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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
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Implementation of Smart Detection in Risk Monitoring and Flood Mitigation in Bengkayang Regency Candra Gudiato; Santi Thomas; Rossi Gawida; Janice Thea Sumar
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.v10i2.29050

Abstract

Bengkayang Regency is one of the areas prone to flooding due to extreme rainfall, poor drainage systems, and low vegetation. This research aims to develop an Internet of Things (IoT)-based Smart Detection system for real-time flood risk monitoring and mitigation. The scientific contribution of this study lies in the integration of three main parameters—water level, rainfall, and Normalized Difference Vegetation Index (NDVI)—which are processed using the Simple Additive Weighting (SAW) method to determine flood vulnerability levels. Test results at four research locations show that Teriak District (Vi = 0.964) and Lumar District (Vi = 0.939) are identified as "safe" areas, while Seluas District (Vi = 0.489) and Ledo District (Vi = 0.640) have "alert" status that requires special attention from the local government. The Smart Detection system is capable of providing early warnings for incoming floods and is expected to contribute to the local government's more holistic mitigation decision-making and enhance community preparedness for potential disasters. ABSTRAK (Bahasa Indonesia) Kabupaten Bengkayang merupakan salah satu wilayah yang rawan banjir, akibat curah hujan ekstrem, buruknya sistem drainase, dan rendahnya vegetasi. Penelitian ini bertujuan untuk mengembangkan sistem Smart Detection berbasis Internet of Things (IoT) untuk pemantauan risiko dan mitigasi banjir secara real-time. Kontribusi ilmiah penelitian ini terletak pada integrasi tiga parameter utama, yaitu ketinggian air, curah hujan, dan tingkat kehijauan lahan (NDVI), yang diolah menggunakan metode Simple Additive Weighting (SAW) untuk menghasilkan keputusan tingkat kerawanan banjir. Hasil pengujian pada empat lokasi penelitian menunjukkan bahwa Kecamatan Teriak (Vi= 0,964) dan Kecamatan Lumar (Vi=0,939) teridentifikasi sebagai wilayah “aman” dari banjir, sedangkan Kecamatan Seluas (Vi= 0,489) dan Kecamatan Ledo (Vi= 0,640) memiliki tingkat kerawanan dengan status “waspada” yang memerlukan perhatian khusus dari pemerintah daerah setempat.  Sistem Smart Detection mampu memberikan peringatan dini akan datangnya banjir dan diharapkan dapat berkontribusi bagi pemerintah daerah setempat dalam pengambilan keputusan mitigasi yang lebih holistik serta meningkatkan kesiapsiagaan masyarakat terhadap potensi bencana banjir.
Innovating Transport Safety Management through Lean Construction in Bridge Infrastructure Projects: Inovasi Manajemen Keselamatan Transportasi Berbasis Lean Construction pada Proyek Infrastruktur Jembatan Citra Pradipta Hudoyo; Muhammad Edwin Rachmanudin; Diyah Ayu Widayanti
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.v10i2.29222

