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All Journal SMARTek MEKTEK Foristek Jurnal MEKANIKAL JURNAL SISTEM INFORMASI BISNIS Jurnal Teknologi dan Manajemen Informatika Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi MEKTRIK CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research SemanTIK : Teknik Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika ScientiCO : Computer Science and Informatics Journal Sains, Aplikasi, Komputasi dan Teknologi Informasi TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Deny Wiria Nugraha
Universitas Tadulako
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Systematic Literature Review of Expert System Mohamad Ilyas Abas; Deny Wiria Nugraha; Asminar Asminar; Isminarti Isminarti
TIN: Terapan Informatika Nusantara Vol 3 No 12 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v3i12.4136

Abstract

Current developments in technological science have had a major impact on our daily lives, one of the impacts is in the health and agricultural sectors which implement automated systems. This research aims to review current topics regarding expert systems and the use of some of the best algorithms used. The literature review was created by systematically reviewing 1000 research articles from the last 5 years (from 2016 to 2020) by collecting through related sources including conference proceedings and journals. The method used in this research is Systematic Literature Review (SLR). SLR is used to identify, study and interpret all relevant specific research. The results of this research reveal several algorithms that are frequently used and have good accuracy values ​​as well as trending topics in the field of expert systems.
Application of simple additive weighting in the decision support system for determining the best location of temporary waste storage places Isminarti Isminarti; Deny Wiria Nugraha; Yuri Yudhaswana Joefrie; Andipa Batara Putra; Widya Wisanty
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1054

Abstract

Waste is a material that is discarded or discarded as a result of human or natural activities. The increase in waste is proportional to the level of human use of goods used in everyday life. Until now, the waste problem has always been a complaint for all people in cities and villages. One effective way to deal with the waste problem is to select the ideal location for a Temporary Waste Storage Site (TPS). This research aims to build a system that can determine the best location for waste TPS using a Decision Support System (DSS) method of Simple Additive Weighting (SAW). In this study, seven assessment criteria were used with 148 test data. The tests showed that the computing time required to get the ranking was 12,528 seconds, and memory usage was 226,2422 kb. Also, this system produces an accuracy level of 93.24%, which shows that it can function well using the SAW method and has a pretty good level of accuracy.   
Employee Attendance System with Facial Recognition Technology Using a Single Shot Detector (SSD) Algorithm Djohari, Riyandi Dwitama; Ngemba, Hajra Rasmita; Hendra, Syaiful; Angraeni, Dwi Shinta; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10869

Abstract

The attendance system is an important thing that is used to assess employee performance. Currently, many companies or organizations have developed attendance systems, but there are still many organizations that have not optimized attendance with a reliable system. This happens at the Palu City Tourism Office, where attendance still uses manual recording in album books. Many problems occur with the manual attendance process, so it is necessary to create an attendance system. This research aims to help the Palu City Tourism Office for attendance management by using face recognition technology using the Single Shot Detector (SSD) algorithm. The system development model uses Waterfall. This research uses 20 different facial poses with four people each with 5 poses. This research uses javascript programming language, Next JS framework, tailwind CSS, PostgreSQL database, and face-api.js library. Tests carried out with blackbox results all features on the system function properly then based on the results of face detection testing, the test results obtained that the Single Shot Detector algorithm can detect facial images recorded on the camera quickly and accurately in medium and bright lighting with a distance of 40-80 cm.  The result of this research is a system that can recognize faces well by matching the training data image with the face image recorded on the camera in realtime
Comparison of Min-Max and EOQ Methods in Web-Based Stock Management Information System Dharmakirti, Dharmakirti; Ngemba, Hajra Rasmita; Hendra, Syaiful; Syahrullah, Syahrullah; Nugraha, Deny Wiria; Dwiwijaya, Kadek Agus
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10983

Abstract

Warehouse stock management is very important for the sustainability of a business, especially building stores that have a lot of goods. Many problems occur in the absence of stock information management in a business. This happens at Pelita Building Store which has a lot of goods so it is very difficult for the owner to decide which items to stock based on priority. This study aims to help business owners to carry out stock management by comparing two inventory methods, namely the Economic Order Quantity (EOQ) and Min-Max methods. The comparison is carried out with the aim of determining the most optimal method that can be used for stock management in the building shop business case. This research uses a system development method by adopting the prototype method. This research was tested by conducting blackbox and accuracy testing. The results of this study based on blackbox testing stated that the features in the application function properly and can be used for stock management. In addition to black box testing, accuracy testing is also carried out. Accuracy testing is done by comparing the data from the information system calculation with the data from the manual calculation. After comparison, an accuracy of 100% is obtained, thus it can be concluded that this application can be used to help shop owners determine priority stock and the most accurate method is to use the EOQ method
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.
Analysis of the Use of MTCNN and Landmark Technology to Improve the Accuracy of Facial Recognition on Official Documents Chandra, Ferri Rama; Ngemba, Hajra Rasmita; Hamid, Odai Amer; Lapatta, Nouval Trezandy; Hendra, Syaiful; Nugraha, Deny Wiria; Syahrullah, Syahrullah
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8814

