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All Journal Jurnal Ners Journal Of Artificial Intelligence And Software Engineering
Rico Halim
Universitas Prima Indonesia
Computer Science & IT Health Professions Medicine & Pharmacology Nursing

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Antibacterial Activity of the Secondary Metabolite of Fusarium LBSU Isolate from the Cat’s Whiskers Plant (Orthosiphon stamineus) Sati Agriani Zega Zega; Rico Halim; Astri Natalia Ginting; Finna Piska
Jurnal Ners Vol. 9 No. 3 (2025): JULI 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v9i3.46029

Abstract

The cat's whisker plant is known to contain bioactive metabolites that have potential as therapeutic agents, while its rhizosphere is a habitat for microorganisms that produce secondary metabolites. Fusarium LBSU isolate were isolate from the rhizosphere of cat’s whiskers plant. Secondary metabolites are produced through liquid fermentation, followed by extraction using ethyl acetate. Antibacterial activity was tested using the disk diffusion method against the Escherichia coli and Staphylococcus aureus. The highest antibacterial activity obtained against Escherichia coli 23.565 mm, higher than Staphylococcus aureus which is 15.3 mm. These findings support the further development of secondary metabolites from rhizosphere isolate as an alternative source of environmentally friendly and effective antibacterial agents.
Prediction Analysis of KIP Student Placement at Universitas Prima Indonesia Using SVM Hyperparameter Optimization Method and Comparative Study Oloan Sihombing; Calvin Wahyu Febrian Sidabutar; Angelica Angelica; Rico Halim; Daniel Agus Towi Sitompul
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9179

Abstract

Penelitian ini bertujuan membangun model prediksi penempatan mahasiswa penerima Kartu Indonesia Pintar (KIP) di Fakultas Sains dan Teknologi Universitas Prima Indonesia menggunakan algoritma Support Vector Machine (SVM) yang dioptimasi hyperparameternya dan dibandingkan dengan K-Nearest Neighbor (KNN). Penelitian menggunakan 269 data mahasiswa yang diperoleh dari instansi akademik dan kuesioner. Tahapan penelitian meliputi pra-pemrosesan data, pembentukan label target, encoding, normalisasi, serta pembagian data 80:20. Optimasi SVM dilakukan menggunakan Grid Search dan 5-fold cross-validation, sedangkan KNN digunakan tanpa optimasi. Hasil penelitian menunjukkan bahwa SVM dengan kernel RBF, parameter C=10 dan gamma=0,1 memperoleh akurasi 88,89%, lebih tinggi dibandingkan KNN sebesar 83,33%. Selain itu, SVM juga menunjukkan performa lebih baik pada metrik precision, recall, dan F1-score, sehingga lebih efektif digunakan untuk mendukung pengambilan keputusan penempatan mahasiswa KIP secara objektif dan tepat sasaran.
Co-Authors Angelica Angelica Astri Natalia Ginting Calvin Wahyu Febrian Sidabutar Daniel Agus Towi Sitompul Finna Piska Oloan Sihombing Sati Agriani Zega Zega