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The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient’s lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.

Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers.

Purpose: The dengue epidemic has an increasing number of sufferers and spreading areas along with increased mobility and population density. Therefore, it is necessary to control and prevent Dengue Hemorrhagic Fever (DHF) by mapping a DHF contingency plan. However, mapping a dengue contingency plan is not easy because clinical and managerial issues, vector control, preventive measures, and surveillance must be considered. This work introduces a cluster-based dengue contingency planning method by grouping patient cases according to their environment and demographics, then mapping out a plan and selecting the appropriate plan for each area.Methods: We used clustering with silhouette scoring to select features, the best cluster formation, the best clustering method, and cluster severity. Cluster severity is carried out by levelling the attributes of the average value to low, medium, high, and extreme, which are related to the plans each region sets for village type and season type.Result: In five years of data (2016-2020) ±15K cases from Semarang City, Indonesia, feature selection results show that environmental and demography group features have the biggest silhouette score. With these features, it is found that K-Means has a high silhouette score compared to DBSCAN and agglomerative with three optimum numbers of clusters. K-Means also successfully mapped the cluster severity and assigned the cluster to a suitable contingency policy.Novelty: Most of the research on DHF cases is about predicting DHF cases and measuring the risk of DHF occurrence. There are not many studies that discuss the policy recommendations for dengue control.

Deformable models are widely used in medical image segmentation methods, to find not only single but also multiple objects within an image. They have the ability to follow the contours of an object of interest, define the boundary of ROI (Region Of Interest) and improve shape recovery. However, these methods still have limitations in cases of low image quality or clutter.This paper presents a new deformable model, the Spring-Charged Particles Model (SCPM). It simulates the movement of positively charged particles connected by springs, attracted towards the contour of objects of interest which is charged negatively, according to the gradient-magnitude image. Springs prevent the particles from moving away and keep the particles at appropriate distances without reducing their flexibility. SCPM was tested on simple shape images and on frontal X-ray images of scoliosis patients. Artificial noise was added to the simple images to examine the robustness of the method. Several configurations of springs and positively charged-particles were evaluated by determining the best spinal segmentation result. The performance of SCPM was compared to the Charged Fluid Model (CFM), Active Contours, and a convolutional neural network (CNN) with U-Net architecture to measure its ability for determining the curvature of the spinal column from frontal X-Ray images. The results show that SCPM is better at segmenting the spine and determining its curvature, as indicated by the highest Area Score value of 0.837, and the lowest standard deviation value of 0.028.

Program vaksinasi menyebabkan perluasan perangkat medis non-elektromedik, terutama dalam diagnostik In Vitro. Barang In Vitro, seperti Antigen Test Covid-19 (Panbio), meningkat sebesar 613,9 persen, memungkinkan perusahaan untuk memenuhi target penjualan 5-10 juta unit tahun ini. Akibatnya, tujuan dari penelitian ini adalah untuk memeriksa kinerja keuangan PT. Itama Ranoraya Tbk, yang sekarang menyaksikan peningkatan penjualan yang signifikan. Jenis penelitian deskriptif ini digunakan dalam penelitian ini. Menghitung dan menganalisis laporan keuangan PT. Itama Ranoraya Tbk pada tahun 2018-2020 berdasarkan rasio likuiditas, rasio solvabilitas, dan rasio profitabilitas menggunakan metodologi analisis data. Sebagai hasil dari temuan penelitian ini, kinerja keuangan perusahaan diklasifikasikan sebagai tidak likuid bila diukur dengan rasio likuiditas, bahkan jika rasio solvabilitas baik dalam rasio DAR, dan diklasifikasikan sebagai belum menguntungkan bila diukur dengan rasio profitabilitas.Kata Kunci: Kinerja, Keuangan, Kesehatan, Non Elektromedik

