cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
Jl. KH. Ahmad Dahlan, PO BOX 202 Purwokerto 53182 Kembaran, Banyumas, Jawa Tengah
Location
Kab. banyumas,
Jawa tengah
INDONESIA
JRST (Jurnal Riset Sains dan Teknologi)
Published by Universitas Muhammadiyah Purwokerto
ISSN : 25799118     EISSN : 25499750     DOI : http://dx.doi.org/10.30595/jrst
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering
JRST (Jurnal Riset Sains dan Teknologi) adalah jurnal peer reviewed dan Open-Acces. JRST merupakan jurnal yang diterbitkan oleh Lembaga Publikasi Ilmiah dan Penerbitan (LPIP) Universitas Muhammadiyah Purwokerto. JRST mengundang para peneliti, dosen, dan praktisi di seluruh dunia untuk bertukar dan memajukan keilmuan di bidang sains dan teknologi yang meliputi bidang Matematika, Kimia, Biologi, Teknologi Rekayasa dan Keteknikan, Farmasi, Geografi, Komputer dan Teknologi Informasi. Dokumen yang dikirim harus dalam format Ms. Word dan ditulis sesuai dengan panduan penulisan. JRST terbit 2 kali dalam setahun pada bulan Maret dan September.
Arjuna Subject : Energi (semua kategori) - Energi (Lain-Lain)
Articles 236 Documents
 20   21   22   23   24 
Detection of the Presence of Ectoparasites in Gourami Fish Cultivation (Ospronemus gouramy) in Tamansari and Singasari Villages, Banyumas Regency: Deteksi Keberadaan Ektoparasit pada Budidaya Ikan Gurami (Ospronemus gouramy) di Desa Tamansari dan Singasari Kabupaten Banyumas Ikhsan Pratama; Dewi Susylowati; Muhammad Azharul Rijal; Hilmy Izzuddin
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 2, September 2026 :JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i2.30153

Abstract

Gourami is one of the leading freshwater fish commodities in the Banyumas area, which is included in the largest contributor to national gourami fish production. One of the problems that often arise among Banyumas farmers, such as gourami mortality caused by ectoparasite infestation in fish, requires further monitoring to prevent losses during the cultivation process. The purpose of this study was to detect the presence of ectoparasites in ponds in Tamansari and Singasari villages. This study was conducted using a case study method. Data collection was carried out by directly observing the condition of pond water quality and fish health conditions, followed by fish sampling. Fish samples were taken at two per pond to avoid stress on other fish. The results of this study detected the presence of several ectoparasites that were slightly different in ponds in both villages. Parasites found in Tamansari village were four types of ectoparasites, namely Trichodina sp., Hennegueya sp., Oodinium sp., and Ichtiophirius sp. with dominance values ​​of 5.88%, 82.88%, 2.67%, and 8.55%, respectively. In Singasari village, two types of ectoparasites were found, namely Trichodina sp. and Dactylogyrus sp. 99.37% and 0.62%, respectively. Based on these results, the use of tarpaulin ponds can actually prevent the presence and development of parasites in farmed fish. The water source used must also always be considered. ABSTRAK (Bahasa Indonesia) Ikan gurami merupakan salah satu komoditas unggulan ikan air tawar di daerah Banyumas yang masuk kedalam daerah penyumbang produksi ikan Gurami Nasional terbesar. Salah satu permasalahan yang kerap muncul di kalangan pembudidaya Banyumas, seperti kematian ikan gurami yang disebabkan oleh infestasi ektoparasit pada ikan, perlu dilakukan pemantauan lebih lanjut untuk mencegah kerugian selama proses budidaya. Tujuan dari penelitian ini adalah untuk mendeteksi keberadaan ektoparasit pada kolam yang berada di desa Tamansari dan Singasari. Penelitian ini dilakukan dengan menggunakan metode studi kasus. Pengumpulan data dilakukan dengan meninjau secara langsung kondisi kualitas air kolam dan kondisi kesehatan ikan, kemudian dilanjutkan dengan pengambilan sampel ikan. Sampel ikan diambil 2 ekor per kolam untuk menghindari stres pada ikan yang lainnya. Hasil penelitian ini mendeteksi keberadaan beberapa ektoparasit yang sedikit berbeda pada kolam di kedua desa. Parasit yang ditemukan desa Tamansari sejumlah empat jenis ektoparasit yaitu Trichodina sp., Hennegueya sp., Oodinium sp., dan Ichtiophirius sp. dengan nilai dominasi masing-masing 5,88%, 82,88%, 2,67%, dan 8,55% serta pada desa Singasari ditemukan dua jenis ektoparasit yaitu Trichodina sp., dan Dactylogyrus sp. 99,37 %, 0,62 %. Berdasarkan hasil tersebut, penggunaan kolam terpal sejatinya dapat mencegah keberadaan dan perkembangan parasit pada ikan budidaya. Sumber air yang digunakan juga harus selalu diperhatikan.
Sensitivity Analysis of Criteria Weights in Selecting a Backend Web Framework Using the SMART Method: Analisis Sensitivitas Bobot Kriteria pada Pemilihan Framework Web Backend Menggunakan Metode SMART Sasa Ani Arnomo; Mhd Adi Setiawan Aritonang; Hendri Kremer; Joni Eka Candra; Faradiba Jabnabillah; M. Yunior Fathurrahman Mahardika
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 2, September 2026 :JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i2.30208

