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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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Comparative Analysis of Linear Regression, Decision Tree, and Gradient Boosting Models for Predicting Drug Corrosion Inhibition Efficiency Using QSAR Descriptors ignasius, Darnell; Akrom, Muhamad; Budi, Setyo
Faktor Exacta Vol 17, No 3 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i3.24679

Abstract

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

Eksplorasi Teknik Web Scraping pada Data Mining: Pendekatan Pencarian Data Berbasis Python Chrisinta, Debora; Simarmata, Justin Eduardo
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.22393

Abstract

Implementasi Metode Perbandingan Eksponensial Dalam Sistem Pendukung Keputusan Pemberian Kredit Nasabah Pada PT Bank DKI Cabang Syariah Wahid Hasyim Fajriah, Riri; Melyana, Melyana; Triyono, Gandung
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.21040

Abstract

PT Bank DKI Cabang Syariah Wahid Hasyim adalah cabang usaha yang melayani segmentasi pasar syariah dari PT Bank DKI sebagai BUMD dari Pemerintah Provinsi DKI Jakarta. Adapun produk bank yang ditawarkan terkait jenis layanan keuangan dan berbagai jenis kredit yang ditawarkan kepada calon debitur, seperti Kredit Pemilikan Rumah (KPR), Mikro UMKM, Kredit Multiguna, Bank Garansi. Permasalahan yang dihadapi saat ini adalah masih cukup signifikan kasus kredit macet di bank akibat kesalahan keputusan dalam pemberian kredit dari data analisa kelayakan calon debitur. Oleh karena itu, tujuan penelitian ini adalah untuk merancang sistem pendukung keputusan dengan menggunakan metode waterfall analysis dengan model perbandingan eksponensial dimana sistem ini akan digunakan oleh Relationship Manager (RM) untuk mengevaluasi kelayakan kredit calon debitur dengan lebih tepat dan akurat. Hasil penelitian ini menyajikan rancangan sistem pendukung keputusan dengan metode perbandingan eksponensial yang dapat membantu proses analisa kredit dari data-data calon debitur yang diproses untuk menghasilkan ranking penilaian kelayakan pemberian kredit, dimana keputusan pemberian kredit diambil berdasarkan nilai tertinggi hasil perhitungan MPE dan hasil ini akan menjadai landasan bagi Relationship Manager sebagai prioritas calon debitur untuk proses selanjutnya mendapatkan persetujuan kredit dari Pemimpin Cabang sebagai penyelia kredit di PT Bank DKI Cabang Syariah Wahid Hasyim.
Pemanfaatan Perpustakaan Digital (E-Library) Sebagai Salah Satu Strategi Peningkatan Kualitas Pendidikan dan Penelitian di Perguruan Tinggi Himawan, Himawan; Kusuma Wardani, Deyana; Kartika Kusuma Winahyu, Raden Rara
Faktor Exacta Vol 17, No 3 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i3.23824

Abstract

The university plays an important role in educating the future generations of the country. One of the universities efforts is to use the facilities and infrastructure in their respective universities to achieve this goal. Astra Polytechnic is one of the higher education institutions in the Cikarang region with unsuitable infrastructure to improve the quality of university services and research activities for all academic communities at the Astra Polytechnic Campus. The infrastructure is the campus library that is still operated in traditional ways, which certainly does not meet the information needs of the digitalization era, which requires the management and delivery of up-to-date and accurate information. Consequently, the Astra University Research and Community Service Institute (LP2M) and the Information Management Study Programme collaborated to establish digital libraries to improve the quality of university services for the entire Astra University College community. In addition, libraries must change (transformation) in order to survive today's digitalization. Ultimately, the use of library information systems is expected to help librarians and library staff manage library collections, memberships and all transactions, so that librarians have more time to do other things and be more efficient in terms of time management.
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.
Fruit Zone : Media Pembelajaran Interaktif Pengenalan Buah Anak Kelompok Belajar Menggunakan ResNet18 Komariah, Siti Ingefatul; Putri, Desti Fitri Aisyah; Rahmawati, Siska Yulia; Fitri, Zilvanhisna Emka; Atmadji, Ery Setiyawan Jullev; Widiastuti, Reski Yulina; Imron, Arizal Mujibtamala Nanda
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.21101

Abstract

Learning media is very important in supporting learning activities in early childhood. Limited learning media and learning methods that are still centered on the ability and experience of teachers are an obstacle to improving learning at Pos Alamanda 105 Jumerto, Jember. An interactive, cheap, easy and accessible learning media is needed to improve students' abilities, especially in fruit recognition using both Indonesian and English. The solution, researchers used Deep Learning method for interactive learning media of fruit introduction in early childhood. The method used is Convolutional Neural Network with Resnet18 architecture. This research uses 21 types of popular fruits and unique fruits equipped with voice features in Indonesian and English. The total data of 2100 fruit images with a learning rate of 0.0002 and a maximum epoch of 100 wereable to classify the fruit with an accuracy rate of 96% (system training) and 95% (system testing).
PENERAPAN METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFKASI KUALITAS DAGING SAPI PADA APLIKASI BERBASIS ANDROID Asmoro, Phaksi Bangun; Solichin, Achmad
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19564

Abstract

The surging demand for beef in Indonesia poses a significant challenge for the food industry, leading to fraudulent practices among meat traders. To meet the high consumer demand and gain higher profits, fresh beef is mixed with spoiled meat. Unfortunately, many consumers are unable to distinguish between fresh and spoiled beef, relying solely on the meat's aroma to determine its quality. However, recognizing spoiled beef requires considering other indicators of spoilage. To address this issue, researchers focused on developing a beef quality classification system using the Convolutional Neural Network (CNN) method. The study involved implementing TensorflowLite on Android devices and training the CNN model with deep learning algorithms to recognize visual patterns in beef images. The Android application provides clear and user-friendly classification results. The developed beef quality classification system achieved remarkable accuracy, with a precision of 97%, a recall of 96%, and an f1 score of 97%. With 100 beef images as test data, the system demonstrated an accuracy rate of 95.69%. This advancement is expected to improve the efficiency and quality of beef processing in Indonesia, ensuring consumers receive genuine and safe products
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

Analisis Trend Topik Penelitian Tesis Pada Program Studi Magister Ilmu Komputer Universitas Budi Luhur Menggunakan Metode Latent Dirichlet Allocation (LDA) Wahyudi, Arief; Bayuaji, Luhur
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.21190

Abstract

Every year, thousands of studies are conducted by researchers from various institutions and places, focusing on various fields and topics. This also applies to thesis research conducted by students of the Master of Computer Science Program at the Faculty of Information Technology, Universitas Budi Luhur. Given the significant amount of research over time at Budi Luhur University's Master of Computer Science Program, it has become increasingly difficult to effectively understand research trends and focus. The purpose of this study is to identify trending thesis research topics in the Master of Computer Science Program at Universitas Budi Luhur. The data used in this research includes thesis research titles conducted from 2016 to 2021. The method used in this research is Latent Dirichlet Allocation (LDA). The results of the study produced the best pass value at 28 and the best number of topics was 5 topics. LDA modeling produces 5 research topics that are trending in the period 2016 to 2021, namely sentiment analysis, data analysis, prediction analysis, decision support systems and machine learning.

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