Bukalapak is one of the largest marketplaces in Indonesia. Reviews on Bukalapak are only in the form of text, images, videos, and stars without any special filters. Reading and analyzing manually makes it difficult for potential buyers. To help with this, we can extract this review by using aspect-based sentiment analysis because an entity cannot be represented by just one sentiment. Several previous research stated that using LSTM-CNN got better results than using LSTM or CNN. In addition, using BERT as word embedding gets better results than using word2vec or glove. For this reason, this study aims to classify aspect-based sentiment analysis from the Bukalapak marketplace with BERT as word embedding and using the LSTM-CNN method, where LSTM is for aspect extraction and CNN for sentiment extraction. Based on testing the LSTM-CNN method, it gets better results than LSTM or CNN. The LSTM-CNN model gets an accuracy of 93.91%. Unbalanced dataset distribution can affect model performance. With the increasing number of datasets used, the accuracy of a model will increase. Classification without using stemming on datasets can increase accuracy by 2.04%. &nbsp, Bukalapak is one of the largest marketplaces in Indonesia. Reviews on Bukalapak are only in the form of text, images, videos, and stars without any special filters. Reading and analyzing manually makes it difficult for potential buyers. To help with this, we can extract this review by using aspect-based sentiment analysis because an entity cannot be represented by just one sentiment. Several previous research stated that using LSTM-CNN got better results than using LSTM or CNN. In addition, using BERT as word embedding gets better results than using word2vec or glove. For this reason, this study aims to classify aspect-based sentiment analysis from the Bukalapak marketplace with BERT as word embedding and using the LSTM-CNN method, where LSTM is for aspect extraction and CNN for sentiment extraction. Based on testing the LSTM-CNN method, it gets better results than LSTM or CNN. The LSTM-CNN model gets an accuracy of 93.91%. Unbalanced dataset distribution can affect model performance. With the increasing number of datasets used, the accuracy of a model will increase. Classification without using stemming on datasets can increase accuracy by 2.04%.

Penelitian ini membahas tentang perbandingan keluaran daya antara panel surya konvensional dengan panel surya yang menggunakan sistem pelacak sinar matahari (solar tracker system). Pada penelitian ini juga membahas bagaimana rancangan sistem fotovoltaik yang akan digunakan untuk mencatu lampu penerangan jalan umum dengan menggunakan lampu LED DC sebesar 5 watt dan rancangan sistem alat pelacak sinar matahari yang berbasis Arduino uno. Perbandingan yang dilakukan adalah dengan mengukur arus dan tegangan keluaran kedua buah panel surya dimana satu panel surya konvensional dan satu panel surya menggunakan sistem pelacak sinar matahari. Pengujian dilakukan dalam dua kondisi yaitu pada kondisi cuaca cerah dan pada kondisi cuaca mendung. Pada cuaca cerah rata- rata selisih efisiensi antara panel surya yang menggunakan sistem pelacak sinar matahari dengan panel surya konvensional adalah sebesar 1,12% dan pada kondisi cuaca mendung adalah sebesar 1,94%. Hasil pengujian menunjukkan bahwa panel surya yang menggunakan sistem pelacak sinar matahari memiliki tingkat efisiensi daya yang lebih tinggi dari pada panel surya konvensional.

Penelitian ini membahas tentang topologi baru dari dc-dc konverter jenis boost, yakni quadratic boost converter berbasis low buffer capacitor stress yang dibandingkan dan dianalisis prinsip kerjanya dengan boost converter. Konfigurasi yang dimiliki oleh kedua topologi rangkaian ini berbeda, sehingga berbeda pula untuk analisis rangkaian, stress tegangan, stress arus tiap komponen semikonduktor dan hasil gain tegangan keluaran yang dihasilkan. Analisis rangkaian yang telah dilakukan dalam penelitian ini menggunakan analisis rangkaian sakelar terbuka dan sakelar tertutup serta menggunakan metode Inductor Voltage Second Balance, kemudian menentukan spesifikasi komponen dan parameter komponen melalui perhitungan. Setelah menentukan spesifikasi dan parameter komponen kemudian rangkaian ini disimulasikan menggunakan software PSIM. Spesifikasi yang digunakan pada kedua komponen konverter ini sama guna membandingkan hasil kerja keluaran konverter. Spesifikasi ini antara lain tegangan masukan 50 V, frekuensi 70kHz, dengan menggunakan duty cycle 10%-50%. Hasil penelitan menunjukan bahwa quadratic boost converter berbasis low buffer capacitor stress dapat menaikan tegangan hingga 4 kali lipat dibandingkan boost converter.

