, Fahmi Fahmi, Wahyu Meka Abstract Porang (Amorphophallus muelleri Blume) is widely found in Indonesia. The high Glucomannan (GM) content in Porang (Amorphophallus muelleri Blume) compared to other tubers becomes the main advantage of Porang. Glucomannan is a low-calorie […]
Semantic Editing of Traffic Near-Miss and Accident Dataset Using Tune-A-Video
Eka Alifia Kusnanti, Chastine Fatichah, Muhamad Hilmil Pradana Abstract Developing effective traffic monitoring systems for accident detection relies heavily on high-quality, diverse datasets. Many existing approaches focus on detecting anomalies in traffic videos. Still, they often fail to account for how varying environmental conditions, such as time of day, weather, or lighting, might influence […]
Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification
Tri Yulianto Nugroho, Umi Laili Yuhana, Daniel Siahaan Abstract Classifying agility levels presents challenges due to variations in team members’ personalities, roles, and undesirable behaviors. This study aims to enhance classification accuracy by comparing the performance of three algorithms: K-Nearest Neighbors (KNN), Random Forest, and Fuzzy-Particle Swarm Optimization (F-PSO) in classifying agility levels using […]
Predicting Failure using Machine Learning and Statistical Based Method: a Production Machine Case Study
Effi Latiffianti, Stefanus Eko Wiratno, Samuel Aditya Christianta Abstract This research investigates the applicability of failure detection models based on machine learning and statistical approaches to reduce unplanned downtime in a food production company. Sensor data is utilized to for identifying early failure symptoms. To capture temporal and sequential dependencies in time-series data, we […]