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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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KOMPARASI KLASTER PENGANGGURAN TERBUKA DI INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19 MENGGUNAKAN K-MEAN CLUSTERING Raihan Maliqi; Kursehi Falgenti
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15108

Abstract

Open unemployment is a productive workforce of secondary education and higher education graduates who have not worked at all. The Indonesian Statistics Bureau (BPS) routinely publishes provincial open unemployment (TPT) data per semester. Many studies have analyzed TPT data by exploring new knowledge using data mining methods with cluster analysis techniques. Researchers have investigated the TPT data cluster under normal conditions before the COVID-19 pandemic. This study aims to find new knowledge from TPT data by comparing the TPT data cluster analysis results before the pandemic (2018-2019) with TPT data during the pandemic (2020-2021). Data mining techniques used are cluster analysis and the k-mean algorithm. The cluster analysis results are regional clusters with low and high unemployment rates before and during COVID-19. In addition, another finding is the movement from high to down clusters. Other interesting results are Covid-19 has the most impact on high unemployment in Aceh, North Sumatra, West Sumatra, Riau Islands, DKI Jakarta, West Java, Banten, East Kalimantan, North Sulawesi, Maluku, and West Papua. Anticipatory steps of the high open unemployment rates in 11 provinces, local government could design the program or policy that could support the BLT policy by the central government.
Development of a Production Machine Maintenance Predictive Model Using the Elman Recurrent Neural Network Algorithm Ajat Zatmika; Harry Dwiyana Kartika; Ali Khumaidi
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15450

Abstract

PT Simba Indosnack Makmur is a factory that produces snacks. In the production process the machine has worked very optimally, the problem that is often faced by the Quality Control department is often finding non-standard product weights. This problem is caused by a machine that already requires maintenance. So far, the maintenance process has to get approval from the manager, which sometimes takes quite a long time to be inspected so that the maintenance process is delayed, which results in reduced production targets. By implementing a predictive maintenance model that utilizes time series data in the production process, applying the Elman Recurrent Neural Network will be able to provide notifications for machine maintenance before the machine is inaccurate in snack production. The Elman structure was chosen because it can make iterations much faster, thus facilitating the convergence process. The input vector used uses windows size. The results of the study using a target error of 0.001 show the smallest MSE value of 0.002833 with windows size 11. Then by using 13 neurons in the hidden layer a minimum error value of 0.003725 is obtained.
IDENTIFIKASI GARIS TELAPAK TANGAN DENGAN METODE MOBILENET CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK SISTEM PRESENSI SISWA Muhammad Hamdi Sukriyandi; Achmad Solichin
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15138

Abstract

The attendance system at SMK Taruna Terpadu 1 with nine majors is still done manually. With a total of about 5,000 students, If attendance is recorded manually, many of these statistics are cumulative, making them difficult to organize and find when needed. Digitization of attendance recording is expected, one of which is the biometric method. Biometrics, the technology that digitally recognizes organic characteristics, can potentially update maps and other identifiers. Biometrics themselves come in physical form, such as faces, irises, fingerprints, and handprints. However, at some point during the COVID-19 pandemic, contact fingerprinting is unavailable and many of the challenges facing facial recognition, starting with skin color, using mask and identical twins. suggest ways to avoid contact. Fingerprint biometrics are an attractive option for more accurate, reliable, and secure contactless human identification technology, but identifying palm features from past images is also an attractive option. I am tasked with inputting some of the palm functions. and lighting fixtures. In this article, the authors propose to apply MobileNeV2's use of augmented facts, ROI detection, and pre-trained convolutional neural community (CNN) models. After testing with the dataset that the author got from SMK Taruna Terpadu 1 by performing data augmentation, ROI detection and identification with the pretrained MobileNetV2 model, it turns out to get the best accuracy results up to 99.98%.
Penggunaan Smartphone Berbasis Android Dalam Penerapan Location Based Service Pada Absensi Karyawan Dengan Metode OOAD Wiyanto Wiyanto; Edora Edora
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15147

