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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 6 Documents
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Search results for , issue "Vol 14, No 1 (2021)" : 6 Documents clear
PERANCANGAN MACHINE VISION UNTUK PEMILAH KUALITAS PRODUK AIR MINUM DALAM BOTOL 600ML DI WTP PUTOI PNJ Nur Alam; Dian Figana
Faktor Exacta Vol 14, No 1 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i1.7652

Abstract

Penelitian ini telah berhasil melakukan perancangan alat untuk memilah kualitas air minum dalam kemasan botol 600ml di WTP PNJ. Air minum yang sangat dibutuhkan oleh tubuh manusia adalah air minum yang bersih, sehat dan higienis. Saat ini mayoritas air minum yang ada khususnya di wilayah Jakarta adalah air minum dalam kemasan. Air sumur yang ada saat ini mayoritas telah tercemar oleh bakteri maupun unsur lain dari berbagai sumber antara lain; dari limbah pabrik, pom bensin, maupun berasal dari limbah rumah tangga lainnya. Politeknik Negeri Jakarta sebagai Perguruan Tinggi Negeri yang saat ini sedang menuju visi unggul berkelas dunia, telah mempersiapkan beberapa fasilitas pembelajaran yang bertujuan untuk meningkatkan kompetensi dosen dan mahasiswa dalam mengembangkan keunggulannya. Salah satu keunggulan tersebut yaitu adanya Pusat Unggulan Teknologi Otomasi Industri (PUTOI). Keunggulan di PUTOI saat ini yang dikembangkan adalah pusat teknologi dan riset mengenai bidang teknologi otomasi industri. PUTOI telah memiliki sistem teknologi otomasi yang canggih yaitu dalam bidang teknologi otomasi Water Treatment Plant (WTP). Dengan demikian sebelum beredar dalam bentuk air minum maka dibutuhkan lagi sebuah alat otomasi untuk memeriksa kualitas dari produk air minum tersebut. Saat ini dibutuhkan sebuah mesin untuk bekerja sebagai Quality Control dari produk air minum WTP PNJ. Peneliti telah berhasil membuat sebuah rancangan mesin pemilah kualitas produk air minum dari WTP PNJ berbasis Machine Vision. Mesin vision ini merupakan metode baru yang sangat diperlukan dalam penentuan kualitas suatu produk secara otomatis. Dimana mesin vision ini dapat memilah kualitas suatu produk air minum berdasarkan level air, kualitas kemasan, kejernihan air, maupun kualitas segel dan barcodenya.
Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi Menggunakan Metode Profile Matching DWI DANI APRIYANI
Faktor Exacta Vol 14, No 1 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i1.9057

Abstract

The calculation of scores that are still comprehensive for each student results in assessments that tend to be less objective. This has an impact on the selection of outstanding students which is less accurate and tends to be non-objective. This requires a decision support system for outstanding students. A decision support system using the profile matching method is one of the most frequently used methods because it can match criteria and has an accurate final decision. The weight criteria used are knowledge and skills, while the criteria for assessing outstanding students are report cards, learning attitudes, extracurricular activities, discipline, and attendance. With the existence of a decision support system for outstanding students, it is hoped that it can help teachers to be more objective in determining student achievement decisions.
ANALISIS GROUND VIBRATION DENGAN METODE PEAK PARTICLE VELOCITY (PPV) Hari Hadi S; Erna Kusuma Wati; Tomas Kristiono
Faktor Exacta Vol 14, No 1 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i1.7833

Abstract

Measurement of Peak Particle Velocity (PPV) mm / sec in the Sabo dam construction project was carried out using seismic accelerometers. This study is to determine the value of PPV produced by construction equipment and then compared with the BS 6472-2: 2008 standard. The measurement method is carried out based on the applicable rules. PPV measurement results produced by each machine are different. In heavy equipment dump trucks, excavators, and front end loaders show PPV values at distances of 50 m, 100 m, 150 m and 200 m under safe conditions referring to the standard which is still in the range of 0.2 - 0.4 mm / sec. while for the pile driving device, demolition, vibrator pile driver at a distance of 50 meters are in unsafe conditions, because more than the range of 0.2 - 0.4 mm / sec, but at a distance of 100, 150, and 200 m PPV values are at safe conditionKey words: PPV, Ground Vibration, Dam sabo 
EVALUASI KUALITAS APLIKASI SISTEM INFORMASI MANAJEMEN KEIMIGRASIAN (SIMKIM) VERSI 2.0 BERBASIS WEB MENGGUNAKAN METODE HUMAN ORGANIZATION TECHNOLOGY FIT (Studi Kasus pada Kantor Imigrasi) Arham Bakri; Anggraeni Ridwan
Faktor Exacta Vol 14, No 1 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i1.8630

Abstract

Immigration office one of the organization implementing information system integration and automation called the immigration management information system (SIMKIM) Version 2.0 for passport services. An evaluation need to find how the system is implemented, the level of success, as far as to which the system contributes to the organizations that use it. SIMKIM Version 2.0 application have three main component namely human organization and technology. The result of the evaluation indicate technology component include system quality getting value 3,24 (good), information quality getting value 3,09 (good), and service quality getting value 3,21 (good). Human component including system user getting value 3,18 (good), user satisfaction getting value 3,07 (good), and the benefits getting value 3,15 (good), and organization structure getting value 3,22 (good). The overall component SIMKIM Version 2.0 implementation obtained value of 3,16 good interpretation based on HOT Fit method.
ANALISIS KLASIFIKASI POPULASI TERNAK KAMBING DAN DOMBA DENGAN MODEL CONVOLUTIONAL NEURAL NETWORK Alusyanti Primawati; Intan Mutia; Dwi Marlina
Faktor Exacta Vol 14, No 1 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i1.8734

Abstract

The number of goat populations is increasing all over the world. Sheep and goats are economically potential for business development because they do not require large areas of land, relatively small investment in business capital, and are easy to market. However, the similarities between goats and sheep can make small breeders who are just starting out in business nervous. Therefore, in goats and sheep, an intensive and efficient Precision Livestock Farming system is required. To answer this problem, goat and sheep objects was studied out using the collaboration software programming R and Python which executed in RStudio editor and Anaconda3 with the Tensor flow package. The sample data of 40 images. The model obtained from the classification results uses 20 pictures of goats and 20 pictures of sheep for training and testing. The accuracy produced shows that the prediction of training data at epoch 70 and 100 has the right accuracy with the actual data. This reinforces that the model used is good (fit) to the training dataset, but when it is applied to the testing dataset, the prediction results are still close to perfect. Epoch 70 identifies there is 1 image of a Goat which is recognized as Lamb.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CNN UNTUK SISTEM PENGENALAN WAJAH Noviana Dewi; Fiqih Ismawan
Faktor Exacta Vol 14, No 1 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i1.8989

Abstract

Face recognition system is generally divided into two stages, face detection system, which is a pre-processing step followed by a facial recognition system. This step will quickly be done by humans but it takes a long time for the computer. This ability of humans is what researchers want to duplicate in the last few years as biometric technology in computer vision to create a model of face recognition in computer. Deep learning becomes a spotlight in developing machine learning, the reason because deep learning has reached an extraordinary result in computer vision. Based on that, the author came up with an idea to create a face recognition system by implementing deep learning using the CNN method and applying library open face. The result of this research is applying deep learning with the CNN method to classification process that resulting percentage of precision of 96%, recall percentage of 100%, and accuracy percentage of 99.8%.

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