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Analisis Model Stokastik Birth-Death pada Populasi Bakteri Esherichia coli dengan Kematian di Bawah Tekanan Antibiotik Anshari, Farhan; Ginting, Puspa Arinda; Aritonang, Anggi Pasha; Hutasoit, Elizabeth; Manihuruk, Oliver Juan Gery; Manullang, Sudianto; Nasution, Alvi Sahrin
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.19442

Abstract

Penelitian membahas model stokastik dinamika populasi bakteri Escherichia coli pada kondisi tekanan antibiotik, khususnya pada konsentrasi Minimum Inhibitory Concentration (MIC), menggunakan proses birth-death linier sebagai kerangka dasar. Dinamika sistem dianalisis melalui master equation untuk memperoleh distribusi probabilitas waktu-ke-waktu, distribusi stasioner, serta waktu rata-rata kepunahan (Mean Time to Extinction) dan probabilitas first-passage time. Pendekatan stokastik mengintegrasikan model berbasis widget pada tingkat sel individu, di mana setiap sel direpresentasikan sebagai unit molekuler minimum. Ketika jumlah widget mencapai ambang batas, sistem mengalami kematian atau pembelahan, masing-masing dengan distribusi binomial simetris. Analisis dilakukan secara teoritis melalui simulasi Monte Carlo berbasis Python. Hasil menunjukkan bahwa meskipun rata-rata populasi tampak stasioner pada kondisi, sistem mengalami fluktuasi stokastik signifikan yang memicu aktivitas mikroskopik intens. Varians populasi meningkat terhadap waktu, menandakan instabilitas tersembunyi. Simulasi menunjukkan bahwa waktu rata-rata kepunahan meningkat subeksponensial terhadap ukuran populasi awal.
Implementasi Metode Markov Chain Untuk Prediksi Laju Inflasi Di Sumatera Utara Muthiah, Ade Naila; Nurjannah, Annisa; Aulia, Ilmi; Kanaya, Nayla Yasyra; Nasution, Tri Annisya Aini; Manullang, Sudianto; Nasution, Alvi Sahrin
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.19461

Abstract

Inflasi merupakan indikator penting dalam menilai kestabilan ekonomi suatu daerah. Di Sumatera Utara, fluktuasi laju inflasi dari tahun ke tahun menuntut adanya metode prediktif yang andal untuk mendukung pengambilan kebijakan. Penelitian ini bertujuan untuk memprediksi laju inflasi di Sumatera Utara dengan menggunakan metode Markov Chain, yang mengandalkan sifat transisi antar kondisi inflasi dari waktu ke waktu. Data historis inflasi dari tahun 2001 hingga 2020 diklasifikasikan ke dalam tiga kategori: rendah, sedang, dan tinggi. Berdasarkan data tersebut, dibentuk matriks transisi probabilitas yang kemudian digunakan untuk menghitung distribusi stasioner. Hasil analisis menunjukkan bahwa kondisi inflasi rendah memiliki peluang jangka panjang tertinggi, yaitu sebesar 84,2%. Tidak ditemukan transisi menuju inflasi tinggi, yang mengindikasikan kestabilan ekonomi regional. Temuan ini menunjukkan bahwa metode Markov Chain dapat memberikan gambaran prediktif yang kuat dan dapat dijadikan alat bantu dalam perencanaan kebijakan ekonomi daerah
DIVERSIFIKASI PRODUK OLAHAN IKAN LELE UNTUK PEMBERDAYAAN EKONOMI MASYARAKAT DESA SIGUMURU Rambe, Muhammad Rahman; Nasution, Alvi Sahrin; Pohan, Rizky Febriani
Jurnal Abdi Insani Vol 12 No 10 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i10.2913

