The utilization of Enterprise Resource Planning (ERP) systems in distribution companies is often not fully integrated with business processes and quality management systems, resulting in operational inefficiencies and weak service control. This condition indicates a gap between the potential of ERP as an integrated system and actual practices that still rely on manual processes and informal coordination. This study aims to analyze, evaluate, and formulate improvement recommendations for the maturity of ERP-related business processes during the post-implementation phase through the integration of the CMMI v3.0 model and the ISO 9001:2015 standard. The research employs a mixed-method approach with an evaluative–diagnostic design. The research subjects consisted of six assessors, three validators, and twenty-seven process users directly involved in operational and service activities. Data were collected through semi-structured interviews, questionnaires based on CMMI–ISO indicators, direct observation, and document review as supporting instruments. Data analysis was conducted using evidence-based SCAMPI C appraisal, quantitative descriptive analysis, and thematic qualitative analysis. The findings indicate that most practice areas remain at initial to basic capability levels, with a high dependence on manual work, weak service governance, and direct impacts on SLA compliance and service timeliness. Overall, the study concludes that the integration of CMMI v3.0 and ISO 9001:2015 is effective as a framework for evaluating and improving the maturity of ERP processes in the post-implementation phase. The implications of this study provide practical guidance for organizations to optimize ERP utilization, strengthen process governance, and foster a quality-oriented culture of continuous improvement.
Respiratory disorders remain a major health concern because conventional monitoring systems are often invasive and expensive, which limits their accessibility for long-term use. The demand for non-invasive, low-cost, and reliable respiratory monitoring technologies is increasing, particularly those that can be integrated into smart healthcare systems. This study aimed to design, develop, and evaluate a respiratory monitoring system based on a spiral Plastic Optical Fiber (POF) sensor integrated with Internet of Medical Things (IoMT) technology. The research employed an experimental design with a quantitative approach. The test subject consisted of one adult participant who was assessed under three breathing conditions: normal, fast, and slow. Data were collected using a POF sensor connected to an Arduino Uno, a phototransistor, an XBee module, and a Raspberry Pi, after which the results were visualized. Data analysis was carried out through sensitivity and resolution calculations. The findings showed that the spiral POF configuration with three windings produced the best performance, with voltage amplitudes of 0.220 V (normal), 0.205 V (fast), and 0.186 V (slow). The corresponding sensitivity and resolution values were 0.412 V/s (0.002 s), 0.246 V/s (0.0040 s), and 0.619 V/s (0.0016 s). These results confirm that the developed system is simple to fabricate, cost-effective, highly sensitive, and capable of providing real-time, non-invasive respiratory monitoring. The study concludes that integrating POF-based sensors into the IoMT platform is feasible for application in smart healthcare services. Furthermore, the findings highlight the potential of POF-based respiratory sensors to be developed into affordable health monitoring technologies.
Leafy vegetable cultivation, such as mustard greens (Brassica juncea) and celery (Apium graveolens), frequently encounters constraints due to unstable microclimatic conditions in open-field environments, including fluctuations in temperature and humidity as well as pest attacks. These conditions lead to significant declines in crop productivity and yield quality. This study aims to analyze the effectiveness and performance of an Internet of Things (IoT) based Smart Greenhouse prototype in optimizing microclimate control and measurably enhancing plant growth. This research employed an experimental approach using an engineering-based experimental design, encompassing system design, sensor calibration, functional testing, and plant growth trials. The experimental subjects consisted of 20 plants. Data were collected through direct observation, automated sensor logging, and plant growth documentation, utilizing validated and reliable instruments including DHT22, BH1750, YL-69, and DS3231 RTC sensors, which were tested for measurement accuracy and consistency. Data analysis was conducted using descriptive quantitative analysis and comparative analysis of mean growth between experimental groups. The results indicate that the Smart Greenhouse system successfully maintained temperature, humidity, and light intensity within optimal ranges, with sensor accuracy exceeding 95%. Furthermore, mustard greens exhibited a 28% higher growth rate compared to open-field cultivation, while celery demonstrated a more moderate improvement. The study concludes that IoT-based microclimate control is effective in enhancing horticultural crop productivity through precise and automated environmental regulation.
