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All Journal Jurnal Digit : Digital of Information Technology Jurnal Teknika : Jurnal Teoritis dan Terapan Bidang Keteknikan Jurnal Ilmiah Raflesia Akuntansi Jurnal Manajemen Riset Inovasi Maeswara: Jurnal Riset Ilmu Manajemen dan Kewirausahaan Mengabdi: Jurnal Hasil Kegiatan Bersama Masyarakat Indonesian Journal of Economics and Strategic Management Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Media Elektrik Jejak digital: Jurnal Ilmiah Multidisiplin Router : Jurnal Teknik Informatika dan Terapan
Aji Saputra
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Hadits dan Hubungannya dngan Al-Qur'an Agus Rifki Ridwan; Susanti; Aji Saputra
Jejak digital: Jurnal Ilmiah Multidisiplin Vol. 1 No. 4 (2025): JULI (Edisi Spesial)
Publisher : INDO PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/xedas970

Abstract

Hadith and the Koran are two main sources in Islamic teachings which have a close relationship in forming Islamic law and life values. This study aims to examine the textual and contextual relationships between Hadith and the Qur’an from the perspective of tafsir and ushul fiqh. Using qualitative methods and a descriptive-analytical approach, this research found that Hadith functions as an explanation, reinforcement and complement to the verses of the Qur’an. Apart from that, this research also highlights the role of Hadith in clarifying mutasyabihat verses and providing details of general provisions in the Al-Qur’an. Understanding this relationship is important to form the correct frame of mind in understanding Islamic teachings holistically.
Pengaruh Corporate Governance (CG) Terhadap Profitabilitas (Studi Empiris pada Perusahaan Manufaktur yang Terdaftar di BEI Periode 2021-2023) Aji Saputra; Winarsih, Winarsih
Jurnal Ilmiah Raflesia Akuntansi Vol. 11 No. 2 (2025): Jurnal Ilmiah Raflesia Akuntansi
Publisher : Politeknik Raflesia Press

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The health of a company can be assessed from the acquisition of profitability through the ROA ratio. To increase company profitability and prevent fraudulent practices requires a good corporate governance component. This study aims to examine the effect of the implementation of corporate governance carried out by the independent board of commissioners, audit committee, managerial ownership, and board of directors on profitability in manufacturing companies in Indonesia. The population in this study are companies in the Chemical Industry, Consumer Goods Industry, and Various Industries sectors that have been listed on the Indonesia Stock Exchange for the period 2021-2023. The sampling technique used purposive sampling and obtained 216 samples. The analysis method used to test the hypothesis is multiple linear regression analysis. The results of this study indicate that the independent board of commissioners, board of directors, and audit committee variables have a positive and significant effect on profitability, while the managerial ownership variable has a negative and significant effect on financial performance in manufacturing companies in Indonesia.
Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization Andy Hermawan; Aji Saputra; Nabila Lailinajma; Reska Julianti; Timothy Hartanto; Troy Kornelius Daniel
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.400

Abstract

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.
Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python Andy Hermawan; Nila Rusiardi Jayanti; Aji Saputra; Army Putera Parta; Muhammad Abizar Algiffary Thahir; Taufiqurrahman Taufiqurrahman
Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan Vol. 2 No. 5 (2024): OKTOBER : Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/maeswara.v2i5.1283

Abstract

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.
Co-Authors Agus Rifki Ridwan Akbar Dwiansyah Andy Hermawan Ari Rhamdani Army Putera Parta Bayu Efendy Bayu Effendy cahaya Tambunan Deny Martha Dian Sulistyorini Wulandari Dzaky Muhammad Baihaqi Edi Triwibowo Fachmi Aditama Fathan Mubina Frendi Tobing Gunawan Toto Hadiyanto Kusnadi Kusnadi Lintang Rizki Ramadhani Muhammad Abizar Algiffary Thahir Muhammad Alif Syahreza Muhammad Dhika Rafi Muhammad Prasetya Muhammad Syadied Nabila Lailinajma Ni Nyoman Sawitri Nila Rusiardi Jayanti Nuur Muhammad Ilham Reska Julianti Rif’ati Dina Handayani Rusliansyah Sri Wahyuni Suriyanti Suriyanti SUSANTI Syafiq Basmallah Taufiqurrahman Taufiqurrahman Timothy Hartanto Troy Kornelius Daniel Veronika Happy Puspasari Wafa Nabila Winarsih Winarsih Wita Kristiana Yilmaz Trigumari Syah Putra Zacharia Bachtiar