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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING KENDALI: Economics and Social Humanities
Putri, Maulina
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Computer Science & IT Economics, Econometrics & Finance

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TELAAH KRITIS TERHADAP PRINSIP LARANGAN GHARAR DAN MAYSIR DALAM PRODUK PASAR MODAL SYARIAH MODERN: KAJIAN PUSTAKA BERBASIS LITERATUR KONTEMPORER Putri, Maulina; Rahmah, Fildatul; Amira, Siti Nur; Putri, Dian Fatma; Hendra K, Joni
KENDALI: Economics and Social Humanities Vol. 4 No. 2 (2025): KENDALI: Economics and Social Sciences Humanities, November 2025
Publisher : ASIAN PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58738/kendali.v4i2.1157

Abstract

Penelitian ini bertujuan memberikan telaah kritis terhadap penerapan prinsip larangan Gharar dan Maysir pada produk pasar modal syariah modern melalui pendekatan penelitian kepustakaan berbasis literatur kontemporer. Kompleksitas instrumen seperti saham syariah, sukuk, reksa dana syariah, ETF syariah, EBA syariah, serta instrumen derivatif modern menuntut reinterpretasi prinsip fiqih muamalah agar tetap relevan dengan dinamika pasar. Metode penelitian dilakukan melalui penelusuran literatur terindeks SINTA, fatwa DSN-MUI, standar AAOIFI, dan dokumen regulator seperti OJK, kemudian dianalisis menggunakan pendekatan analisis isi dan analisis komparatif. Hasil kajian menunjukkan bahwa meskipun prinsip Gharar dan Maysir telah diterapkan dalam berbagai instrumen pasar modal, perkembangan teknologi seperti high-frequency trading, algoritmic trading, margin trading, dan short selling menimbulkan risiko spekulatif dan ketidakpastian sehingga memerlukan pengawasan syariah lebih ketat. Instrumen derivatif syariah seperti sukuk derivatif, tawarruq derivative, dan urbun options dinilai memiliki potensi penyimpangan dari maqāṣid al-syarī’ah apabila tidak dibatasi secara jelas. Perbandingan kebijakan Indonesia dengan yurisdiksi seperti Malaysia dan GCC menunjukkan bahwa Indonesia lebih konservatif dalam inovasi, namun unggul dalam kepastian syariah. Penelitian ini menegaskan adanya kesenjangan antara teori normatif syariah dan praktik operasional pasar modern, yang memerlukan pendekatan metodologis baru, peningkatan edukasi investor, serta inovasi instrumen yang lebih berlandaskan aset riil. Kajian ini berkontribusi dalam memperkuat landasan teoretis dan rekomendasi kebijakan untuk mengembangkan pasar modal syariah yang lebih adil, stabil, dan sejalan dengan maqāṣid al-syarī’ah.
Analysis of the Best Social Media Platforms for Promotion Using Machine Learning and RFE Feature Selection: A Comparative Study of Gradient Boosting, XGBoost, CNN, and SVR Putri, Maulina; Hendrawan, Aria
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12049

Abstract

This study aims to identify the most effective social media platforms for digital marketing. The use of social media for promotion continues to grow, yet many businesses still struggle to determine which platforms have the greatest impact. Therefore, this study compares the performance of various machine learning platforms to predict the best platform. The algorithms used are Gradient Boosting Regressor, XGBoost Regressor, Convolutional Neural Network (CNN), and Support Vector Regression (SVR) to estimate digital conversion potential based on user reviews, ad reach, and content trend patterns. A Knowledge Discovery in Databases (KDD) workflow is used to identify the most important key factors. This process includes data preprocessing, TF-IDF feature extraction, sentiment analysis, feature engineering, and feature elimination (RFE). The results showed that the CNN algorithm excelled in prediction, with the highest R² score of 0.74 and the lowest RMSE of 14.78. CNN predictions showed YouTube topping the list in terms of conversion potential, followed by Facebook and TikTok. These results highlight the higher promotional effectiveness of video-based platforms and the importance of machine learning in digital marketing decision-making. However, this study is limited by its reliance on static user review and ad reach data, which may not fully capture the dynamic changes of social media platforms.
Co-Authors Amira, Siti Nur Aria Hendrawan, Aria Hendra K, Joni Putri, Dian Fatma Rahmah, Fildatul