In recent years, global energy supply has remained heavily dependent on fossil fuels, highlighting the urgent need for renewable and clean energy sources. One promising alternative is hydrogen fuel cell technology. This study aims to examine the effect of variations in the electrode cross-sectional area immersed in the electrolyte on the electrical voltage generated by a hydrogen fuel cell system. The research employed an experimental method using two types of electrodes: ASTM C561 as the anode and AISI 304 stainless steel as the cathode. Three experimental variables were investigated, namely the percentage of electrode cross-sectional area immersed in the electrolyte, the concentration level of NaHCO₃, and the duration of electrical input applied to generate hydrogen and oxygen. The results indicate that at 100% immersion of the electrode cross-sectional area with a 50% NaHCO₃ concentration, the generated voltages were 1.91 V at 30 seconds, 1.93 V at 60 seconds, and 2.12 V at 90 seconds. The findings demonstrate that the hydrogen fuel cell is capable of producing stable and relatively constant electrical voltage, consistent with the principles of electrochemical reactions between hydrogen and oxygen. The experimental results further reveal that the magnitude of the generated voltage is influenced by factors such as hydrogen concentration, electrode surface area, as well as system temperature and humidity. The implications of this study provide a strong scientific basis for the potential utilization of hydrogen fuel cells as a clean and sustainable energy source. These findings may serve as a reference for the development of environmentally friendly alternative energy technologies, particularly in efforts to reduce dependence on fossil fuels and mitigate carbon emissions.
Rabbits are highly sensitive to temperature fluctuations, particularly during the neonatal phase when thermoregulatory mechanisms are not yet fully developed. Unstable environmental temperatures can induce thermal stress, reduce feed intake, inhibit growth, and increase mortality rates. This study aims to design and evaluate a temperature control system based on a Proportional–Integral–Derivative (PID) controller integrated with a carbon fiber filament heating element for rabbit kits housing, and to compare its performance with a conventional heating system. An experimental–applied approach was employed using a Completely Randomized Design (CRD), involving two treatments: carbon fiber heating with PID control and conventional heating with an on–off thermostat. Data collected included real-time cage temperature, energy consumption (kWh), rabbit welfare indicators (feed intake, respiration rate, and mortality), as well as qualitative data obtained through observation and farmer interviews. Quantitative analysis was conducted using descriptive statistics, ANOVA, Root Mean Square Error (RMSE), and Integral of Absolute Error (IAE), while qualitative data were analyzed thematically. The results demonstrate that the PID–carbon fiber system maintained temperature stability with an average deviation of 0.12 °C, significantly outperforming the control system (1.45 °C). Energy consumption was reduced by 23%, feed intake increased by 21%, respiration rate decreased by 16%, and mortality was lower under the PID-based system. The discussion highlights that the integration of carbon fiber filament heating with PID control enhances energy efficiency, ensures homogeneous heat distribution, and improves overall rabbit welfare. In conclusion, this system shows strong potential to reduce operational costs by up to 30% while increasing productivity, although challenges related to humidity effects on filament durability warrant further investigation.
