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All Journal Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer VARIANSI: Journal of Statistics and Its Application on Teaching and Research SMART: Jurnal Pengabdian Kepada Masyarakat
Rahmanda, Lalu Ramzy
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Physics Social Sciences Other

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Analisis Model Jalur Longitudinal Berbasis Glmm Pada Kasus Pasien Penderita Tuberkulosis Paru: Studi Simulasi Fernandes, Adji; Rahmanda, Lalu Ramzy; Solimun
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128415

Abstract

Evaluasi dan monitoring pada pasien penderita tuberkulosis paru membutuhkan pengobatan yang tepat. Dalam beberapa tahun terakhir, pada tahap monitoring telah ditemukan penanda biologis yang disebut suPAR (soluble urokinase plasminogen activator receptor) berpotensi sebagai biomarker untuk mendiagnosis, prognosis, dan evaluasi penyakit paru. Penelitian ini bertujuan untuk menyelidiki faktor-faktor yang berhubungan dengan pasien tuberkulosis paru dan menentukan faktor yang paling signifikan berdasarkan waktu pengamatan, indeks massa tubuh, dan laju endapan darah. Sampel sebanyak 60 pasien tuberkulosis paru di Malang dievaluasi secara longitudinal setiap dua minggu sekali selama 13 periode. Dalam penelitian ini, kami menggunakan analisis jalur dengan generalized linear mixed model (GLMM) dengan metode Weighted Least Square (WLS) untuk menyelidiki hubungan antarvariabel terhadap kadar monosit dan kadar suPAR pada pasien tuberkulosis paru. dan membandingkan hasilnya dengan Ordinary Least Square (OLS). Hasil penelitian menunjukkan bahwa model terbaik adalah GLMM dengan struktur kovarian unstructured dengan AIC terkecil dan R2 terbesar. Selain itu, indeks massa tubuh memiliki pengaruh yang paling signifikan terhadap kadar monosit dan suPAR pada pasien TB paru. Oleh karena itu, sangat penting untuk mempertimbangkan indeks massa tubuh pasien dalam evaluasi dan monitoring pasien tuberkulosis paru.   Abstract Evaluating and monitoring patients with pulmonary tuberculosis needs an appropriate treatment. In recent years, a biological marker called suPAR (soluble urokinase plasminogen activator receptor) has been identified in the monitoring stage and has the potential as a biomarker for diagnosing, prognosing, and monitoring disease. This study aims to investigate the factors associated with patients with pulmonary tuberculosis and determine the most significant factor based on observation time, BMI, and ESR. A total of 60 patients diagnosed with pulmonary tuberculosis in Malang were included and evaluated longitudinally every two weeks over 13 periods. In this study, we use path analysis with the generalized linear mixed model (GLMM) using the Weighted Least Square (WLS) method to investigate the relationship between the variables on monocyte and suPAR levels in pulmonary tuberculosis patients and compare the results those obtained using the Ordinary Least Square (OLS) method. The results demonstrate that the optimal model is the GLMM with an unstructured covariance structure, exhibiting the smallest AIC and the largest R2. Additionally, body mass index exerts the most significant effect on monocyte and suPAR levels in patients with pulmonary tuberculosis. Consequently, considering patient’s BMI when evaluating and monitoring patients with pulmonary tuberculosis is imperative.
PERFORMANCE OF NEURAL NETWORK IN PREDICTING MENTAL HEALTH STATUS OF PATIENTS WITH PULMONARY TUBERCULOSIS: A LONGITUDINAL STUDY Rahmanda, Lalu Ramzy; Fernandes, Adji Achmad Rinaldo; Solimun, Solimun; Ramifidiosa, Lucius; Zamelina, Armando Jacquis Federal
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.124-135

Abstract

Comorbidity between pulmonary tuberculosis and mental health status requires effective psychiatric treatment. This study aims to predict anxiety and depression levels in patients with pulmonary tuberculosis and consider future mental health treatment for patients. A sample of 60 pulmonary tuberculosis patients in Malang were involved and evaluated longitudinally every two weeks over 13 periods. In this study, we use the Generalized Neural Network Mixed Model (GNMM) to obtain better results in predicting anxiety and depression levels in patients with pulmonary tuberculosis and compare the results with the Generalized Linear Mixed Model (GLMM). The flexibility of GLMM in modeling longitudinal data, and the power of neural network in performing a prediction makes GNMM a powerful tool for predicting longitudinal data. The result shows that neural network's prediction performance is better than the classical GLMM with a smaller MSPE and fairly accurate prediction. The MSPEs of the three compared models: 1-Layer GNMM, 2-Layer, and GLMM, respectively are 0.0067, 0.0075, 0.0321 for the anxiety levels, and 0.0071, 0.0002, and 0.0775 for the depression levels. Furthermore, future research needs to investigate the data with a larger sample size or high dimensional data with large network architectures to prove the robustness of GNMM.
Pelatihan Pembuatan Media Pembelajaran Interaktif Bagi Guru SMA Negeri 7 Takalar Hafid, Hardianti; Nusrang, Muhammad; Fahmuddin, Muhammad; Rahmanda, Lalu Ramzy
SMART: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2025): Oktober
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/smart.v5i2.75904

