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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Sistem Monitoring Suhu dan Kelembaban Berbasis Internet of Things (IoT) Pada Ruang Data Center Kusumah, Rafik; Islam, Hajar Izzatul; Sobur, Susilawati
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5199

Abstract

The Internet of Things (IoT) has brought significant advancements in various fields, including environmental monitoring. This study presents the implementation of an IoT-based temperature and humidity monitoring system specifically designed for a data center. The monitoring device consists of a DHT11 sensor, water level sensor, OLED I2C display, and ESP8266 (NodeMCU) microcontroller, enabling accurate and consistent measurements of temperature and humidity levels. Through extensive hardware testing, the developed device has demonstrated its effectiveness in accurately measuring temperature and humidity within the data center. Comparative analysis of the hardware data with readings from the HTC-2 device revealed minimal error rates, with an average of 1.7% for temperature and 2.1% for humidity, affirming the reliability and consistent performance of this device. Software testing showcased the monitoring application's efficiency in displaying temperature and humidity data through an intuitive dashboard. Users can easily access real-time data and statistical graphs, facilitating effective monitoring and analysis of the environmental conditions in the data center.
Optimasi Pengendalian Persediaan dengan Metode Reorder Point dalam Pengembangan Aplikasi Kontrol Stok Berbasis Web Maulidi, Rakhmad; Listianti, Prima
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5204

Abstract

Stock control is a process of keeping track of owned items, their location, and their movements in and out of storage. It helps businesses to manage their inventory efficiently and meet consumer demand while reducing the cost of storing goods. However, online shops like Omah Mode often struggle to meet the demand of consumers due to the lack of a stock control system, resulting in penalties from the marketplace. To solve this problem, a web-based stock control application was designed using the reorder point method. This method determines the minimum stock of goods that should be in the warehouse and the right time to order goods with low stock from suppliers. The purpose of this research is to develop an inventory control system that displays the minimum stock of goods needed in the warehouse. The study used the SDLC Prototype method, consisting of needs analysis, prototype making, prototype evaluation, system coding, system testing, system evaluation, and system use. The results showed that the system created has an 85.71% accuracy rate based on a comparison of manual calculations and system calculations of 28 sample items.
Penerapan Goal Programming untuk Optimalisasi Penjadwalan Jam Kerja Satuan Pengamanan Pradjaningsih, Agustina; Rohmatul Aulia, Indriyani; Riski, Abduh
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5322

Abstract

One common challenge in security system management is the scheduling of security guards' work. Proper work scheduling is essential to prevent physical and psychological fatigue, which can negatively impact their performance. The scheduling process is influenced by factors such as the number of security personnel and the shift arrangements. This study aims to apply the goal programming method to optimize the scheduling of security guards. The research utilizes LINGO 17.0 software for assistance. The research process includes problem identification, data collection, determination of variables and parameters, formulation of goal programming models, solving these models using LINGO 17.0 software, analysis of the results, and the compilation of work schedules for security guards. The study's findings indicate that the established constraints have been met, and the number of working hours and days off for security guards has been optimized, resulting in an efficient schedule.
Predicting Missing Value Data on IEC TC10 Datasets for Dissolved Gas Analysis using Tertius Algorithm Ardi, Noper; S, Supardianto; Irmansyah Lubis, Ahmadi
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5361

Abstract

IEC TC10 is the most widely used Dissolved Gas Analysis (DGA) measurement dataset nowadays. Many DGA-based studies have been carried out using conventional methods and methods based on Artificial Intelligence Techniques (AITs). DGA is a diagnostic test performed on power transformers to detect and diagnose potential faults. The test involves analyzing the gases that are dissolved in the transformer oil, which can provide important information about the condition of the transformer. DGA is a widely used technique for transformer monitoring and maintenance in the power industry. However, this dataset is not perfect. There are still many problems in this dataset, one of which is the problem of missing value data. This problem will be significant if not appropriately handled. More reliable data from DGA measurement results is an in-dispensable reference in diagnosing faults in power transformers. This study focuses on dealing with the problem of missing value data using the Tertius algorithm, then testing the results using the J48 and Random Forest algorithms. The results obtained are pretty significant. Of the total 56 missing data, 36 could be predicted perfectly. And received the results of measuring accuracy using the J48 method of 62.73% and the Random Forest method of 70.71%. This result shows that the approach we applied is relatively good for handling missing values in IEC TC10 datasets.
Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan Ikhsal, Muhammad Fachry; Dermawan, Budi Arif; Adam, Riza Ibnu
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5562