Penelitian ini berfokus pada pentingnya penerapan strategi pengajaran yang lebih inovatif dalam sistem pendidikan. meskipun penerapan strategi pengajaran masih mengandalkan metode tradisional pendekatan tersebut sering kali tidak mampu memenuhi kebutuhan kualitas pendidikan saat ini. Tujuan dari penelitian ini untuk mengembangkan media digital interaktif berbasis Ispring Suite 11. Metode yang digunakan dalam penelitian ini yaitu Research and Development (R&D) menggunakan model Hannafin & Peck. Teknik pengumpulan data mengunakan lembar validasi, angket respon peserta didik dan tes. Teknik analisis data menggunakan persentase. Hasil penelitian menunjukan skor validitas ahli materi 94%, ahli bahasa 80% dan ahli media 95% dengan kategori sangat valid. Untuk penilaian keefektifan perangkat pembelajaran uji coba kelompok kecil sebanyak enam orang mendapatkan 92% dan uji coba kelompok besar sebanyak tiga belas orang mendapatkan 96% dengan kategori tinggi. Hasil pengembangan media ini berpengaruh terhadap peningkatan literasi sains yang dibuktikan pada penilaian tes yang dibuktikan dengan hasil statistik nilai Sig < nilai alpha atau 0,00<0,05, sehingga hipotesis penelitian diterima. Berdasarkan hasil penelitian tersebut maka media digital interaktif berbasis Ispring Suite 11 pada pelajaran IPAS muatan IPA kelas IV valid dan layak digunakan.
Cassava peel waste is a biomass that has not been optimally utilized, even though it has the potential as a raw material for activated carbon. The problems that arise are the low added value of this waste and the need for efficient technology to produce highly porous activated carbon. This study aims to analyze the effect of variations in microwave irradiation time on the pore characteristics of cassava peel-based activated carbon. This study uses a quantitative approach with a laboratory experimental design. The research subjects were activated carbon resulting from chemical and microwave activation treatments, with three trial groups, each consisting of one activated carbon sample processed for 10, 15, and 20 minutes. Data collection methods were carried out through characterization using X-ray Fluorescence (XRF), Scanning Electron Microscopy (SEM), and Ultraviolet-Visible (UV-Vis) spectroscopy instruments. Data were analyzed descriptively and comparatively to observe pore structure development, elemental composition, and optical properties between samples. The results showed that the longer the microwave activation time, the more the porosity and surface structure of the activated carbon developed, with sample AC20 showing the best pore morphology and the highest absorption value. The conclusion of this study indicates that a combination of chemical and microwave-based activation is effective in producing highly porous activated carbon from cassava peels. This research implies that cassava peel waste can be an alternative solution in developing environmentally friendly adsorbent materials for pollutant remediation applications in aquatic environments.
Water hyacinth (Eichhornia crassipes) is a fast-growing aquatic plant, floats on the surface of the water and is often considered a weed because it can cover the water surface, inhibit photosynthesis, reduce oxygen levels, clog airways, and disrupt human activities such as fishing, transportation, and irrigation. Therefore, an effective tool is needed to clean waters filled with water hyacinth. This study aims to design a water hyacinth cutting tool concept based on an airboat. In this study, a master design approach method is used to obtain an airboat design quickly and efficiently. The research subjects involved are the airboat structure design, including the hull shape and propulsion system, and ship stability calculations. The data collection method in this study involves a literature study of previous technical references, empirical data from similar ships, and the technical parameters of the engine and hull structure. Data analysis was carried out using marine engineering calculation methods. The study results show that the resulting airboat design meets the stability criteria according to IMO (International Maritime Organization) standards, and general arrangements can be produced quickly. Through this research, we can create innovative and applicable solutions to the water hyacinth problem, which often disrupts irrigation activities, water transportation, and tourism.
In the digital age and the 4.0 industrial revolution, the use of artificial intelligence (AI) technology in education has become increasingly urgent, particularly in technical vocational learning such as Network Security. This study aims to analyze the effect of using Gemini AI on students' interest and learning outcomes in Network Security at vocational high schools. This study employs a quantitative approach with a quasi-experimental design in the form of a non-randomized control group pre-test post-test design. The sample consists of two Grade XI TKJ classes at SMKN 1 Banyuanyar selected through purposive sampling. Data collection techniques include a questionnaire to measure learning interest and pre-test and post-test assessments to evaluate learning outcomes. Data were analyzed using the independent samples t-test, and effectiveness was assessed using Cohen’s d. The results showed a significant increase in both learning interest (d = 1.821) and learning outcomes (d = 3.560) in the experimental class after implementing Gemini AI, with a significance level of p < 0.001. These findings confirm that AI-based learning can be an effective solution in improving the quality of vocational education in the field of technology. This study contributes practically and theoretically to the development of adaptive learning methods and is recommended to be expanded to other contexts and subjects.
