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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
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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XTEA CRYPTOGRAPHY IMPLEMENTATION IN ANDROID CHATTING APP Irfan Syamsuddin; Siska Ihdianty; Eddy Tungadi; Kasim Kasim; Irawan Irawan
Journal of Information Technology and Its Utilization Vol 3, No 2 (2020)
Publisher : Sekolah Tinggi Multi Media (STMM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.3.2.3622

Abstract

Information security plays a significant role in information society. Cryptography is a key proof of concept to increasing the security of information assets and has been deployed in various algorithms. Among cryptography algorithms is Extended Tiny Encryption Algorithm. This study aims to describe a recent Android Apps to realize XTEA Cryptography in mobile form. In addition, a thorough example is presented to enable readers gain understanding on how it works within our Android Apps.
User Experience Analytics pada Sistem Informasi Politeknik Negeri Ujung Pandang HM, Nurfaida; Tungadi, Eddy; Syamsuddin, Irfan
Jurnal Teknologi Elekterika Vol. 19 No. 1 (2022): Mei
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v6i1.3503

Abstract

The Ujung Pandang State Polytechnic (PNUP) manages several departments, consisting of the Department of Electrical Engineering, the Department of Machinery, the Department of Civil, the Department of Chemistry, the Department of Accounting, and the Department of Commerce Administration. In managing how many significant programs, PNUP requires a quality information system to support academic activities by evaluating the system's quality that has been implemented. The PNUP Information System with the URL address https://.www.poliupg.ac.id/ has never been evaluated, where there are several complaints from students and admins regarding obstacles to using the information system. Complaints about the information system's use prompted researchers to research the quality of the PNUP information system. Therefore, the User Experience was analysed from the usability aspect, which was tested with several tools, namely Woorank, SEOquake, PageSpeed and using the SUS method. Testing with the usability aspect approach was carried out on the information system at PNUP. The test results using several tools, namely Woorank, got a score of 78. It was considered suitable for facilitating searches on search engines. Page Speed scored 67 for desktop use and 28 for mobile use. It was considered very bad in loading website pages which were considered to disturb user satisfaction and using the SUS method, with 180 respondents getting a score of 58.65, which is considered usable and has the potential to reduce visitors to the PNUP information system. It can be judged that the PNUP information system has not met the usability aspect.
Logistic Supply Chain Management System Modeling Using Blockchain Solihin, Muhammad Alif; Nur, Dahliah; Tungadi, Eddy; Yusri, Iin Karmila
Jurnal Teknologi Elekterika Vol. 21 No. 2 (2024)
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v21i2.5110

Abstract

Indonesia's economic growth in the logistics sector is becoming increasingly significant, alongside the rising demand for goods. However, the complexity of logistics supply chain management in Indonesia still faces challenges, including security, human error, and lack of transparency. This research proposes the use of blockchain technology for a logistics supply chain management system, which is capable of storing transaction data in a decentralized and distributed manner. Ganache is used as a local blockchain server, Metamask for transactions, and smart contracts to manage user flows on a web3 dapps-based website. The research procedure includes literature study, design, development, and system testing. The test results indicate that all features and functions of the system operate as expected. The system not only successfully handles high demand and transaction spikes but also demonstrates enhanced data security, improved transparency through real-time tracking, and robust performance under high workloads. Overall, this blockchain-based logistics supply chain management system effectively secures and manages transaction data while enabling data tracking and tracing, thereby increasing transparency and handling high concurrent transactions efficiently.
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.
Enhancing Web Performance for E-learning Platform using Content Delivery Network (CDN) and Varnish Cache Utomo, Muhammad Nur Yasir; Tungadi, Eddy; Khartika, Widya
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.993

Abstract

Along with the development of technology, the web has become a very popular platform for providing information services and digital content, especially in education sector. The popularity of web services such as e-learning is directly proportional to the increasing number of users. The increase in the number of users is often a problem because it can lead to decreased web performance and potential downtime. To overcome this problem, this study proposes Content Delivery Network (CDN) and Varnish Cache as solutions. Web performance evaluation was carried out in a campus internal network using Apache JMeter with a load of 1,000 users. Based on the evaluation, there was a 175.5% increase in throughput, from 51.9 to 142.9 requests per second. In terms of response time, it improved by 54.3%, decreasing from 16,476 ms to 7,526 ms. Additionally, latency was reduced by 82.4%, from 3,555.8 ms to 624.8 ms. The error rate also decreased from 31.4% to 17.2%. These results indicate that CDN implementation can effectively improve web server performance and provide an optimal user experience, especially under high load conditions.
Klasifikasi Kualitas Biji Kopi Ekspor Menggunakan Jaringan Saraf Tiruan Backpropagation OLIVYA, MEYLANIE; TUNGADI, EDDY; RANTE, NOVYANTI BUA’
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 3 No 2 (2018): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1068.076 KB) | DOI: 10.24252/instek.v3i2.6227

Abstract

Klasifikasi kualitas biji kopi, khususnya pada PT. Sulotco Jaya Abadi, menggunakan teknik sortasi manual dengan mengambil sampel dan melakukan observasi sesuai format yang disediakan. Teknik tersebut memiliki beberapa kelemahan, yaitu lamanya waktu yang diperlukan untuk memilah jenis biji yang baik, serta lemahnya mata manusia yang jika bekerja terlalu lama akan mengurangi kualitas penilaian biji kopi. Oleh karena itu, diperlukan sebuah aplikasi yang dapat membantu melakukan klasifikasi biji kopi secara otomatis menggunakan Jaringan Saraf Tiruan (JST) Backpropagation. Pengujian terhadap 10 citra biji kopi, diperoleh tingkat akurasi sebesar 80%. Pengujian pengaruh jumlah input terhadap akurasi, diperoleh tingkat akurasi sebesar 74% pada 8 input. Kata Kunci: Klasifikasi kualitas, citra biji kopi, Jaringan Saraf Tiruan Backpropagasi
Indonesian Automated Essay Scoring: A Comparative Study of Pretrained Transformer Models Pulung Hendro Prastyo; Eddy Tungadi; Shaifudin Zuhdi
Information Technology Education Journal Vol. 4, No. 2, May (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i2.8069

Abstract

Manual essay scoring is often characterized by inefficiency and inconsistency. This process is notably time-consuming, leading to delayed feedback and increased susceptibility to evaluator fatigue and subjective bias, thereby posing significant challenges. Automated Essay Scoring (AES) offers a scalable, robust, and consistent solution to these issues. However, the performance of AES models can vary considerably depending on the specific application. Therefore, this study evaluated ten Indonesian pretrained transformer models from Hugging Face for AES tasks, using 300 essay responses from a Research Methodology quiz at Politeknik Negeri Ujung Pandang. Performance was assessed using Root Mean Square Error (RMSE) and Quadratic Weighted Kappa (QWK). Among the evaluated models, Indobenchmark/indobert-base-p2 (BERT-02) demonstrated superior performance. It achieved the lowest RMSE of 5.664 and the highest QWK score of 0.6745. The findings suggest that BERT-02 is the most effective model for Indonesian AES tasks. Future research could explore larger datasets and different models to further enhance the performance and understanding of Indonesian AES systems.
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