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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 345 Documents
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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.
Adversarial Training For Robust Defense In Cnn Models For Lung And Colon Histopathological Images Nisa', Chilyatun; Suciati, Nanik; Yuniarti, Anny
IPTEK The Journal for Technology and Science Vol 35, No 2 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i2.19630

Abstract

Cancer stands as the world’s second-leading cause of death, arising from abnormal cell growth that invades the body’s cells and tissues. Simultaneous occurrences of lung and colon cancer are not uncommon, with lung cancer often emerging as the second primary cancer in colon cancer patients. While Deep Learning (DL) approaches have shown promise in accurate cancer classification, recent studies highlight the susceptibility of DL models to perturbations in input images. Merely achieving accuracy is insufficient; models must demonstrate resilience against even the slightest perturbations by applying adversarial defence methods. This study aims to enhance the reliability of the Convolutional Neural Network (CNN) algorithm in the face of adversarial attacks by implementing adversarial training. Leveraging the LC25000 dataset and various pre-trainedCNNmodels for classification,we employ adversarial attack methods such as Carlini and Wagner, DeepFool, and SaliencyMap alongside adversarial training for defence. Evaluation metrics include precision, recall, F1-score, accuracy. Our assessment involves scrutinizing adversarial attacks and defences on histopathology images related to lung and colon issues, representing a state-of-the-art endeavour. The results indicate a significant improvement in susceptibility to adversarial attacks on histopathological images of the lungs and colon, from 0% to 81%.
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.
A New Indoor Positioning Approach based on Weighted K-Nearest Algorithm Akanni, Jimoh; Isa, Abdurrhaman Ademola; Abdulrahman, Amuda Yusuf; Alao, Atanda Rasaq; Ogunbiyi, Olalekan
IPTEK The Journal for Technology and Science Vol 35, No 2 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i2.20249

Abstract

Many contemporary technological services rely heavily on precise location data within smartphone applications, making accuracy a crucial aspect of indoor positioning systems. However, the variability in received signal strength (RSS) poses a challenge for achieving exact locations in Wi-Fi indoor positioning algorithms. Traditional weighted k-nearest neighbor (WkNN) techniques typically utilize RSS spatial distance for selecting reference points (RPs) to estimate locations. To enhance position accuracy, this study introduces a novel indoor positioning method based on WkNN. By incorporating three geometrical distances of RSS (physical, spatial, and Canberra), this approach selects RPs and conducts position estimation using a fusion weighted strategy based on these distances. Experimental findings indicate that the newly proposed method outperforms the nearest neighbor (NN) technique. Moreover, comparative investigations demonstrate its superiority over k-nearest neighbor (kNN) and weighted k-nearest neighbor (WkNN) algorithms. Compared to NN, kNN, and WkNN algorithms, this novel technique improves positioning accuracy by approximately 49.9%, 32%, and 25%, respectively.
Analysis of Taxpayer Behavior to Predict Motor Vehicle Tax Payments Using the Weighted Majority Voting Ensemble Approach Wahyuwidayat, Raditia; Saikhu, Ahmad; Hidayati, Shintami Chusnul
IPTEK The Journal for Technology and Science Vol 35, No 2 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i2.19196

Abstract

Taxpayer non-compliant behavior impacts Motor Vehicle Tax (MVT) revenues not following the predetermined targets. This behavior results in reduced income, and several regional development targets may not be achieved. Therefore, Regional Governments need to predict MVT payments to formulate future targets better. This research aims to analyze taxpayer behavior in predicting future MVT payments, whether the payments are compliant or late or non-payment. The proposed approach starts by analyzing and obtaining a dataset of taxpayer behavioral features. An ensemble classifier method based on Weighted Majority Voting (WMV) is used to predict payments. WMV was developed using the GridSearchCV technique to find optimal hyperparameter values to increase the model accuracy value for individual classifiers. The weight determined from the model accuracy value is converted into a ranking of the number of votes to maximize model performance. Next, feature ablation analysis is carried out to understand the contribution of each feature to model performance. The performance of the proposed system is evaluated using the confusion matrix, accuracy, precision, recall, and f1-score. The research results show that the WMV method performs better, with an accuracy of 96.247%, compared to the proposed individual classifier method in predicting MVT payments based on taxpayer behavior.
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.
Assessing Water Quality in Nigerian Villages: An IoT-Based Monitoring of Three Rivers Osifeko, Martins; Oduwole, Olamide; Kafar, Rasheedat
IPTEK The Journal for Technology and Science Vol 35, No 2 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i2.20524

