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p-Index From 2020 - 2025
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All Journal IQTISHODUNA Indonesian Journal of Electrical Engineering and Computer Science Journal of Language Development and Linguistics
Saleem, Muhammad
Unknown Affiliation
Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Corpus-Based Approach to Sociolinguistic Study of Offensive Words: Gender, Time and Register Differences Doung, Dara; Ny, Sun; Saleem, Muhammad
Journal of Language Development and Linguistics Vol. 2 No. 2 (2023): September 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/jldl.v2i2.3960

Abstract

The unpleasant expressions (offensiveness) always emotionally affect to the human psyches, and exists in all languages in less or strong degrees and directly or indirectly. Therefore, this study aimed to investigate over the changes of them by corporally-derived data and their registers, times and genders were the pilot targets. BNC and COCA Corpora were put for data collections while the list of previous insulting words was selected to reuse, especially the ones with highest frequencies. The results suggested 4 words damn, shit, fuck and dick had the highest degrees of uses but those from BNC were comparatively fewer than the rest. Moreover, the top four were emerged up to the different periods of times and contexts while men used them considerably more often than the women did.
A comparative study on electricity load forecasting using statistical and deep learning approaches Butt, Tehreem Fatima; Tameer, Sana; Saleem, Muhammad; Ur Rehman, Jawwad Sami; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1540-1552

Abstract

Load forecasting has become reproving aspect of an energy management system (EMS). It gives basic advantage to grid stability, cost effectiveness and battery storage system (BSS). For this purpose, machine learning (ML) is widely adopted to forecast the electricity load. This research paper investigates the performances of various time series estimating models applied to electricity load data for an Irish company. The research mainly adopts the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) networks and transformer neural network (TNN) to forecast the electricity load. A comparison evaluation is conducted encompassing various quantifying measures such as root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The results are then compared to get an understanding whether the TNN using attention-based mechanism is better than the two state of the art models. Hence provides a complete understanding about which of the model needs improvements in its architecture for enhancement of operational efficiency and cost effectiveness in the realm of EMS.
The Role of Electronic Customer Relationship Management and Affecting Customer Loyalty Saleem, Muhammad; Hidayati, Nur; Pardiman, Pardiman; Zaed, Ali
IQTISHODUNA IQTISHODUNA (Vol. 21, No. 2, 2025)
Publisher : Fakultas Ekonomi, UIN Maliki Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/iq.v21i2.36585

Abstract

This article aims to analyze the impact of E-CRM mediated by satisfaction on consumer loyalty among Facebook and Instagram users in Malang City. The research sample consisted of 89 users. The questionnaire was distributed via Google Forms as direct data collection from respondents. The data was then analyzed using structural equation modeling partial least squares (SEM-PLS), which was used to answer the research hypothesis. The results of the study indicate that E-CRM does not affect loyalty. In contrast,E-CRM significantly affects satisfaction, which in turn affects loyalty, and satisfaction mediates the effect of E-CRM on loyalty. Implications for further research suggest comparing loyalty on other online shopping applications.
Co-Authors Butt, Tehreem Fatima Doung, Dara Nur Hidayati Ny, Sun Pardiman Pardiman Selvaperumal, Sathish Kumar Tameer, Sana Ur Rehman, Jawwad Sami Zaed, Ali