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Journal : Jurnal Teknik Informatika (JUTIF)

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
SENTIMENT ANALYSIS OF THE SAMBARA APPLICATION USING THE SUPPORT VECTOR MACHINE ALGORITHM Firdaus, Thoriq Janati; Indra, Jamaludin; Lestari, Santi Arum Puspita; Hikmayanti, Hanny
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.2024.5.4.2673

Abstract

Rapid technological developments have opened up new opportunities for public services by utilizing digital application innovations. One example is the West Java Samsat Mobile (SAMBARA) designed by the West Java Provincial Revenue Agency (BAPENDA). The SAMBARA application is expected to accelerate annual vehicle tax payment obligations, but several reviews on the Playstore show user dissatisfaction with SAMBARA's performance. This study aims to conduct a sentiment analysis of SAMBARA application reviews using the Support Vector Machine algorithm. SAMBARA user review data on Google Playstore was collected using the python programming language google play scraper library on google colabolatory resulting in 1620 data on January 2, 2024. The data pre-processing stage involves various steps such as data cleaning, lowercase conversion, tokenization, stemming, stop words removal, normalization, and the use of the TF-IDF method. The data is then labeled positive and negative, positive for reviews with scores of 4 and 5 and negative labels for reviews with scores of 1 to 3. The Support Vector Machine (SVM) algorithm is used for classification, a well-known method for accurate classification. Model evaluation was conducted using a confusion matrix to calculate the precision, recall, and F1-Score values. The evaluation results provide an overview of the performance of the classification algorithm in grouping user reviews into positive and negative categories. The evaluation results show that the SVM algorithm provides quite good performance with an accuracy value of 88.75%, precision 87.51%, recall 81.25%, and F1-Score 83.71% which can be the basis for improving the quality of service of the SAMBARA application. Because the Sambara application has a negative sentiment of 73.4%, it can be concluded that it still gets a bad rating in terms of use.
INTRODUCTION NATIONAL IDENTIFICATION NUMBER AND NAME ON ID CARD USING OCR (OPTICAL CHARACTER RECOGNITION) METHOD Holila, Holila; Pratama, Adi Rizky; Lestari, Santi Arum Puspita; 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.2024.5.4.2242

Abstract

This study examines the use of Optical Character Recognition (OCR) methods for the automatic recognition and extraction of text from images of Identity Cards (KTP). The aim is to provide an effective solution to the problems of document forgery and duplication, particularly in the use of KTP as an identity verification tool. Utilizing the Tesseract library, this research involves preprocessing steps such as conversion to grayscale, perspective transformation, and noise reduction to enhance OCR accuracy. Testing was conducted with 50 different KTP images using Python programming, achieving an Optical Character Recognition accuracy rate of 91%. Additionally, tests conducted with a dataset of 50 KTP images containing NIK and name variables showed that all images were successfully detected with an accuracy rate of 90%. This study confirms that the OCR method is effective in reading text from KTP images in real-time, thus it can be implemented for automatic identity verification.
Co-Authors AA Sudharmawan, AA Abdul Gapur Achmad, Syifa Latifah Adi Rizky Pratama Agung Susilo Yuda Irawan Ahmad Afifur Rahman Ahmad Fauzi Ahmad Fauzi Ahmad Rahman Al Fathir Rizal Januar Alif Kirana Amansyah, Ilham Anton Romadoni Junior Apriade Voutama April Hananto Ardiansyah, Fikri Arif Nurman Arip Solehudin Aris Martin Kobar Arum Puspita Lestari, Santi Asep Jamaludin Aviv Yuniar Rahman Awal, Elsa Elvira Ayu Juwita Azis Saputra Azzahra, Wava Lativa Baihaqi, Kiki Ahmad Cici Emilia Sukmawati Dadang Yusup Deden Wahiddin Deny Maulana Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Eko Pramono Fadmadika, Fadilla Faisal, Sutan Fauzi Ahmad Muda Fauzi, Ahmad Firdaus, Thoriq Janati Firmansyah Maulana Fitri Nur Masruriyah, Anis Garno . Garno, Garno Gugy Guztaman Munzi Hananto, Agustia Hanny Hikmayanti Handayani Hanung Pangestu Rahman Hilda Fitriana Dewi Hilda Novita Hilda Yulia Novita Holila, Holila Irma Putri Rahayu Juwita, Ayu Ratna Karyanto, Dony Dwi Khoirull Munazzal Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M Andrian Agustyan Maharina, Maharina Maliah Andriyani Mudzakir, Tohirin Al Muhammad Arya Suhendi Muhammad Cesar Afriansyah Arief Muhammad Deden Miftah Fauzi Muhammad Imam Naufal Muhammad Khoiruddin Harahap Muhammad Raja Nurhusen Muhammad Romadhon Nazori AZ Novalia, Elfina Nugraha, Najmi Cahaya Nurdin, Cherry Januar Nurlaelasari, Euis Nursyawalni, Reva Paryono, Tukino Pratama, Adi Rizky Purnama, Ariya Purnomo, Indarto Aditya Rahmat Hidayat Rahmat Rahmat Rahmat Rahmat Ratna Juwita, Ayu Rifaldi, Rizky Rija Nur Hijriyya Rissa Ilmia Agustin Rizki, Lutfi Trisandi Robinson Nababan Rohana, Tatang Romlah Saefulloh, Nandang Sandi Susanto Santi Lestari Sihabudin Sihabudin, Sihabudin Siregar, Amril Mutoi Siti Robiah Suparno Sutan Faisal Syahrul Azis Tatang Rohana Tatang Rohana Tia Astiyah Hasan Tohirin Al Mudzakir Tohirin Mudzakir Toif Muhayat Tri Vicika, Vikha Ulfa Amelia Vikha Tri Vicika Wahiddin, Deden Wildan Amin Wiharja Yana Cahyana Yogi Firman Alfiansyah