Garuda - Garba Rujukan Digital

Sistem Informasi Pengukuran Efektivitas Produksi BerbasisWeb (Studi Kasus : PT. Beiersdorf Indonesia) Rakhmadani, Diovianto Putra; Wicaksono, Soetam Rizky
Jurnal Rekayasa Sistem Industri Vol 4, No 2 (2015): Jurnal Rekayasa Sistem Industri
Publisher : Jurnal Rekayasa Sistem Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.605 KB)

The effectiveness of the production is a must knowledge in a manufacture company, because it is closelylinked to the success level of a company, if effectiveness reaching high level, it will lead to be successful interms of meeting production targets. It is also happened at PT Beiersdorf Indonesia which requires method tomeasure the effectiveness of production in their companies, particularly in Elastoplast production division.PT Beiersdorf Indonesia requires certain method IPC computerized checklist which the system can giveinformation related to the evaluation of the effectiveness of production. Based upon these problems, it is clearlystated that they need to use innovation in Effectiveness Level Measurement Information System Production.This information system is designed to help the all involved employees the process, documenting, and directlycontrols the activity of production. in this study, by creating the new system will help the company to conductan evaluation of the production process.

Sistem Informasi Pengukuran Efektivitas Produksi BerbasisWeb (Studi Kasus : PT. Beiersdorf Indonesia) Rakhmadani, Diovianto Putra; Wicaksono, Soetam Rizky
Jurnal Rekayasa Sistem Industri Vol 4, No 2 (2015): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

The effectiveness of the production is a must knowledge in a manufacture company, because it is closelylinked to the success level of a company, if effectiveness reaching high level, it will lead to be successful interms of meeting production targets. It is also happened at PT Beiersdorf Indonesia which requires method tomeasure the effectiveness of production in their companies, particularly in Elastoplast production division.PT Beiersdorf Indonesia requires certain method IPC computerized checklist which the system can giveinformation related to the evaluation of the effectiveness of production. Based upon these problems, it is clearlystated that they need to use innovation in Effectiveness Level Measurement Information System Production.This information system is designed to help the all involved employees the process, documenting, and directlycontrols the activity of production. in this study, by creating the new system will help the company to conductan evaluation of the production process.

Peningkatan Kualitas Citra pada Citra Digital Gelap Adhinata, Faisal Dharma; Wardhana, Ariq Cahya; Rakhmadani, Diovianto Putra; Jayadi, Akhmad
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 4 No 2 (2020)
Publisher : Politeknik Dharma Patria Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v4i2.373

Salah satu tahap utama dalam pemrosesan citra digital adalah peningkatan kualitas citra. Citra yang berwarna gelap tidak terlihat detail informasi yang terkandung pada citra. Bahkan objek yang tampak pada citra bisa tidak terlihat karena pengambilan citra dilakukan pada pencahayaan kurang. Citra gelap perlu dilakukan peningkatan kualitas citra supaya detail informasi citra dapat terlihat secara visual. Beberapa algoritma peningkatan kualitas citra digital diantaranya negative transformation, log transformation, contrast stretching, bit plane slice, dan histogram equalization. Pada penelitian ini akan dikaji beberapa algoritma peningkatan kualitas citra untuk melihat hasil terbaik dari kasus citra gelap. Berdasarkan hasil percobaan, diperoleh hasil terbaik menggunakan algoritma histogram equalization. Algoritma histogram equalization menghasilkan histogram citra yang tersebar rata sehingga detail informasi citra dapat dilihat secara visual.

