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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Ceddi Journal of Information System and Technology (JST)
Vijay Nath
Birla Institute of Technology
Computer Science & IT Decision Sciences, Operations Research & Management

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Study and Design of 40 nW CMOS Temperature Sensor for Space Applications Abhishek Pandey; Vijay Nath
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.1426

Abstract

In this paper, a novel CMOS temperature sensors based on sub-threshold MOS operation has been presented, which is designed for space and satellite applications. This proposed CMOS temperature sensor is enunciated good linearity between temperatures range from -55OC to 150OC with inaccuracy of 0.85 OC/V. This circuit is operated at supply 1V and static power consumption 40nW is achieved. The proposed circuit is based on the MOS threshold voltage and mobility. There are two type of sensor output in presented circuit first, voltage proportional to absolute temperature (PTAT) due to threshold voltage and second, negative temperature coefficient (NTC) due to mobility. This circuit is designed & simulated using Cadence analog & digital system design tools UMC90nm CMOS technology. The layout area of the circuit is 17.213μm  6.655μm. This is a low power and longer battery life for harsh environment, wireless sensor network applications etc. 
Rental Housing Recommendation System Model Based on Multi-Criteria Decision Making and Machine Learning with Technology Acceptance Model Integration Robbi Rahim; Vijay Nath
Ceddi Journal of Information System and Technology (JST) Vol. 5 No. 1 (2026): April
Publisher : Yayasan Cendekiawan Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v5i1.164

Abstract

The selection of rental housing is a multi-criteria decision-making problem involving factors such as price, location, facilities, security, and environment, making it difficult for prospective tenants to determine the option that best suits their needs. This study aims to develop a rental housing recommendation system model by integrating Multi-Criteria Decision Making (MCDM), Machine Learning, and Technology Acceptance Model (TAM). The Analytical Hierarchy Process (AHP) is used to determine the criteria weights. In contrast, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to rank rental housing alternatives. Furthermore, a Machine Learning approach using the Random Forest algorithm is applied to predict user preferences from historical data. System evaluation is carried out by comparing recommendation performance between AHP–TOPSIS and Simple Additive Weighting (SAW), testing the performance of the Machine Learning model, and analysing user acceptance using Structural Equation Modelling based on Partial Least Squares (SEM-PLS) within the Technology Acceptance Model framework. The results show that the AHP–TOPSIS model outperforms SAW in recommendation performance, achieving 89.4% compared to 84.7%. The Random Forest-based Machine Learning model also demonstrated good performance in predicting user preferences. The SEM results showed that perceived ease of use significantly influenced perceived usefulness and behavioural intention; perceived usefulness significantly influenced behavioural intention; and behavioural intention significantly influenced actual use, with p-values < 0.05. The R-square value of 0.67 indicated that the model had strong explanatory power for user acceptance. This study developed a rental housing recommendation system model that not only objectively provides the best alternative but also adjusts its recommendations to user preferences, achieving high acceptance. This model has the potential to be applied in the development of recommendation systems in the housing sector and artificial intelligence-based decision support systems.
Co-Authors Abhishek Pandey Robbi Rahim