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All Journal Jurnal Pendidikan Matematika (Kudus) International Journal of Engineering, Technology and Natural Sciences (IJETS) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Ilmu Terapan Universitas Jambi Sewagati
Santoso, Noviyanti
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Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics 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. 
Implementation of Artificial Neural Network with Particle Swarm Optimization Algorithm for Financial Distress Prediction of Private Banks in Indonesia Alfin, Muhammad; Firdianto, Dafa Rifqi; Santoso, Noviyanti
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 7 No 2 (2025): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v7i2.458

Abstract

Banking stability, particularly the risk of financial distress in private commercial banks, remains a critical issue that requires accurate and reliable prediction models. This study aims to analyze the characteristics of financial distress in Indonesian private commercial banks and to evaluate the effectiveness of Artificial Neural Networks (ANN) and ANN optimized with Particle Swarm Optimization (ANN-PSO) in predicting financial distress. Using financial data from 59 private commercial banks over the 2020–2023 period, this research employs five financial ratios as input variables and applies ANN and ANN-PSO models, with parameter selection conducted through a trial-and-error and optimization process. The results show that financial distress peaked in 2022–2023 with 32 distressed banks, while descriptive statistics indicate differences between distress and non-distress banks, including average NPLs of 1.40% versus 1.04%, ROA of 0.36% versus 0.75%, and LDR of 93.89% versus 92.39%, respectively. In predictive performance, both ANN and ANN-PSO achieved identical test accuracy of 95.74%, sensitivity of 93.75%, specificity of 96.77%, and an F1 score of 93.75%, although ANN-PSO demonstrated better model stability with lower training accuracy (98.40%) compared to ANN (99.47%), indicating reduced overfitting. Despite these promising results, this study is limited to a relatively short observation period and a fixed set of financial ratios; therefore, future research is recommended to incorporate longer time horizons, additional macroeconomic variables, and alternative optimization techniques to further enhance prediction robustness and generalizability.
PORTFOLIO OPTIMIZATION UNDER CARDINALITY CONSTRAINTS: A METAHEURISTIC MEAN-VARIANCE APPROACH Ihzza, Juwita Nur; Santoso, Noviyanti; Nafis, Moch Abdillah
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 2 (2026): Volume 10, Nomor 2, April 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i2.53849

Abstract

The growing emphasis on sustainability in investment decisions necessitates portfolio optimization models that incorporate practical constraints, particularly asset cardinality. This study applies a Cardinality-Constrained Mean–Variance (CCMV) framework, which increases computational complexity due to the limited number of selected assets. To address this, two metaheuristic algorithms, namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are implemented. Using data from 25 stocks observed between March and August 2025, the analysis includes asset screening based on the risk-free rate, portfolio optimization, and performance evaluation using expected return, risk, and the Sharpe ratio. PSO produces an optimal portfolio of 8 assets with a Sharpe ratio of 27.594%, while ABC selects 9 assets and achieves a slightly higher Sharpe ratio of 27.599%. Although the difference is marginal, ABC demonstrates superior computational efficiency. The findings highlight the effectiveness of metaheuristic approaches, particularly ABC, in solving constrained portfolio optimization problems.
Classification of Hepatitis Patients Using Logistic Regression and Support Vector Machines Methods Nurlaily, Diana; Irfandi, Yoga Prastya; Santoso, Noviyanti; Qomariyah, Siti; Wibowo, Dandy
Jurnal Pendidikan Matematika Vol 5, No 2 (2022): Jurnal Pendidikan Matematika (Kudus)
Publisher : Universitas Islam Negeri Sunan Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jpmk.v5i2.17052

Abstract

Hepatitis is an inflammatory disease of the liver. The virus often causes hepatitis and it becomes the number one world health problem. From 2019 to 2020, there were 1.5 million new cases of hepatitis B and C infection per year. WHO (World Health Organization) aims to eliminate hepatitis by 2030. Based on this problem, it is necessary to classify which health indicators may be vulnerable to the survival of hepatitis patients. This research aims to obtain the best method for classifying hepatitis patients by comparing the logistic regression method and SVM (Support Vector Machines). The classification using logistic regression and SVM is the suitable alternative for this case because the response category is binary data. This research is quantitative research and the researcher uses the hepatitis data set obtained from the UCI repository learning machine. The hepatitis data set contains 19 predictive variables (6 continuous and 13 discrete variables). The patients are divided into two groups, living, and dead patients’ groups. The results show that the best accuracy value produced by using the logistic regression method is 79.3%, and by using the SVM method is 81.94%. Thus, the best classification result for the hepatitis data set is the holdout stratified SVM method using Kernel radians with an accuracy value of 81.94%. This result indicates that the holdout stratified SVM method using Kernel radians can classify hepatitis patients’ data. Hepatitis adalah penyakit peradangan pada hati. Hepatitis sering disebabkan oleh virus. Hepatitis termasuk masalah kesehatan dunia. Tahun 2019 sampai dengan 2020, terdapat 1,5 juta kasus baru infeksi hepatitis B dan C per tahun. WHO (World Health Organization) bertujuan untuk menghilangkan penyakit hepatitis pada tahun 2030. Berpondasikan masalah tersebut, perlu adanya pengklasifikasian untuk mengetahui indikator kesehatan mana yang mungkin rentan terhadap kelangsungan hidup pasien hepatitis. Tujuan penelitian ini untuk mendapatkan metode terbaik dalam mengklasifikasikan pasien hepatitis dengan cara membandingkan metode regresi logistik dan SVM (Support Vector Machines). Klasifikasi menggunakan regresi logistik dan SVM merupakan alternatif yang tepat untuk kasus ini, karena kategori respon adalah data biner. Penelitian ini merupakan penelitian kuantitatif. Penelitian ini menggunakan dataset hepatitis yang diperoleh dari UCI machine learning repository. Kumpulan data hepatitis berisi 19 variabel prediksi (6 variabel kontinu dan 13 variabel diskrit). Pasien dibagi menjadi dua kelas yaitu hidup dan mati. Hasil penelitian menunjukkan bahwa nilai akurasi terbaik yang dihasilkan metode regresi logistik adalah 79.3% sementara menggunakan metode SVM adalah 81.94%. Jadi hasil klasifikasi terbaik untuk dataset hepatitis adalah metode SVM holdout stratified menggunakan kernel radian dengan akurasi sebesar 81,94%. Hasil ini mengindikasikan bahwa metode SVM holdout stratified menggunakan kernel radian dapat digunakan untuk mengklasifikasikan data pasien hepatitis.
Co-Authors Alfin, Muhammad Destri Susilaningrum Dwi Endah Kusrini Firdaus, Nelwan Topan Firdianto, Dafa Rifqi Habibi, Mochammad Reza Hibatullah , Fausania Ihzza, Juwita Nur Indriadika, Ayu Irfandi, Yoga Prastya Mukti Ratna Dewi Nafis, Moch Abdillah Nafis, Mochammad Abdillah Nurlaily, Diana Siti Qomariyah Suprih Ulama, Brodjol Sutijo Wibowo, Dandy