Article Per Year (5 Year)
p-Index From 2021 - 2026
1.006
P-Index
This Author published in this journals
All Journal JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Informasi dan Teknologi Jurnal Statistika dan Matematika (Statmat) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Masyarakat SENSASI
Kartika Maulida Hindrayani
Unknown Affiliation
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 Mathematics Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
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.
Prediksi Volatilitas Saham KINO dan MRAT menggunakan Model BEKK-MGARCH Renaldy Al Ikhsan; Wahyu Syaifullah Jauharis Saputra; Kartika Maulida Hindrayani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.15639

Abstract

This study analyzes the volatility prediction of KINO and MRAT stocks using the BEKK-MGARCH model during January 2019 to December 2024. Both stocks exhibit high price fluctuations, with volatility significantly influenced. Persistence effects are more dominant, with asymmetric spillover where KINO's influence on MRAT is stronger. Conditional correlation shows a shift from positive to negative in the 30-day forecast. Model evaluation demonstrates low RMSE values of 2.05×10⁻⁵ (KINO), 3.38×10⁻⁵ (MRAT), and 4.42×10⁻⁵ (covariance), indicating excellent predictive performance and confirming the reliability of the BEKK-MGARCH model with exponential smoothing in forecasting the volatility dynamics and relationship between these two stocks with high precision. However, the Jarque-Bera test rejects residual normality (p < 2.2×10⁻¹⁶), and the Ljung-Box test detects autocorrelation, suggesting the need for more complex models such as Student-t distribution or asymmetric models. These findings provide important insights for investors in managing risk and portfolio diversification strategies in the cosmetics sector.
Komparasi Hasil Segmentasi Metode K-Means dan Agglomerative Hierarchical Terhadap Provinsi di indonesia Berdasarkan Profil Perjalanan Wisata Tahun 2024 Ni Luh Ayu Nariswari Dewi; Azizah Zalfa Assyadida; Steffany Marcellia Witanto; Muhammad Nasrudin; Kartika Maulida Hindrayani
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.49999

Abstract

Indonesia merupakan negara dengan kekayaan alam dan budaya yang beragam sehingga memiliki potensi pariwisata yang sangat besar. Salah satu faktor penting dalam pertumbuhan sektor pariwisata adalah pergerakan wisatawan nusantara. Kegiatan wisata yang dilakukan oleh wisatawan nusantara memiliki berbagai tujuan, seperti liburan, kunjungan keluarga, keagamaan, maupun urusan pekerjaan. Keanekaragaman tersebut mencerminkan adanya perbedaan karakteristik lokasi wisata di setiap provinsi sehingga diperlukan analisis lebih lanjut untuk mengelompokkan provinsi berdasarkan profil perjalanan wisata. Penelitian ini bertujuan untuk membandingkan hasil segmentasi menggunakan metode K-Means dan Agglomerative Hierarchical Clustering (AHC) terhadap provinsi di Indonesia berdasarkan data perjalanan wisata tahun 2024 yang bersumber dari Badan Pusat Statistik (BPS). Evaluasi hasil cluster dengan metode K-Means menunjukkan terbentuknya 3 cluster dengan Silhouette Score sebesar 0,662. Sedangkan, dengan metode Agglomerative Hierarchical Clustering (AHC) terbentuk 3 cluster yang memiliki nilai Silhouette Score sebesar 0,9535 menggunakan pemilihan jarak average linkage. Hal tersebut menunjukkan bahwa objek atau data sudah berada pada cluster yang sesuai. Indonesia is a country with diverse natural and cultural resources, giving it enormous tourism potential. One important factor in the growth of the tourism sector is the movement of domestic tourists. Domestic tourists engage in various types of tourism activities, such as vacations, family visits, religious pilgrimages, and business trips. This diversity reflects the differing characteristics of tourist destinations across provinces, necessitating further analysis to group provinces based on travel profiles. This study aims to compare the results of segmentation using the K-Means method and Agglomerative Hierarchical Clustering (AHC) for provinces in Indonesia based on 2024 travel data sourced from the Central Statistics Agency (BPS). The evaluation of the cluster results using the K-Means method shows the formation of 3 clusters with a Silhouette Score of 0.662. Meanwhile, using the Agglomerative Hierarchical Clustering (AHC) method, 3 clusters were formed with a Silhouette Score of 0.9535 using the average linkage distance selection. This indicates that the objects or data are already in the appropriate clusters.
Klasifikasi Gerakan Bahasa Isyarat Indonesia (Bisindo) menggunakan Arsitektur Transfer Learning Xception Amelia, Meisya Vira; Wahyu Syaifullah Jauharis Saputra; Kartika Maulida Hindrayani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.15674

Abstract

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 98% during testing
Co-Authors Aang Kisnu Darmawan Amelia, Meisya Vira Azizah Zalfa Assyadida Dwi Arman Prasetya Endang Tri Wahyurini Hilya Zada Mardhatilla Al Haadiy KARTINI Maudi Adella Meisya Vira Amelia Muhammad Nasrudin Ni Luh Ayu Nariswari Dewi Renaldy Al Ikhsan Rizky Fatkhur Rohman Steffany Marcellia Witanto Wahyu Syaifullah Jauharis Saputra Yosua Satria Bara Harmoni