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All Journal Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) International Journal of Health, Engineering and Technology
Rido Favorit Saronitehe Waruwu
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Dentistry Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Analisis Algoritma Certainty Factor dalam Menentukan Pembagian Warisan Hukum Perdata Menggunakan Metode RDR Muhammad Syahputra Novelan; Syahputri, Maulisa; Rido Favorit Saronitehe Waruwu; Sella Monika Br Tarigan; Heri Eko Rahmadi Putra
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 4 (2025): EDISI JULI 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i4.11482

Abstract

Dalam surah Al-Jasiyah ayat 18 dijelaskan mengenai prosedur atau hukum yang telah ditetapkan Allah bagi hamba-Nya untuk diikuti, baik yang berkaitan dengan aqidah, ibadah, akhlak, maupun muamalah. Di antara hukum yang harus dipenuhi adalah hukum waris. Warisan dikenal dengan istilah ‘faraid’, yaitu bentuk peraturan yang mengatur pemindahan hak milik seseorang yang telah meninggal kepada ahli warisnya agar dapat digunakan untuk meningkatkan kesejahteraan dan mengubah kehidupan mereka yang ditinggalkan. Dalam proses pembagian warisan juga menggunakan perhitungan yang akurat dan adil guna menghindari potensi konflik di antara ahli waris. Selain hukum waris Islam, terdapat pula hukum waris yang diadopsi dari negara-negara Barat, yaitu hukum waris sipil. Hukum perdata menjelaskan bagian-bagian yang diperoleh berdasarkan pembagian kelompok. Dari penelitian yang dilakukan menggunakan algoritma Certainty Factor (CF) dan metode Ripple Down Rules untuk mendapatkan pembagian warisan kelompok pertama dengan nilai CF sebesar 0,424.
Machine Learning–Based Prediction of Oil Palm Plantation Yield Using Random Forest Regression Mayang Modelina Cynthia; Sigit Prabowo; Jheki Pranta Singarimbun; Muhammad Akbar Firdaus; Hafizh Al-Ghifari Rangkuti Rangkuti; Rido Favorit Saronitehe Waruwu; Muhammad Amin
International Journal of Health Engineering and Technology Vol. 4 No. 5 (2026): IJHESS JANUARY 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v4i5.572

Abstract

The rapid development of digital technology has led to a significant increase in the volume and diversity of customer transaction data, making big data a crucial asset for organizations in designing business strategies. However, abundant data will not provide meaningful value if it is not analyzed appropriately. This study aims to implement data science techniques to extract insights from big data of customer transactions using the Python programming language. The research adopts a descriptive–exploratory quantitative approach by utilizing customer transaction datasets as secondary data. The analysis stages include data preprocessing, exploratory data analysis (EDA), and the application of data science algorithms such as clustering and predictive analysis using Python libraries including pandas, numpy, matplotlib, and scikit-learn. The results show that the data science approach is capable of identifying customer behavior patterns based on spending value, transaction frequency, and purchasing habits over a specific period. Furthermore, the clustering model successfully groups customers into several segments with distinct characteristics, providing valuable insights that can be used as a basis for more effective and personalized marketing decision-making. Therefore, this study confirms that the implementation of data science using Python can assist companies in transforming big data of customer transactions into high-value information that supports improved business strategies and customer retention.
Co-Authors Hafizh Al-Ghifari Rangkuti Rangkuti Heri Eko Rahmadi Putra Jheki Pranta Singarimbun Mayang Modelina Cynthia Muhammad Akbar Firdaus Muhammad Amin Muhammad Syahputra Novelan Sella Monika Br Tarigan Sigit Prabowo Syahputri, Maulisa