The detection process is often hampered by the limitations of edge devices, high latency of sending messages to the cloud, message loss due to burst traffic, and decreased model accuracy due to concept drift. The main objective of this research is to improve the accuracy and speed of IoT anomaly detection through a combination of speed layers and batch layers in an MQTT-based Lambda architecture, reducing end-to-end latency, suppressing the detection error rate, and testing the system's reliability under high data traffic conditions and when concept drift occurs. The type of research used is experimental research with a quasi-experimental approach. The research subjects involved data streams from approximately 40 IoT devices. The data collection method was carried out using an MQTT broker (Mosquitto/EMQX) connected to a stream processor for the speed layer. The research instruments included system logs, latency tracers, throughput meters, and a confusion matrix. Data analysis was done through preprocessing, anomaly detection modeling, and a performance comparison between the batch-only baseline and Lambda integration. The results show that integrating the Lambda architecture with the MQTT protocol can significantly improve the performance of IoT anomaly detection. The F1 score increased from 0.81 to 0.90, the end-to-end latency decreased from 1.8 seconds to 0.35 seconds, and the false positive rate decreased by 32% compared to the batch-only method. The system also proved more reliable under high data loads, with less than 0.2% message loss when using quality of service 1. Furthermore, the six-hour, despite concept drift training strategy successfully maintained detection accuracy at F1 ≥ 0.88 despite concept.
The low ability of students in understanding pronunciation indicates that English learning still faces serious challenges. This occurs because innovative learning media have not been applied optimally, resulting in conventional and less interactive teaching. This study aims to develop and test the effectiveness of Scratch 3.0 as a learning medium in improving students’ pronunciation comprehension by analyzing, evaluating, and validating the media comprehensively. The type of research used is development research with the ADDIE model. The research subjects consist of two trial groups: an experimental class with 30 students and a comparison group with 32 students. Research data were collected through media expert validation, material expert validation, student response questionnaires, as well as learning achievement tests in the form of pretests and posttests. The research instruments included validation sheets, questionnaires, and test items to ensure the quality and effectiveness of the media. Data analysis was conducted using descriptive quantitative methods through validity tests, feasibility assessments, and calculations of students’ learning improvement. The results of the study show that Scratch 3.0 is proven to be valid, feasible, and effective in significantly improving students’ pronunciation comprehension. Thus, it can be concluded that the development of this media successfully addresses the problem of students’ low understanding of pronunciation. The implication of this research is that Scratch 3.0 can be used as an alternative innovative learning medium that encourages students to be more active, independent, and motivated in learning English.
Waste level monitoring is still often done manually, making it inefficient in preventing accumulation. Ultrasonic sensors are widely used because they are practical and affordable, but their accuracy is often affected by environmental and hardware conditions. This study aims to compare the Kalman Filter and Exponential Moving Average methods to improve the accuracy of ultrasonic sensor readings in an automated waste monitoring system. The type of research used is an experiment with a microcontroller-based system that is tested on various waste height variations. The Kalman Filter combines previous estimates with new data, while the Exponential Moving Average gives more weight to the most recent value. The performance of both methods is assessed based on measurement consistency and error rate.The data was then analyzed quantitatively using Root Mean Square Error (RMSE).The results show that the Kalman Filter produces lower errors and more stable data compared to the Exponential Moving Average or raw data. In conclusion, the Kalman Filter is more effective in improving the reliability and accuracy of the automated waste monitoring system. The implications of this research suggest that selecting the right sensor type can significantly improve system performance in detecting waste capacity in real time.
