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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Pendas : Jurnah Ilmiah Pendidikan Dasar JATI (Jurnal Mahasiswa Teknik Informatika) G-Tech : Jurnal Teknologi Terapan TEMATICS : TECHNOLOGY MANAGEMENT AND INFORMATICS RESEARCH JOURNALS Journal of Dinda : Data Science, Information Technology, and Data Analytics Innovative: Journal Of Social Science Research International Journal of Multidisciplinary Approach Research and Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Muhammad Fahrury Romdendine
Politeknik Imigrasi
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Public Health Social Sciences Other

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Modelling The Nexus between Parenting Style and Anti Social Behavior using Ensemble Learning Approach Angga Aditya Permana; Muhammad Fahrury Romdendine
G-Tech: Jurnal Teknologi Terapan Vol 7 No 4 (2023): G-Tech, Vol. 7 No. 4 Oktober 2023
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v7i4.3304

Abstract

Contemporary society is grappling with issues of anti-social behavior in children and adolescents, one of which is influenced by parenting styles. This research employs machine learning technology, particularly ensemble learning, to model the relationship between parenting styles and anti-social behavior. The research data is derived from previous studies encompassing parenting style parameters and anti-social behavior. This data is preprocessed and feature-engineered, then used in modeling through the Random Forest (RF) and Adaptive Boost (AdaBoost) methods. Modeling is conducted in two phases: vanilla modeling and hyperparameter tuning. The results of the tuned models indicate that RF performs better (accuracy=91%) than AdaBoost (accuracy=72%). In conclusion, RF, as a bagging ensemble learning technique, effectively models the relationship between parenting styles and anti-social behavior. Future studies are recommended to gather more training data and develop an early detection system for use by child psychologists in the field.
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.
Ensemble Learning Approach Reveals Significant Clinical Attributes from Real-World Breast Cancer Cases Angga Aditya Permana; Muhammad Fahrury Romdendine
G-Tech: Jurnal Teknologi Terapan Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i2.4044

Abstract

Breast cancer has become on of the leading causes of death in Indonesia. This study contributes to global efforts to combat breast cancer by improving patient outcome prediction accuracy. This study employed ensemble learning techniques such as Random Forest, XGBoost, and LightGBM. The results of the study demonstrates LightGBM's superior performance (accuracy=85%, ROC-AUC=81%, AUPR=85%). Notably, all three algorithms identify key clinical attributes: "Relapse Free Status (Months)", "Overall Survival (Months)", "Nottingham Prognostic Index", and "Lymph Nodes Examined Positive". LightGBM uniquely highlights "pam50_LumA" as significant, suggesting reduced fatality risk for Luminal A subtype patients, while others prioritize "Tumor Size". This research lays groundwork for intelligent systems to predict breast cancer outcomes, potentially transforming patient care and clinical practice.
TWITTER SOCIAL NETWORK ANALYSIS AND SENTIMENT IDENTIFICATION OF “VAKSIN BOOSTER” KEYWORD Romdendine, Muhammad Fahrury; Martadireja, Okky Pratama; Danutirta, Alif Sofa; Sulasno, Mitsal Shafiq
TEMATICS: Technology Management and Informatics Research Journals Vol 6 No 2 (2024): TEMATICS: Technology ManagemenT and Informatics Research Journals
Publisher : Polteknik Imigrasi

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

Abstract

Abstract. The low acceptance level and limited coverage of booster vaccines, despite their critical importance for public health, highlight the need for deeper insights into societal perceptions and behaviors. Social networks, as a significant medium for information dissemination, offer a valuable opportunity to understand public discourse and identify influential factors. This study leverages graph topology analysis to map and analyze the dynamics of vaccine-related discussions within social networks. By identifying key individuals who play pivotal roles in spreading booster vaccine information, the analysis reveals the structure and flow of information within the network. Furthermore, sentiment analysis indicates that neutral interactions dominate these discussions, followed by negative and positive sentiments. Notably, the neutral content largely pertains to travel procedures, which aligns with the "mudik" tradition during the data collection period. These findings provide a framework for understanding the sociotechnical landscape of vaccine acceptance and offer actionable insights for designing targeted, effective strategies to enhance booster vaccine uptake.
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.
Systematic Literature Review : Population Density Mapping Using Data Mining Maftuh, Naufal; Nursanto, Gunawan Ari; Romdendine, Muhammad Fahrury
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1805

