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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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Analisis dan Optimasi Sistem Kendali Robot Falcon Millenium: Automatic Robot Palletizer Menggunakan PLC Omron Wijaya, Ahmad Reynaldi; Mandasari, Raden Deasy; Rosano, Andi
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.19593

Abstract

Deteksi Pelat Nomor Dengan Menggunakan Optical Character Recognition Berbasis Algoritma LSTM Wardhani, Elisa; Dwiasnati, Saruni
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19216

Abstract

Illegal Parking which has become a frequent occurrence in the Jakarta area, encourages system transformation which includes the addition of automatic parking system facilities based on the building infrastructure and facilities regulation. The development of an automatic parking system can utilize license plate number detection to minimize the need to manually input license plate numbers into the parking system. In this study, LSTM algorithm training is done and implemented on Optical Character Recognition to detect license plate numbers accurately. Based on the evaluation results, the LSTM algorithm has a good performance in detecting license plate numbers with an accuracy rate of 86,36%. However, the LSTM algorithm performance improved when implemented on Optical Character Recognition with an accuracy rate of 95,8%. Hence, based on the evaluation, the LSTM algorithm that has been implemented on Optical Character Recognition is considered a preferable choice in license plate number detection as it has a higher level of accuracy compared to the use of the LSTM algorithm alone.
LPG Gas Leak Detection System and LPG Fire Classification Based on Internet of Things and Artificial Intelligence with Telegram Bot as a Monitoring Tool Adjhi, Dhimaz Purnama; Hanafi, Mohamad Rizal; Suteddy, Wirmanto
Faktor Exacta Vol 17, No 3 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i3.21920

Abstract

LPG gas leaks pose a serious threat in industrial kitchens as they can cause costly fires, both in terms of material and safety. To improve safety, an accurate detection system is required. This research focuses on developing an LPG gas leak detection system and LPG fire classification with Internet of Things and Artificial Intelligence technology. Supported by Telegram Bot as an emergency notification monitoring tool, this system uses MQ-2 sensors to detect LPG gas leaks and ESP32-Cam to classify LPG fires along with Pretrained-model technology such as Cascade Fire Detection on OpenCV Cloud Server. As the output of this system, the use of PWM control and automation oversees regulating the Exhaust Fan according to the detected leakage. FreeRTOS is also used for system task efficiency, and Port Forwarding with Ngrok Local Server allows public access to the ESP32-Cam. System testing was conducted by Black-Box testing, then evaluating the performance of the MQ-2 sensor against 400 ppm and 1500 ppm thresholds for LPG testing distances in open kitchens and closed kitchens, as well as analyzing system response and delay via HTTP protocol. The results demonstrated the system's success in detecting gas leaks, classifying LPG fires and facilitating emergency communication.
Membaca Sinyal Electroencephalogram (EEG) Dalam Menangkap Tingkat Emosi (Berdasarkan Ontologi) Devianto, Yudo; Sediyono, Eko; Prasetyo, Sri Yulianto Joko; Manongga, Danny
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.20878

Abstract

Philosophically based EEG (electroencephalography) signal data processing is an engaging interdisciplinary approach and opens up new perspectives in understanding brain function. In this context, it is necessary to examine data from a technical or biological point of view and consider its metaphysical, epistemological and even ontological aspects. Ontology is a branch of metaphysics that deals with objects and the types of objects that exist according to one's metaphysical (or even physical) theory, their properties, and their relationship. This article attempts to provide a philosophical view of science based on ontology for processing EEG signal data, the data source of which is taken from brain waves. With the results of trials using the Artificial Neural Network (ANN) classification, an accuracy value of 46.73 was obtained. The Convolutional Neural Network (CNN) algorithm can also be used to process EEG signal data to determine a person's emotional level; this is proven in research results; although the overall accuracy of emotion recognition has increased significantly, several problems cause low accuracy in the DEAP and DREAMER data sets. There are also results of other experiments carried out using CNN, and the experimental results show that the weight of channels related to emotions is greater than that of different channels. The Continuous Capsule Network (CCN) algorithm and Deep Neural Network (DNN) algorithm can also be used to process EEG signal data to determine the level of emotion.
PENERAPAN ALGORITMA NAIVE BAYES CLASSIFIER UNTUK ANALISIS SENTIMEN KOMENTAR TWITTER PROYEK PEMBAGUNAN IKN Zamzami, Faiz; Hidayat, Rahmat; Fathonah, Rina
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.22265

Abstract

Penerapan Metode Convolution Neural Network (CNN) Dalam Proses Pengolahan Citra Untuk Mendeteksi Cacat Produksi Pada Produk Masker Arvio, Yozika; Kusuma, Dine Tiara; Sangadji, Iriansyah BM; Dewantara, Erno Kurniawan
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.20073