Bioremediasi limbah agroindustri kopi berupa air limbah dan kulit kopi, serta eceng gondok menghasilkan dua jenis luaran yakni biogas sebagai sumber energi dan lumpur berpotensi dimanfaatkan sebagai pupuk kompos. Tujuan penelitian ini yakni mengidentifikasi nilai C/N dan merekomendasikan kompos terbaik dari lumpur hasil bioremediasi limbah agroindistri kopi dan eceng gondok dalam mendukung pertumbuhan vegetatif tanaman tomat. Tahapan kajian terdiri atas penyiapan lumpur biogas dari tiga jenis feeding yakni limbah pengolahan kopi, eceng gondok, dan campuran limbah pengolahan kopi dan eceng gondok, pembenihan dan pemeliharaan tanaman tomat, identifikasi unsur hara dan nilai C/N, aplikasi kompos, pengamatan perubahan vegetatif yakni luas daun, diameter batang, dan tinggi tanaman, serta analisis data. Nilai C/N kompos lumpur proses biogas dari limbah pengolahan kopi dan eceng gondok yakni 20 – 35. Kompos lumpur proses biogas campuran kulit kopi dan eceng gondok menunjukkan hasil terbaik dalam mendukung pertambahan parameter luas daun tanaman tomat sebesar 26,50 ± 0,09%.
Climate change is the long-term shift in weather patterns from the tropics to the polar regions. This global threat is starting to materialize and is putting pressure on many industries. This study aims to test and compare the performance of eight machine learning models in classifying sentiment related to climate change to find the most accurate and effective model. Thousands of people share their thoughts daily through tweets on the popular microblogging platform Twitter (X). Twitter (X) is a fantastic source for information about public opinion and perceived risks of problems. One of the hot topics being discussed on Twitter is climate change. Climate change is a well-known and rapidly growing topic of study in sentiment analysis in NLP and text classification. This study used LR, SVM, XGB, DT, RF, NB, KNN, and GBM algorithms to examine the issue of climate change. The dataset was obtained from Kaggle and grouped into four sentiment polarities: "News," "Pro," "Neutral," and "Anti," which were then divided into 80% training data and 20% testing data. SMOTE was used to handle imbalanced data in the sentiment polarity classes. With an accuracy of 73.92%, an F1-Score (Macro) of 0.645, and an F1-Score (Weighed) of 0.727, the SVM-Linear algorithm outperformed all algorithms used in the study. In conclusion, the BERT model provides the highest accuracy in classifying climate change-related sentiment compared to the other seven models. This implication provides a scientific basis for selecting the most accurate and efficient machine learning model for detecting public sentiment related to climate change, thus supporting more responsive environmental policymaking.
Fly ash dan bottom ash (FABA) di Kota Sawahlunto dimanfaatkan sebagai material penimbunan kembali lahan bekas tambang. Penimbunan FABA dalam jumlah besar dikhawatirkan berdampak negatif terhadap air tanah. Penelitian ini bertujuan untuk menganalisis konsentrasi logam Pb dan persebarannya menggunakan metode response surface methodology in design of experiments, serta merumuskan rekomendasi berdasarkan kajian. Penelitian dilakukan melalui 3 kali pengambilan sampel air tanah pada 3 lokasi dengan jarak 0 m, 365 m, dan 730 m. Metode pengambilan sampel mempedomani SNI 6989.58.2008, dan dianalisis di laboratorium mempedomani SNI 6989-84:2019. Kualitas air tanah lokasi kajian tidak memenuhi standar kualitas air minum dimana konsentrasi Pb 0,144 mg/L melebihi baku mutu (0,01 mg/L). Penimbunan FABA pada areal bekas tambang berkontribusi terhadap pencemaran air tanah dengan parameter logam Pb. Pola persebaran logam Pb menurun berdasarkan pertambahan jarak, dan cenderung naik seiring dengan bertambahnya waktu. Penelitian selanjutnya perlu mengkaji jarak aman lokasi penimbunan FABA dengan sumber air tanah.