Abstract

Bridge infrastructure projects in Indonesia often face transport safety challenges due to overlapping construction areas with active traffic lanes. This study aims to identify waste categories in bridge projects and analyze their implications for transport safety. A mixed-methods approach was employed, combining literature review, questionnaire surveys of 197 respondents (contractors, consultants, and field workers), expert interviews, and field observations. The research instrument was tested for validity (r > 0.3) and reliability (Cronbach's Alpha = 0.872), while risk analysis was conducted using Failure Mode and Effects Analysis (FMEA) with parameters of Severity, Occurrence, and Detection. The findings indicate that all eight Lean Construction waste categories were identified. Defects/Rework had the highest perceived risk for workers (mean = 4.1), while Transportation ranked highest for the public (mean = 4.2). FMEA analysis confirmed the highest RPN values for Defects/Rework (381), Transportation (364), and Inventory (333) all classified as high risk. Mitigation strategies focused on quality improvement, logistics optimization (including Just-in-Time), and managerial reinforcement through daily huddles and visual management. This study concludes that Lean Construction is not only effective in reducing waste but also serves as a strategic approach to enhancing transport safety in bridge projects. ABSTRAK (Bahasa Indonesia) Proyek infrastruktur jembatan di Indonesia sering menghadapi tantangan keselamatan transportasi karena tumpang tindih area konstruksi dengan jalur lalu lintas aktif. Penelitian ini bertujuan mengidentifikasi kategori waste pada proyek jembatan serta menganalisis implikasinya terhadap keselamatan transportasi. Pendekatan mixed methods digunakan, menggabungkan studi literatur, kuesioner terhadap 197 responden (kontraktor, konsultan, dan pekerja lapangan), wawancara pakar, serta observasi lapangan. Instrumen penelitian diuji validitas (r > 0,3) dan reliabilitas (Cronbach's Alpha = 0,872), sedangkan analisis risiko dilakukan dengan Failure Mode and Effects Analysis (FMEA) menggunakan parameter Severity, Occurrence, dan Detection. Hasil penelitian menunjukkan bahwa delapan kategori waste Lean Construction teridentifikasi. Defects/Rework memiliki persepsi risiko tertinggi bagi pekerja (mean = 4,1), sementara Transportation tertinggi bagi publik (mean = 4,2). Analisis FMEA mengonfirmasi nilai RPN tertinggi pada Defects/Rework (381), Transportation (364), dan Inventory (333) semua tergolong risiko tinggi. Strategi mitigasi difokuskan pada peningkatan mutu pekerjaan, optimalisasi logistik (termasuk Just in Time), serta penguatan manajemen melalui daily huddle dan visual management. Penelitian ini menyimpulkan bahwa Lean Construction tidak hanya efektif mengurangi pemborosan, tetapi juga menjadi pendekatan strategis untuk meningkatkan keselamatan transportasi pada proyek jembatan
Selected Pharmacognostic Characters of Vasconcellea pubescens Leaf Crude Drugs Artha Sudibyo; Dilla Maulida; Elza Sundhani; Alwani Hamad; Dwi Hartanti
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.v10i2.29232

Abstract

Mountain papaya (Vasconcellea pubescens A.DC.) is a highland species introduced to the Dieng Plateau in Central Java. It has strong cultural significance but limited scientific documentation on its medicinal use. No crude drugs from these plants have been included in the Indonesian Herbal Pharmacopeia. This study aimed to establish baseline pharmacognostic specifications for mountain papaya leaf crude drugs collected from three areas of the Dieng Plateau. The plant materials were collected from three locations in Dieng, and their identities were authenticated accordingly. The microscopical morphology, thin-layer chromatography (TLC) profile, ash values, water-extractable value, total flavonoid content (TFC), and total phenolic content (TPC) of the crude drugs were analyzed in accordance with the IHP standard methods. Botanical authentication confirmed the samples as Vasconcellea pubescens A.DC. Powder microscopy revealed characteristic fragments, including parenchyma cells, trichomes, mesophyll tissues, calcium oxalate crystals, and spiral vessels. TLC profiling demonstrated a consistent phytochemical fingerprint across samples, with 12 resolved spots, and caffeic acid was tentatively identified as a reference marker (Rf 0.63). Physicochemical evaluation showed high total ash (9.92-10.18%) and acid-insoluble ash (1.76-3.0%) values. Water extractable value (28.95-40.72%) varied across sample collection locations. Quantitative analysis showed a relatively high TPC (5.33±0.09%) compared with TFC (0.38±0.01%). Collectively, these findings provide the first pharmacognostic characteristics for mountain papaya leaf crude drugs from Dieng and offer reference data to support quality control and standardization.
Drug Demand Forecasting Using Holt's Linear Trend and ARIMA Methods (A Case Study of Omeprazole at Primary Clinic UIN Sunan Kalijaga, Indonesia): Peramalan Penggunaan Obat Menggunakan Metode Holt's Linear Trend dan ARIMA (Studi Kasus Obat Omeprazole di Klinik Pratama UIN Sunan Kalijaga, Indonesia) Alfinatul Aisyah; Muhammad Naufal Daffa Satria; Titi Sari
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.v10i2.29505

