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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 9 Documents
 1 
Search results for , issue "Vol 16, No 4 (2023)" : 9 Documents clear
Penerapan Algoritma Sweep dan Particle Swarm Optimization (PSO) sebagai Alternatif Menentukan Rute Distribusi Fauzi, Ilham Saiful; Wardani, Imaniah Bazlina; Putra, Indra Lukmana; Puspitasari, Peni
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.18962

Abstract

One aspect of marketing activities is distribution. In the process of distributing goods, it is important to determine the optimal route that minimize mileage and reduce costs. This study aims to provide alternative solutions in determining distribution routes with the shortest distance which has implications for shorter travel times and lower costs. This research adapts the Capacitated Vehicle Routing Problem (CVRP) model with the approach of sweep and Particle Swarm Optimization (PSO) algorithm to determine the route. To generate a comparison route, we use the Nearest Neighbor (NN) algorithm. The result was that 100 agents were divided into 6 clusters and the total distance of the PSO-generated route is 218.115 units or 85.70% of the route distance generated by Nearest Neighbor algorithm.
Perancangan Diagnosa Covid-19 Menggunakan Metode Case Based Reasoning (CBR) Untuk Mengidentifikasi Tingkatan Gejala Pasien Covid-19 Rismawati, Nofita; Mulya, Muhamad Femy
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19767

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.
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.
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
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.
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
Clustering the K-means Algorithm with the Approach to Student Interpersonal Communication Patterns in Selecting Secondary Schools Wulan, Rayung; Widaningsih, Themotia Titi; Yanuar, Fit
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.20852

Abstract

This research aims to understand students' communication patterns in choosing secondary schools by identifying existing group patterns, and understanding the factors that influence students' decisions in choosing secondary schools. Using the k-means algorithm clustering method, the dataset was obtained from student data, psychological test scores and interpersonal communication in three grade 9 junior high schools in West Jakarta. The dataset obtained was 317, the results of data clearing were 259 students who were eligible to be tested. The results of tests carried out with 4 clusters show an accuracy value close to 0, with cluster 2 having a value of -0.150. The results show that students who choose a secondary school based on their psychological test results and interpersonal communication between parents, homeroom teachers and the school are the dominant values in the continuity of selecting a senior secondary school
Analisis Model Matematika dan Simulasi Pada Penyebaran Hepatitis Non HepA-E Akut di Indonesia Ristiawan, Rifki; Wahyudi, Farrell; Selvia, Noni
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19670

Abstract

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