Garuda - Garba Rujukan Digital

R. DJoko Andreas Navalino

Universitas Pertahanan Republik Indonesia

Author-ID : 4231178

Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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ANALISIS PENGEMBANGAN APLIKASI PSYCHOMOTOR VIGILANCE TASK (PVT) SEBAGAI ALAT UJI KESIAPAN BERTUGAS PENGEMUDI KENDARAAN TEMPUR Uman Sukmada; Sigit Permana; Hardianto Iridiastadi; R. DJoko Andreas Navalino
Citizen : Jurnal Ilmiah Multidisiplin Indonesia Vol. 2 No. 2 (2022): CITIZEN: Jurnal Ilmiah Multidisiplin Indonesia
Publisher : DAS Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53866/jimi.v2i2.91

Fatigue has been stated as one of the main factors causing accidents in various countries. One of the efforts that can be made to prevent the risk of an accident is to detect as early as possible the driver's condition before starting the trip. One example of a driving profession is the driver of a combat vehicle. Busy work schedules and changing service schedules can result in combat vehicle drivers experiencing a decrease in the quality and quantity of sleep associated with increased fatigue at work. Therefore, this study aims to evaluate the level of fitness for the duty of soldiers. The research was held at the TNI Soldier Education Center using a type of Tank fighting vehicle. This study used the Psychomotor Vigilance Task (PVT) application as a measuring tool for data collection, with a total of 8 respondents. Data processing using Wilcoxon's non-parametric statistical test. The results showed that there was no significant difference in the average value of response time and drowsiness, but the number of misses showed a significant difference in conditions after driving for 2 hours, and after driving for a total of 3 hours.

Analisis Laporan Keuangan: Mengukur Kesehatan Kinerja Keuangan PT. PAL Indonesia Periode 2018-2020 A. Firmansyah Buhroni; R. Djoko Andreas Navalino; Luhut Simbolon
AURELIA: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Vol 1, No 2 (2022): December 2022
Publisher : CV. Rayyan Dwi Bharata

AbstrakPenelitian ini bertujuan untuk mengetahui kinerja keuangan perusahaan pada PT PAL Indonesia periode tahun buku 2018-2020, berdasarkan Keputusan Menteri BUMN Nomor KEP-100/MBU/2002. Teknik analisis data yang digunakan berdasar pada Surat Keputusan Menteri BUMN Nomor KEP-100/MBU/2002. Populasi pada penelitian ini adalah keseluruhan laporan keuangan tahunan PT PAL Indonesia, sedangkan sampel dalam penelitian ini adalah laporan posisi keuangan (neraca) dan laporan laba/rugi PT PAL Indonesia periode 2018-2020. Pengumpulan data dilakukan dengan menggunakan teknik dokumentasi dan studi literatur. Berdasarkan hasil analisis kinerja keuangan pada PT PAL Indonesia, yang dapat dilihat dari aspek keuangannya adalah, pada tahun 2018 mendapatkan predikat Kurang Sehat dengan kategori BB, selanjutnya pada tahun 2019 mengalami peningkatan akumulasi bobot indikator sehingga mendapatkan predikat Sehat dengan kategori A, dan pada tahun 2020 perusahaan mengalami penurunan akumulasi bobot indikator sehingga mendapatkan predikat Kurang Sehat dengan kategori BBB.Kata Kunci: Kinerja Keuangan, Keputusan Menteri Badan Usaha Milik Negara Nomor KEP-100/MBU/2002, Rasio Keuangan, PAL Indonesia, IndikatorÂ AbstractThis study aims to determine the company's financial performance at PT PAL Indonesia for the 2018-2020 financial year, based on the Decree of the Minister of SOEs Number KEP-100 / MBU / 2002. The data analysis technique used is based on the Decree of the Minister of SOEs Number KEP-100 / MBU / 2002. The population in this study is the entire annual financial statements of PT PAL Indonesia, while the samples in this study are statements of financial position (balance sheet) and profit/loss statements of PT PAL Indonesia for the 2018-2020 period. Data collection is carried out using documentation techniques and literature studies. Based on the results of the financial performance analysis at PT PAL Indonesia, what can be seen from the financial aspect is, in 2018 it received the title of Unhealthy with the BB category, furthermore, in 2019 there was an increase in the accumulation of indicator weights so that they received the Healthy predicate with category A, and in 2020 the company experienced a decrease in the accumulation of indicator weights so that it received the Unhealthy predicate with the BBB category.Keywords: Financial Performance, Decree of the Minister of State-Owned Enterprises Number KEP-100/MBU/2002, Financial Ratio, PAL Indonesia, Indicators

The AI-Enabled Pharmacovigilance for Defence Health Surveillance: Automatic Detection of Adverse Drug Events from Patient Reviews Using BioClinical ModernBERT: Farmakovigilans Berbasis AI untuk Pengawasan Kesehatan Pertahanan: Deteksi Otomatis Kejadian Efek Samping Obat dari Ulasan Pasien Menggunakan BioClinical ModernBERT Nanang Yulian; R. Djoko Andreas Navalino; Linus Yoseph Wawan Rukmono; Riduan Riduan
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1192

Pharmacovigilance is a critical component of post-marketing drug safety, yet conventional adverse drug event (ADE) reporting systems remain constrained by substantial underreporting. In defence health systems, the timely detection of medication-related safety signals is not only a clinical concern but also a matter of force health protection, medical readiness, and operational resilience. Patient-generated health narratives from online forums, drug review platforms, and social media provide a complementary source of pharmacovigilance intelligence, but their informal, unstructured, and context-dependent nature creates significant challenges for automated analysis. This study evaluates BioClinical ModernBERT, a biomedical–clinical long-context encoder based on the ModernBERT architecture, for automatic ADE detection from patient reviews. Its performance is compared with three representative BERT-based transformer baselines: BERT-base, BioBERT, and ClinicalBERT. Experiments were conducted using the CSIRO Adverse Drug Event Corpus (CADEC), a benchmark corpus of patient-reported medication experiences from online health forums. The corpus was pre-processed through sentence segmentation, text cleaning, medical entity normalization, and sentence-level label alignment for binary ADE/non-ADE classification. All models were fine-tuned using a 70:15:15 training, validation, and test split and evaluated using accuracy, precision, recall, and F1-score. The results show that BioClinical ModernBERT achieved the highest overall performance, with an F1-score of 0.891, outperforming ClinicalBERT (0.847), BioBERT (0.832), and BERT-base (0.798). Confusion matrix analysis further indicates that BioClinical ModernBERT reduced false negative errors, particularly in long, multi-clause, and clinically implicit patient narratives. These findings suggest that combining biomedical–clinical domain adaptation with long-context representation provides a meaningful advantage for detecting ADE signals in complex patient-generated text. From a defence health perspective, such capability may support the development of AI-enabled pharmacovigilance surveillance systems that enhance medication safety, health intelligence, and readiness-oriented risk monitoring across civilian–military health ecosystems.

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