Abstract

The failure of the accident detection system on CCTV cameras can affect the increase in the death rate on the highway. The use of the CNN method in the construction of CCTV accident detection systems has been widely used before. However, common problems that are often encountered are dirty lenses and varifocal zooms that don't automatically focus, causing the quality of the resulting CCTV images to decrease, thus affecting system performance. In this research, a model was developed to detect accidents on CCTV images using the MobileNetV2 pre-trained model which was optimized by upscaling the dataset using the Real-ESRGAN model to produce more optimal performance. This study uses a CCTV image dataset totaling 989 and consisting of 2 types of prediction classes including accident and non-accident. The results showed that the MobileNetV2 model succeeded in producing 94% testing accuracy and an average inference time of 3.33 seconds in the GT test scenario. During the testing process, it was found that the model was not optimal if it identified new data with clustered objects. In addition, based on the test scenarios X2, X4, X8 it was found that the image quality calculated based on PSNR and SSIM values greatly influences classification performance such as accuracy, precision, recall, and AUC score.
Pengenalan Wajah Resolusi Rendah Menggunakan Arsitektur Lightweight VarGFaceNet dengan Adaptive Margin Loss Ramadani, Daffa Tama; Adam, Riza Ibnu; Jaman, Jajam Haerul; Rozikin, Chaerur; Garno, G.
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5831

Abstract

Face recognition is a modern security solution that is quickly and easily integrated into most existing devices, so this system is widely applied to several domains as one of the security authorizations. Developing face recognition models using mainstream architectures (AlexNet, VGGNet, GoogleNet, ResNet, and SENet) will make it difficult to implement the models on mobile devices and embedded systems. In addition, low resolution images, such as those from CCTV surveillance cameras or drones, pose challenges for the models to recognize faces, as the images lack sufficient details for identification. Therefore, this research aims to analyze the performance of a face recognition model developed using the lightweight VarGFaceNet architecture with the adaptive margin loss AdaFace on a low-resolution image dataset. Based on the evaluation results on the LFW dataset, an accuracy of 99.08% was achieved on high-resolution data (112x112 pixels), while on the lowest synthetic low-resolution data (14x14 pixels), an accuracy of 79.87% was obtained with the assistance of the Real-ESRGAN and GFP-GAN super-resolution models. On the TinyFace dataset, without fine-tuning, a Rank-1 accuracy of 46.08% was achieved without using super-resolution models and 45.03% when utilizing super-resolution models.
Perbandingan Metode Klasterisasi Data Bertipe Campuran: One-Hot-Encoding, Gower Distance, dan K-Prototype Berdasarkan Akurasi (Studi Kasus: Chronic Kidney Disease Dataset) Fadilah, Zahra Rizky; Wijayanto, Arie Wahyu
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5857

Abstract

This study aims to compare the one-hot-encoding method, Gower distance combined with k-means, DBSCAN, and OPTICS algorithms, and k-prototype for clustering mixed data types based on accuracy. The dataset used in this research is the chronic kidney disease (CKD) dataset sourced from the UCI Machine Learning Repository. Based on the evaluation using the silhouette index, it is found that k-prototype with the number of clusters k=2 is the most optimal clustering method because it provides the highest silhouette index value compared to the other four methods, with a value of 0,3796. Cluster 1 contains 175 observations, while cluster 2 contains 225 observations. When associated with the labels on the dataset, the clustering results provide an accuracy value of 81,25 percent.
Analisa Performa Arsitektur Transfer Learning Untuk Mengindentifikasi Penyakit Daun Pada Tanaman Pangan Winanto, Tawang Sahro; Rozikin, Chaerur; Jamaludin, Asep
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5991