Abstract

Ensuring safe and clean water is crucial for public health, especially in regions with limited access to reliable water quality testing. This study focuses on assessing water quality in three Nigerian villages using an IoT-based system. Traditional water quality monitoring methods are often expensive, time-consuming, and require specialized personnel and laboratory facilities. To overcome these challenges, we propose a low-cost, real-time water quality monitoring system utilizing the ESP32 microcontroller equipped with sensors for temperature, pH, dissolved oxygen, and conductivity. Our system collects and transmits data for continuous monitoring and analysis. The deployment in Nigerian villages along three rivers reveals that while pH levels are within safe limits, turbidity levels in two rivers exceed acceptable drinkingwater standards, highlighting the presence of particulate contamination. The system’s real-time capabilities and cost-effectiveness demonstrate its potential for broader application in resource-constrained areas. This study underscores the importance of IoT technologies in enhancing water quality monitoring and contributes to achieving the Sustainable Development Goals related to clean water and sanitation
Predicting Failure using Machine Learning and Statistical Based Method: a Production Machine Case Study Latiffianti, Effi; Wiratno, Stefanus Eko; Christianta, Samuel Aditya
IPTEK The Journal for Technology and Science Vol 36, No 1 (2025)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v36i1.22501

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 employ one of potential network based method so called the Long Short Term Memory (LSTM) Autoencoder. Furthermore, we contrast the performance of the result with the traditional statistical method, the multivariate Exponentially Weighted Moving Average (EWMA). While both models successfully detected all failures, LSTM-AE demonstrated superior performance by reducing false alarms and providing true alarms with a longer time-to-failure. The findings highlight the potential of leveraging limited data for failure prediction, demonstrating the effectiveness of both models in detecting anomalies while emphasizing their role in enhancing productivity through early failure detection.
Public Transport Passenger Satisfaction in Surabaya Using Ordinal Logistic Regression and Importance Performance Analysis Azzahro, Amilia; Chamid, Mutiah Salamah; Azizah, Nur
IPTEK The Journal for Technology and Science Vol 34, No 3 (2023)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v34i3.13847

Abstract

Public transportation has an important role in everyday life, namely reducing congestion and avoiding traffic accidents. However, in reality, public transportation tends to be abandoned by the people of Surabaya. The tendency of public transportation to be abandoned by the people of Surabaya City is due to reduced public satisfaction with the quality of public transportation services. This study aims to determine the factors that affect the satisfaction of public transport passengers in the city of Surabaya using Ordinal Logistics Regression and identify what service quality needs to be improved and maintained in urban transportation using the Importance Performance Analysis method. The results of this study indicate that 70% of public transport passengers in Surabaya are dissatisfied with the quality of the public transportation service. Variables that affect passenger satisfaction are reliability (the ability of the public transportation to provide the promised service immediately), responsiveness (the ability of the driver to assist passengers and provide responsive and fast service), assurance (the knowledge and ability of the driver to maintain passenger trust), empathy (care driver to passenger needs), direct evidence (passenger attractiveness to the physical facilities of the public transportation), and transportation fares. The service quality variable that becomes the main priority for improvement is the ability of the driver to provide the promised service immediately, the knowledge, ability, courtesy and safety of the driver in carrying out their duties as well as attractiveness to physical facilities.

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