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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics e-Jurnal Arus Elektro Indonesia Jurnal Inspiration JOIV : International Journal on Informatics Visualization SemanTIK : Teknik Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Sebatik ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar JURNAL IT: Media Infromasi STMIK Handayani Makassar EPI International Journal of Engineering Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Journal of System and Computer Engineering Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Innovative: Journal Of Social Science Research Inspiration: Jurnal Teknologi Informasi dan Komunikasi Bulletin of Social Informatics Theory and Application
Andani Achmad
Teknik Elektro UNHAS
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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SISTEM KONTROL TINGKAT KEKERUHAN DAN PH PEMBERIAN PAKAN PADA AQUARIUM MENGGUNAKAN ATMEGA328 VIA IoT Bakrim, La Ode; Achmad, Andani; Adnan, Adnan
SemanTIK : Teknik Informasi Vol 7, No 1 (2021): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (462.453 KB) | DOI: 10.55679/semantik.v7i1.18120

Abstract

Rutinitas dalam pemberian pakan serta nilai parameter lingkungan akuarium perlu senantiasa diawasi dan dijaga pada rentang tertentu. Dengan memanfaatkan teknologi IoT peneliti ingin menggabungkan parameter pH dan kekeruhan air sehingga pengontrolan dapat dilakukan secara real time dalam waktu yang bersamaan untuk mengefisienkan waktu, dengan memanfaatkan wifi dan ATmega328. Untuk itu dibangun sistem kontrol tingkat kekeruhan, dan pH pemberian pakan pada aquarium menggunakan ATmega328, yang merancang pengontrolan kekeruhan dan pH pemberian pakan pada aquarium secara real time, bagaimana implementasi penggunaan ATmega328 menggunakan wifi dengan memanfaatkan teknologi IoT dan melakukan pengontrolan kekeruhan dan pH pemberian pakan pada aquarium secara real time.  ATmega328 dengan fungsi untuk mengontrol pH dan kekeruhan air dan dapat di monitoring via IoT. Pada pengujian respon motor servo dan relay telah berjalan dengan baik dimana motor servo dapat membuka katup 450 dan delay 5 detik dengan waktu pukul 8.00 dan 13.00, relay yang di kontrol ATmega328 menggunakan IDE Arduino melalui parameter air tidak normal untuk mengontak kedua pompa dalam menetralkan air berjalan dengan baik sesuai referensi. Peneliti telah berhasil menciptakan website dengan nama web IoT untuk memonitoring semua sensor dan komponen dalam perangkat via IoT.Kata kunci; ATmega328, Internet of Things (IoT), Kekeruhan, pH Air, dan Pemberian Pakan
IMPLEMENTASI HONEYPOT DAN PORT KNOCKING DALAM MENDETEKSI SERANGAN DDoS ATTACK PADA SERVER JARINGAN Suliman, Suliman; Achmad, Andani; Adnan, Adnan
SemanTIK : Teknik Informasi Vol 7, No 1 (2021): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (842.206 KB) | DOI: 10.55679/semantik.v7i1.17953

Abstract

Sistem keamanan jaringan semakin hari kian makin berkembang, begitu pula serangan pada sistem jaringan yang berbeda-beda metode dan perkembangannya, khususnya pada server yang menjadi pengendali utama dalam sistem jaringan menjadi target utama. Oleh karena itu pentingnya menggunakan sistem keamanan jaringan dalam mendeteksi dan menggagalkan serangan.Metode serangan yang digunakan adalah Distributed Denial of Service (DDoS attack) dengan berbagai jenis serangan seperti DDoS attack request flooding, DDoS attack traffick flooding, DDoS brute force attack dan DDoS attack SQL injection. Sedangkan untuk mendeteksi dan menggagalkan serangan yaitu menggunakan metode honeypot dan port knocking yang akan berkolaborasi dalam mengamankan sistem server jaringan pada sistem operasi Windows. Hasil saat dilakukan 5 kali proses pengujian sebelum serangan rata-rata performance kinerja CPU usage history (5%-18%) dan networking LAC (1%-5%). Setelah serangan CPU usage history (72%-90%) dan networking LAC (22%-32%). Sedangkan pada saat ada serangan namun server jaringan sudah terpasang honeypot dan port knocking performance kinerja CPU usage history menjadi stabil mencapai rata-rata (5%-21%) dan networking LAC (1%-6%).Kata kunci; Server Jaringan, DDoS Attack, Honeypot dan Port Knocking
Sosialisasi dan Pelatihan Teknologi Tatakelola Perkuliahan Berbasis Smart Things Palantei, Elyas; Achmad, Andani; Syarif, Syafruddin; Arief, Azran Budi; Rachmaniar, Ida; Hasanuddin, Zulfajri Basri; Panggalo, Samuel; Waris, Tajuddin; Achmad, Andini Dani; Baharuddin, Merna; Palantei, Idris
JURNAL TEPAT : Teknologi Terapan untuk Pengabdian Masyarakat Vol 7 No 1 (2024): Community Development
Publisher : Faculty of Engineering UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jurnal_tepat.v7i1.418