Abstract

There are differing perceptions regarding framework usage. A key challenge in developing enterprise-scale Software as a Service (SaaS) applications is selecting the right backend web framework. This study addresses this challenge by applying the Simple Multi-Attribute Rating Technique (SMART) method. A total of 15 popular frameworks were evaluated based on eight relevant criteria. The criteria used were code quality & Structure, Stability & Mature, Ecosystem & Support, Security, Scalability, Performance, Maintenance Cost, and Ease of Integration. Evaluation data and initial scores (scale 1–10) were collected using expert judgment by five experts through a standardized utility value function, synthesized with quantitative testing data from TechEmpower Web Framework Benchmarks and empirical metrics from GitHub repositories. The initial SMART scores placed Spring Boot (Java) and ASP.NET Core (C#) as the top choices, followed by Django (Python), Laravel (PHP), and NestJS (Node.js). To validate these decisions, a Sensitivity Analysis was conducted by varying the weighting of the security and performance criteria. The combination of the SMART method and sensitivity analysis offers a transparent and adaptive framework for multi-criteria decision-making. The first-ranked framework, Spring Boot (Java), demonstrated a very high level of robustness to changes in evaluation criteria priorities. ABSTRAK (Bahasa Indonesia) Terdapat perbedaan persepsi mengenai penggunaan framework. Permasalahan kunci dalam pengembangan aplikasi Software as a Service (SaaS) berskala enterprise adalah memilih framework web backend yang tepat. Studi ini mengatasi tantangan tersebut dengan menerapkan metode Simple Multi-Attribute Rating Technique (SMART). Sebanyak 15 framework populer dievaluasi berdasarkan delapan kriteria relevan. Kriteria yang digunakan adalah kualitas kode & Struktur, Stabilitas & Mature, Ekosistem & Dukungan, Keamanan, Skalabilitas, Performa, Biaya Maintenance, dan Kemudahan Integrasi. Data evaluasi dan skor awal (skala 1–10) dihimpun menggunakan metode expert judgment oleh 5 orang pakar melalui fungsi nilai utilitas terstandardisasi, yang disintesis dengan data pengujian kuantitatif TechEmpower Web Framework Benchmarks serta metrik empiris repositori GitHub. Hasil skor SMART awal menempatkan Spring Boot (Java) dan ASP.NET Core (C#) sebagai pilihan utama, diikuti oleh Django (Python), Laravel (PHP), dan NestJS (Node.js). Untuk memvalidasi keputusan ini, Analisis Sensitivitas dilakukan dengan memvariasikan bobot kriteria keamanan dan performa. Kombinasi metode SMART dan Analisis Sensitivitas menawarkan kerangka kerja transparan dan adaptif untuk pengambilan keputusan multi-kriteria. framework peringkat pertama yaitu Spring Boot (Java) menunjukkan tingkat kekokohan (robustness) yang sangat tinggi terhadap perubahan prioritas kriteria evaluasi.
Potential of Alum–Chitosan Adjuvant Combination in Enhancing the Immunogenicity of Virus-Like Particles (VLP)-Based Vaccines: A Literature Review: Potensi Kombinasi Adjuvan Alum–Chitosan dalam Meningkatkan Imunogenisitas Vaksin Berbasis Virus-Like Particles (VLP): Tinjauan Literatur Ardhista Shabrina Fitri; Adhyatmika; Soni Siswanto
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 2, September 2026 :JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i2.30289

