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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science Sewagati
Santoso, Noviyanti
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Arts Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Pelatihan Dashboard Monografi untuk Peningkatan Efisiensi Penyusunan RAB Tahunan Desa Bagi Staf Kelurahan di Kecamatan Wonosari, Wonoasri Kabupaten Madiun Santoso, Noviyanti; Suprih Ulama, Brodjol Sutijo; Kusrini, Dwi Endah; Susilaningrum, Destri; Dewi, Mukti Ratna; Hibatullah , Fausania; Habibi, Mochammad Reza; Nafis, Mochammad Abdillah
Sewagati Vol 9 No 4 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i4.2477

Abstract

Penyusunan Rencana Anggaran Belanja (RAB) tahunan desa merupakan langkah krusial dalam pengelolaan keuangan yang transparan, akuntabel, dan efektif. Namun, proses ini seringkali menghadapi kendala berupa kurangnya efisiensi dalam pengumpulan dan analisis data monografi desa. Pelatihan Dashboard Monografi ini bertujuan untuk meningkatkan kapasitas staf kelurahan di Kecamatan Wonosari, Wonoasri, Kabupaten Madiun, dalam menyusun RAB tahunan secara lebih efisien dan akurat. Pelatihan yang dilaksanakan mencakup pengenalan aplikasi berbasis Excel yang dirancang khusus untuk penyusunan RAB infrastruktur desa, termasuk estimasi anggaran jalan paving. Metode kegiatan melibatkan ceramah, diskusi, praktik langsung, dan pendampingan untuk memastikan pemahaman peserta terhadap materi. Hasil evaluasi menunjukkan peningkatan signifikan dalam efisiensi dan akurasi penyusunan RAB desa. Pelatihan ini berhasil memberikan solusi digital berbasis data kepada staf kelurahan, sehingga mendukung proses perencanaan anggaran yang lebih efektif dan tepat sasaran. Keberhasilan kegiatan ini menunjukkan pentingnya pelatihan berkelanjutan, pendampingan teknis, dan evaluasi berkala untuk memastikan keberlanjutan manfaat dari aplikasi yang diimplementasikan.
Exploring User Experience by User Review Using LDA-Topic Modeling and HEART Framework: A Systematic Literature Review Indriadika, Ayu; Santoso, Noviyanti
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2247

Abstract

This study aims to evaluate the integration of the HEART framework (Happiness, Engagement, Adoption, Retention, and Task Success) with computational modeling techniques such as Latent Dirichlet Allocation (LDA) for measuring User Experience (UX). A Systematic Literature Review (SLR) was conducted on articles published between 2015 and 2025, selected from reputable databases including Scopus. The selected studies emphasize the use of HEART metrics in conjunction with machine learning approaches, particularly LDA, and were assessed based on the Scimago journal quartile ranking system. The findings categorize the studies into five main research objectives: predicting user satisfaction and emotional response, optimizing usability, analyzing user-generated content, evaluating learning performance through gamified systems, and assessing system requirements in relation to UX. This classification reveals growing trends in applying hybrid methods that combine qualitative metrics with automated modeling techniques. The results underline the importance of developing more adaptive and scalable UX evaluation frameworks that align human-centered insights with machine learning-driven analysis. This study offers a foundational reference for future research in building integrative models that advance the depth and scale of UX assessments in complex digital environments.
Early Warning Systems for Financial Crisis Prediction: A Systematic Literature Review of Econometrics, Machine Learning and Uncertainty Indices Firdaus, Nelwan Topan; Santoso, Noviyanti
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2314

Abstract

This study evaluates the integration of econometric methods, machine learning models, and uncertainty indices within the framework of Early Warning Systems (EWS) for financial crisis prediction in stock markets. A Systematic Literature Review (SLR) was conducted on studies published between 2008 and 2024, sourced from reputable databases such as Scopus, IEEE, and other international publishers. The review identifies three main objectives. First, the development of predictive models for market volatility and systemic risk using econometric and machine learning approaches. Second, the diagnosis of risk factors by incorporating macroeconomic indicators, commodity prices, geopolitical uncertainty, and sentiment data from big data sources. Third, the evaluation of policy implications and the role of composite indicators in maintaining financial stability. The dominant data categories include market data (prices, returns, volatility, sectoral indices), macroeconomic indicators (production, interest rates, leading indicators), commodities and energy (oil and gold), and measures of risk and uncertainty (GPR, GEPU, TPU, sentiment). Methodologically, studies employ time series econometrics (ARIMA, GARCH, VAR, spillover), machine learning, hybrid approaches, and indicator or policy-based frameworks. The findings reveal a growing trend toward multivariate and hybrid models, yet highlight limited integration across methods and data domains. 
Co-Authors Destri Susilaningrum Dwi Endah Kusrini Firdaus, Nelwan Topan Habibi, Mochammad Reza Hibatullah , Fausania Indriadika, Ayu Mukti Ratna Dewi Nafis, Mochammad Abdillah Suprih Ulama, Brodjol Sutijo