Abstract

A mask manufacturer in Indonesia with a production of 4 million masks per day for various types of masks. However, in the production process there are still many defective and unsalable masks that enter the stock of goods to be sent, this is due to the quality control process that is still manual. So that to reduce product defects, it is necessary to mitigate by creating a system that can detect defective products, to facilitate the quality control process, an intelligent computing system is needed so that it is expected to reduce mask production defects to build this computational model will be carried out in several stages. The first stage will be a field study to obtain samples of defective and perfect products. The second stage builds a computational model, this model is built based on the Convolution Neural Network (CNN) method and the third stage builds a system that suits the needs in the field and tests the system against the company's needs. The purpose of this research is to produce a good and perfect defective product detection system so that it can be useful for reducing defective products that pass the quality control stage. From this research, if the process is run by entering existing data, it produces an accuracy percentage of 99% of the 750 data tested. While in real time testing, a percentage of 96.4% was obtained using 28 data.
Sistem Pendukung Keputusan Penerima Bantuan Renovasi Rumah Pasca Gempa Cianjur Menggunakan Multi Attribute Decision Making dengan Metode SAW Maskur A, Moch Riyadi; Triyono, Gandung
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.22760

Abstract

Dalam situasi darurat bencana diperlukan keefektifan dan efisiensi dalam pengambilan keputusan. Dalam penelitian ini yaitu keputusan untuk menentukan penerima bantuan renovasi rumah pasca gempa. Pada saat ini, salah satu permasalahan adalah ketidakakuratan dalam pengambilan keputusan. Hal tersebut dapat menyebabkan ketidaktepatan dalam pemberian bantuan. Adanya permasalahan tersebut, maka diperlukan sebuah sistem pendukung keputusan menentukan penerima bantuan renovasi pasca gempa. Pada penelitian ini diusulkan sebuah model sistem pendukung keputusan terbaik dengan pendekatan MADM dengan metode SAW. Model yang dikembangkan menggunakan 8 kriteria, yaitu kondisi pondasi, kondisi sloof, kondisi penutup atap, kondisi rangka atap, kondisi lantai, kondisi dinding, kondisi ring balok, dan kondisi kolom. Pada penelitian ini digunakan 20 data calon penerima bantuan, dengan rincian 5 data yang termasuk rumah dengan renovasi kategori rusak berat, 9 data untuk renovasi rumah yang rusak sedang, 4 data untuk renovasi rumah yang rusak ringan, dan 2 data untuk yang tidak layak mendapatkan bantuan renovasi rumah. Hasil pengujian model dihasilkan akurasi 98% untuk akurasi nilai, dan 95% untuk akurasi kelayakan yang dibandingkan dengan data aktual yang didapat.
IMPLEMENTASI SISTEM AUTOMATIC TEXT SUMMARIZATION BERBASIS FITUR DAN METODE JARINGAN SYARAF TIRUAN PROPAGASI BALIK Syaddad, Muhammad Sulthan; Syafrullah, Mohammad
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.20006

Abstract

In the era of Industry 4.0, information has become a primary necessity for society today as it enables people to know about various current events worldwide. With the rapid development of information technology and the internet, there has been an abundance of documents available that we can search according to our needs. Text Summarization Machines have the function of presenting essential information from the original documents in a shorter format while still preserving the main content and helping users understand the information from lengthy documents faster. In this case, the method used is the Text Summarization Feature-Based approach, utilizing the Backpropagation Artificial Neural Network algorithm for sentence prediction calculations. The Backpropagation Artificial Neural Network algorithm seeks the most optimal weights during its process. In the testing process with five document samples, the final result obtained was a text summary model that could predict the overall number of labels correctly. However, it struggled in predicting which ones should be labeled as "true" and which ones should be labeled as "false".
Kombinasi algoritma base64 dan caesar cipher pada aplikasi Devianto, Yudo; Gunawan, Wawan; Sukowo, Bambang; Susafaati, Susafaati
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.20680

Abstract

Digital information systems must also pay attention to data security because it is confidential, there are many problems with data security which result in loss of data or damage caused by irresponsible parties. This research will combine the BASE64 and CAESAR CIPHER algorithms in applications to maintain the security of financial data so that it cannot be seen by users who do not have access to the application. The system development in this research looks like in Figure 1 which uses the Extreme Programming method. The testing carried out was using Black box and white box testing which produced the same cyclomatic complexity value, namely 4. So it can be concluded that the system is running well because the testing produces the same value
Perancangan Sistem Informasi Hino Service on Site (Studi Kasus : Dealer Hino, PT. Persada Lampung Raya) Astuti, Renita Dwi; Firmansyah, Firmansyah; hasibuan, muhammad said
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19447

Abstract

Service on site is one of the after-sales services for Hino Truck vehicles. The Service on Site contract program provides customer vehicle service services at the customer's location or site by placing a mechanic at the customer's location. In its implementation, several obstacles were encountered, such as the vehicle service history was not recorded, there were no service reports so the customer did not know the vehicle's performance. So this research develops the design of the Hino Service on Site Information System using observational research methods, literature review, and documentation. To build the system, Use Case diagrams were designed and then measured using Use Case Points (UCP) to assist management in expanding the Servicee on Site Information System. UCP will assist management when making decisions regarding system development in terms of time, human resources and finances. Software measurement using UCP in the Service on Site Information System at Hino Dealers PT. Persada Lampung Raya has a Use Case Point (UCP) score of 38.448 and is categorized as a small software size project, which is smaller than 99. With the proposed design of this system, it can simplify and speed up the on site service administration process and can provide information in the form of vehicle performance reports to customers so that they can improve service on site program

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