Abstract

Mapping population density plays a crucial role in designing and developing urban policies. Traditional methods are often unable to capture complex spatial patterns, making the application of data mining techniques crucial. In this study, we conducted a Systematic Literature Review (SLR) of various data mining techniques, including K-Means, KDE, DBSCAN, Random Forest, linear regression, Cellular Automata, and Fuzzy C-Means. The findings of this study show that although K-Means proved to be effective, it is quite sensitive to the presence of outliers. On the other hand, DBSCAN successfully detects irregular distributions, while KDE is able to track trends despite being computationally intensive. Random Forest and linear regression can predict growth, but both require large datasets to provide accurate results. Meanwhile, Cellular Automata and Fuzzy C-Means offer flexibility, but also require comprehensive data. For future optimization, we recommend using AI-GIS hybrid models.
The Effect of Auto Gate Systems on The Traveler Profiling System at Soekarno-Hatta International Airport Martadireja, Okky Pratama; Romdendine, Muhammad Fahrury
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16282

Abstract

The growing numbers of international flights have resulted in the improvement of immigration processes at the airports. Therefore, self-service biometric gates have been developed to guarantee more speed and security at the border. In this paper, the authors evaluate the effects of implementing auto gate systems at Soekarno-Hatta International Airport, specifically looking into the effect of operational efficiencies, privacy, and data security. This study uses a qualitative approach to literature analysis. The author uses previous studies, Government papers, and industry documents to identify the mechanisms that facilitate effective implementation and acceptance of these systems and their relationship to privacy and data security issues. Implementation of the proper auto gate systems might ease immigration processes and encourage traveling around the world, but addressing internal aspects like perception, data safety, and even human beings will always be vital. The findings from this analysis recommend an increase in the data protection models in place, enhancing the accuracy of the systems and making the procedures more open.
Graph Analysis for the Discovery of Key Proteins in Type 2 Diabetes Mellitus Permana, Angga Aditya; Romdendine, Muhammad Fahrury; Perdana, Analekta Tiara
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 5 No. 4 (2023): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v5i4.189

Abstract

One of the metabolic diseases with a rising prevalence in Indonesia is Type 2 Diabetes Mellitus (T2DM). A collective effort from various sectors is required to seek solutions for T2DM. The proteomic approach, which focuses on proteins and their interactions related to T2DM, can be used to understand this condition. This research aims to model protein interactions associated with T2DM using a network graph, enabling the identification of key proteins that have the potential to serve as therapeutic targets or T2DM biomarkers. The graph analysis method used in this study involved four centrality measures: degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The validation method used to confirm the identified proteins is gene set enrichment analysis. The results obtained from the graph analysis using four centrality measures highlighted that seven out of 27 T2DM-related proteins are key proteins; these are: ABCC8, HNF4A, INS, KCNJ11, NEUROD1, PDX1, and SLC30A8. This study concludes that graph analysis on the interaction graph of T2DM-related proteins successfully identified key proteins that could potentially serve as T2DM biomarkers. Further medical investigation is imperative because computational identification alone is not sufficient to confirm the validity of the findings in this study.
SYSTEMATIC LITERATURE REVIEW: SISTEM ANTRIAN PADA PELAYANAN PUBLIK Dwi Handoko Putra; Wilonotomo; Muhammad Fahrury Romdendine
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 02 (2025): Volume 10, Nomor 02 Juni 2025 t
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i02.23555

Abstract

Queue management systems are an essential and fundamental component in all service and product sectors. These systems are crucial because they are the main point of interaction between an organization and its clients. This study seeks to explore the theoretical foundations and methodological approaches applicable to the analysis of queuing systems. The study used a literature review methodology with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, examining scientific papers from Google Scholar published between 2020 and 2025. The findings revealed that the Technology Acceptance Model (TAM) emerged as the dominant theoretical framework, while qualitative research methods, particularly survey-based approaches, were the most frequently used methodologies. In addition, SPSS software stood out as the primary analytical tool among researchers in this field.
Co-Authors Albert Putra Pratama Alif Shofa Danutirta Angga Aditya Permana Angga Aditya Permana Annisa Annisa Ardhi Baskara, Arya Bilkis Praharani Cakra Trinata Cakra Trinata Danutirta, Alif Sofa Dewanto, Rafi Dwi Handoko Putra Fatriani, Rizka Maftuh, Naufal Maharani Piranti, Nurul Mala Nurilmala Martadireja, Okky Pratama Mila Rosmaya Nursanto, Gunawan Ari Nurul Maharani Piranti Okky Pratama Martadireja Perdana, Analekta Tiara Sulasno, Mitsal Shafiq Vita Nurul Fathya Wilonotomo Wisnu Ananta Kusuma