Abstract

Virus-like particle (VLP)-based vaccines are a promising vaccine platform because they mimic the structure of viruses without containing genetic material, making them safe and highly immunogenic. However, VLPs often require adjuvants to enhance an optimal immune response. Alum is a classic, long-used adjuvant known to primarily induce humoral immune responses through the T helper 2 (Th2) pathway, while chitosan is a natural biopolymer capable of enhancing humoral and cellular immune responses through the activation of antigen-presenting cells and the inflammasome. The purpose of this article is to review the mechanisms of action of alum and chitosan as adjuvants in VLP vaccines and to evaluate the potential of their combination in enhancing immune responses. The method used was a literature review of research articles published after 2017 and accessible online. This review demonstrates two important points. First, alum plays a role in enhancing antibody formation and Th2-type immune responses through mechanisms such as antigen depot formation and NOD-like receptor family pyrin domain-containing 3 (NLRP3) activation. Second, chitosan helps increase antigen absorption, supports dendritic cell function, and produces a more balanced immune response between T helper 1 (Th1) and Th2. These differences in mechanisms indicate that the alum-chitosan combination is complementary, resulting in a synergistic effect by simultaneously enhancing the humoral immune response by alum and the cellular immune response by chitosan. Therefore, the combination of these two adjuvants has the potential to increase the effectiveness of VLP vaccines against various infectious diseases. ABSTRAK (Bahasa Indonesia) Vaksin berbasis virus-like particles (VLP) merupakan platform vaksin yang menjanjikan karena mampu meniru struktur virus tanpa mengandung materi genetik, sehingga aman dan sangat imunogenik. Namun, VLP sering memerlukan adjuvan untuk meningkatkan respons imun yang optimal. Alum merupakan adjuvan klasik yang telah lama digunakan dan diketahui terutama menginduksi respons imun humoral melalui jalur T helper 2 (Th2), sedangkan chitosan merupakan biopolimer alami yang mampu meningkatkan respons imun humoral dan seluler melalui aktivasi sel penyaji antigen dan inflammasome. Tujuan artikel ini adalah untuk meninjau mekanisme kerja alum dan chitosan sebagai adjuvan pada vaksin VLP serta mengevaluasi potensi kombinasi keduanya dalam meningkatkan respons imun. Metode yang digunakan adalah studi literatur terhadap artikel penelitian yang dipublikasikan setelah tahun 2017 dan dapat diakses secara daring. Kajian ini menunjukkan dua poin penting. Pertama, alum berperan dalam meningkatkan pembentukan antibodi dan respons imun tipe Th2 melalui mekanisme seperti pembentukan depot antigen dan aktivasi NOD-like receptor family pyrin domain containing 3 (NLRP3) dan pembentukan depot antigen. Kedua, kitosan membantu meningkatkan penyerapan antigen, mendukung kerja sel dendritik, serta menghasilkan respons imun yang lebih seimbang antara T helper 1 (Th1) dan Th2 yang lebih seimbang. Perbedaan mekanisme ini menunjukkan bahwa kombinasi alum–chitosan bersifat komplementer, sehingga menghasilkan efek sinergis melalui peningkatan simultan respons imun humoral oleh alum dan respons imun seluler oleh chitosan. Dengan demikian, penggunaan kombinasi kedua adjuvan ini berpotensi meningkatkan efektivitas vaksin VLP terhadap berbagai penyakit infeksi.
Exploration of Lignocellulose-Degrading Bacteria from Sugarcane Bagasse for Potential Bioaugmentation in Biogas Production Arinta Agnie Dewantari; Francisco Marceliano Putra Hidayanta; Uswatun Nurkhasanah; Dewi Kartika; Kholifatur Rosyida; Mitha Fhusfita; Sisilia Claudia Dhana
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 2, September 2026 :JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i2.30298