Abstract

Catfish is a type of freshwater fish with high economic value, both in terms of sales value and nutritional value. Furthermore, this fish is easy to grow, with a high protein content, soft, white meat, and affordable prices. However, suboptimal cultivation results in low yields, which reduces the income of catfish farmers. One effort to increase the income of catfish farmers is through diversification of processed catfish products (catered fish paste, catfish meatballs, and catfish satay). This activity aims to improve the knowledge and skills of the Sigumuru Village community in processing catfish into a variety of processed products. Furthermore, this activity can also increase the income of the Sigumuru Village community. The methods used in this activity were location surveys, socialization, demonstrations, marketing, and monitoring. The participants of this activity were the Fish Cultivation Group (FCG) "Sigumuru Jaya" which consisted of 12 people. This activity was carried out for 8 months. The activity evaluation method was carried out using a pre-test, post-test, preference questionnaire, and monitoring questionnaire. The results of this activity showed an average increase in community empowerment outcomes of 83.89% which consisted of increased skills, knowledge, business sustainability, and partner income. However, the overall activities carried out showed an increase in community empowerment outcomes of 91.11%. The results of the preference questionnaire showed that the majority of the community (50%) chose pecal lele as the most popular processed catfish product because it tastes good (37.5%). The results of the monitoring questionnaire showed an assessment score of 75. Pecal lele is the most popular food among the community. Thus, the PKM team recommends that target partners further increase pecal lele production to increase their income and business sustainability.
SELIMUT ANTI API INOVASI PENCEGAHAN DAN MITIGASI KEBAKARAN TAHAP AWAL BERSAMA MITRA PBKM KESUMA MARELAN MEDAN Suyanti, Retno Dwi; Pardosi, Sri Masnita; Nasution, Alvi Sahrin; Prima, Heppy Setya
Jurnal Abdi Insani Vol 12 No 11 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i11.2838

Abstract

Fires are sudden disasters that can cause significant losses, both material and non-material. Prompt early response is crucial to prevent the spread of fire, but many communities lack simple and easy-to-use fire extinguishers. A five-member community service team, in collaboration with the Kesuma Community Learning Center (PKBM), led by Ms. Siti Nurhayati, located in Lk 2 Pasar 2 Barat Terjun, Medan Marelan, and involving 40 community members as partners, conducted training and developed an innovation called a "Fulerene Patterned Fire Blanket." This blanket is made from heat-resistant fullerene-patterned fiberglass and coated with sodium bicarbonate (NaHCO₃), which accelerates fire extinguishing by lowering the temperature and inhibiting oxidation. This combination of materials results in a device that is high-temperature resistant, lightweight, and reusable after washing. This activity included outreach, demonstrations, hands-on training, and involved the local community in simple production of the device. The results demonstrated an increase in community knowledge and skills in dealing with small fires. Enthusiasm for this safe and economical design has also increased disaster awareness and preparedness at the community level. The positive response has opened up opportunities for broader program development. Scientific studies have shown that fullerene-patterned fire blankets are effective as a preventative solution, enhancing community preparedness for early fire risks.
An optimized kernel SVM framework for game review sentiment analysis using particle swarm optimization Karo Karo, Ichwanul Muslim; Dewi, Sri; Nasution, Alvi Sahrin
Joutica Vol 11 No 1 (2026): MARET
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jti.v11i1.1633

Abstract

The digital game industry’s rapid growth has increased interaction between developers and players through online review platforms. These reviews contain vital information about gaming experiences and satisfaction, serving as guides for future game versioning. Sentiment analysis provides a strategic approach to automatically classify reviews, offering data-driven insights for developers. This study focuses on enhancing sentiment analysis performance for Player Unknown's Battlegrounds (PUBG) reviews by integrating Kernel Support Vector Machine (SVM) with Particle Swarm Optimization (PSO). A dataset of 1,205 reviews from the Google Play Store was analyzed using TF-IDF feature extraction and 5-fold cross-validation. While default Kernel SVM achieved 76.78% accuracy, it suffered from low precision (56.9%). Implementing PSO for parameter optimization significantly improved performance, reaching 86.42% accuracy, 70.98% precision, and an 83.03% F1-score. Comparisons with Naïve Bayes, basic SVM, BERT, and Lexicon + SVM confirm that the Kernel SVM + PSO model provides superior and more stable performance. These findings highlight PSO’s effectiveness in SVM parameter tuning. Future research should investigate combining metaheuristic optimization with deep learning models to improve model generalization.