When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.

SMEs are one of the components driving economic growth which is supported by the existence of Microfinance Institutions. This study aims to look at the pattern of preferences and the factors that influence the preferences of SMEs in finding sources of financing for Microfinance Institutions. Cluster random sampling was used by distributing questionnaires and structured interviews with samples of MSMEs in Yogyakarta City, especially Umbulharjo District and Kotagede District. Meanwhile, a questionnaire was also given to MFIs to see their perceptions of SMEs. The data is processed using descriptive statistics and will be interpreted quantitatively and qualitatively. From the results of the analysis of respondents' preferences in general, some factors determine the selection of MFIs by customers in each MFI forming the segmentation. The conclusion is that banking MFIs are distinguished from non-banking MFIs. The determining factor for banking MFIs is the selection of MFIs by customers to serve both administrative speed and MFIs that are easily accessible from the location. The average determinant of non-bank MFIs in selecting good services is administrative speed and proximity to public transportation. The average segmentation preferences of SMEs for each MFI are: for respondents, banking MFIs are used to dealing with banks and are more oriented toward business development, looking for non-banking MFIs in the southwest, choosing MFIs because they are related to cooperatives, respondents have a high level of religion. relatively strong, simple administration with a fast time and there is an emotional connection between customers with cooperatives and BMT. This shows that cooperatives are trying to get customers from micro, business and individual income are each below IDR 5 million, stating the case that BMT is trying to get customers from micro, but business and individual incomes are smaller than customers in the cooperative segment which is below IDR 1 million. Keywords: Preference, UMKM, LKM Abstrak UKM merupakan salah satu komponen penggerak pertumbuhan ekonomi yang didukung oleh keberadaan Lembaga Keuangan Mikro. Penelitian ini bertujuan melihat pola preferensi dan faktor-faktor yang mempengaruhi preferensi UKM dalam mencari sumber pembiayaan Lembaga Keuangan Mikro. Melalui penyebaran kuesioner dan wawancara terstruktur dengan sampel UMKM yang terpilih melalui cluster random sampling di Kota Yogyakarta khususnya Kecamatan Umbulharjo dan Kecamatan Kotagede. Sementara itu, kuesioner juga diberikan kepada LKM untuk melihat persepsi mereka terhadap UKM. Data diolah dengan menggunakan statistik deskriptif dan akan diinterpretasikan secara kuantitatif dan kualitatif. Hasil analisis preferensi responden secara umum, terdapat faktor penentu pemilihan LKM oleh nasabah pada setiap LKM pembentuk segmentasi. Kesimpulan bahwa LKM perbankan dibedakan dengan LKM non perbankan. Faktor penentu LKM perbankan dipilih oleh nasabah karena kecepatan administrasi pelayanan dan LKM yang mudah dijangkau dari lokasi. Rata-rata faktor penentu LKM non bank dipilih adalah karena pelayanan yang baik dengan kecepatan administrasi dan dekat dengan angkutan umum. Preferensi segmentasi rata-rata UKM untuk masing-masing LKM adalah: LKM perbankan sudah terbiasa berurusan dengan bank dan lebih berorientasi pada pengembangan usaha sedangkan LKM non perbankan karena berkaitan dengan koperasi, responden memiliki tingkat religi yang relatif kuat, administrasi yang sederhana dengan waktu yang cepat serta adanya hubungan emosional antara nasabah dengan koperasi dan BMT. Hal ini menunjukkan bahwa koperasi berusaha mendapatkan nasabah dari mikro, pendapatan usaha dan individu masing-masing di bawah Rp5 juta. BMT berusaha mendapatkan pelanggan dari mikro, tetapi pendapatan usaha dan individu lebih kecil dari pelanggan segmen Koperasi yang dibawah Rp1 juta Kata kunci: Preference, UMKM, LKM