Abstract

Indonesia is still highly dependent on fossil fuels, with renewable energy contributing only around 15% of the national energy mix by the end of 2025. This remains far from the national New and Renewable Energy (EBT) policy target of 23% by 2025. One EBT processing potential that has not been fully explored is agro-industrial waste, especially bagasse produced from sugarcane processing. This waste has a high lignocellulose content, so it has great potential to be processed into biogas as an alternative energy source. To increase biogas production from biomass, lignocellulose requires pretreatment. This research uses bioaugmentation technology with indigenous microbes isolated from bagasse in the pretreatment process. This research aimed to obtain indigenous bacterial isolates with lignocellulose-degrading enzyme activity from bagasse. Methods included isolating indigenous bacteria, quantifying bacterial populations, characterizing lignocellulose-degrading bacteria, identifying bagasse bacterial isolates, and performing molecular identification and phylogenetic analysis of the isolates. The results obtained were two bacterial isolates from bagasse samples. Isolate code B showed lignocellulose-degrading activity, with a significant cellulolytic index (IS) of 1.15, indicating its potential as a bioaugmentation candidate for enhancing biogas production.  Based on macroscopic and microscopic observations, the bacterial isolate is suspected to belong to the genus Bacillus sp. Based on sequence analysis using BLAST against the NCBI database and phylogenetic analysis using the Neighbor-Joining method, isolate B was found to belong to the Bacillus subtilis species complex.
Clustering of Rainfall Patterns in Selected Regions of Central Java Using Gaussian Mixture Models: Klasterisasi Pola Curah Hujan di Wilayah Terpilih di Jawa Tengah Menggunakan Gaussian Mixture Model Tiara Safitri; Hindayati Mustafidah; Elindra Ambar Pambudi; Maulida Ayu Fitriani
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 2, September 2026 :JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i2.32038