Penerapan Metode Simple Additive Weighting (SAW) pada Sistem Informasi Pemilihan Asisten Praktikum Gustalika, Muhamad Azrino; Rakhmadani, Diovianto Putra; Segara, Alon Jala Tirta
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3065

Each campus has technology that is used to communicate and exchange information using technology in the form of a website. The use of the website itself has penetrated into the world of education, one of which is the laboratory on campus. Laboratory on campus cannot be separated from the existence of a practicum assistant. Every semester the faculty of informatics opens registration for practicum assistants, but there are obstacles that candidates who register still use the manual method in their selection. So they need the Simple Additive Weighting (SAW) method. This study uses the Simple Additive Learning method which will increase the highest score level of 1.39 with the weight indicator used in the selection of practicum assistants and get an average score of 4.9 out of 5.0 so that it is very effective for admins (laborers) to manage and lecturers, to see recommendations for prospective practicum assistants, the best are web-based

Sistem Informasi Pengukuran Efektivitas Produksi BerbasisWeb (Studi Kasus : PT. Beiersdorf Indonesia) Diovianto Putra Rakhmadani; Soetam Rizky Wicaksono
Jurnal Rekayasa Sistem Industri Vol. 4 No. 2 (2015): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

The effectiveness of the production is a must knowledge in a manufacture company, because it is closelylinked to the success level of a company, if effectiveness reaching high level, it will lead to be successful interms of meeting production targets. It is also happened at PT Beiersdorf Indonesia which requires method tomeasure the effectiveness of production in their companies, particularly in Elastoplast production division.PT Beiersdorf Indonesia requires certain method IPC computerized checklist which the system can giveinformation related to the evaluation of the effectiveness of production. Based upon these problems, it is clearlystated that they need to use innovation in Effectiveness Level Measurement Information System Production.This information system is designed to help the all involved employees the process, documenting, and directlycontrols the activity of production. in this study, by creating the new system will help the company to conductan evaluation of the production process.

Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method Faisal Dharma Adhinata; Diovianto Putra Rakhmadani
Teknika Vol 10 No 1 (2021): Maret 2021
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v10i1.328

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

PERANCANGAN SISTEM INVENTORY RUANG KELAS DENGAN PENDEKATAN METODE QUALITY CONTROL STATISTICAL SAMPLING BERBASIS WEB STUDI KASUS : INSITUT TEKNOLOGI TELKOM PURWOKERTO Diovianto Putra Rakhmadani; Faisal Dharma Adhinata; Ariq Cahya Wardhana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 6 No 1 (2021): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v6i1.1620

The classroom is the principal place in the implementation of teaching and learning activities. Classroom comfort indirectly affects the learning atmosphere and the comfort level of students in implementing teaching and learning activities. In its operational activities, classrooms have several supporting tools such as blackboards, projectors, air conditioners, chairs, and desks. These objects often experience problems such as minor damage, decreased function, to heavy damage. The existence of these obstacles can make teaching and learning activities less qualified in an academic classroom. Facilities and infrastructure to support quality teaching and learning activities require an inventory information system with quality control methods to ensure the classroom's quality of facilities is maintained. This research uses the Waterfall software development method and produces a web-based computerized system that can be used to monitor the quality of classroom facilities and follow-up. With this system, the quality of facilities and infrastructure in the classroom is maintained.

YOLO Algorithm for Detecting People in Social Distancing System Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Alon Jala Tirta Segara
Jurnal Transformatika Vol 19, No 1 (2021): July 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i1.3582

Social distancing is an effort to prevent the spread of the coronavirus. Several systems for monitoring social distancing have been developed. People detection is an essential step in implementing a social distancing system. Failure to detect people causes the social distancing system to be inaccurate. Two people who communicate cannot occur violations of social distancing because one person is not detected. Therefore, we propose a precise person detection method for the social distancing system. The proposed social distancing system uses the YOLOv3 method for people detection and Euclidean Distance for measuring the distance of social distancing. YOLOv3 can detect people's objects precisely, even people who are caught small by the camera. Experiments on two outdoor video datasets result in an F1 value of more than 0.8. This proposed system can serve as a reference for future social distancing research.

A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Merlinda Wibowo; Akhmad Jayadi
JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.

Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Danur Wijayanto
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.22-30

Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes.

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