The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of a country. Current research trends regarding the detection of student entrepreneurial potential have the greatest challenge in the unequal ratio of datasets. This study proposes a rule-generation model in an imbalanced situation to classify student entrepreneurship based on the Theory of Planned Behavior (TPB). The result is a ruleset that is used for the early detection of student entrepreneurial potential. The proposed method consists of three main stages, namely preprocessing data to classify data based on TPB variables, generating a dataset by clustering and selecting attributes by sampling to balance the data, and finally generating a ruleset. Furthermore, the results of the detecting ruleset have been evaluated with actual data from the student tracer study as ground truth. The evaluation results show high accuracy so that the ruleset can be applied to the higher education environment in the future.

Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users' body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds.

Bank merupakan lembaga keuangan yang berperan sebagai pendorong perekonomian guna meningkatkan taraf hidup masyarakat di seluruh dunia. Pada sebuah bank sangat diperlukan model antrean untuk mengatasi panjangnya antrean. Penelitian ini bertujuan untuk meneliti kinerja dalam sistem antrean yang terjadi saat transaksi serta bagaimana cara mengoptimalkan pelayanan nasabah dengan menerapkan model antrean di PT. Bank Syariah Indonesia, Tbk Kantor Cabang Kediri Trade Center. Penelitian ini menggunakan teknik analisis deskriptif dengan pendekatan kuantitatif. Dalam mengumpulkan data dalam penelitian ini dilakukan dengan cara pengamatan serta interview sehingga dapat mengetahui antrean yang terjadi. Objek pada penelitian ini adalah Business Operational System Manager atau customer service. Hasil penelitian ini menunjukkan bahwa kinerja sistem antrean yang terjadi saat ini belum sepenuhnya optimal dengan tersedianya dua customer service.Kata Kunci: Bank, Antrean, Multiple Channel Query System

High level of fault currents has become one of the most challenging issues, along with increased electrical energy consumption and power grid expansion. However, some of the previous work was limited to installing limiters on designs and installing them on small plans. In this study, the optimal placement of a resistive-type superconducting fault current limiter (RSFCL) to achieve good power system transient stability is proposed. The proposed method has several advantages. First, procedure fault analysis is applied to consider the magnitude of the short-circuit currents and the line located close to the generator for the placement of the device. Compared with other methods, it can directly find the exact line to be installed RSFCL. Second, the stability assessment is validated using the time domain simulation by installing limiters with varying capacities in any location. Third, parameters were analysed to evaluate the proposed method’s effectiveness, including angular speed, rotor angular velocity, and generator rotor angle in a multi-machine system. The result reveals that the placement of the limiter on the line with the most significant short-circuits value on the line closest to the generator increases the power system stability more than the placement in other locations.

Penelitian ini bertujuan menganalisis pengaruh kepemimpinan transformasional dan kepuasan kerja pada authoritative citizenship conduct dengan komitmen organisasi sebagai variabel mediasi. Metode pengumpulan data menggunakan information preliminary yang diperoleh dari kuesioner dengan teknik relative arbitrary inspecting kepada 92 orang perawat RSUP Daha Husada, Kediri. Metode analisis data primer menggunakan Regresi Linier Berganda dan uji Sobel sebagai uji efek mediasi. Hasil penelitian menemukan bahwa kepemimpinan transformasional dan kepuasan kerja berpengaruh signifikan pada komitmen organisasi, dan kepemimpinan transformasional, kepuasan kerja, dan komitmen organisasi juga berpengaruh signifikan pada Perilaku Warga Organisasi. Penelitian ini mendapatkan bahwa komitmen organisasi merupakan anteseden yang kuat pada hierarchical citizenship conduct dan merupakan variabel mediasi yang baik antara kepemimpinan transformasional dan kepuasan kerja pada Perilaku Warga Organisasi