Abstract

Regional differences in rainfall characteristics, driven by varying meteorological and geographical conditions, necessitate clustering methods capable of identifying rainfall patterns more representatively. This study aims to cluster rainfall patterns in selected regions of Central Java using the Gaussian Mixture Model (GMM), based on average temperature (TAVG), average relative humidity (RH_AVG), sunshine duration (SS), and average wind speed (FF_AVG). Daily meteorological data for 2025 were obtained from the BMKG, covering Banjarnegara Regency, Cilacap Regency, Tegal Regency, and Semarang City. The research process involved data preprocessing, normalization via MinMax Scaling, GMM modeling, determining the optimal number of clusters using the Bayesian Information Criterion (BIC), and evaluating cluster quality using the Silhouette Score and Davies–Bouldin Index (DBI). The results indicate that the optimal number of clusters is eight, with a minimum BIC value of −29305.52589. Evaluations using the Silhouette Score and Davies–Bouldin Index show that Cluster 7 (Cool-Humid Pattern) exhibits consistent clustering quality across both metrics, while Cluster 5 (Cool-Very Humid Pattern) also demonstrates good cluster quality; meanwhile, characteristic analysis identified meteorological pattern differences between coastal areas—which tend to be warm and sunny—and mountainous areas, which are cooler and more humid. This study contributes by applying the Gaussian Mixture Model as a probabilistic clustering approach to identify rainfall patterns based on meteorological characteristics, while providing information that could support water resource management, the agricultural sector, and hydrometeorological disaster mitigation. ABSTRAK (Bahasa Indonesia) Karakteristik curah hujan yang berbeda antarwilayah akibat variasi kondisi meteorologi dan geografis memerlukan metode klasterisasi yang mampu mengidentifikasi pola curah hujan secara lebih representatif. Penelitian ini bertujuan melakukan klasterisasi pola curah hujan di wilayah terpilih di Jawa Tengah menggunakan Gaussian Mixture Model (GMM) berdasarkan suhu rata-rata (TAVG), kelembapan relatif (RH_AVG), lama penyinaran matahari (SS), dan kecepatan angin rata-rata (FF_AVG). Data meteorologi harian tahun 2025 diperoleh dari BMKG yang mencakup Kabupaten Banjarnegara, Kabupaten Cilacap, Kabupaten Tegal, dan Kota Semarang. Tahapan penelitian meliputi preprocessing, normalisasi menggunakan MinMax Scaling, pemodelan GMM, penentuan jumlah cluster optimal menggunakan Bayesian Information Criterion (BIC), serta evaluasi kualitas cluster menggunakan Silhouette Score dan Davies–Bouldin Index (DBI). Hasil penelitian menunjukkan bahwa jumlah cluster optimal yang diperoleh adalah delapan cluster dengan nilai BIC terendah sebesar −29305,52589. Evaluasi menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan bahwa Cluster 7 (Pola Sejuk Lembap) memiliki kualitas pengelompokan yang konsisten pada kedua metode evaluasi, sedangkan Cluster 5 (Pola Sejuk Sangat Lembap) juga menunjukkan kualitas cluster yang baik., sedangkan analisis karakteristik mengidentifikasi perbedaan pola meteorologi antara wilayah pesisir yang cenderung hangat dan cerah dengan wilayah pegunungan yang lebih sejuk dan lembap. Penelitian ini memberikan kontribusi melalui penerapan Gaussian Mixture Model sebagai pendekatan klasterisasi berbasis probabilistik untuk mengidentifikasi pola curah hujan berdasarkan karakteristik meteorologi serta menyediakan informasi yang berpotensi mendukung pengelolaan sumber daya air, sektor pertanian, dan mitigasi bencana hidrometeorologi.
Sentiment Analysis of User Reviews of Purwokerto Station Using TF-IDF and the Random Forest Method : Analisis Sentimen Ulasan Pengguna Terhadap Stasiun Purwokerto menggunakan TF-IDF dan Metode Random Forest Diva Nur Syafika; Hindayati Mustafidah; Maulida Ayu Fitriani; Elindra Ambar Pambudi
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 2, September 2026 :JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i2.32087