Abstract

One of the most important activities in a company is the presence and absence of employees, this needs to be done for the continuity of activities within the company, the current system at PT. XYZ for attendance activities uses a finger print system, while for absenteeism it still uses a manual system, problems often occur in the finger print machine and are still conventional. The purpose of this study is to implement an automated employee attendance system using an Android-based location-based service so that it can make it easier for employees to perform attendance and absenteeism. This application system was developed using the OOAD (Object Oriented Analysis and Design) method, designed using UML (Unified Modeling Language) with tools from Android Studio and tested using blackbox testing
Rancang Bangun Sistem Keamanan Rumah Terintegrasi Telegram Menggunakan Mikrokontroler ATMega328 Habib Nurfaizal
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15902

Abstract

Crime can happen anywhere and anytime. The security system is important. Much will be done to make conditions secure, one of them is home security. Home is a place to stay that must be guarded. Everyone will anxious if leaving home when empty. Usually home security uses CCTV cameras. Many theft cases show that using a CCTV camera is less effective. It is not enough to use a camera to monitor the state of the house, then we need a system that can give real-time notifications to home-owners via Telegram. Telegram is a cloud-based and free instant messaging service. Telegram also provides a bot system. When creating a new bot on a telegram, can monitor the state of the house by receiving telegram messages through sensors that have been identified. This system is made using ATMega 328 Arduino board as a microcontroller and NodeMcu ESP8266 as a wifi that can give notifications if there is movement, fire, and gas leakage in the house.
APPLICATION OF SOCIAL MEDIA PLATFORM TECHNOLOGY IN PUBLICITY STRATEGY PROSUMPTION OF DIGITAL WORKERS IN THE MARKETPLACE Rayung Wulan; Udi Rusadi
Faktor Exacta Vol 16, No 2 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i2.17343

Abstract

The existence of social media platforms in the post-covid-19 pandemic era has increased sharply and penetrated various sectors among workers, especially digital workers. Various efforts to apply social media platform technology continue to increase in efforts to publicity strategies for the production of digital workers. platformsSocial media as a form of publicity expression for the production of digital workers has spread to various devices with the help of social media platforms with various applications. Social media platform technology as an effort to improve digital worker production publicity strategies that can increase the current marketplace rating. The purpose of this study is to apply social media platform technology which can be a strategy in publicity for digital workers in the marketplace. The method used in this study with the approachcomparative causal quantitative using surveys from several digital consumers who often use various marketplaces in their daily lives. , by adopting the slovin theory. In the testing phase of 368 respondents, there is a truth hypothesis from 105 digital consumers who are eligible for further testing in the marketplace with a simple linear regression analysis. Generated based on calculations with slovin theoryThe Publicity Strategy for Producing Digital Workers in the dominant Marketplace using the Social Media Platform shows a result of 95.2%, this result shows how high the presentation of these results is.
Implementasi Metode Support Vector Machine Dengan Algoritma Genetika Pada Prediksi Konsumsi Energi Untuk Gedung Beton Bertulang Asep Syaputra; Buhori Muslim; Nanda S. Prawira; Edowinsyah edowinsyah
Faktor Exacta Vol 16, No 2 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i2.16657

