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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 11 Documents
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Search results for , issue "Vol 17, No 2 (2024)" : 11 Documents clear
Implementasi Metode Simple Additive Weighting Pada Perancangan Sistem Penilaian Reseller di Showroom Aska Motor Garage Ananda, Rizki; Yulianingsih, Yulianingsih; Megiati, Yunita Endra
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.20768

Abstract

Aska Motor Garage Showroom is a company engaged in motorcycle sales. As a sales-oriented business, Aska Motor Garage needs a strategy to boost its sales. One of the strategies that can be implemented is by establishing partnerships with resellers through the provision of rewards in the form of incentives. Therefore, there is a need for an objective and measurable assessment system. The Simple Additive Weighting method is one of the techniques used to determine the best value based on criteria and weights that can be customized according to the partners' needs, and it is considered quite appropriate for use in this research. The result of this system design is a decision support system that provides information about the assessment of the top resellers, using four supporting criteria, including monthly sales, innovation, work quality, and adherence to target pricing.
Analisis Sentimen Terhadap Kontroversi Putusan MK Mengenai Usia Capres-Cawapres Menggunakan Multi-Layer Perceptron Dengan Teknik SMOTE Sasmita, Sasmita; Jariah S.Intam, Rezki Nurul; Surianto, Dewi Fatmarani; B, Muhammad Fajar
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.22442

Abstract

In October 2023, the Constitutional Court's decision on age limit requirements for presidential and vice-presidential candidates stirred controversy, perceived as favoring a specific vice-presidential candidate. Public reactions flooded social media platforms, particularly on Najwa Shihab's YouTube channel, where sentiment analysis was conducted on 505 comments under the video titled "Putusan MK: Publik memang Seharusnya Marah" (Constitutional Court Decision: The Public Should Indeed Be Angry). The comments were categorized into three sentiment classes: 425 negative, 42 neutral, and 38 positive. The study employed Multi-Layer Perceptron (MLP) models tested on both imbalanced and balanced data using the SMOTE oversampling technique. Two feature extraction methods, TF-IDF weighting and countvectorizer, were applied. Results showed that the combination of TF-IDF with balanced data yielded the most effective classification model, boasting a remarkable accuracy, precision, recall, and F1-score, each at 99%.
Electronic Voting (e-voting) sebagai Aplikasi Terdesentralisasi pada Vexanium Blockchain Bramasto, Suryo; Savitri, Sandriana Febia; Djuwitaningrum, Endang Ratnawati
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.19405

Abstract

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.
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".
Application of Ensemble Tree Algorithm for Installment Payment Arrears Prediction at Makmur Bersama Credit Union Khumaidi, Ali; Darmawan, Risanto; Reztrianti, Diajeng
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.21819

Abstract

PENERAPAN ALGORITME BACKPROPAGATION NEURAL NETWORK UNTUK ESTIMASI JUMLAH KASUS DBD BERDASARKAN DATA CUACA Raissa, Benita Hasna; Rusdah, Rusdah
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.20473

Abstract

Dengue fever is widespread throughout the tropics which tends to have a seasonal pattern, namely before and after the rainy season. Infection is caused by one of the closely related dengue viruses, commonly called a serotype, which causes mild symptoms to symptoms that require medical treatment and hospitalization, even death can occur if the case is severe. Based on surveillance data, the number of cases in 2022 will be 3,190 people. One of the efforts to reduce the incidence of DHF is by forecasting the incidence of DHF to prevent an increase in DHF cases which continues every year. This research was forecasted using the independent variables average temperature, average humidity, average rainfall, and wind speed. The data used is public through surveillance and the BMKG website and the data used is data from 2018 to 2022. In this study using the backpropagation neural network algorithm, the model used is 4-3-1, where there are 4 variables in the input layer, 3 units in the hidden layer, 1 unit in the output layer with a learning rate value of 0.04, and momentum of 0.09 and the results are RMSE 4,347.
Komparasi Pengaruh Model Klasifikasi Naive Bayes dan Support Vector Machine Pada Analisis Data Sentimen Di Bidang Pendidikan Fajriah, Riri; Kurniawan, Denni
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.22342

Abstract

Application of Data Mining to Prediction of New Students' Interested Departements With an Approach Naive Bayes Algorithm Harsanti, Niken; Wibowo, Arief
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.20625

Abstract

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