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INDONESIA
Faktor Exacta
Published by Universitas Indraprasta PGRI
ISSN : 1979276X     EISSN : 2502339X     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Faktor Exacta is a peer review journal in the field of informatics. This journal was published in March (March, June, September, December) by Institute for Research and Community Service, University of Indraprasta PGRI, Indonesia. All newspapers will be read blind. Accepted papers will be available online (free access) and print version.
Arjuna Subject : -
Articles 523 Documents
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PENERAPAN METODE EARNED VALUE ANALYSIS MENGGUNAKAN SOFTWARE PRIMAVERA PROJECT PLANNER PADA PEMBANGUNAN INSTALASI PENGOLAHAN AIR LIMBAH Nadila Agnessia; Drajat Indrajaya
Faktor Exacta Vol 15, No 1 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i1.10456

Abstract

PT Gasd Geosby Indonesia is a company engaged in environmental services and construction. PT Gasd Geosby Indonesia is handling a Wastewater Treatment Plant (IPAL) construction project in Cikarang for an industrial company. The project experienced delays due to several factors supporting activities. Factors that can affect delays include people, materials, costs, and the tools used. Delay cannot be avoided but can be controlled. One form of project control is to calculate the project completion time and the amount of costs that will be incurred until the project is completed. One of the methods used to estimate the time and cost of the project according to the budget according to the work that has been completed is Earned Value Analysis (EVA). The result of data processing using the Primavera application is Estimate At Completion (EAC) in the 4th week of Rp. 185.682.084, the estimated cost is greater than the project planning cost of Rp. Rp. 147.794.994 if the trend of project implementation during the visit did not change until the end of implementation. From the data obtained based on the results of the field visit at week 4, the estimated completion of the project for 43 days or 14 days from the last day of visit was obtained.
Survey Perangkat Lunak Untuk Manajemen Big Data Menggunakan Metode SLR (Systematic Literature Review) Muhammad Andryan Wahyu Saputra; Hamim Tohari; U'un Setiawati
Faktor Exacta Vol 15, No 1 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i1.11735

Abstract

Big data technology is a whole technology that can handle processing related to data analysis to explore the potential in it. Some uses of big data are based on the largest sources of data traffic, such as social media, financial transactions, public data, sensor data, and enterprise data. In the research conducted there is a problem, namely how to determine and find out the best software to manage big data. The following problems can be solved by conducting a software comparison survey to find out the best software that can be used and is suitable for big data management problems. The software survey research for big data management uses secondary data from national and international journals that have been published on Scopus, Semantic Scholar, and Google Scholar in the period 2017-2021 as data reference. The software survey in this study used the Systematic Literature Review (SLR) method. The results of research using SLR software state that the software that can be used to determine big data management is 7 software, Apache Hadoop software is software that is often used to perform big data analysis because Apache Hadoop software can have many features and managed to obtain the minimum results to maximize the results of the analysis so that this is different from the application of other software and also the application of software.
Automatic Railway Gate System for Commuter Line Train Based on Sensor Accelerometer and Microcontroller Erna Kusuma Wati
Faktor Exacta Vol 15, No 1 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i1.11380

Abstract

The design of the automatic system of crossbar gates has been done a lot to reduce the number of accidents at the railroad crossing, but no one has been tested directly when the real train passes. In this study will design an automatic system of railroad crossing gates that will be tested on the passing KRL commuter trains so that they can display the vibration acceleration data. There are two systems in this study; system 1 is a vibration detector, and system two is an automatic system at the railway gate. The Accelerometer sensor is a passing train vibration detector, and Arduino UNO will give the command or control of the train gate to open and close automatically with a servo motor and turn on the LED and Buzzer. The system test is carried out when ten commuter line trains cross the train line on the Jakarta-Depok, Indonesia route on each variation of distance. Variations in the interval between system 1 to system two namely 200m, 450m, and 650m are performed to obtain the time difference between the railroad gates closing entirely and the time when the train passes at the gate crossing, which is close to the standard time. The test results at a distance of 200 meters have a time difference of 2.9 seconds with an average value of 10.01 m / s2, at a distance of 450 meters for 14.1 seconds; g 10.01 m / s2, and at a range of 650 for 36.7 seconds the value of g is 10.02 m / s2. Thus the results of research on the design of an automatic system based on the accelerometer and microcontroller railroad gates placed with the distance between the systems as far as 650 meters can be recommended to PT. Commuter Indonesia makes all railroad crossing gate systems work automatically and to improve road safety and safety at railroad crossings..
Pengujian IaC Berbasis DevOps dan Ansible Menggunakan Metode Black Box Testing I Putu Agus Eka Pratama; Putu Bayu Suarnata Wahyu Putra
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.12039