Abstract

This community service activity is a form of implementation of the results of research and innovation in the Department of Electrical Engineering related to the development of smart things-based lecture technology. The technology disseminated in this activity includes hardware, software and work procedures to support campus operations towards an intelligent system. It is hoped that the aim of this activity will be to develop similar technology by the Muhammadiyah University (Unismuh) Bone campus as a partner in this activity. This Community Service (PkM) activity program is a structured and sustainable effort that can be implemented by partners to increase efficiency in lecture management, reduce routine workload, and improve student learning experiences. Many infrastructure and strategic objects on the Unismuh Bone campus have the potential to be connected to the internet network so that governance arrangements are easier, more effective and optimal. This PkM program is designed to introduce and integrate advanced technology such as the Internet of Things (IoT), sensors, data analysis and automation into lecture management which currently relies on manual processes. Lecturers, administrative staff and students at partner universities have been involved in various integrated activities such as workshops/seminars, tutorials and demonstrations/practical training aimed at providing the understanding and skills needed to adopt technology. With the help of digital technology, partners are expected to be able to further optimize class management, evaluation, attendance monitoring, and utilization of physical and digital resources. To evaluate the success of the activity, a questionnaire was administered before (pre-test) and after (post-test) the activity. The evaluation results show that there is an increase in partner understanding related to smart things-based governance technology.
Fermented and Unfermented Cocoa Beans for Quality Identification Using Image Features Basri, Basri; Indrabayu, Indrabayu; Achmad, Andani; Areni, Intan Sari
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2578

Abstract

Fermented cocoa bean products are one of the high-quality requirements of the cocoa processing industry. On an automated industrial scale, early identification of cocoa bean quality is essential in the processing industry. This study aims to identify the condition of quality cocoa beans based on fermentation and non-fermentation characteristics. This study applies analysis based on static images taken using a camera with a distance variation of 5 cm, 10 cm, and 15 cm in both classes, with 500 image data each. The Feature extraction Approach uses the Oriented Gradient (HOG) method with a Support Vector Machine (SVM) classification technique. Image analysis of both object classes was also performed with a color change to show the dominance of the color pattern on the skin of the cocoa beans to be analyzed. The results showed that fermented cocoa beans show a color pattern and texture that tends to be darker and coarser than non-fermented cocoa beans. Computational results with performance analysis using Receiver Operating Characterisic (ROC) on both classes showed the results that the distance of 5 cm and 15 cm has 100% accuracy, but based on the best performance, comprehensively seen in terms of Precision, Recall, and F1-Score shows the best value is at a distance of 15 cm. The results of this research based on the literature review conducted have better achievements, thus enabling further research on the development of conveyor models with real-time video data for automation systems.
Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas Nugraha, Deny Wiria; Ilham, Amil Ahmad; Achmad, Andani; Arief, Ardiaty
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1383

Abstract

This study proposes a framework for improving performance and exploring the application of Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately produce aerial imagery semantic segmentation of natural disaster-affected areas. This study utilizes two models: U-Net and Pyramid Scene Parsing Network (PSPNet). Extensive study results show that the Grid Search algorithm can improve the performance of the two models used, whereas previous research has not used the Grid Search algorithm to improve performance in aerial imagery segmentation of natural disaster-affected areas. The Grid Search algorithm performs parameter tuning on DCN, data augmentation criteria tuning, and dataset criteria tuning for pre-training. The most optimal DCN model is shown by PSPNet (152) (bpc), using the best parameters and criteria, with a mean Intersection over Union (mIoU) of 83.34%, a significant mIoU increase of 43.09% compared to using only the default parameters and criteria (baselines). The validation results using the k-fold cross-validation method on the most optimal DCN model produced an average accuracy of 99.04%. PSPNet(152) (bpc) can detect and identify various objects with irregular shapes and sizes, can detect and identify various important objects affected by natural disasters such as flooded buildings and roads, and can detect and identify objects with small shapes such as vehicles and pools, which are the most challenging task for semantic segmentation network models. This study also shows that increasing the network layers in the PSPNet-(18, 34, 50, 101, 152) model, which uses the best parameters and criteria, improves the model's performance. The results of this study indicate the need to utilize a special dataset from aerial imagery originating from the Unmanned Aerial Vehicle (UAV) during the pre-training stage for transfer learning to improve DCN performance for further research.
AN EVALUATION OF THE POWER SUPPORT INTERNET INFRASTRUCTURE OF MAKASSAR CITY IN TELEMEDICINE FRAME Muhammad, Figur; Achmad, Andani; Adnan, Adnan; Mubarak, Abdul; Muis, Abdul
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7785