Abstract

Drug inventory management in primary healthcare facilities often faces challenges due to unstable demand fluctuations, leading to risks of stockouts or excess stock accumulation. This study aims to analyze the historical usage patterns of omeprazole and compare the accuracy of Holt's Linear Trend and ARIMA methods as a basis for recommending a forecasting system to support drug inventory management at Pri-mary Clinic UIN Sunan Kalijaga. The data used consist of 57 monthly observations from January 2021 to September 2025, partitioned into 80% training data and 20% testing data. Holt's Linear Trend was applied with parameter estimation using SSE minimization through Microsoft Excel Solver, yielding optimal values of α = 0.195919 and β = 1. ARIMA was applied through order identification, parameter estimation, and residual diagnostic stages, producing the best model ARIMA(0,1,1) which satisfied the white noise and residual normality assumptions. Evaluation results indicate that Holt's Linear Trend achieved better accuracy on testing data with an MSE of 17,256.57 and MAPE of 22.75%, compared to ARIMA(0,1,1) with an MSE of 21,325.42 and MAPE of 27.86%. The superiority of Holt's Linear Trend is supported by data characteristics reflecting an increasing trend without a dominant seasonal pattern, along with a β value of 1 that enables the trend component to be updated maximally in response to data dynamics. Based on these findings, Holt's Linear Trend is recommended as a forecasting model to support inventory control efficiency and reduce the risk of drug shortages at Primary Clinic. ABSTRAK (Bahasa Indonesia) Pengelolaan persediaan obat di fasilitas pelayanan kesehatan primer seringkali menghadapi tantangan akibat fluktuasi permintaan yang tidak stabil, sehingga memicu risiko kekosongan atau penumpukan stok. Penelitian ini bertujuan untuk menganalisis pola historis penggunaan obat Omeprazole serta membandingkan akurasi metode Holt’s Linear Trend dan ARIMA sebagai dasar rekomendasi sistem peramalan untuk manajemen persediaan obat di Klinik Pratama UIN Sunan Kalijaga. Data yang digunakan adalah data bulanan periode Januari 2021 hingga September 2025 sebanyak 57 observasi yang dipartisi menjadi 80% data training dan 20% data testing. Metode Holt’s Linear Trend diterapkan dengan estimasi parameter menggunakan minimisasi SSE melalui Solver Microsoft Excel, menghasilkan nilai optimal α = 0,195919 dan β = 1. Metode ARIMA diterapkan melalui tahap identifikasi orde, estimasi parameter, dan diagnostik residual, menghasilkan model terbaik ARIMA(0,1,1) yang memenuhi asumsi white noise dan normalitas residual. Hasil evaluasi menunjukkan bahwa Holt’s Linear Trend memberikan akurasi lebih baik pada data testing dengan MSE 17.256,57 dan MAPE 22,75%, dibandingkan ARIMA(0,1,1) yang menghasilkan MSE 21.325,42 dan MAPE 27,86%. Keunggulan Holt’s Linear Trend didukung oleh karakteristik data yang menunjukkan tren meningkat tanpa pola musiman yang dominan, serta nilai β = 1 yang memungkinkan komponen tren diperbarui secara maksimal mengikuti dinamika data. Berdasarkan temuan ini, Holt’s Linear Trend direkomendasikan sebagai model peramalan untuk mendukung efisiensi pengendalian persediaan dan menekan risiko kekurangan obat di Klinik Pratama.
Secondary Structure Analysis of Fermented Bovine Collagen with Varying Fermentation Times Using FTIR: Analisis Struktur Sekunder Kolagen Sapi Terfermentasi dengan Variasi Waktu Menggunakan FTIR Dhiksa Yudhanata Eksa Pratama; Luthfi Ahmad Muchlashi; Tri Yudani Mardining Raras
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.v10i2.29560