Abstract

Informasi tentang konsumsi energi sangat penting dalam mengukur efisiensi energi dan penghematan energi dalam bangunan. Konsumsi energi ini mengacu pada jumlah energi yang dibutuhkan untuk memberi daya pada bangunan pada waktu tertentu. Dengan mengetahui informasi ini, kita dapat mengevaluasi konsumsi energi yang ada dan membuat perubahan yang diperlukan untuk mengurangi penggunaan energi yang tidak perlu. Dalam jangka panjang, penghematan energi dapat membantu mengurangi biaya dan juga memberikan manfaat bagi lingkungan dengan mengurangi emisi gas rumah kaca yang dihasilkan oleh bangunan. Oleh karena itu, memperoleh informasi konsumsi energi yang akurat sangat penting bagi semua pihak yang terlibat dalam perencanaan, pembangunan, dan pengelolaan bangunan. Selama beberapa dekade terakhir, konsumsi energi di bangunan terus meningkat di seluruh dunia, dan sebagian besar konsumsi energi ini berasal dari Pemanasan, Ventilasi, dan Penyejuk Udara (HVAC) di dalam bangunan. Untuk mengatasi masalah ini, penelitian dilakukan dengan membuat model mesin vektor dukungan yang menggunakan algoritma genetika untuk memprediksi konsumsi energi di bangunan secara akurat. Dalam penelitian ini, dua model mesin vektor dukungan diuji, yaitu support vector machine dan support vector machine yang menggunakan algoritma genetika. Hasil pengujian menunjukkan bahwa model support vector machine memberikan nilai RMSE sebesar 2,6. Selanjutnya, algoritma genetika digunakan untuk mengoptimalkan parameter C dan memilih variabel prediktor yang paling relevan, dan hasilnya adalah nilai RMSE sebesar 1,7 dan hanya 3 variabel prediktor yang dipilih. Pada tahap selanjutnya, optimasi parameter dan pemilihan fungsi dilakukan untuk mencapai nilai RMSE terendah yang mungkin, dan hasilnya adalah RMSE sebesar 1,537. Dengan demikian, algoritma mesin vektor dukungan yang menggunakan algoritma genetika dapat memberikan solusi yang akurat dan efektif dalam memprediksi konsumsi energi di bangunan dengan nilai kesalahan terkecil.
Analisa Perbandingan Penerapan Metode SARIMA dan Prophet dalam Memprediksi Persediaan Barang PT XYZ Wawan Gunawan; Misbah Ramadani
Faktor Exacta Vol 16, No 2 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i2.13803

Abstract

Determining the right level of inventory is very important because it relates to the flow of money and can affect the performance of an organization. Too much inventory of goods can cause accumulation of storage space (warehouse) and reduce capital. The research will use data on sales of tires and wheels to be predicted using the SARIMA and Prophet methods, then the results will be compared for accuracy using RMSE. Based on the research results, it can be concluded that SARIMA (0, 0, 0)x(0, 1, 1, 12) with an RMSE evaluation result of 3.61 is superior to Prophet in predicting Dunlop product sales with an RMSE evaluation result of 4.02. SARIMA has the advantage in predicting because in the process there are features to find the best parameters to be implemented in the model.
Algoritma K-Means Untuk Mengetahui Minat Siswa Terhadap Jurusan Teknik Informatika putri dina mardika
Faktor Exacta Vol 16, No 2 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i2.17067

Abstract

There are already many information technology-based companies in the capital city, many job vacancies may be opened because they require experts with an educational background in informatics engineering. The research was conducted on YMIK 2 Jakarta high school students who concentrated on science and social studies. YMIK 2 High School students are familiar with information technology devices because there is a computer lab as a student facility for conducting computer learning activities. And there is an internet network in the form of free wifi at school. Researchers used data mining techniques with the K-Means algorithm and RapidMiner tools to process data to produce some conclusions about groupings related to whether or not YMIK 2 Jakarta High School students are interested in the Informatics Engineering major. The researcher divided the clusters into 2 groups consisting of cluster_0 which means students who are interested in informatics engineering and cluster_1 which means students who are not interested in informatics engineering. The data set used in this study was 50 data, according to the students participating in SMA YMIK 2 Jakarta. From the results of this study, it is known that students who are interested in majoring in informatics engineering are more numerous than students who are not interested in majoring in informatics engineering based on the k-means clustering algorithm.
Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization Puji Catur Catur Siswipraptini; Abdul Haris; Winda Novita Sari
Faktor Exacta Vol 16, No 2 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i2.15900

Abstract

The problem often occurs in chili leaves is organisms that interfere with chili plants which can reduce chili production. There are chili plant diseases that are difficult for farmers to recognize by using their eyes and without using tools. The purpose of this study was to produce a model capable of identifying chili leaf diseases based on leaf colour in order to make it easier for farmers to identify chili leaf diseases, especially  Phytophthora, Anthracnose, and Cercospora diseases, using the Learning Vector Quantization (LVQ) classification algorithm. Data was collected in the form of digital images of 30 chili leaves which were processed by resizing and transforming RGB to HSV which then proceeded to Canny Edge detection process with the aim of getting patterns from images of chili leaves. The result of testing LVQ algorithm using a confusion matrix get an accuracy of 80%, the precision value of 80%, recall value of 82%, and f-1 score of 81%. 

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