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IPTEK The Journal for Technology and Science
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 08534098     EISSN : 20882033     DOI : -
Core Subject : Science,
Computer Science & IT
IPTEK The Journal for Technology and Science (eISSN: 2088-2033; Print ISSN:0853-4098), is an academic journal on the issued related to natural science and technology. The journal initially published four issues every year, i.e. February, May, August, and November. From 2014, IPTEK the Journal for Technology and Science publish three times a year, they are in April, August and December in online version.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 35, No 3 (2024)" : 5 Documents clear
Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification Nugroho, Tri Yulianto; Yuhana, Umi Laili; Siahaan, Daniel
IPTEK The Journal for Technology and Science Vol 35, No 3 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i3.21992

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 simple feature scaling as part of the data preprocessing. Simple feature scaling is employed to ensure that all parameters are on the same scale, thereby improving the model’s effectiveness in learning classification patterns. F-PSO was selected for its ability to perform adaptive global search optimization within a fuzzy environment, while KNN and Random Forest serve as benchmarks. The study involved 160 participants from various Scrum teams to evaluate the effectiveness of these algorithms. The parameters considered included team members’ personalities (based on the Keirsey model), roles within the team, and the identification of negative behavior patterns (antipatterns). The results indicated that the F-PSO algorithm significantly outperformed KNN and Random Forest in terms of accuracy, improving from an average accuracy of 25% before optimization to 93.75% after applying F-PSO. This approach enables Scrum teams to identify and address obstacles affecting agility, facilitating earlier problem prediction and resolution, leading to more adaptive and effective teams.
Semantic Editing of Traffic Near-Miss and Accident Dataset Using Tune-A-Video Kusnanti, Eka Alifia; Fatichah, Chastine; Pradana, Muhamad Hilmil
IPTEK The Journal for Technology and Science Vol 35, No 3 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i3.22186

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 the occurrence of near-misses or accidents. In this study, we explore the potential of Tune-A-Video to apply semantic editing techniques to an existing traffic near-miss and accident dataset. By modifying the visual environment, such as changing the time of day, weather, or lighting, we aim to generate realistic footage variations without altering the core events like near-miss incidents or accidents. This method enhances the dataset with more varied and realistic traffic conditions, improving its representativeness of real-world scenarios. The primary objective is not to create a new dataset but to assess the impact of semantic editing on the dataset’s diversity and its effect on model performance. The results show that using Tune-A-Video for semantic editing can enrich the dataset, making it more suitable for training machine learning models. This approach helps improve the accuracy and robustness of computer vision models, particularly for traffic monitoring and accident detection applications, offering a promising tool for traffic safety systems.
The Utilization of Ethanol as Antisolvent to Enhance the Glucomannan Content of Tuber of Amorphophallus muelleri Blume Rachmaniah, Orchidea; Putri, Hazira Larasati Novida; Maharany, Cindy Shofya; Hendrianie, Nuniek; Juliastuti, Sri Rachmania; Darmawan, Raden; Fahmi, Fahmi; Meka, Wahyu
IPTEK The Journal for Technology and Science Vol 35, No 3 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i3.20190

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 fibre polysaccharide with a β-1,4 bond composed of D-glucose and D-mannose. However, its Calcium Oxalate (CaC2O4) content is an issue and should be minimized when consumed. The research focused on increasing the GM content of raw Porang tuber donated from PT ABS., Bogor. Initially, the rawmaterialwas roughly sliced and soaked in NaCl solution (10% or 15 %-w), washed with gently warm water (approx. 40 °C), and air dried to result in Crude Konjac Flour (CKF). Further purification of CKF will produce Konjac Glucomannan (KGM). The KGM has a higher content of glucomannan. Ethanol (EtOH) was used as an antisolvent in the purification step. The final product of KGM was analyzed regarding GM, calcium oxide, moisture, and ash content. A 15%-w of NaCl solution followed by warm water washing could reduce the 59% content of Calcium Oxalate. Meanwhile, further purification using 50% EtOH increased 627.85% GM content from their initial value in Porang tubers. Hence, the treatment combination of NaCl-warm water washing and EtOH purification maximally removes calcium oxide and increases the GM content in the KGM.
Catalytic Cracking of Polypropylene-Low Density Polyethylene Pyrolysis Oil over Hierarchical H-ZSM-5 Catalyst Dwi Cahyani, Silvia; Marlinda, Lenny; Evrianti, Yuli; Rimawan, Bagas; Heriyanti, Heriyanti; Rahmi, Rahmi; Sutrisno, Sutrisno
IPTEK The Journal for Technology and Science Vol 35, No 3 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i3.19188

Abstract

The conversion of plastic waste into liquid fuel has become the center of attention by researchers as a way to overcome environmental problems. According to last research, the polypropylene (PP) and low-density polyethylene (LDPE) pyrolysis oil mixture was classified into hydrocarbon range diesel which have the number of carbon atom from C12-C24. Because that, the effect of LDPE/PP pyrolysis oil over hierarchical H-ZSM-5 catalyst on hydrocarbon composition of liquid fuel oil using the catalytic cracking reactor at 200 oC was investigated. Hierarchical H-ZSM-5 catalyst (hH-ZSM-5) was obtained from H-ZSM-5 modified by desilication method using NaOH. Gas chromatography-mass spectrometry showed that the liquid fuel oil from the catalytic cracking of all variations consisted of aliphatic hydrocarbons with some cycloaliphatic compounds. In ratio of 250 mL/0.5 g (oil/catalyst) aromatic hydrocarbon increased from 1.69 area% to 15.58 area% over H-ZSM5 and hH-ZSM-5 respectively. The higher oil quality with shorter hydrocarbon chains in all variations was obtained over hH-ZSM-5 catalyst, namely in the range of C7-C14 or comparable to hydrocarbon range gasoline and kerosene.
Biocompatibility of Silicone Elastomer Incorporated with Chitosan: Morphology, Mechanical, Biodegradation Assessment and the Potential for Injectable Biomaterials Rasyida, Amaliya; Maharsipu, Yohannes Gabriel Adven Christy; Purniawan, Agung; Fikri Kurniawan, Muhammad; Tri Wicaksono, Sigit; Ardhyananta, Hosta
IPTEK The Journal for Technology and Science Vol 35, No 3 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i3.22015

Abstract

One of the major concerns associated with the use of silicone material is microorganisms and fungal growth which can result in degradation of the material, inflammation, and chronic infection. Thus, the development of antimicrobial silicone elastomer is becoming necessary. The aim of this study was to evaluate the effect of adding different concentrations of chitosan particles into the silicone matrix. The samples were characterized using a Universal Testing Machine (UTM), Scanning Electron Microscopy (SEM), MTT Assay, antibacterial and hydrolytic material degradation for a month. The addition of 50% chitosan recorded the highest value in the pore area of 29.282 with the widest zone of bacterial inhibition of 6.4 ± 0.4 mm as well as the highest% cell viability of 80.08 ± 1.21%, the furthermore, the shortest lifetime predicted from biodegradation test was around 36 weeks.

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