Abstract

Collagen consumption as a supplement for skin health and musculoskeletal diseases is increasing in Indonesian society. However, due to its large molecular size, it reduces the efficiency of the delivery process. Therefore, collagen hydrolysis is carried out by fermentation into collagen peptides. This study aims to analyze the effect of the duration of the fermentation process of bovine collagen (FBCP = Fermented Bovine Collagen Peptide) on the secondary structure of collagen using an FTIR instrument. A comparative study of the infrared spectra profile was carried out on unfermented collagen (K-), fermented bovine collagen for 1, 2, 3, 4 and 5 weeks, and commercially hydrolyzed collagen peptides (K+). The results showed that the bovine collagen fermentation method successfully converted collagen into collagen peptides accompanied by changes in the secondary structure of collagen. Statistical tests showed significant differences between unfermented collagen (K-) and collagen fermented for 2, 3, 4 and 5 weeks with p = 0.065, p = 0.023, p = 0.005 and p = 0.002 respectively. There were also significant differences between hydrolyzed collagen peptides (K+) and fermented collagen peptides for 4 (p = 0.036) and 5 weeks (p = 0.018), with more peptide bond cleavage in the fermented group. The present study concludes that the duration of bovine collagen fermentation affects changes in the secondary structure of collagen and the formation of collagen peptides that are equivalent to or better than non fermented hydrolyzed collagen peptides. ABSTRAK (Bahasa Indonesia) Konsumsi kolagen sebagai suplemen kesehatan kulit dan penyakit muskuloskleletal semakin meningkat di masyarakat Indonesia. Namun demikian karena ukuran molekul yang besar, meyebabkan berkurangnya efisiensi proses penghantaran. Untuk itu dilakukan hidrolysis kolagen dengan cara fermentasi menjadi peptida kolagen. Studi ini bertujuan menganalisis pengaruh durasi proses fermentasi pada kolagen sapi terhadap struktur sekunder (FBCP=Fermented Bovine Collagen Peptide) melalui instrumen FTIR. Studi komparatif profil spektra inframerah dilakukan pada kolagen yang tidak difermentasi (K-), kolagen sapi terfermentasi selama 1, 2, 3, 4 dan 5 pekan, dan peptida kolagen hasil hidrolisis komersial (K+). Hasil penelitian menunjukkan bahwa metode fermentasi kolagen sapi berhasil mengonversi kolagen menjadi peptida kolagen yang disertai perubahan pada struktur sekunder kolagen. Uji statistik memperlihatkan perbedaan yang signifikan antara kolagen yang tidak difermentasi (K-) dan kolagen yang difermentasi selama 2, 3, 4 dan 5 pekan dengan p= 0,065, p = 0,023, p = 0,005 dan p = 0,002 berturut-turut. Terdapat pula perbedaan signifikan antara peptida kolagen hasil hidrolisis (K+) dan peptida kolagen hasil fermentasi selama 4 (p=0,036) dan 5 pekan (p=0,018), dengan pemutusan ikatan peptida yang lebih banyak pada kelompok fermentasi. Dari studi ini disimpulkan bahwa lama fermentasi kolagen sapi berpengaruh terhadap perubahan struktur sekunder kolagen dan pembentukan peptida kolagen yang setara atau lebih baik dibandingkan peptida kolagen hasil hidrolisis dengan proses nonfermentasi.
Efficient Three-Class Sentiment Classification of IMDb Reviews Using LoRA-Based Fine-Tuning on Pseudo-labeled Data Puguh Hiskiawan; Wendy Tjung; Dustin Darmawan Isya Widjaja; Stevan Wiyandi
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.v10i2.29622