Abstract

Kegiatan Pengabdian kepada Masyarakat ini dilaksanakan di SMA Negeri 7 Takalar bertujuan untuk meningkatkan kompetensi guru dalam pembuatan media pembelajaran interaktif berbasis teknologi digital. Fokus utama kegiatan adalah pelatihan penggunaan platform Wordwall, yang memungkinkan guru menciptakan media ajar berbentuk kuis dan permainan edukatif. Metode pelaksanaan meliputi sosialisasi, pelatihan interaktif, praktik langsung, serta diskusi terbuka. Kegiatan ini diikuti oleh 25 guru dari berbagai mata pelajaran, serta mendapat dukungan penuh dari kepala sekolah. Hasil kegiatan menunjukkan bahwa para peserta sangat antusias dan mulai mampu mengembangkan media ajar yang lebih menarik dan relevan dengan kebutuhan pembelajaran masa kini. Kegiatan ini diharapkan dapat menjadi langkah awal menuju transformasi digital dalam proses pembelajaran di sekolah mitra.
Perbandingan Model Value-at-Risk (VaR) Hybrid GARCH-EVT dan Model Standar dalam Pengukuran Risiko Ekstrem pada Portofolio Saham Sektoral di Indonesia Annisa Syalsabila; Ikhwana, Nur; Utomo, Agung Tri; Rahmanda, Lalu Ramzy; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm461

Abstract

This study aims to construct an optimal portfolio and compare the accuracy of various Value-at-Risk (VaR) models in measuring the risk of stock portfolios in the Indonesia Stock Exchange (IDX). The optimal portfolio is formed using the Minimum Variance Portfolio (MVP) method based on 11 sector-representative stocks for the period 2019–2025. The risk performance of this portfolio is then evaluated using six VaR models: Variance–Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), and the hybrid GARCH–EVT model. Model accuracy is assessed through backtesting using the Kupiec Proportion of Failures (POF) test and the Christoffersen Conditional Coverage (CC) test at the 95% and 99% confidence levels. The optimization results indicate that the MVP portfolio is dominated by defensive sectors such as consumer non-cyclicals (ICBP.JK) and large-cap banking (BBCA.JK). Backtesting results show that although all models perform adequately at the 95% level, standard models (VC, MC, GARCH) fail to capture extreme risk at the 99% level. In contrast, the GARCH–EVT model satisfies the backtesting criteria and emerges as the most accurate and superior model for predicting extreme losses.Penelitian ini bertujuan untuk membangun portofolio optimal dan membandingkan akurasi berbagai model Value-at-Risk (VaR) dalam mengukur risiko portofolio saham di Bursa Efek Indonesia (BEI). Portofolio optimal dibentuk menggunakan metode Minimum Variance Portfolio (MVP) dari 11 saham perwakilan sektor periode 2019-2025. Kinerja risiko portofolio ini kemudian diukur menggunakan enam model VaR: Variance-Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), dan model hybrid GARCH-EVT. Akurasi model diuji menggunakan backtesting Uji Kupiec (POF) dan Uji Christoffersen (CC) pada tingkat kepercayaan 95% dan 99%. Hasil optimisasi menunjukkan portofolio MVP didominasi oleh sektor defensif seperti consumer non-cyclicals (ICBP.JK) dan perbankan big-cap (BBCA.JK). Hasil backtesting menunjukkan bahwa meskipun semua model akurat pada tingkat 95%, model standar (VC, MC, GARCH) gagal mengukur risiko ekstrem pada tingkat 99%. Sebaliknya, model GARCH-EVT terbukti memenuhi uji dan menjadi model yang paling akurat dan superior untuk memprediksi kerugian ekstrem.
Co-Authors Annisa Syalsabila Fahmuddin, Muhammad Fernandes, Adji Fernandes, Adji Achmad Rinaldo Hardianti Hafid Ikhwana, Nur Nusrang, Muhammad Rais, Zulkifli Ramifidiosa, Lucius Solimun Solimun, Solimun Utomo, Agung Tri Zamelina, Armando Jacquis Federal