Article Per Year (5 Year)
p-Index From 2021 - 2026
1.873
P-Index
This Author published in this journals
All Journal Indonesian Journal of Business Analytics (IJBA) Wadiah : Jurnal Perbankan Syariah Istithmar: Jurnal Studi Ekonomi Syariah Coreid Journal Al-Muhasib: Journal of Islamic Accounting and Finance Al-Intaj : Jurnal Ekonomi dan Perbankan Syariah Welfare: Jurnal Pengabdian Masyarakat
Rofik Efendi
Institut Agama Islam Negeri Kediri
Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Integration of Corporate Governance and Social as a Catalyst for Islamic Banking Financial Performance; Evidence in Indonesia Sri Anugrah Natalina; Grahita Chandrarin; Maxion Sumtaxy; Rofik Efendi; Yuliani; Dijan Novia Saka
Indonesian Journal of Business Analytics Vol. 4 No. 4 (2024): August 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijba.v4i4.10233

Abstract

Corporate governance can be assessed from how the company has been run openly and transparently, to gain public trust in market mechanisms. Islamic banking in Indonesia shows a pattern that social activities are an obligation that must be carried out. Social is a characteristic of Indonesian banking, a financial institution that has implemented the concept of sharia. In this study, the sample used amounted to 132 data on BUS's financial statements for 11 years from 2010-2020. As a result, improved governance, actually led to a decline in financial performance, by the regression value of -0.1682. Meanwhile, the social variable shows that if social performance increases, financial performance also increases by 0.282.
The Impact of The 2020 Health Crisis on Exchange Rates and Stock Prices in Indonesia : (Study on PT. Jasa Marga (Persero) Tbk.) Efendi, Rofik; Yuliani; Natalina, Sri Anugrah
Al-Muhasib: Journal of Islamic Accounting and Finance Vol. 3 No. 2 (2023)
Publisher : Department of Islamic Accounting, The Faculty of Islamic Economics and Business, State Islamic Institute of Kediri [IAIN Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/almuhasib.v3i2.734

Abstract

The infrastructure sector during the health crisis had an impact by declining by around 12% until the end of 2020. One that can cause a decrease in stock prices is the exchange rate. PT Jasa Marga (Persero) Tbk. is one of the companies that showed active stock price activity during the health crisis period. The data sample of this study is to take daily data from exchange rates and stock prices during 2020 with a sample number of 242. Empirical results show that the correlation coefficient is -0.89, hence the relationship between the exchange rate and stock price is very strong and contradictory. Ha's partial test results were accepted and Ho was rejected, i.e. the exchange rate was partially significant and affected the stock price. The R square results of 79.8 percent that the exchange rate .
Short Message Spam Classification using Decision Tree, Naive Bayes, and Logistic Regression Aulia, Citra; Dinah, Azalia Fathimah; Zahratunnisa, Dzilan Nazira; Efendi, Rofik
CoreID Journal Vol. 3 No. 3 (2025): November 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i3.146

Abstract

The increasing use of Short Message Service (SMS) in digital communication has been accompanied by a rise in spam messages, which threaten user convenience and information security. This study presents a comparative analysis of three classical machine learning algorithms—Decision Tree, Naïve Bayes, and Logistic Regression—for SMS spam classification. The research follows the CRISP-DM methodology, including data collection, understanding, preparation, modeling, and evaluation. The dataset used is the SMS Spam Collection (A More Diverse Dataset) from Kaggle, comprising 5,574 SMS messages labeled as spam or ham. Text preprocessing is performed through cleaning operations and feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The models are evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. Experimental results indicate that Logistic Regression achieves the most balanced performance, with an accuracy of 97.13%, precision of 99.23%, recall of 80.75%, F1-score of 89.04%, and an AUC of 98.72%. Naïve Bayes demonstrates high efficiency and perfect precision but lower recall, while Decision Tree offers interpretability with comparatively lower classification performance. The results suggest that Logistic Regression is the most suitable model for lightweight and reliable SMS spam detection systems, balancing accuracy and misclassification risk. This study provides practical insights for implementing efficient spam filtering solutions and serves as a reference for future research in text classification and natural language processing, particularly for short-message communication.
Co-Authors Abdulghani, Naser Ali Akbar, Ahmad Riszki Al-Akhsan, Ahmad Ghaffar Amir Maliki Abitolkha Anwar, Ahmad Saiful Aulia, Citra Caspirosi, Loris Caspirosi Dijan Novia Saka Dinah, Azalia Fathimah Fani Ma’sumatul Maghfiroh Fikriyah, Ashfa Grahita Chandrarin Iskandar Tsani, Iskandar Ladia Ayu Sekar Sari Mada, Qonita Al Mahfudhotin, Mahfudhotin Maxion Sumtaxy Natalina, Sri Anugrah Natalina Nur Khasan Rahmawati, Afifah Suci Ryski Pebriana Shella Dwiyanti SRI ANUGRAH NATALINA Sri Anugrah Natalina Sri Anugrah Natalina Sri Anugrah Natalina, Sri Anugrah Sufirmansyah Sufirmansyah Wiwik Kusumaning Asmoro Wulandari, Afrilia YULIANI Yuliani Yuliani Yuliani Yuliani Yusnita Agustin Zahratunnisa, Dzilan Nazira Zahro’, Afi Faridatas