Dillenia alata (Dilleniaceae) digunakan oleh masyarakat lokal Papua sebagai obat tradisional. Namun, informasi ilmiah tentang fitokimia dan bioaktivitas tumbuhan D. alata masih sangat terbatas. Penelitian ini bertujuan menguji kandungan senyawa (fitokimia) secara kualitatif, kandungan fenolik dan flavonoid, serta aktivitas antioksidan berbagai ekstrak kulit batang tumbuhan Dillenia alata. Penelitian ini menggunakan metode eksperimen yang meliputi beberapa tahap yaitu persiapan sampel, uji kualitatif fitokimia, uji TPC dan TFC serta uji antioksidan. Ekstrak metanol D. alata mempunyai nilai TPC dan TFC tertinggi yaitu sebesar 279,89 mg GAE pergram ekstrak kering dan 38 mg QE pergram ekstrak kering. Ekstrak metanol juga menunjukkan kapasitas antioksidan tertinggi secara uji DPPH, ABTS, dan FRAP dengan nilai IC50 DPPH 1,19 μg/mL, IC50 ABTS 4,55 μg/mL, dan nilai FRAP 156,42 μM Fe2+/g. Hasil-hasil ini menunjukkan bahwa kulit batang D. alata merupakan salah satu sumber antioksidan yang baik.
Salah satu peran data mining yang populer adalah peran klasifikasi, peran klasifikasi adalah teknik mengelombokan data dengan pola tertentu. Akan tetapi peran klasifikasi sering menghadapi permasalah pada proses pemindaian yang lamban pada dataset berdimensi tinggi sehingga nilai akurasi tidak konsisten serta tidak maksimal. Sehingga berdampak pada kualitas informasi yang dihasilkan dan berpotensi mengganggu proses pengambilan keputusan serta menetukan suatu kebijakan. Algoritma klasifikasi yang sangat populer serta memiliki kinerja yang baik adalah algoritma C45 dan KNN, namun kedua algoritma ini memiliki teknik pendekatan yang berbeda dalam mengolah data, Meskipun keduanya memiliki keunggulan masing-masing, performanya masih dapat ditingkatkan melalui pendekatan ensemble learning. Penelitian ini mengusulkan integrasi metode Bagging dan Stacking untuk meningkatkan kinerja algoritma C4.5 dan KNN. Metode bagging (Bootstrap Aggregating) adalah teknik dalam pembelajaran mesin yang bertujuan untuk meningkatkan akurasi dan stabilitas model klasifikasi. Ini dilakukan dengan cara menggabungkan beberapa model yang dilatih pada subset data yang berbeda, yang dipilih melalui teknik bootstrap sampling (sampling dengan pengembalian). Sementara itu stacking adalah teknik ensembel dalam pembelajaran mesin yang menggabungkan prediksi dari beberapa model dasar menggunakan meta-learner untuk menghasilkan prediksi akhir yang lebih akurat. Pengujian dilakukan menggunakan empat dataset, yaitu Bank Marketing, Credit Card, Credit Risk Assessment, dan Credit Card Defaulter. Hasil penelitian menunjukkan bahwa metode Bagging dengan algoritma C4.5 secara konsisten menghasilkan performa terbaik pada tiga dataset dengan akurasi masing-masing 91,21%, 97,73%, dan 92,11%. Sedangkan pada dataset keempat, metode Bagging dengan KNN memperoleh akurasi tertinggi sebesar 97,06%. Temuan ini membuktikan bahwa teknik ensemble, khususnya Bagging, efektif dalam meningkatkan akurasi dan konsistensi kinerja algoritma klasifikasi dasar. Dengan demikian, integrasi Bagging dan Stacking dapat menjadi solusi potensial untuk mengatasi permasalahan klasifikasi dalam data mining, terutama pada domain keuangan seperti prediksi kredit macet.