Abstract

Salah satu faktor gagal panen ialah serangan penyakit yang menyerang pada bagian daun pada tanaman. Solusi dari permasalahan ini yaitu dengan melakukan identifikasi dini penyakit tanaman pangan dengan memanfaatkan image classification dan deep learning menggunakan objek citra daun untuk mempercepat proses identifikasi penyakit pada daun tanaman pangan sehingga tidak mempengaruhi hasil produksi tanaman. Banyak penelitian yang sudah membuat penelitian memanfaatkan Image classification untuk klasifikasi penyakit tanaman berdasarkan citra daun menggunakan metode Transfer Learning. Namun pada penelitian terdahulu hanya menggunakan satu dua atau tiga arsitetur dan hanya mengunakan satu dataset saja untuk proses pengujian yang membuat tidak terlalu memberikan jawaban arsitektur mana yang mempunyai performa terbaik untuk membuat model klasifikasi penyakit berdasarkan citra daun tanaman. oleh karena itu diperlukan adanya perbandingan performa dari tiap model arsitektur untuk mengetahu arsitektur mana yang terbaik. Maka dari itu penelitian ini, peneliti akan melakukan eksperimen menggunakan lima arsitektur dan tiga dataset yang berbeda dengan enam sekenario pelatihan model dan selanjutnya kami melakukan anlisis perbandingan kinerja tiap sekenario pelatihan model. Hasilnya Penelitian ini dilakukan analisa hasil pelatihan dan pengujian yang sudah dilakukan arsitektur VGG 16 memiliki performa yang paling baik dibandingkan dengan arsitektur lainnya yang diujikan.
Sales Analysis Using Apriori Algorithm in Data Mining Application on Food and Beverage (F&B) Transactions Marselina, Sonia; Jaman, Jajam Haerul; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.5026

Abstract

The current business landscape has compelled many companies to compete in boosting their company's revenue, particularly in the F&B sector. Existing sales transaction data has not been fully maximized in determining the business strategy of companies. Therefore, the implementation of data mining is necessary to analyze and explore available data to discover new information that is more beneficial for the company. In this study, we analyze sales transaction data using the a priori algorithm method because this algorithm efficiently handles the data mining process on a large scale with a substantial amount of data. The results of this study indicate that the formed association rules can determine patterns of product purchases that are frequently bought together. The established association rules successfully combine sales transaction data into two-item combinations, namely green tea latte and french fries, with a support value of 16% and a confidence level of 83%. These rules can be used as a reference in determining the company's business strategy.
Sentiment Analysis on Fuel Purchase Policy Through MyPertamina Application Using NB and SVM Methods Optimized by PSO as Weight Optimation Rousyati, Rousyati; Pratmanto, Dany; Ardiansyah, Angga; Aji, Sopian
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.5131

Abstract

Sentiment analysis on the MyPertamina application can serve as a means to extract customer opinions about the application. This method involves collecting reviews from users who have utilized the MyPertamina application and classifying these reviews as positive or negative using sentiment analysis algorithms. After the reviews are classified, themes discussed in positive and negative reviews can be extracted, such as ease of use, payment speed, or technical issues. This provides a general overview of user expectations for the MyPertamina application and areas that may need improvement. Sentiment analysis of MyPertamina application comments using Naïve Bayes (NB) and Support Vector Machine (SVM) methods is a process to evaluate whether user comments on the MyPertamina application are positive or negative. NB and SVM are machine learning methods used to predict the category of an input based on given training data. In this study, user comments on the MyPertamina application are used as input and classified as positive, negative, or neutral based on previous training data. The goal of this sentiment analysis is to understand user perceptions of the MyPertamina application and enhance its quality. The research concludes that the implementation of data mining can assist in categorizing sentiments of MyPertamina reviews. The NB algorithm with the addition of Particle Swarm Optimization (PSO) proves to be the most effective method in this study compared to NB alone, SVM, and SVM + PSO. The NB algorithm with PSO optimization yields an accuracy of 79.49%, the highest precision of 79.57%, recall of 79.38%, and the highest AUC of 95.30%, falling into the category of excellent classification.

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