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All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) INTEK: Jurnal Penelitian Journal of Information Systems and Informatics Jurnal Teknologi Elekterika Journal of Information Technology and Its Utilization Information Technology Education Journal Journal of Informatics and Computer Engineering Research Journal of Information Technology and its Utilization
TUNGADI, EDDY
Politeknik Negeri Ujung Pandang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Journal : Journal of Informatics and Computer Engineering Research

 1 
INOVASI SISTEM PEMERIKSAAN UJIAN SELEKSI CALON MAHASISWA BARU DI POLITEKNIK NEGERI UJUNG PANDANG Dzulmukmin, Andi Abdul; Tungadi, Eddy; Abduh , Ibrahim
Journal of Informatics and Computer Engineering Research Vol. 1 No. 1 (2024)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v1i1.4918

Abstract

The inspection system for the entrance exam to the state polytechnic in Ujung Pandang is a process for checking participant computer answer sheets (LJK) to obtain the scores of participants who took the exam so that it becomes a benchmark for participants' graduation of the desired study program. The previous UMPN inspection system had drawbacks, namely the program inspection system still needed to be developed in terms of accuracy and effectiveness. Designing an FSI inspection program that has high accuracy requires data collection such as FSI along with references to similar inspection programs. The LJK inspection program uses the python language with the image processing library, namely openCV. LJK will be scanned and produce 100dpi and 200dpi LJK images which will be tested in the FSI inspection program. Based on the program testing conducted, it is recommended to use a 15x15 pixel resolution of 100dpi with an average accuracy of 99.31% and for a 200dpi resolution it is recommended to use 30x30 pixels with an average accuracy of 99.98%.
PENERAPAN ALGORITMA OPPOSITION-BASED WHALE DALAM KLASIFIKASI SVM UNTUK ANALISIS SENTIMEN TERHADAP KEBIJAKAN PPKM Said, Asrul; Tungadi, Eddy; Olivya , Meylanie
Journal of Informatics and Computer Engineering Research Vol. 1 No. 2 (2024)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v1i2.5178

Abstract

Corona Virus Disease 2019 (COVID-19) has had a serious impact, forcing the Indonesian government to establish various policies to deal with the spread of COVID-19. One of these policies is the Implementation of Restricting Community Activities (PPKM). During its implementation, this policy raised pros and cons in the community, especially on Twitter social media. The existence of this public opinion, can be used as an effective source of information to assist the government in taking and evaluating policies. The purpose of this study was to determine public sentiment towards PPKM policies based on the classification of tweets or public opinion on Twitter. This process is carried out by applying the OBWOA method for selecting appropriate and optimal SVM parameters and feature selection to select the best features thereby reducing computation time and producing good classification performance. The results of the optimization of the SVM parameters obtained C = 4.99522643 and gamma = 1.4236435 with an average accuracy of 75.20%, precision of 79.73%, recall of 71.65%, and f measure of 70.88%. The feature selection results obtained an average accuracy of 82.40%, precision of 84.23%, recall of 79.63%, and f-measure of 78.96%. In addition, the sentiment classification of 1,389,481 public tweet data in the period January to December 2021 obtained 53% of tweets with Negative sentiment, 25% of Tweets with Neutral sentiment, and 22% of tweets with Positive sentiment.
IMPLEMENTASI DEEP LEARNING UNTUK PENDETEKSIAN PENGGUNA MASKER PADA CCTV: STUDI KASUS PUSKESMAS SUDIANG RAYA Trisnaningrun, Ainun; Tungadi, Eddy; Syamsuddin, Irfan
Journal of Informatics and Computer Engineering Research Vol. 1 No. 2 (2024)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v1i2.5184

Abstract

The use of mask is one of the things that needs attention when you want to leave the house to implement health protocols to avoid diseases that are currently troubling people in the world or commonly known as Covid-19. Currently, people are reluctant to come to the hospital for fear of being exposed to the Covid-19 virus, so people who need treatment prefer to visit the puskesmas near their home. However, there are still many people who do not use masks on the grounds that the intended location is close to home. To overcome this can be done by detecting visitors' faces using the camera. So a system is proposed, namely the detection of mask users with the Convolutional Neural Network (CNN) method. One of the widely applied CNN methods for processing image data is YOLO. YOLO (You Only Look Once) is a deep learning-based model developed to detect an object in real-time. YOLO works by looking at the image as a whole, then using a neural network and automatically detecting existing objects. So that in this study the YOLO model, namely YOLOv4, was used as an object detection model in a mask detection system with CCTV video media whose data is sent in real-time.
CATTLE DISEASE DIAGNOSIS SYSTEM USING RANDOM FOREST CLASSIFICATION METHOD Syam, Muh. Fadhil; Utomo, Muhammad Nur Yasir; Tungadi, Eddy
Journal of Informatics and Computer Engineering Research Vol. 2 No. 1 (2025)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v2i1.5563

Abstract

Cattle farming plays an important role in Indonesia's economy, but its productivity can decline due to livestock health issues. To address this, this study develops a cattle disease diagnosis system based on machine learning using the Random Forest classification method. The system helps farmers identify diseases independently based on input symptoms. The model is built using the Random Forest algorithm, trained on 1745 primary data obtained from the Barru Regency Department of Agriculture. The data undergoes a comprehensive pre-processing stage, including cleaning to remove inconsistencies, One-Hot Encoding for categorical feature transformation, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE) to ensure fair representation of all disease categories. Model evaluation using a Confusion Matrix demonstrates a high accuracy of 91%, indicating strong predictive performance. Based on the model, a mobile application based on Android is developed to assist farmers in the early detection of cattle diseases.
Co-Authors Abduh , Ibrahim Asrul Said Dzulmukmin, Andi Abdul Hayani Hayani HM, Nurfaida Irawan Irawan Irfan Syamsuddin Irfan Syamsuddin Kasim Kasim Khartika, Widya Meylanie Olivya Nur, Dahliah Olivya , Meylanie Pulung Hendro Prastyo Raharjo, Muh. Fajri RANTE, NOVYANTI BUA’ Shaifudin Zuhdi Siska Ihdianty Solihin, Muhammad Alif Syam, Muh. Fadhil Trisnaningrun, Ainun Utomo, Muhammad Nur Yasir Yusri, Iin Karmila