Penelitian ini bertujuan untuk mensintesis hijau nanopartikel Fe₃O₄ (NPs) menggunakan ekstrak daun Moringa Oleifera dan menguji kemampuan absorpsinya terhadap pewarna metilen biru. Difraksi sinar-x menunjukkan tidak adanya puncak difraksi pada sudut 2theta, ini menunjukan bahwa struktur materialnya bersifat amorf. Analisis ukuran distribusi partikel mengonfirmasi keberadaan material berukuran nano sebesar 34,7%. Spektrum inframerah transformasi fourier menunjukkan gugus fungsi seperti C–H, C–O, C–C, dan C–N yang mengindikasikan keberhasilan sintesis hijau. Selain itu, keberadaan gugus fungsi Fe–O pada rentang 570–580 cm⁻¹ juga mendukung terbentuknya NPs. Efektivitas absorpsi nanopartikel Fe₃O₄ diuji terhadap larutan metilen biru dengan variasi massa 1 g, 2 g, dan 3 g. Efisiensi absorpsi menunjukkan penurunan yang signifikan seiring dengan peningkatan massa nanopartikel, yang diperkuat dengan perubahan visual warna larutan. Penelitian ini membuktikan bahwa sintesis nanopartikel Fe₃O₄ secara hijau dapat menjadi metode yang efektif dan ramah lingkungan untuk pengolahan limbah pewarna.
Pantai Lais mengalami perubahan garis pantai yang sangat cepat yang disebabkan abrasi dan sedimentasi. Penelitian ini bertujaun untuk mengidentifikasi karakteristik dinamika angin dan longshore current, dan pemetaan perubahan garis pantai di segmen Pantai Lais. Metode yang digunakan adalah pengukuran in situ parameter pembangkit longshore current, visualisasi menggunakan drone untuk memetakan arah longshore current, dan pemetaan perubahan garis pantai menggunakan data citra satelit USGS. Hasil penelitian diperoleh pada musim peralihan I angiin beirheimbus darii barat laut dengan kecepatan 0,50 - 3,60 m/s. Musim peralihan II angiin beirheimbus darii teinggara dengan kecepatan 0,50 – 5,70 m/s. Kecepatan longshorei curreint dii musiim peiraliihan I seibeisar 0,13 m/s - 1,49 m/s, dan musiim peiraliihan IiIi seibeisar 0,18 m/s - 1,12 m/s. Rata-rata abrasii seilama 10 tahun teirakhiir seibeisar 13,03 m/tahun dengan niilaii abrasii teirtiinggii 20,5 m itahun 2015, dan rata-rata sedimentasi sebesar 10,60 m/tahun, dengan nilai sedimentasi tertinggi 29, 5 m. tahun 2017.
The need for a more automated, precise, and integrated antenna measurement system has become urgent. The main objective of this research is to develop and test an automatic antenna measurement system based on LabVIEW that can improve the accuracy and efficiency of measuring the gain and radiation pattern of a 4x1 microstrip antenna. This research is a study of the development and validation of an experimental system using a 4×1 microstrip antenna array as the research subject, with an automatic measurement system based on an Arduino microcontroller (AT-Mega328) and stepper motor, as well as LabVIEW software for instrument control, data acquisition, real-time transmission, and user interface. Data collection was performed automatically via a roll-over-azimuth rotator. In data analysis, the results of the automatic system were quantitatively compared with CST simulation results and a semi-automatic system using 2D correlation coefficients and gain differences. At the same time, the benefits of improved time efficiency were also measured. The findings of this research indicate that the developed automatic measurement system provides a practical, efficient, and cost-effective solution for educational laboratories and industrial antenna testing, with potential for further development to support higher frequency ranges and three-dimensional measurements. The findings of this study have implications for improving the efficiency, accuracy, and flexibility of microstrip antenna testing and for offering practical and economical solutions for laboratory and industrial needs.