Abstract

User reviews on Google Maps serve as a valuable source of information for evaluating the service quality of public facilities, including train stations. This study aims to analyze the sentiment of user reviews for Purwokerto Station using the Random Forest algorithm and to identify dominant Bigrams within each sentiment class to provide insights for station management. The dataset was obtained by scraping Google Maps, yielding 2,232 reviews after data cleaning. The research process involved sentiment labeling based on ratings; text preprocessing (cleaning, case folding, tokenization, non-standard word normalization, stopword removal, and single-token deduplication); and feature extraction using Bigram-based Term Frequency–Inverse Document Frequency (TF–IDF). The dataset was split into 80% training data and 20% testing data, then classified using the Random Forest algorithm. Results indicate that the model achieved 90.16% accuracy on the test data. However, evaluation revealed that while the model excelled at recognizing the positive class, it struggled to identify negative and neutral classes; this was evidenced by a Macro F1-score of 0.32 and precision, recall, and F1-score values ​​of 0.00 for both the negative and neutral classes. This outcome was driven by an imbalanced data distribution, causing the model to lean toward predicting reviews as positive. Bigram analysis showed that the phrase "clean and comfortable"  dominated positive sentiment, whereas phrases such as "waiting room" and "ticket counter staff" more frequently in negative sentiment. ABSTRAK (Bahasa Indonesia) Ulasan pengguna pada platform Google Maps menjadi salah satu sumber informasi yang dapat dimanfaatkan untuk mengevaluasi kualitas pelayanan suatu fasilitas publik, termasuk stasiun kereta api. Penelitian ini bertujuan menganalisis sentimen ulasan pengguna Stasiun Purwokerto menggunakan algoritma Random Forest serta mengidentifikasi Bigram yang dominan pada setiap kelas sentimen sebagai bahan evaluasi bagi pengelola stasiun. Dataset diperoleh melalui scraping Google Maps dan menghasilkan 2.232 ulasan setelah melalui proses pembersihan data. Tahapan penelitian meliputi pelabelan sentimen berdasarkan rating, preprocessing text yang terdiri atas cleaning, case folding, tokenizing, normalisasi kata tidak baku, stopword removal, dan duplikasi token tunggal, kemudian dilanjutkan dengan ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF–IDF) berbasis Bigram. Dataset dibagi menjadi data pelatihan sebesar 80% dan data pengujian sebesar 20%, kemudian diklasifikasikan menggunakan algoritma Random Forest. Hasil penelitian menunjukkan bahwa model memperoleh akurasi sebesar 90,16% pada data pengujian. Namun, hasil evaluasi juga menunjukkan bahwa model memiliki kemampuan yang sangat baik dalam mengenali kelas positif, tetapi belum mampu mengidentifikasi kelas negatif dan netral, yang ditunjukkan oleh nilai Macro F1-score sebesar 0,32 serta nilai precision, recall, dan F1-score sebesar 0,00 pada kedua kelas tersebut. Kondisi tersebut dipengaruhi oleh distribusi data yang tidak seimbang sehingga model cenderung memprediksi ulasan ke kelas positif. Analisis Bigram menunjukkan bahwa frasa bersih nyaman mendominasi sentimen positif, sedangkan frasa seperti ruang tunggu dan petugas loket lebih banyak muncul pada sentimen negatif. Penelitian ini menunjukkan bahwa representasi TF–IDF Bigram mampu menangkap konteks frasa yang membedakan setiap kategori sentimen serta memberikan informasi mengenai aspek pelayanan yang paling sering diapresiasi maupun dikeluhkan oleh pengguna.  

Page 24 of 24 | Total Record : 236

 20   21   22   23   24 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Volume 10 No. 2, September 2026 :JRST Volume 10 No. 1, March 2026: JRST Volume 9 No. 2 September 2025: JRST Volume 9 No. 1 Maret 2025: JRST Volume 8 No. 2 September 2024: JRST Volume 8 No. 1 Maret 2024: JRST Volume 7 No. 2 September 2023: JRST Volume 7 No. 1 Maret 2023: JRST Volume 6 No. 2 September 2022: JRST Volume 6 No. 1 Maret 2022: JRST Volume 5 No. 2 September 2021: JRST Volume 5 No. 1 Maret 2021: JRST Volume 4 N0. 2 September 2020: JRST Volume 4 No. 1 Maret 2020: JRST Volume 3 N0. 1 Maret 2019: JRST Volume 3 No. 2 September 2019: JRST Volume 2 No. 2 September 2018: JRST Volume 2 No. 1 Maret 2018: JRST Volume 1 No. 2 September 2017: JRST Volume 1 No. 1 Maret 2017: JRST More Issue