Tujuan penelitian ini untuk mengetahui estimasi kebutuhan bahan baku untuk periode berikutnya, sebagai dasar menentukan penerapan metode EOQ, dan mengetahui perbandingan dalam penerapan EOQ periode lalu dengan periode yang akan datang, berdasar pada estimasi kebutuhan bahan baku. Hasil penelitian 1) diperoleh model tren linier regresi yang terbaik adalah metode tren kuadrat terkecil, dengan Standar Kesalahan Estimasi terkecil, dengan model tren linier regresi = Y’= 2.044,70 – 23,70(x). Maka hasil estimasi kebutuhan bahan baku periode 2021 (n+1) adalah sebesar 22,545 Kg lebih rendah 8% dari kebutuhan bahan baku periode 2020 (n), sebesar 24.536 Kg. 2) EOQ menggunakan data historis yaitu 2020 (n) diperoleh besar EOQ adalah 1.566 Kg, dan SS sebesar 167 Kg, dan Rop sebesar 1.068 Kg. Sedangkan dengan menggunakan data kebutuhan bahan baku estimasi (peramalan) untuk periode 2021 (n+1) EOQ terletak pada 1.498 Kg, dan SS pada 154 Kg, dan RoP sebesar 942 Kg. Jika perusahaan menggunakan data historis tahun 2020 untuk kebijakan pengendalian perusahaannya akan berefek pada kelebihan bahan baku sebesar 1.962 Kg pada akhir tahun 2021 (n+1).

The increase of the classification accuracy level has become an important problem in machine learning especially in diverse data-set that contain the outlier data. In the data stream or the data from sensor readings thatproduce large data, it allows a lot of noise to occur. It makes the performance of the machine learning model is disrupted or even decreased. Therefore, clean data from noise is needed to obtain good accuracy and to improve the performance of the machine learning model. This research proposes a two-stages for detecting and removing outlier data by using the covariance estimator and isolation forest methods as pre-processing in the classification process to determine fine motor skill (FMS). The dataset was generated from the process of recording data directly during cursive writing by using a digitizer. The data included the relative position of the stylus on the digitizer board. x position, y position, z position, and pressure values are then used as features in the classification process. In the process of observation and recording, the generated data was very huge so some of them produce the outlier data. From the experimental results that have been implemented, the level of accuracy in the FMS classification process increases between 0.5-1% by using the Random Forest classifier after the detection and outlier removal by using covariance estimator and isolation forest. The highest accuracy rate achieves 98.05% compared to the accuracy without outlier removal, which is only about 97.3%.

Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset.

Currently, volatility, uncertainty, complexity, and ambiguity (VUCA) have become unavoidable problems. In addition, knowledge or information that is not managed properly can result in inappropriate decision-making processes within an organization. Business Intelligence conception is then becoming an essential view for converting unstructured data and information into a more actionable strategic plan that allows organizations to make competitive decisions. Village Government (VG) is the smallest organization in the Indonesian government system because VG implemented regulation and development programs in all areas of a national government. VG executes a series of tasks every year starting from planning, budgeting, administrating, executing, and reporting. However, the important role of VG in the development of a country brings also some drawbacks such as corruption and other domino effects. Several factors have been identified that cause those problems such as lack of capabilities in managing village organization and human resources quality. Monitoring and evaluation regarding those VG performances normally have been done each year. However, measurable evaluation standard for VG performance until recently has not been determined nationally. This study is intended to make a comprehensive standard of village government performance assessment through a Good Governance Framework approach. This study involved 198 villages from Madiun Regency as a case study. Seventy-four measured parameters were proposed to evaluate VG performance mapping. Fuzzy TOPSIS is implemented to rank those 198 villages into 4 groups of VG performance levels. The fuzzy TOPSIS classification result has been validated by using manual scoring and the accuracy reached 86,4%.