Abstract

Sentiment analysis has become an important task in natural language processing for understanding public opinions expressed in online reviews. However, most publicly available IMDb datasets are limited to binary sentiment labels, which restricts the ability of sentiment analysis systems to capture neutral opinions. This study proposes an efficient sentiment analysis framework that transforms the binary IMDb dataset into a three-class sentiment classification problem consisting of positive, neutral, and negative sentiments. The proposed approach integrates pseudolabeling with Parameter-Efficient Fine-Tuning (PEFT) using the Low-Rank Adaptation (LoRA) technique on the Longformer architecture. Experimental results show that the model achieves an accuracy of 77.06%, a weighted F1-score of 72.17%, and a Matthews Correlation Coefficient (MCC) of 0.6232. The results demonstrate that LoRA-based fine-tuning can significantly reduce computational requirements while maintaining competitive performance in sentiment classification tasks. These findings indicate that the proposed framework provides a practical and computationally efficient solution for large-scale sentiment analysis, particularly for environments with limited computational resources.
Formulation of A Biofarmaka Mosquito Repellent Losion Using Kalamansi (Citrofortunella microcarpa) Essential Oil: Formulasi Losion Biofarmaka Repellent Nyamuk Berbasis Minyak Atsiri Jeruk Kalamansi (Citrofortunella microcarpa) Evi Renas Sidauruk; Yessy Rosalina; Devi Silsia
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.v10i2.29898

Abstract

Mosquito repellent lotions containing synthetic active ingredients such as N,N-diethyl-meta-toluamide (DEET) are highly effective but may cause skin irritation, and other adverse health effects, highlighting the need for safer plant-based alternatives. This study aimed to develop a mosquito repellent lotion formulated with calamansi (Citrofortunella macrocarpa) essential oil as a natural active ingredient and to evaluate its physicochemical characteristics and repellent efficacy. The study employed a non-factorial completely randomized design (CRD) consisting of five concentrations of calamansi essential oil (0%, 5%, 10%, 15%, and 20%), with three replications per treatment. The evaluated parameters included emulsion stability, pH, homogeneity, spread ability, and mosquito repellent activity. The results showed that all formulations exhibited 100% emulsion stability, pH values within the acceptable range (7.53-7.56), and good homogeneity based on both visual and microscopic observations. Spreadability increased from 6.32 cm at 0% essential oil concentration to 7,53 cm at 20% concentration. Repellent efficacy increased significantly with increasing essential oil concentration, with the longest protection time of 118 minutes achieved by the formulation containing 20% calamansi essential oil. These findings indicate that increasing the concentration of calamansi essential oil enhances the mosquito repellent efficacy, demonstrating its potential as natural alternatives to synthetic DEET-based mosquito repellent lotions. ABSTRAK (Bahasa Indonesia) Losion antinyamuk berbahan aktif sintetis seperti N,N-diethyl-meta-toluamide (DEET) diketahui efektif, namun berpotensi menimbulkan iritasi kulit dan efek samping lainnya, sehingga diperlukan alternatif repelen yang lebih aman berbasis bahan alam. Studi ini bertujuan mengembangkan formulasi losion dengan komponen aktif minyak atsiri kalamansi (Citrofortunella microcarpa) sebagai opsi repelen alami, serta mengevaluasi karakteristik fisikokimia dan efektivitasnya sebagai penolak nyamuk. Penelitian menggunakan Rancangan Acak Lengkap (RAL) nonfaktorial dengan lima taraf konsentrasi minyak atsiri kalamansi, yaitu 0%, 5%, 10%, 15%, dan 20%, masing-masing diulang tiga kali. Parameter yang diamati meliputi stabilitas emulsi, pH, homogenitas, daya sebar, dan aktivitas repelen terhadap nyamuk. Hasil penelitian menunjukkan bahwa seluruh formulasi memiliki stabilitas emulsi 100%, pH berada pada rentang aman (7,53–7,56), serta homogen secara visual dan mikroskopis. Daya sebar meningkat dari 6,32 cm pada konsentrasi 0% menjadi 7,53 cm pada konsentrasi 20%. Aktivitas repelen meningkat signifikan seiring bertambahnya konsentrasi minyak atsiri, dengan waktu proteksi terpanjang sebesar 118 menit pada konsentrasi 20%. Hasil penelitian menunjukkan bahwa peningkatan konsentrasi minyak atsiri kalamansi mampu meningkatkan efektivitas losion sebagai repelen tanpa menurunkan mutu fisik sediaan, sehingga berpotensi dikembangkan sebagai alternatif repelen alami pengganti losion berbahan aktif sintetis.
The Effect of Increasing the Dataset Size on the Performance of Sentiment Analysis Models for Tokopedia Reviews Using SVM, Naïve Bayes, and Perceptron Based on TF-IDF: Pengaruh Peningkatan Dataset terhadap Kinerja Model Analisis Sentimen Ulasan Tokopedia Menggunakan SVM, Naïve Bayes, dan Perceptron Berbasis TF-IDF A'yun Fa'yuni; Afril Efan Pajri; Ita Aristia Sa’ida
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.v10i2.29997

