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All Journal Jurnal Informasi dan Teknologi Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Masyarakat SENSASI
Kartika Maulida Hindrayani
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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IMPLEMENTASI PEMBELAJARAN PADA PROGRAM STUDI INDEPENDEN BIDANG MACHINE LEARNING DI PT DICODING AKADEMI INDONESIA Meisya Vira Amelia; Kartika Maulida Hindrayani
Jurnal Pengabdian Masyarakat SENSASI Vol. 4 No. 2 (2024): Jurnal Pengabdian Masyarakat SENSASI
Publisher : Faculty of Economics and Bussiness, UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/sensasi.v4i2.78

Abstract

Merdeka Belajar – Kampus Merdeka merupakan bagian dari kebijakan Merdeka Belajar oleh Kementerian Pendidikan, Budaya, Riset, dan Teknologi yang memberikan seluruh mahasiswa untuk mengasah kemampuan sesuai bakat dan minat dengan terjun langsung ke dunia kerja sebagai langkah persiapan karir. Dari berbagai pilihan program yang disediakan oleh pihak Merdeka Belajar – Kampus Merdeka, peneliti memilih untuk mengikuti kegiatan Magang dan Studi Independen Bersertifikat (MSIB), khususnya adalah kegiatan studi independen yang diadakan oleh PT Dicoding Akademi Indonesia, yaitu Bangkit Academy. Penelitian implementasi pembelajaran pada Bangkit Academy dilakukan dengan metode kualitatif deskriptif. Hasil penelitian menunjukkan bahwa implementasi pembelajaran pada Bangkit Academy sudah dilakukan dengan baik. Dimulai dari metode self-paced learning yang diterapkan untuk meningkatkan motivasi belajar peserta, banyaknya akses materi yang diberikan, ragam perancangan soal agar menarik, dan diakhiri dengan proyek akhir secara kelompok untuk mengaplikasikan seluruh pengetahuan yang didapatkan menjadi aplikasi yang berguna. Selain itu juga fasilitas yang diberikan berupa pendampingan dari mentor Bangkit Academy dilakukan dengan baik dan instruktur yang dihadirkan merupakan orang-orang yang telah berpengalaman.
Optimizing Categorical Boosting Model with Optuna for Anti-Tuberculosis Drugs Classification Yosua Satria Bara Harmoni; Kartika Maulida Hindrayani; Dwi Arman Prasetya
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.ijeeemi.v7i2.92

Abstract

Tuberculosis is one of the leading causes of death globally, with death rate reaching 1.30 million by 2022, an increase of 3.2% compared to the previous year. Indonesia is one of the countries with the highest number of tuberculosis cases in the world. The Directly Observed Treatment Short-course (DOTS) plays a role in improving the effectiveness of tuberculosis therapy by ensuring the availability of appropriate anti-tuberculosis drugs. However, errors in drug selection can lead to therapy failure, relapse, and Multi-Drug Resistant (MDR) cases. To overcome this, classification models based on patient medical record data can be used to improve the accuracy of drug selection. This research focuses on developing classification model to determine the type of drug using Categorical Boosting algorithm optimized with Optuna using Tree-structured Parzen Estimator. The data consisted of numerical variables, such as age, treatment duration, and categorical variables, such as history of diabetes mellitus, HIV status, drug combination. The CatBoost algorithm was chosen due to its ability to handle categorical data. Hyperparameter optimization was performed to obtain the best parameters. The preprocessing stage involved memory reduction, feature normalization, and encoding on 620 data samples, which were then divided into 90% training and 10% test data. Experimental results show CatBoost model produces an initial accuracy of 90%. After applying parameter optimization techniques using Optuna, the accuracy increased to 96%, showing 6% improvement. The model is able to accurately classify drugs combination, which can support the selection of more effective therapies for tuberculosis patients. Thus, the use of SMOTE to address class imbalance combined with Optuna for hyperparameter optimization was shown to improve the accuracy of CatBoost-based classification models. This finding confirms the effectiveness of SMOTE and Optuna methods in improving the accuracy of prediction models for drug type classification, contributing the improvement of tuberculosis treatment strategies.
Interpretive Structural Modeling-Based Decision Support System for Marine Tourism Strategy Kartini; Kartika Maulida Hindrayani; Endang Tri Wahyurini; Aang Kisnu Darmawan; Hilya Zada Mardhatilla Al Haadiy; Maudi Adella; Rizky Fatkhur Rohman
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.649

Abstract

Marine tourism in Madura has great potential for economic growth, but its unsustainable management threatens the ecosystem and community welfare. A development strategy is needed that balances economic, social, and environmental aspects. The main challenge is the complexity of sustainable marine tourism development, where various factors are interrelated and require a holistic approach. Previous studies have identified factors that influence marine tourism, but have been lacking in integrating them into a comprehensive decision-making framework. This study aims to fill this gap by developing a Decision Support System (DSS) to help stakeholders formulate sustainable marine tourism development strategies. The main objective of this study is to develop a DSS based on Interpretive Structural Modeling (ISM) to map the relationships between key variables and provide strategy recommendations. The ISM approach is used to identify, analyze, and interpret the relationships between key variables. Data were collected through expert interviews, surveys, and literature studies. The study produced a hierarchical model that describes the influence and relationships between variables, as well as a DSS that is able to provide development strategy recommendations based on priorities and objectives. This study contributes to providing a structured and evidence-based decision-making tool for sustainable marine tourism development in Madura. The originality of this study lies in the integration of ISM into DSS for sustainable marine tourism, offering a new perspective in strategic decision-making.
Co-Authors Aang Kisnu Darmawan Dwi Arman Prasetya Endang Tri Wahyurini Hilya Zada Mardhatilla Al Haadiy KARTINI Maudi Adella Meisya Vira Amelia Rizky Fatkhur Rohman Yosua Satria Bara Harmoni