ANALISA PENGARUH FILTRASI TERHADAP PENURUNAN BOD DAN COD PADA LIMBAH RUMAH TANGGA DI KELURAHAN CACABAN, KECAMATAN MAGELANG, TENGAH KOTA MAGELANG

<p>Penurunan kinerja Instalasi Pengolahan Air Limbah (IPAL) Domestik di wilayah Kelurahan Cacaban, Kecamatan Magelang Tengah, Kota Magelang, menyebabkan penurunan kualitas outlet IPAL. Hasil uji laboratorium pada tanggal 30 Juli 2020, menunjukkan kadar BOD (<em>Biological Oxygen Demand</em>) sebesar 148,64, kadar COD (<em>Chemical Oxygen Demand</em>) sebesar 307, sedangkan rasio BOD/COD sebesar 0,48. Karena kadar BOD dan COD lebih tinggi dari Baku Mutu Limbah Domestik, maka perlu diadakan penelitian untuk mengurangi kadar BOD dan COD agar sesuai dengan serta mengurangi rasio BOD/COD menjadi 0,2.</p><p>Penelitian ini menggunakan perlakuan filtrasi dengan tiga komposisi filter. Komposisi pertama adalah ijuk, kapur dan arang aktif. Komposisi kedua adalah ijuk dan arang kapur, dan komposisi ketiga ijuk dan kapur. Parameter senyawa yang ditinjau adalah BOD, COD, serta perbandingan antara BOD dan COD. Analisa data yang digunakan yaitu dengan uji regresi polinomial.</p><div><p>Hasil penelitian menunjukkan penyisihan tertinggi BOD dan COD dihasilkan oleh Model Filter 3, yaitu ijuk dengan tebal 7,5 cm, kapur dengan tebal 7,5 cm, dan arang aktif dengan tebal 12 cm, yang dapat menyisihkan kadar BOD sebesar 131,307 mg/L dan menyisihkan kadar COD sebesar 220,67 mg/L. Dan perbandingan BOD dan COD yang mendekati 0,2 dihasilkan oleh Model Filter 3, yaitu ijuk, kapur, dan arang aktif yaitu 0,201.</p></div>

Klasifikasi COVID-19 menggunakan Filter Gabor dan CNN dengan Hyperparameter Tuning

ABSTRAKPenyakit COVID-19 dapat timbul karena berbagai faktor sebab dan akibat, sehingga penyakit ini memiliki efek buruk bagi penderita. Pencitraan CT-Scan memiliki keunggulan dalam memproyeksikan kondisi paru-paru pasien penderita, sehingga dapat membantu dalam mendeteksi tingkat keparahan penyakit. Dalam studi ini, penelitian dilakukan untuk mendeteksi penyakit COVID-19 melalui citra CT-Scan menggunakan metode Filter Gabor dan Convolutional Neural Networks (CNN) dengan Hyperparameter Tuning. Data yang digunakan yaitu citra CT-Scan SARSCoV-2 berjumlah 2481 gambar. Sebelum melatih model, dilakukan preprocessing data, seperti pelabelan, pengubahan ukuran, dan augmentasi gambar. Pengujian Model dilakukan dengan beberapa skenario uji. Hasil terbaik diperoleh pada skenario untuk model Filter Gabor dan CNN dengan Hyperparameter Tuning mendapatkan akurasi sebesar 97,9% dan AUC sebesar 99% dibandingkan dengan model tanpa Hyperparameter Tuning dan Filter Gabor.Kata kunci: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification ABSTRACTCOVID-19 disease can arise due to various causal and causal factors, so it has an adverse effect on patients. CT-Scan imaging has an advantage in projecting the lung condition of patients with the patient, so it can help in detecting the severity of the disease. In this study, research was conducted to detect COVID-19 disease through CT-Scan imagery using Gabor Filter method and Convolutional Neural Networks (CNN) with Hyperparameter Tuning. The data used is CT-Scan SARSCoV-2 imagery amounting to 2481 images. Before training the model, preprocessing data is performed, such as labeling, resizing, and augmentation of images. Model testing is performed with multiple test scenarios. The best results were obtained in scenarios for The Gabor Filter model and CNN with Hyperparameter Tuning getting 97.9% accuracy and AUC by 99% compared to models without Hyperparameter Tuning and Gabor Filter.Keywords: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification

The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds

Hybrid systems are subject to multiple dynamic models, or so-called modes. To estimate the state, the sequence of modes has to be estimated, which results in an exponential growth of possible sequences. The most prominent solution to handle this is the interacting multiple model filter, which can be extended to smoothing. In this paper, we derive a novel generalization of the interacting multiple filter and smoother to manifold state spaces, e.g., quaternions, based on the boxplus-method. As part thereof, we propose a linear approximation to the mixing of Gaussians and a Rauch–Tung–Striebel smoother for single models on boxplus-manifolds. The derivation of the smoother equations is based on a generalized definition of Gaussians on boxplus-manifolds. The three, novel algorithms are evaluated in a simulation and perform comparable to specialized solutions for quaternions. So far, the benefit of the more principled approach is the generality towards manifold state spaces. The evaluation and generic implementations are published open source.

Experimental and Theoretical Research on Pressure Drop Changes in a Two-Stage Air Filter Used in Tracked Vehicle Engine

The effect of mineral dust in the air sucked in by an engine on accelerated component wear and reduction in performance was presented. The necessity to use two-stage air filters (multicyclone-paper insert) for military vehicles was shown. The results showed that placing an air filter in the path of the air entering the engine causes an additional pressure drop (air filter resistance increase), which leads to engine power decrease and increased fuel consumption. An analysis of model filter beds’ pressure drop changes (depending on bed parameters, aerosol flow parameters, and dust content) was carried out. It was revealed that it is very difficult to model changes in pressure drop in filter beds for actual conditions that appear during vehicle operation. The air filter pressure drop measurement results of more than 20 tracked vehicles operating in variable air dust concentration conditions were presented. The forms of selected regression models of the “life curve” type, best suited to the actual changes in air filters pressure drop as a function of the vehicle mileage, were determined. Significant differences were found between the same model values for different units of the tested vehicles. The quality of forecasting pressure drop value by selected functions was assessed by extrapolating them to the value of the next measurement and comparing the forecast and actual value. It was found that for the performed experiment, sufficiently good results of experimental data approximation and forecasting were obtained for a simple linear model.