Abstract

The growth in user reviews on e-commerce platforms generates a large volume of data that has the potential to be utilised in sentiment analysis. This study aims to analyse the impact of increasing data volume on the performance of sentiment analysis models. The data was obtained through web scraping of 4,071 Tokopedia reviews on the Google Play Store. The data was then processed through a pre-processing stage comprising cleaning, normalisation, tokenisation, stopword removal, and stemming. Sentiment labelling was performed using a rating-based method supported by a lexicon, employing three identical machine learning algorithms: Support Vector Machine (SVM), Multinomial Naïve Bayes, and Perceptron, with TF-IDF feature representation. The results of the study show that Multinomial Naïve Bayes achieved the highest accuracy of 90.35%, followed by SVM at 88.93%, and Perceptron at 85.46%. All models showed an improvement in performance compared to previous research using a smaller dataset. This study demonstrates that the size of the dataset influences the accuracy and stability of sentiment analysis models ABSTRAK (Bahasa Indonesia) Pertumbuhan ulasan pengguna pada platform e-commerce menghasilkan volume data yang besar dan berpotensi dimanfaatkan dalam analisis sentimen. Penelitian ini bertujuan untuk menganalisis pengaruh penambahan jumlah data terhadap kinerja model analisis sentimen. Data diperoleh melalui web scraping ulasan Tokopedia di Google Play Store sebanyak 4.071 data. Data kemudian diproses melalui tahap pre-processing yang meliputi cleaning, normalisasi, tokenisasi, stopword removal, dan stemming. Pelabelan sentimen dilakukan menggunakan metode rating based dengan dukungan leksikon, dengan menggunakan tiga algoritma pembelajaran mesin yang sama, yaitu Support Vector Machine (SVM), Multinomial Naïve Bayes, dan Perceptron dengan representasi fitur TF-IDF. Hasil penelitian menunjukkan bahwa Multinomial Naïve Bayes memperoleh akurasi tertinggi sebesar 90.35%, diikuti SVM sebesar 88.93%, dan Perceptron sebesar 85.46%. Seluruh model mengalami peningkatan performa dibandingkan penelitian sebelumnya dengan dataset lebih kecil. Penelitian ini menunjukkan bahwa jumlah dataset berpengaruh terhadap akurasi dan stabilitas model analisis sentimen
Computational Modeling of Acoustic Resonance in Fluid-Filled Cavities for Physics Learning Rodika Utama; Moh. Toifur; Djamaluddin Perawironegoro
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.v10i2.30006