Abstract

This research aims to find the quality of the internet in Makassar City. It uses a 10 Mbps service from Indihome to support telemedicine. The study is a case study of sending raw MRI image data to the AWS cloud. The research uses a virtual server from the AWS cloud. It stores raw MRI image data. The data will be sent via the FTP client FileZilla. The tests were carried out eight times. They used the quality of service standard formula from TIPHON. The results come from 8 tests. In the tests, MRI image data was sent to the AWS cloud. The results show that the average throughput value was 4.53 Mbps with an index of 4. This result is excellent. Packet loss is low at 0.01% with an index of 4, which is very good. The delay is 1.7 ms with an index of 3, which is good. The jitter is 1.69 ms with an index of 3, which is good. The quality of service test results are based on TIPHON standards. They show that sending Raw MRI image data to the AWS cloud at 10 Mbps from Indihome in Makassar City is good.
Optimization of Herbal Plant Classification Using Hybrid Method Particle Swarm Optimization With Support Vector Machine Amriana, Amriana; Ilham, Amil Ahmad; Achmad, Andani; Yusran, Yusran
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2576

Abstract

The classification process applied in this study helps identify the many kinds of herbal plants. Herbal plant leaf features are used based on color, shape, and texture. Particle Swarm Optimization and Support Vector Machine (PSO-SVM) hybridization are applied in the classification process to increase classification and identification accuracy. A well-liked metaheuristic approach for solving optimization issues is Particle Swarm Optimization (PSO). Particles look around the search area for the best responses.  A particle swarm is initially initialized randomly within the search area via the PSO algorithm. Every particle's mobility is determined by both its own experience and the experiences of the other particles in the swarm. Each particle keeps track of the best solution it has ever found and the swarm's most extraordinary remedy that has so far been discovered. The Hybrid approach concurrently selects features for the SVM and optimizes its parameters. The kernel function's gamma value non-linearly maps an input space to a high-dimensional feature space. At the same time, the C parameter determines the trade-off between fitting error minimization and model complexity. The Gaussian kernel parameter is set to determine the optimal parameter value of the RBF kernel function. Feature selection solves the issue by eliminating redundant, associated, and irrelevant features. A confusion matrix is utilized in the evaluation to gauge the system's performance. The results demonstrated an improvement in accuracy, with the hybrid PSO-SVM using test data achieving an accuracy of 98% compared to the SVM method, achieving a 91% accuracy.
A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars Rahmat, Muhammad Abdillah; Indrabayu, Indrabayu; Achmad, Andani; Salam, Andi Ejah Umraeni
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2665

Abstract

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.
Cocoa bean quality identification using a computer vision-based color and texture feature extraction Basri, Basri; Indrabayu, Indrabayu; Achmad, Andani; Areni, Intan Sari
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1609

Abstract

The current pressing issue in the downstream processing of cocoa beans in cocoa production is a strict quality control system. However, visually inspecting raw cocoa beans reveals the need for advanced technological solutions, especially in Industry 4.0. This paper introduces an innovative image-processing approach to extracting color and texture features to identify cocoa bean quality. Image acquisition involved capturing video with a data acquisition box device connected to a conveyor, resulting in image samples of Good-quality and Poor-quality of non-cutting cocoa beans dataset. Our methodology includes multifaceted advanced pre-processing, sharpening techniques, and comparative analysis of feature extraction methodologies using Hue-Saturation-Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) with correlated features. This study used 15 features with the highest correlation. Machine Learning models using Support Vector Machine (SVM) with some parameter variation value alongside an RBF kernel. Some parameters were measured to compare each approach, and the results show that pre-processing without sharpening achieves better accuracy, notably with the HSV and GLCM combination reaching 0.99 accuracy. Adequate technical lighting during data acquisition is crucial for accuracy. This study sheds light on the efficacy of image processing in cocoa bean quality identification, addressing a critical gap in industrial-scale implementation of technological solutions and advancing quality control measures in the cocoa industry.
Analisis Metode Decision Tree dan Regresi Logistik Sebagai Sistem Rekomendasi Kenaikan Golongan Berdasarkan Kinerja Pegawai pada Universitas Lamappapoleonro Aksa, Andi Nurul; Achmad, Andani; Arda, Abdul Latief
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.782