Abstract

The development of information technology, which is followed by an increase in the need for devices and computing resources for services on computer networks, requires cost and time for the configuration and development process. Infrastructure as Code (IaC) based on DevOps using Ansible, is a solution to this problem, by combining development and operational processes. However, post-implementation, it is necessary to test on the application side to determine the functionality of the running system. For this reason, in this research, a Black Box Testing method with three steps is proposed for testing the implementation of DevOps-based IaC using Ansible. The test results show that the implementation of Ansible for DevOps-based IaC was successfully carried out by configuring the host node and running the Ansible playbook from the host server.
Model Machine Learning Klasifikasi Data Sekolah TK Berdasarkan Status dan Kabupaten abdu rahman; Fiqih Ismawan
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.13211

Abstract

Klasifikasi status sekolah menjadi parameter khusus bagi beberapa kalangan orang tua dalam melakukan pemilihan sekolah untuk anak yang dinginkan, beberapa pertimbangan khusus dalam penentuan sekolah salah satunya adalah status sekolah, jumlah sekolah, jumlah guru, jumlah murid dan jumlah ruang kelas. Makalah ini melaporkan bahwa data status sekolah TK kabupaten dan kota administrasi provinsi DKI Jakarta dapat dilakukan klasifikasi berdasarkan cluster dan domain data, dengan mempartisi data ke dalam cluster sehingga data yang memiliki karakteristik yang sama dikelompokkan ke dalam satu cluster yang sama dan data yang mempunyai karateristik yang berbeda dikelompokan ke dalam cluster yang lain. Metode klasifikasi yang digunakan adalah Levenshtein Distance dan K-Means Clustering, sumber data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh data.jakarta.go.id. Data sekunder yang digunakan adalah data sekolah dari 12 record kabupaten dan kota di Jakarta. Penelitian ini bertujuan untuk membuat model dan menentukan kriteria serta menganalisis akurasi klasifikasi antara ketiga metode tersebut dalam klasifikasi Data Sekolah TK Berdasarkan Status dan Kabupaten/Kota Administrasi Provinsi DKI Jakarta. Setelah dilakukan pengujian maka hasil Silhouette Score berdasarkan Average dari 4 atribut yaitu Cluster C1 dari score 0,691355 sampai 0,718406, Cluster C2 dari score 0,745171 sampai 0,747778 dan Cluster C3 dari score 0,601115 sampai 0,647377. Hasil Penelitian ini berupa pemodelan data dengan menggunakan parameter yang diambil dari data.jakarta.go.id kemudian diuji menggunakan beberapa model klasifikasi yang terdapat pada Machine Learning.
Rancang Bangun Sistem Kendali Pintu Pagar Otomatis Berbasis Pengolahan Citra Digital Pelat Nomor Kendaraan Menggunakan Metode Optical Character Recognition (OCR) Syah Alam; Firman Fauzi; Gunawan Tjahjadi; Ridzki Saputro Sya’ban
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.12922

Abstract

The gate is the main access to enter and exit the vehicle. In general, the gate is opened and closed manually by humans so it takes time and effort. This study proposes the design of an automatic gate control system based on digital image processing of vehicle number plates using the Optical Character Recognition (OCR) method to be able to recognize vehicle number plates. The vehicle number plate image will be recorded by a USB camera and processed using MATLAB to recognize each character on the vehicle number plate. Then the results of processing the vehicle number plate image are compared with the number plate database that has been inputted into the system. If the number plate is registered in the database, MATLAB will forward the command to Arduino Uno to drive the servo motor to open the gate. From the test results, it takes 7 seconds to process the vehicle number plate image processing until the gate is open. The percentage of successful reading of vehicle number plate characters by the MATLAB system is 100% of the 6 number plates tested with an accuracy of 100%. This research can be recommended as an automatic gate control system for security in buildings and homes.
Prediksi Daya Output Sistem Pembangkit Listrik Tenaga Surya (PLTS) Menggunakan Regresi Linear Berganda Suryo Bramasto; Dian Khairiani
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.13254

Abstract

The power generated by Solar Power Plants (Pembangkit Listrik Tenaga Surya/PLTS) from time to time is fluctuating due to the influence of weather and other external conditions. This study predicts the output power of PLTS Sumalata in North Gorontalo Regency with data analytics on datasets obtained from measurements at 2 plants in PLTS Sumalata. Data analytics to predict the output power of PLTS Sumalata is using a multiple linear regression approach, which is applied by implementing the Cross-industry standard for data mining (CRISP-DM) process model. The tools used are the Weka 3.0 application and Jupyter Notebook with the Python programming language. With data analytics using Weka 3.0 on datasets obtained from measurements at 2 plants in PLTS Sumalata, multiple linear regression equations were obtained as well as evaluation of prediction results using Correlation Coefficient (CC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). The equation formed from the prediction of the output power in Plant 1 is Y = -22216632810.1123 - 771640073.1888 X1 + 2349039057.8254 X2 -25796134709.3552 X3. While the equation formed from the prediction of the output power in Plant 2 is Y = -2784.107 + 300.0146 X1 – 173.7016 X2 + 21773.3845 X3. Based on the test, the correlation coefficient on the Plant 1 dataset is 0.52 and the Plant 2 dataset is 0.92. Those can be concluded that the irradiation data, module temperature, and ambient temperature have a significant effect of 52% on the output power generated in the PLTS system at Plant 1 and 92% on Plant 2. Then the MAE, RMSE, RAE, and RRSE values in the Plant 1 dataset are higher than Plant 2, while the relationship between the independent variables and the dependent variables in the Plant 2 dataset is stronger than the Plant 1 dataset. In order to improve the accuracy of the prediction that can be used for evaluating the performance of the PLTS system, measurement data with a minimum measurement duration of one year is needed to be able to represent seasonal conditions throughout the year, such as the dry season, rainy season, and extreme weather conditions.
Text Mining of PeduliLindungi Application Reviews on Google Play Store Irwansyah Saputra; Taufik Djatna; Riki Ruli A. Siregar; Dinar Ajeng Kristiyanti; Hasbi Rahma Yani; Andri Agung Riyadi
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.10629

