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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 8 Documents
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Search results for , issue "Vol 15, No 2 (2022)" : 8 Documents clear
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.
Perancangan Sistem Penerimaan Siswa Baru Berbasis Web Pada Sekolah Dasar Islam Plus Baitul Maal Herfandi Herfandi; Saruni Dwiasnati; Kiki Ahmad Baihaqi; reza Avrizal
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.12894

Abstract

Islamic-based education in Indonesia is an educational institution that focuses on forming the character and knowledge of Islamic religious values. Islamic Elementary School Education Institute Plus Baitul Maal in carrying out educational administration procedures still uses the conventional system. The procedure that took place has not been efficient because prospective students are required to complete the registration form sheet by manual means and still stored in the folder. The implementation of information system technology in the education sector is able to provide convenience, especially in terms of efficientness, accuracy and novelty of information. Therefore, the design and creation of a website-based new student admission information system is expected to be able to solve the problem. This research resulted in a website-based new student admission information system with the main Page having Home, About Us, How to Apply, and Contact functionality. The student dashboard page has Home, Student Profile, Document functionality. The admin dashboard page has the functionality of Home, Website Content, Management, Document Completeness, and Settings as an admin user management serves to set up admin accounts (Create, Record, Update and Delete (CRUD) for admin accounts), development methods using waterfalls, research methods using qualitative and information system testing using black box testing that gets conclusions according to various functionality tests. This system is expected to ease the work of new student admission administrators as well as adjustments to education in the Industrial 4.0 era.

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