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All Journal Finansha: Journal of Sharia Financial Management Indonesian Journal of Economics and Management Coreid Journal
Ilham Nurjaman, Ilham
Yayasan Pendidikan Sahida
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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The Effect of Investment Decisions & Dividend Policies on The Value of Companies In The Industrial Goods Sub-Sector Iskandar, Zeinku Putri; Asih, Vemy Suci; Nurjaman, Ilham; Kusuma, Suteja Wira Dana
Indonesian Journal of Economics and Management Vol. 5 No. 3 (2025): Indonesian Journal Of Economics and Management (July 2025)
Publisher : Jurusan Akuntansi Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/ijem.v5i3.6625

Abstract

Firm value and share price are closely related. A high stock price indicates a strong company value in the capital market. The Industrial Goods sub-sector is a sector that provides capital to be used in the production process in various industries, in the form of machinery, equipment and equipment for manufacturing, construction and goods and services. This study aims to determine whether investment decisions represented by the Price Earning Ratio (PER) and dividend policy represented by the Dividend Payment Ratio (DPR) affect firm value represented by Price per Book Value (PBV) in industrial goods sub-sector companies for the period 2019 to 2023 which are officially listed on the IDX. The associative method with a quantitative approach was used in this study, with a sample size of six companies out of 40 companies with a period of 5 years. It was found in this study that PER as a proxy for investment decisions contributed positively to increasing firm value as measured by PBV but was not significant. Meanwhile, Dividend Payment Ratio (DPR) as a representation of dividend policy significantly and positively affects Price Book Value (PBV) as a representation of firm value.
IHTIKAR IN HADITH: A FIQH AL-HADITH ANALYSIS OF HOARDING PRACTICES IN THE ISLAMIC ECONOMIC SYSTEM Handika, Caca; Fithriany Rahmah, Yulia; Nurjaman, Ilham; Fauzi Nazar, Reza; Nurdiansyah, Irdan; Priyanti, Gita
Finansha: Journal of Sharia Financial Management Vol. 6 No. 2 (2025): Finansha: Journal of Sharia Financial Management
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/fjsfm.v6i2.51110

Abstract

The practice of hoarding goods poses a serious challenge to the realization of distributive justice within the Islamic economic system. This study aims to examine the prohibition of hoarding through a comprehensive fiqh al-hadith approach by tracing the legal status of relevant hadiths and analyzing the implications of hoarding practices for market dynamics and socio-economic stability. Employing a qualitative method with a descriptive-analytical design, this study is based on an extensive literature review that integrates tahrij al-hadith, syarh al-hadith, and a normative analysis grounded in maqasid al-shariah. The findings reveal that the prohibition of hoarding articulated in the hadith literature is not merely rooted in religious injunctions but also embodies profound ethical and social considerations that remain highly relevant to contemporary issues of goods distribution. Hoarding practices generate artificial scarcity, induce price inflation, and disrupt market equilibrium, ultimately imposing welfare losses on consumers. Accordingly, this study underscores the urgency of strengthening sharia-based regulatory oversight and enhancing public education on muamalah ethics as preventive measures against manipulative economic behavior. Overall, this study contributes to the development of a more just, ethical, and sustainable Islamic distribution framework in addressing the challenges of modern and globalized economies.
Comparison of Classification Models for Predicting Admission Outcomes of Prospective Students with Disabilities Anwar, Rosihon; Irfan, Mohamad; Nurjaman, Ilham
CoreID Journal Vol. 4 No. 1 (2026): March 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i1.147

Abstract

Students with disabilities are a group that requires special attention in the admission process at universities, especially at State Islamic Higher Education Institutions (PTKIN). Although inclusive policies have been implemented, challenges in implementation in the field are still quite significant, especially in terms of equal access and the readiness of educational institutions. This study aims to analyze the opportunities and challenges of accepting students with disabilities at PTKIN through a machine learning approach to predict the factors that influence selection graduation. The research data consists of 80 prospective students with disabilities who participated in the PTKIN selection, covering variables such as gender, province of origin, previous education, school accreditation, and type of disability. The research process included data cleaning, feature engineering (including categorical encoding and recategorization of disability variables), and data balancing using the SMOTE method. Next, model training was carried out using three main algorithms, namely Support Vector Machine (SVM), Random Forest, and XGBoost, as well as model combination (ensemble voting classifier) for performance comparison. The results show that the SVM (RBF kernel) model provides the best performance with an accuracy of 80% and an F1-score of 0.88 for the “Pass” class. This model outperforms Random Forest and XGBoost, which have an accuracy of 65% each. The most influential factors for graduation are the province of origin, disability category, and previous form of education. These findings indicate that the acceptance of students with disabilities at PTKIN is still influenced by geographical factors and educational background, so affirmative policies need to be directed at expanding access for people with disabilities from certain regions and backgrounds. The machine learning approach has proven to be effective as a tool for analyzing inclusive education policies in the PTKIN environment.
Co-Authors Fithriany Rahmah, Yulia Handika, Caca Iskandar, Zeinku Putri Kusuma, Suteja Wira Dana Mohamad Erihadiana, Mohamad Mohamad Irfan, Mohamad Nazar, Reza Fauzi Priyanti, Gita Rizal Anshorudin Rosihon Anwar, Rosihon Supriyatna, Dede Vemy Suci Asih, Vemy Suci