Abstract

Aplikasi PeduliLindungi merupakan aplikasi buatan pemerintah indonesia  untuk melakukan pelacakan dan penghentian  penyebaran Covid-19. Ulasan terkait aplikasi tersebut tidak seluruhnya baik, hal ini dibuktikan dengan beragamnya peringkat bintang yang diberikan pengguna sehingga terjadinya kesulitan dalam melihat sentimen positif atau negatif terkait aplikasi tersebut. Penelitian ini bertujuan untuk mengklasifikasi ulasan mengenai aplikasi PeduliLindungi kepada dua kelas, yakni sentimen positif dan sentimen negatif. Algoritma klasifikasi yang digunakan adalah klasifikasi Naive Bayes Classifier (NBC). Hasil Menunjukkan Accuracy  85%, Precision 77,7%, Recall 98%, dan F1-Score 86,7%.
PENINGKATAN KUALITAS PRODUK NORMAL NOODLE DENGAN MENGGUNAKAN METODE SIX SIGMA DAN FUZZY FMEA Ririn Regiana Dwi Satya; Nurdeni Nurdeni
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.13429

Abstract

PT Indofood CBP Sukses Makmur Tbk is one of the companies engaged in the food sector, namely producing instant noodles. Preliminary research shows that PT Indofood Tbk Suskes Makmur Tbk has products that are not suitable and are formed as a result of sampling the production process, sometimes even exceeding the standards set by the company. The four production processes are cutting, frying, cooling and wrapping. In the four processes produced a number of different defective products, namely: In the cutting process the defective products produced were 30,586 pcs, in the frying process as many as 21,569 pcs, in the cooling process as many as 11,735 pcs and in the wrapping process as many as 42,000 pcs. %. In improving the quality, Six sigma and fuzzy FMEA methods are used. From the results of calculations using the conventional FMEA method and from the results of calculations with fuzzy logic for the value of FRPN using MATLAB software, it has different results, where the highest FRPN value is failure mode 1 (F1) or a risk factor for product defects because many noodle blocks are tucked away which can result in material wasteful with a value of 150 as a rating of 1. With this method, it is hoped that using the Six sigma method and fuzzy FMEA the company can improve quality and reduce the percentage risk of defects in the production process.
Algorithm Analysis of K-Means and Fuzzy C-Means for Clustering Countries Based on Economy and Health Lily Wulandari; Bima Olga Yogantara
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.12106

Abstract

Clustering adalah teknik pembelajaran mesin tanpa pengawasan yang membagi populasi menjadi beberapa kelompok atau klaster sedemikian rupa sehingga data dalam kelompok yang sama mirip satu sama lain, dan data dalam kelompok yang berbeda tidak serupa. Algoritma clustering yang ada diantaranya algoritma K-Means dan Fuzzy C-Means. Pada makalah ini proses clustering dilakukan untuk mengelompokkan negara-negara di dunia menjadi dua kategori utama yaitu negara maju dan negara berkembang berdasarkan tingkat kesejahteraan masyarakatnya. Makalah ini membahas tentang perbandingan algoritma K-Means dan Fuzzy C-Means. Algoritma K-Means menghasilkan 32 negara maju dan 135 negara berkembang. Algoritma Fuzzy C-Means menghasilkan 33 negara maju dan 134 negara berkembang. Hasil analisis pengujian performa menggunakan parameter Davies Bouldin Index pada algoritma K-Means memiliki nilai paling kecil artinya lebih baik yaitu sebesar 0.6606398 DB. Sedangkan hasil pengujian parameter Silhouette Coefficient pada Fuzzy C-Means semakin besar nilainya semakin baik dan didapatkan nilainya sebesar 0.896 S. Pengujian yang cukup signifikan terlihat pada penilitian ini adalah hasil pengukuran parameter Execution Time pada algoritma K-Means sebesar 0.00199 detik dan jauh lebih cepat.

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