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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Information Systems Engineering and Business Intelligence JOIV : International Journal on Informatics Visualization Cakrawala: Jurnal Litbang Kebijakan Darmabakti Cendekia: Journal of Community Service and Engagements Jurnal Layanan Masyarakat (Journal of Public Service) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Nusantara Journal of Computers and its Applications Jurnal Informatika: Jurnal Pengembangan IT
Rimuljo Hendradi
Bachelor Of Information Systems Study Program, Faculty Of Science And Technology, Universitas Airlangga, Surabaya-Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Public Health Social Sciences Other

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PENERAPAN METODE RULE BASED PADA QUESTION ANSWERING SYSTEM TERJEMAH AL-QURAN BERTEMA SAINS Vita Aulia, Ade; Zaman, Badrus; Hendradi, Rimuljo
NJCA (Nusantara Journal of Computers and Its Applications) Vol 9, No 1 (2024): June 2024
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v9i1.368

Abstract

Al-Qur’an merupakan kitab suci umat islam dimana sekitar seperdelapannya merupakan ayat-ayat yang mengisyaratkan tentang dasar-dasar ilmu pengetahuan (sains). Meskipun ayat-ayat al-Qur’an telah terklasifikasikan menurut temanya, namun diperlukan waktu untuk mencari kembali ayat-ayat sesuai dengan tema sains pada permasalahan tertentu. Untuk memudahkan pencarian tersebut dapat digunakan Question Answering System (QAS), sehingga didapatkan jawaban yang relevan dengan pertanyaan. Metode yang digunakan dalam QAS terdiri dari tiga tahap, yaitu question analysis, passage retrieval, dan answer extraction. Tahap question analysis dilakukan untuk melakukan analisis pertanyaan. Tahap passage retrieval dilakukan untuk mengambil dokumen-dokumen ayat terjemahan menggunakan TF-IDF dan cosine similarity. Sedangkan tahap answer extraction dilakukan untuk ekstraksi hasil jawaban dengan menggunakan metode word-match dan rule-based. Data yang digunakan untuk ujicoba sebanyak 387 ayat yang terbagi dalam 10 sub-tema.  Berdasarkan hasil evaluasi, sistem menunjukkan hasil kinerja yang baik. Hal ini terlihat hasil evaluasi sistem dengan menggunakan 20 pertanyaan didapatkan hasil precision, recall dan f-measures berturut-turut sebesar 0.75, 0.72 dan 0.74.
Advanced inferential statistics and data mining for chlorophyll distribution clustering Felix Reba; Toha Saifudin; Rimuljo Hendradi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2081-2091

Abstract

This study proposes an integrated statistical framework to analyze chlorophyll distribution in marine environments by combining probability distribution modeling, goodness-of-fit (GoF) evaluation, and machine learning-based clustering. Eight probability distribution models—half normal, inverse Gaussian, Rician, Birnbaum–Saunders, Nakagami, extreme value, t location-scale, and stable—were evaluated using observational chlorophyll-a data from the Copernicus Marine Service. Model performance was assessed through the Kolmogorov–Smirnov (KS) and Anderson Darling (AD) GoF tests, along with five statistical information criteria. The results indicate that the inverse Gaussian and extreme value distributions consistently offered the best statistical fit and ecological relevance across varying sample sizes. Clustering analysis, performed using the k-means algorithm and validated via the silhouette index, further confirmed the robustness of these two models in forming stable and well-separated clusters. In contrast, the half-normal distribution showed poor performance and instability, especially with smaller sample sizes. The proposed taxonomy and spatial visualizations enable empirical classification of model behavior and support integration into real-time marine decision support systems (DSS) for ecosystem monitoring. Overall, the study contributes to the development of accurate, data-driven analytical tools that aid sustainable marine resource management, aligned with sustainable development goal (SDG) 14 on marine ecosystem protection.
Co-Authors Abdjan, Muhammad Ikhlas Alfinda Novi Kristanti Alsya Firdausi Nuzula Aminy, Aisyah Ardi Kurniawan Army Justitia Azalea Rahma Septianti Badrus Zaman Baharin, Siti Nor Atika Eto Wuryanto Faried Effendy Fatmawati Fatmawati Felix Reba Ganden Suprianto Hadi Helmi Md Zuraini Herdianto, Muhammad Hendra Khoso, Mir Hasan Khusnul Wardah Mukharomah Marisa Nur Shahadatin Mochamad Zakki Fahmi Nanik Siti Aminah Nita Citrasari Novitasari, Dian C Rini Nur Chamidah NUrina Fitriani, NUrina Ofusori, Lizzy Oluwatoyin Retrialisca, Fitri Rico Ramadhan, Rico Rinda Nariswari Rizal Amegia Saputra Rizqi, M. Najib Wafirur Roslinda Salim Rosyidan, Fikri Yoma Satya Candra Wibawa Sakti Siregar, Naufal Ramadhan Al Akhwal Takaya, Yoshiaki Taufik Taufik Tio Christiawan Bramayudha Toha Saifudin Vita Aulia, Ade Waidah Ismail Wulandari, Nuryuliana Zahra, Adisha Putri Az