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All Journal EKSAKTA: Journal of Sciences and Data Analysis PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia) Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Science and Technology Journal of Fundamental Mathematics and Applications (JFMA) Smart Society: Community Service and Empowerment Journal AMMA : Jurnal Pengabdian Masyarakat Indonesian Journal of Applied Mathematics Journal of Social Sciences and Technology for Community Service Theta: Journal of Statistics
Mika Alvionita Sitinjak
Institut Teknologi Sumatera
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Social Sciences Other

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Peningkatan Kompetensi Web Modern melalui Pelatihan Framework Next.js bagi siswa SMKN 7 Bandar Lampung Febri Dwi Irawati; Mika Alvionita Sitinjak; Ardika Satria; Ahmad Luky Ramdani; Ira Safitri; Tirta Setiawan; Luluk Muthoharoh; Rohmi Dyah Astuti; Yoga Aji Sukma; Dimas Dwi Randa; Vina Nurmadani; Yustida Bellini
Journal Social Science And Technology For Community Service Vol. 7 No. 1 (2026): Volume 7 Nomor 1 Maret 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v7i1.1689

Abstract

Kesenjangan kompetensi siswa Sekolah Menengah Kejuruan (SMK) dengan kebutuhan industri pengembangan perangkat lunak yang dinamis menjadi latar belakang utama pelaksanaan pengabdian ini. Framework Next.js, sebagai teknologi modern berbasis React, dipilih untuk meningkatkan kapasitas teknis siswa dalam membangun aplikasi web modern dan berkinerja tinggi. Kegiatan ini bertujuan untuk meningkatkan pemahaman dan keterampilan praktis siswa SMKN 7 Bandar Lampung melalui metode project-based learning. Subjek pengabdian terdiri dari 56 siswa yang dievaluasi menggunakan instrumen pre-test dan post-test. Analisis data dilakukan secara komprehensif menggunakan statistika deskriptif dan inferensia non-parametrik Wilcoxon Signed-Rank Test karena distribusi data yang tidak normal (p < 0,05). Hasil analisis menunjukkan adanya peningkatan skor rata-rata yang signifikan dari 28,39 menjadi 37,04. Wilcoxon test menghasilkan nilai statistik V = 3 dengan signifikansi p < 0,001, yang mengonfirmasi adanya perbedaan kompetensi yang nyata setelah intervensi diberikan. Selain itu, capaian effect size sebesar 0,571 (kategori besar) memperkuat bukti bahwa metode pelatihan berbasis praktik efektif dalam mereduksi disparitas pemahaman siswa, terutama pada indikator inisiasi proyek dan struktur folder. Kesimpulannya, pelatihan ini berhasil mentransformasi pengetahuan siswa dari kategori ragu-ragu menjadi paham, sekaligus memberikan fondasi teknologi yang relevan dengan standar industri terkini.
Virtual Screening of BACE1 Inhibitors Using Machine Learning: A Comparative Study of Random Forest and XGBoost Tirta Setiawan; Meezan Ardhanu Asagabaldan; Mika Alvionita Sitinjak; Kharisma Gumilang; Dede Masita; Divania Ramadhani; Eunike Bunga; Ramadhita Atifa Hendri; Ericson Chandra Sihombing
PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia) Article in Press
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive decline due to the accumulation of amyloid-β plaques in the brain. Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) plays a crucial role in forming amyloid-β peptides, making it a key therapeutic target in Alzheimer’s treatment development. This study aims to identify potential BACE1 inhibitors using a machine learning approach with the Random Forest and Extreme Gradient Boosting (XGBoost) algorithms, two distinct ensemble strategies (bagging and boosting). The dataset consisted of molecular representations as Morgan fingerprints (radius = 2, nBits = 2048), derived from compounds obtained through virtual screening and public databases such as ChEMBL. The models were trained and evaluated using a stratified 70:30 train–test split to maintain class balance across bioactivity levels (active, intermediate, and inactive). Model performance was evaluated using accuracy, F1-macro, precision-macro, recall-macro, and ROC-AUC-macro metrics. The results indicate that both models show strong classification capability in distinguishing BACE1 inhibitor activity, with XGBoost slightly outperforming Random Forest. Three compounds, CHEMBL3670318, CHEMBL5984885, and CHEMBL4740498, were predicted to be active and are potential candidates for further validation through molecular dynamics, in vitro, and in vivo studies. These findings suggest that ensemble-based machine learning can serve as an early-stage screening tool to prioritize BACE1 inhibitor candidates that still require experimental validation.
Co-Authors Ardika Satria Ariyanto, Adisty Syawalda Christyan Tamaro Nadeak Danni Gathot Harbowo Dede Masita Devia Gahana Cindi Alfian Dhea Sukma Agustiana Dimas Dwi Randa Dimas Wahyu Saputro Divania Ramadhani Ericson Chandra Sihombing Eristia Arfi Eunike Bunga Fadia Dilla Sabine Febri Dwi Irawati Fuji Lestari Ira Safitri Kharisma Gumilang Lis Nuraini Luluk Muthoharoh Luluk Muthoharoh Luluk Muthoharoh Meezan Ardhanu Asagabaldan Muliawati Muthoharoh, Luluk Napitupulu, Leonard Andreas Nuramaliyah, Nuramaliyah Rahwani, Putri Hazizah Ramadhita Atifa Hendri Ramdani, Ahmad Luky Rekzi P. Manullang Riri Lestari Rohimatul Anwar Rohmi Dyah Astuti Satria, Ardika Sihaloho, Ryantoni Surya, Annisa Cahyani Syaiful, Achmad Taj Shavira Tirta Setiawan Tirta Setiawan Vina Nurmadani Yoga Aji Sukma Yustida Bellini