Abstract

This research focuses on the importance of employee performance in supporting organizational success, especially in the promotion process at Lamappapoleonro University which is still done manually. Therefore, this research aims to develop a recommendation system for promotion using the Decision Tree and Logistic Regression methods, which is expected to speed up and simplify the decision-making process regarding employee promotions. The Decision Tree algorithm is used to classify employee performance in the form of sufficient, good, and excellent variables, while the Logistic Regression algorithm is used to predict the feasibility of employee promotion with the variable feasible or inappropriate. The data used in this study includes 12 independent variables, such as attendance, discipline, responsibility, and innovative ability. The analysis results show that the Decision Tree and Logistic Regression methods are able to produce accurate predictions, with an accuracy rate of 91.67% and 100% respectively. The main factors that influence promotion are honesty, discipline, and innovation ability. With this recommendation system, the employee promotion process becomes more efficient and accurate, providing significant benefits for human resource management at Lamappapoleonro University.
Co-Authors -, Sofyan Abd. Salam Abdul Latief Arda Abdul Muis Abdullah, Alfiah Achmad Zubair Adnan Adnan Ahmad Abdullah Ahmad Ilham, Amil Akbar Iskandar Akhmad Qashlim, Akhmad Aksa, Andi Nurul Al Kautsar Amil Ahmad Ilham Amriana Amriana Andini Dani Achmad Andini Dani Achmad, Andini Dani Ansar Ansar Ansar Suyuti Anshar, Muh Arda, Abdul Latif Ardiaty Arief . Areni, Intan Sari Arief, Ardiaty Arief, Azran Budi Armin Lawi Asnimar Awal Kurniawan Azran Budi Arief Baital, Muhammad Syarif Bakrim, La Ode Basri Basri Basri, - Budiansyah, Anugrah Christoforus Y. Deny Wiria Nugraha Dewi Kusumawati, Dewi Dewiani . Dewiani Dewi Djamaluddin Dewiani Dewiani Dhimas Tribuana Edwin Adrin Wihelmus Sanad Ejah Umraeni Elyas Palantei Faizal A. S. Faizal A. Samman . Faizal Arya Samman Faizal Arya Samman Fighi S. Permadi . Figur Muhammad Gassing - Gassing . Hasanuddin, Zulfajri Basri Hazriani, Hazriani Husain, Muhammad Fadhil Ida Rachmaniar Sahali Ida Rachmaniar Sahali Indrabayu Indrabayu Indrabayu, - Ingrid Nurtanio Intan Sari Areni Irma Pratiwi Sayuti Konate, Siaka Latif, Nuraida M. Hasanuddin Mansyur Martani, Ahmad Merna Baharuddin Merna Baharuddin Milleneo . Mubarak, Abdul Muh Anshar Muh. Anshar . Muhammad Abdillah Rahmat, Muhammad Abdillah Muhammad Akbar Muhammad Niswar Nappu, Muhammad Bachtiar Palantei, Elyas Palantei, Idris Panggalo, Samuel Pasra, Nurmiati Phie Chyan Rachmaniar, Ida Rahman, Ariastuti Ramdan Satra Rhiza S. Sadjad Rhiza S. Sadjad . Rifaldy Ramadhan Latief S, Mulyadi Salam, Andi Ejah Umraeni Salama Manjang Samuel Panggalo Sarmila, Sarmila Suliman, Suliman Supriadi Sahibu Syafruddin Syarif Syafruddin Syarif Tajuddin Waris Usman Usman Utomo, Tri Panji Sugi Wahyudi Sofyan Wardi . Wardi Wardi Yudha, Muh. Reza Eka Yulis, Nurlina Yusran . Yusran Yusran Yuyun Yuyun, Yuyun Zaenab . Zahir Zainuddin Zahir Zainuddin