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All Journal International Journal of Electrical and Computer Engineering Jurnal Mahasiswa Matematika Vidya Karya Sistemasi: Jurnal Sistem Informasi JRPM (Jurnal Review Pembelajaran Matematika) Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Mathematics and Mathematics Education (JMME) Jurnal Varian EduLine: Journal of Education and Learning Innovation Malcom: Indonesian Journal of Machine Learning and Computer Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Interdisciplinary Explorations in Research Journal (IERJ)
Tesdiq Prigel Kaloka
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Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Pengaruh Sistem Keuangan Digital, Literasi Keuangan, Dan Akses Permodalan Terhadap Kinerja Keuangan Usaha Mikro, Kecil, Dan Menengah Di Kalimantan Selatan Muhammad Rizalun Nashoha; Tesdiq Prigel Kaloka; Hamrani Hamrani
Interdisciplinary Explorations in Research Journal Vol. 4 No. 2 (2026)
Publisher : PT. Sharia Journal and Education Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62976/ierj.v4i2.1932

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh sistem keuangan digital, literasi keuangan, dan akses permodalan terhadap kinerja keuangan Usaha Mikro, Kecil, dan Menengah (UMKM) di Kalimantan Selatan. Penelitian menggunakan pendekatan kuantitatif dengan teknik survei terhadap 136 responden UMKM yang dipilih melalui stratified random sampling dari tiga wilayah utama Kalimantan Selatan. Data dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan software SmartPLS v4.0. Evaluasi model pengukuran menunjukkan seluruh indikator memenuhi kriteria outer loading > 0,70, AVE > 0,50, Composite Reliability > 0,70, dan HTMT < 0,90. Hasil pengujian model struktural melalui bootstrapping (5.000 subsampel) menunjukkan bahwa: (1) sistem keuangan digital berpengaruh positif dan signifikan terhadap kinerja keuangan UMKM (β=0,412; T=4,872; p<0,01); (2) literasi keuangan berpengaruh positif dan signifikan (β=0,318; T=3,614; p<0,01); (3) akses permodalan berpengaruh positif dan signifikan (β=0,276; T=3,187; p<0,01); dan (4) ketiga variabel mampu menjelaskan 69,8% variasi kinerja keuangan UMKM (R²=0,698; Q²=0,387). Temuan ini mengimplikasikan perlunya kebijakan terpadu yang mengintegrasikan literasi digital keuangan dengan program pembiayaan inklusif untuk mendorong ketahanan ekonomi UMKM di Kalimantan Selatan.
Multi-Modal Ensemble Framework for Mental Health Disorder Prediction: A Novel Machine Learning Approach M. Fadli Ridhani; Tesdiq Prigel Kaloka; Yazid Aufar; Annisa Rizqiana
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.300

Abstract

Mental health disorders constitute a major global public health concern, affecting millions of individuals across diverse socioeconomic and cultural contexts. Accurate prediction of mental health outcomes at the population level remains challenging due to the complex and non-linear relationships among co-occurring disorders. Previous studies relying on traditional statistical approaches, particularly linear regression, have reported limited predictive performance, with an R² of approximately  0.7175. This limitation highlights the need for more advanced analytical frameworks capable of capturing comorbidity patterns and non-linear interactions among mental health conditions. This study proposes and evaluates a novel multi-modal ensemble machine learning framework to improve the prediction accuracy of eating disorder prevalence using global mental health data. The analysis utilizes country-level prevalence data for schizophrenia, depression, anxiety, bipolar disorder, and eating disorders across multiple countries and years. Eating disorder prevalence is modeled as the primary target variable, while other mental health disorders are incorporated as predictive features to represent clinically established comorbidity relationships. To enhance the representational capacity of the data, an extensive feature engineering strategy was applied, generating 19 additional features through polynomial transformations, interaction terms, ratio-based indicators, and aggregate burden measures. Unsupervised clustering techniques, including K-Means, DBSCAN, and hierarchical clustering, were employed to identify natural groupings of countries based on their mental health profiles. Furthermore, ten machine learning algorithms were systematically evaluated, including linear models, tree-based methods, neural networks, and support vector regression. The best-performing models were subsequently integrated into a stacking ensemble architecture. Experimental results demonstrate that the proposed stacking ensemble achieved a test R² score of 0.9955, corresponding to a 42.2% improvement over the baseline linear regression model. These results indicate that multi-modal ensemble approaches substantially enhance predictive accuracy and provide valuable insights to support evidence-based global mental health policy and targeted intervention planning.
Co-Authors Ahmadi Marta Annisa Rizqiana Azzahra, Hurul A’ini Sekar Bustamam, Alhadi Danang Yugo Pratomo Dedi Kuswandi Desy Lutfiani Pratiwie Febrianto Afli Hamrani Hamrani Indah Gumilang Dwinanda Kadek Ayu Cintya Adelia M Fadli Ridhani M. Junaidi Marhusin , Mariyam Mariyam Muhammad Aditia Muhammad Rizalun Nashoha Mustika, Widya Fajar Nadila Deryza Syofrin Punaji Setyosari Ridhoni, Wahyu Saida Ulfa Salmah Salmah Siswantining, Titin Swasti, Olivia Tampubolon, Patuan P Wahyu Ridhoni Widagdo, Muhammad Rudito Wilda, Robiatul Witari Yazid Aufar Yazid Aufar Yudhistira, Danang Bagus