Abstract

Acoustic resonance in closed cavities is a fundamental phenomenon in wave physics influenced by system geometry, medium properties, and acoustic boundary conditions. This study aims to analyze the variation of acoustic resonance frequencies in fluid-filled cavities while exploring the role of computational modeling in supporting physics learning. The method employs a mathematical modeling approach based on the one-dimensional acoustic wave equation with impedance boundary conditions at the fluid–air interface. Numerical simulations are conducted to evaluate resonance frequency variation as a function of fluid height, and Fast Fourier Transform (FFT) analysis is used to identify dominant frequencies. The results show that increasing fluid height reduces the effective air-column length, leading to systematic shifts in resonance frequency. Furthermore, computational representations enable dynamic visualization of the relationship between physical variables and frequency changes, thereby supporting conceptual understanding in physics learning. These findings suggest that computational modeling can serve as an effective approach to bridge theoretical concepts and observable acoustic phenomena in educational contexts.
IoT-Based PLC – Factory I/O Integration for Optimizing Production Process Control: Case Study of Box Sorting Systems: Integrasi PLC – Factory I/O Berbasis IoT Untuk Optimalisasi Kendali Proses Produksi: Studi Kasus Sistem Penyortiran Box Angga Wahyu Aditya; Mentari Putri Jati; Hilmansyah; Ihsan; Al Ford Erlangga; Slamet Widodo; Desak Made Ristia Kartika
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.v10i2.30048

Abstract

Optimization of production processes through virtual technology has been developed across many aspects, including system design and control. This aims to streamline the production costs of optimization systems in industry. The approach is to design a box-sorting system using Factory IO, while the control system uses a Haiwell AC10S0R PLC and a B7H-E HMI. The box sorting control system can be done directly or indirectly. Direct control of the sorting system is carried out via connecting switches, either in physical form via a PLC or in virtual form on the HMI. Sensors are used to classify boxes into three categories: large, medium, and small. This box separation uses three actuators that will insert boxes into each container. The first actuator will insert large boxes into the first container if sensors 1, 2, and 3 are active high. The second actuator will insert medium boxes into the second container if sensors 1 and 2 are active high and sensor 3 is inactive. The third actuator will insert a small box if sensor 1 is active high and sensors 2 and 3 are inactive. Test results show the system can sort all three box categories according to the control logic, with a sorting success rate of 98% and average response times of 1.2 seconds for direct control and 2.2 seconds for remote control. It can also be controlled remotely based on IoT via Haiwell Cloud. ABSTRAK (Bahasa Indonesia) Optimalisasi proses produksi melalui teknologi virtual telah dikembangkan dalam banyak aspek, baik dalam desain sistem maupun pengendalian. Hal ini bertujuan dalam mengefisiensikan biaya produksi sistem optimalisasi pada dunia industri. Pendekatan yang digunakan adalah desain sistem penyortiran box menggunakan Factory IO, sedangkan sistem kendali menggunakan PLC Haiwell AC10S0R dan HMI B7H-E. Sistem kendali penyortiran box dapat dilakukan secara langsung maupun tidak langsung. Kendali sistem penyortiran secara langsung dilakukan berbasis saklar penghubung, baik dalam bentuk fisik melalui PLC maupun saklar penghubung virtual yang terdapat pada HMI. Sensor – sensor digunakan untuk mengklasifikasikan box ke dalam tiga kategori, yakni box besar, sedang, dan kecil. Pemisahan box ini menggunakan tiga buah aktuator yang akan memasukkan box ke masing-masing penampung. Aktuator pertama akan memasukkan box besar ke dalam penampung pertama apabila sensor 1, 2, dan 3 aktif high. Aktuator kedua akan memasukkan box sedang ke dalam penampung kedua apabila sensor 1 dan 2 aktif high dan sensor 3 tidak aktif. Aktuator ketiga akan memasukkan box kecil apabila sensor 1 aktif high dan sensor 2 dan 3 tidak aktif. Hasil pengujian menunjukkan sistem mampu menyortir ketiga kategori box sesuai logika kendali dengan tingkat keberhasilan penyortiran sebesar 98 % dengan rata-rata waktu respons sebesar 1,2 detik pada kendali langsung dan 2,2 detik pada kendali jarak jauh. Serta dapat dikendalikan dari jarak jauh berbasis IoT melalui Haiwell Cloud.

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