The problem of power factor in the industry is critical. This is due to the issue of low power factor that can make the vulnerability of industrial equipment damaged. This problem has been resolved in various ways, one of which is the Automatic Power Factor Correction, with the most popular device called capacitor bank. There are also many methods used, but several methods require certain calculations so the system can adapt to the new plant. In this study, researchers proposed a capacitor bank control system that can adapt to plants with different capacitor values without using any calculations by using an Artificial Neural Network with a closed-loop controller. The system is simulated using Simulink Matlab to know the performance with two testing scenarios. The first is changing the value of the power factor on the system and changing the value of the capacitor power at each bank, the second comparing it with the conventional methods. The results show that the system has been able to adapt to different capacitor power values and has a better performance than the conventional method in power factor oscillation due to the extreme power factor interference

The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.

KAPET (Kawasan Pengembangan Ekonomi Terpadu) merupakan program pemerintah yang ditujukanuntuk mendorong percepatan pembangunan ekonomi di wilayah Timur Indonesia. Program inidiluncurkan pada periode pemerintahan Presiden Suharto pada tahun 1996 dan dimatangkan padatahun 1998 melalui Keppres No 9 Tahun 1998. KAPET Palapas merupakan salah satu KAPET di PulauSulawesi, berlokasi di Provinsi Sulawesi Tengah yang merupakan relokasi dari KAPET Batui. Secaraefektif KAPET Palapas beroperasi pada tahun 2008. Pengembangan KAPET Palapas bertujuan untukmendorong pertumbuhan ekonomi di Provinsi Sulawesi Tengah dengan Kota Palu sebagai pusatpengembangan wilayah. Studi ini mengevaluasi kinerja KAPET Palapas dengan memeriksa indikatorindikatorekonomi makro yang merupakan turunan dari Produk Domestik Regional Bruto di ProvinsiSulawesi Tengah. Proses evaluasi menunjukkan bahwa dalam kurun waktu empat tahunpengembangan KAPET Palapas belum mampu memberikan tenaga penggerak perekonomian diProvinsi Sulawesi Tengah.Kata Kunci: evaluasi, kinerja, KAPET

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The impact of pollution resulting from exhaust gas in the form of coal ash in the Steam Power Plant makes the Electrostatic Precipitator an equipment that can handle these problems. The equipment has a voltage level regulation system using a thyristor which with a certain value can create an electric field capable of capturing charged ash. The thyristor system has a disturbance known as harmonics. Good handling to overcome harmonics is to measure using a certain index, one of which is the Total Harmonic Distortion (THD) and to know its effect on the power factor. In this research, an analysis of the electrostatic precipitator system at PLTU Pangkalan Susu will be carried out by collecting the necessary data and the firing angle installed in the company. From the test results, it can be said that the THD value in the Electrostatic Precipitator system at PLTU Units 3 & 4 Pangkalan Susu is in the normal range. To find out the range of compatible firing angles on the system, it is also necessary to provide a test variable angle. Based on the test, it is known that the firing angle installed in the company is 46 degree with THDv and THDi of 4.01 percent and 3.61 percent  respectively and the Power Factor is 0.84876. Whereas with the test variable angle, it is known that the good or normal THDv and THDi values are in the range 30 degree to 60 degree where the THDv value is in the range of 3.83 percent to 4.91 percent and the THDi value is in the range of 3.50 percent to 4. 21 percent.