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All Journal International Journal of Artificial Intelligence Research JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Jusikom: Jurnal Sistem Informasi Ilmu Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research Jurnal Algoritma
Wahiddin, Deden
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
PERSONAL PROTCTIVE EQUIPMENT DETECTION FOR OCCUPATIONAL SAFETY AND HEALTH USING YOLOV8 IN MANUFACTURING COMPANIES: DETEKSI ALAT PELINDUNG DIRI (APD) UNTUK KESELAMATAN DAN KESEHATAN KERJA MENGGUNAKAN YOLOV8 Gapur, Abdul; Wahiddin, Deden; Mudzakir, Tohirin Al; Indra, Jamaludin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2619

Abstract

According to data from BPJS Keltelnagakelrjaan, 265,333 cases of work accidents were recorded in 2022. The use of personal protective equipment (PPE) is very important in reducing and preventing work accidents in the company. Although PPE cannot eliminate all risks, it is possible to minimise the number of work accidents in manufacturing companies. The aim of this research is to automatically select Personal Protective Equipment (PPE) in the form of hard hats and vests and to improve the accuracy results using the YOLOv8 model. With a dataset of 500 helmet and velst images for deltelksi which will be categorised into 4 classes namely hellelm, velst, no-hellelm, no-velst. The dataset used is 500 data, which is then divided into three datasets, namely: training data as much as 70%, validation data as much as 20%, and telst data as much as 10%, from the dataselt telrselbut the best results of testing data values from 50 dataselt the accuracy results obtained are 0.98. It is hoped that with the use of Meltode and accuracy results using Yolo v8, it can be used in companies by detecting Personal Protective Equipment (PPE) with fast and accurate results, so that it can be applied in monitoring the use of PPE in manufacturing companies to reduce the risk of work accidents in manufacturing companies
CLASSIFICATION OF RICE ELIGIBILITY BASED ON INTACT AND NON-INTACT RICE SHAPES USING YOLO V8-BASED CNN ALGORITHM Hastari, Nazwa Putri; Rohana, Tatang; Masruriyah, Anis Fitri Nur; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2413

Abstract

The large amount of unfit rice has an impact on the quality of rice provided to the community. This is due to the lack of supervision of the quality of existing rice, so that the quality of rice distributed to the community has a lot of unfit quality. Rice production for public consumption reached 21.69 million tons in 2021, according to data from the Central Statistics Agency (BPS). Rice is the main food of the Indonesian people because most Indonesians are farmers and the vast amount of agricultural land makes Indonesia one of the largest rice producing countries in Southeast Asia, this has a huge impact on people's habits in consuming rice as the main food provider. The Government of the Republic of Indonesia started a Social Assistance rice distribution program through the Ministry of Social Affairs in 2018. This program is named Prosperous Rice Social Assistance (Bansos Rastra). Classification of rice eligibility can be the first step to ensure that the rice received from the government is of high quality and can meet the daily needs of households in Indonesia. CNN algorithm based on YOLOv8 system can automatically recognize the form of rice given by the government whether it is feasible or not. In the research stages there are dataset collection, preprocessing, training models to evaluation. Based on the results obtained in this study, the accuracy achieved is 79% for the Eligible class and 79% for the Ineligible class with Confidence score reaching a value of 1.00. The results of this study can be used as a decent and unfit rice classification detection model by looking at the shape of the rice. So that the rice distributed to the community has decent rice quality.
CLASSIFICATION OF FAMILY HOPE PROGRAM RECIPIENTS USING NAIVE BAYES AND C4.5 METHODS Fauzi, Farras Ahmad; Rohana, Tatang; Juwita, Ayu Ratna; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.3697

Abstract

Receiving PKH assistance in Rawamerta District does not always go well, so there are people who are not entitled to receive assistance. This is because there is still no system that can facilitate the process of classifying PKH assistance recipients. The application of data mining can facilitate classification with high speed and accuracy. The purpose of this study is to classify PKH assistance recipients using the Naïve Bayes and C4.5 methods to determine the eligibility of PKH for people facing social welfare problems. The data used is PKH data in Rawamerta District, Karawang Regency in 2023, totaling 1834 data. The results of naive bayes accuracy of 98.89%, precision 98.25%, recall 98.51%, F1-score 98.89%, and AUC 1.00 are included in the excellent classification because they are in the range of 0.90-1.00, while the C4.5 algorithm produces Accuracy values ​​of 99.26%, Precision 99.25%, Recall 99.25%, F1-score 99.25% and AUC 0.99 are included in the excellent classification because they are in the range of 0.90-1.00. The C4.5 algorithm is superior to Naive Bayes, because the accuracy produced is higher.
Co-Authors AA Sudharmawan, AA Abdul Gapur Ajie, Prasetyo April Lia Hananto Awal, Elsa Elvira Baihaqi, Kiki Ahmad Enjelia, Lola Faisal, Sutan Fauzi, Ahmad Fauzi, Farras Ahmad Fauzi, Rifqi Arul Fitri Nur Masruriyah, Anis Gozali, Gozali Halif, Jenny Hanny Hikmayanti Handayani Hartini, Dina Hastari, Nazwa Putri Iman Sanjaya Indra, Jamaludin Juwita, Ayu Ratna Lestari, Santi Arum Puspita Mudzakir, Tohirin Al Novita, Hilda Yulia Nurlaelasari, Euis Nurmayanti, Trisya Nursyawalni, Reva Pratiwi, Gita Risky Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Rizky Agung Ridho Pratama, Ilham Rohana, Tatang Sukmawati, Cici Emilia Yana Cahyana