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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JATI (Jurnal Mahasiswa Teknik Informatika)
Fahrury Romdendine, Muhammad
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Computer Science & IT Engineering

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Machine learning for potential anti-cancer discovery from black sea cucumbers Fahrury Romdendine, Muhammad; Fatriani, Rizka; Ananta Kusuma, Wisnu; Annisa, Annisa; Nurilmala, Mala
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3157-3163

Abstract

Despite being an abundant marine organism in Indonesia, black sea cucumbers (Holothuria atra) is still underutilised due to its slightly bitter taste. This study aims to identify potential anti-cancer compounds from black sea cucumbers using machine learning (ML) to perform drug discovery. ML models were used to predict interactions between compounds from the organism with cancer-related proteins. Following prediction, all compounds were computationally validated through molecular docking. The validated compounds were then screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) Lab 2.0 to assess their druglike properties. The results showed that ML predicted seven out of 86 compounds were interacted with cancer-related proteins. Computational validation from the results showed that four out of seven compounds demonstrated stable interaction with proteins where only one compound meet the criteria of drug-like compound. The framework of ML and computational validation highlighted in this study shows a great promise in the future of drug discovery specifically for marine organisms. Since computational method only works in prediction realms, wet lab validation and clinical trials are imperative before the drug candidate can be produced as actual anti-cancer drug.
FRAMEWORK DATA MINING: SEBUAH SURVEI Ardhi Baskara, Arya; Maharani Piranti, Nurul; Fahrury Romdendine, Muhammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13803

Abstract

Perkembangan pesat dalam ranah teknologi informasi telah meningkatkan kebutuhan akan metode data mining untuk menganalisis dan mengolah data dalam jumlah besar. Berbagai metodologi telah dikembangkan untuk mendukung proses ini, di antaranya Knowledge Discovery in Databases (KDD), Cross-Industry Standard Process for Data Mining (CRISP-DM), dan Sample, Explore, Modify, Model, and Assess (SEMMA). Penelitian ini bertujuan untuk mengevaluasi popularitas dan efektivitas masing-masing metodologi melalui pendekatan Systematic Literature Review berbasis PRISMA. Sebanyak 52 artikel dari tahun 2021 hingga 2025 dianalisis guna mengidentifikasi tren penggunaan metodologi dalam berbagai bidang, termasuk kesehatan, bisnis, teknologi, dan pendidikan. Hasil studi menunjukkan bahwa CRISP-DM adalah metodologi yang paling sering diterapkan karena fleksibilitasnya dalam berbagai sektor. Sementara itu, KDD dan SEMMA lebih banyak digunakan dalam konteks yang lebih spesifik. Studi ini menyoroti pentingnya pemilihan metodologi yang sesuai untuk memastikan efektivitas ekstraksi informasi dari data. Temuan penelitian ini diharapkan dapat menjadi referensi bagi akademisi, praktisi, dan peneliti dalam menentukan metodologi yang paling relevan berdasarkan karakteristik data dan tujuan analisis.
LEVERAGING BIG DATA FOR INDONESIA’S IMMIGRATION POLICY: OPPORTUNITIES AND LIMITATIONS Dewanto, Rafi; Trinata, Cakra; Fahrury Romdendine, Muhammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13908

Abstract

Indonesia's fragmented immigration data systems, exacerbated by its archipelagic geography and institutional complexity, pose significant challenges to effective immigration management. These issues result in inefficiencies in border security, difficulties in tracking irregular migration, and substantial economic losses due to undocumented migrant workers. For instance, discrepancies between official data and World Bank estimates reveal a gap of 5.3 million unrecorded migrant workers, highlighting systemic failures in data integration and enforcement. This study explores the potential of big data analytics to address these challenges by integrating disparate systems and enhancing decision-making processes. Using a normative juridical approach, the research examines Indonesia's legal frameworks and proposes a comprehensive implementation framework. This framework includes centralized data integration using Hadoop and Spark technologies, predictive analytics for migration patterns, and robust privacy safeguards to protect vulnerable populations. The findings emphasize that big data can significantly improve operational efficiency, enhance national security, and support evidence-based policy development. However, the study also identifies critical barriers such as privacy concerns, technical limitations, and institutional coordination gaps. By addressing these challenges, the proposed framework offers actionable insights into leveraging big data for effective immigration policing in Indonesia while safeguarding civil liberties.
Co-Authors Annisa Annisa Ardhi Baskara, Arya Dewanto, Rafi Fatriani, Rizka Maharani Piranti, Nurul Mala Nurilmala Trinata, Cakra Wisnu Ananta Kusuma