Abstract

A face recognition system consists of two stages: face detection and face recognition. Detection of features such as eyes and mouth is important in facial image processing, especially for official documents such as identity cards. To ensure identification accuracy, this research applies facial landmark extraction technology and MTCNN (Multi-Task Cascaded Convolutional Neural Network). The purpose of this research is to evaluate the accuracy of MTCNN in detecting facial features at the Department of Population and Civil Registration (dukcapil) Palu City, using facial landmarks and waterfall methods as an application development methodology. The evaluation results show that MTCNN has high face recognition accuracy and good positioning ability regardless of what GPU in use as long have right CPU and System Operation. In comparison, the Viola-Jones algorithm is effective for high-speed applications, while SSD offers balanced performance with GPU device requirements for optimal performance. While MTCNN proved to be effective, challenges still exist, such as false positives and false negatives, especially in poor lighting conditions and extreme poses. Image and camera quality, including resolution and facial expression, also affects detection accuracy. These findings suggest that the application of MTCNN can improve face recognition accuracy for official documents, although it requires addressing existing challenges. With this technology, it is expected that errors in facial recognition can be minimized, resulting in more reliable data that meets the standards for issuing identity documents. This research contributes to the development of a more accurate and efficient face recognition system for personal identification applications.
Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah Ngemba, Hajra Rasmita; Raivandy, I Made Randhy; Hendra, Syaiful; Ardiansyah, Rizka; Dwi Wijaya, Kadek Agus; Nugraha, Deny Wiria; Irfan, Mohamad
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10235

Abstract

Agriculture is a highly robust sector in Indonesia. This is evidenced in Central Sulawesi Province, where the gross domestic product (GDP) from the agricultural sector, based on constant prices from 2018 to 2021, continues to experience growth. Such conditions suggest that commodities in the agricultural sector have the potential to become export products, enabling a greater economic boost for the region. Before engaging in exports, it is necessary to identify which commodities have potential. One way to determine this is by applying Klassentypology. To simplify the process, it can be implemented in machine learning using the K-Nearest Neighbor algorithm. K-Nearest Neighbor is chosen because this algorithm can handle data containing noise and has good adaptability when given new data. In this research, two machine learning models were developed. The first model is used to classify whether a commodity is advancing or lagging, while the second model is used to classify commodities that grow rapidly and slowly. The highest accuracy obtained from the first model is 96.23%. Meanwhile, the highest accuracy from the second model is 93.49%.
IMPLEMENTASI SVM DAN SMOTE PADA ANALISIS SENTIMEN MEDIA SOSIAL X TERHADAP PELANTIKAN AGUS HARIMURTI YUDHOYONO Fajriyah, Nurul; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Laila, Rahmah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6246

Abstract

Pelantikan Agus Harimurti Yudhoyono sebagai Menteri Agraria dan Tata Ruang/Badan Pertanahan Nasional (ATR/BPN) telah memicu berbagai reaksi publik yang terekam dalam media sosial X. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap pelantikan tersebut menggunakan algoritma Support Vector Machine (SVM) dan teknik Synthetic Minority Oversampling Technique (SMOTE). Data yang digunakan dalam penelitian ini diambil dari komentar masyarakat di media sosial X, yang kemudian diolah untuk membedakan antara sentimen positif, negatif, dan netral. Dalam proses analisis, data awal yang diperoleh cenderung tidak seimbang, dengan jumlah data sentimen negatif yang lebih banyak dibandingkan dengan sentimen positif dan netral. Oleh karena itu, teknik SMOTE diterapkan untuk mengatasi ketidakseimbangan kelas dan meningkatkan performa model. Algoritma SVM kemudian digunakan untuk melakukan klasifikasi sentimen. Hasil penelitian menunjukkan bahwa model SVM yang diimbangi dengan SMOTE memiliki tingkat akurasi yang tinggi dalam mengklasifikasikan sentimen publik dibandingkan dengan model tanpa SMOTE dengan akurasi sebesar 0.93, presisi sebesar 0.93 dan recall sebesar 0.93.
Analysis and Design of Food Price Data Processing Information System Priska, Salsa Dilah; Syahrullah, Syahrullah; Nugraha, Deny Wiria; Lapatta, Nouval Trezandy; Lamasitudju, Chairunnisa Ar
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3904

Abstract

Food prices have an important role in maintaining economic stability and public welfare, as price fluctuations can have a direct impact on purchasing power and inflation. The manual process of recording and reporting food price data at the Department of Agriculture and Food Security of Palu City leads to inefficiencies, data inaccuracies, and difficulties in tracking historical information. These limitations highlight the need for a structured system that can support accurate and efficient data management. This study applies a prototyping method to develop a web-based information system tailored to the needs of the institution. The development process involves continuous interaction between users and developers to ensure the system meets practical requirements. Data were collected through interviews, observations, and documentation. System functionality was tested using black box testing, while usability was assessed using the System Usability Scale (SUS) questionnaire. The results indicate that the system's features, including daily price input, automatic average calculations, report submission, and approval workflows, function correctly. Users are able to interact with the system efficiently, and the SUS results show that the system falls into the acceptable usability category, indicating that it is easy to use. In conclusion, the development of this web-based information system improves the efficiency and accuracy of food price data processing and reporting. It provides a reliable tool for managing information within the department and supports better operational performance.
Implementation of ResNet-50-Based Convolutional Neural Network For Mobile Skin Cancer Classification Asriani, Asriani; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Amriana, Amriana; Wirdayanti, Wirdayanti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9696

Abstract

The skin is one of the most important parts of the human body, serving vital functions such as protecting internal organs from injury, shielding against direct bacterial exposure, regulating body temperature, and more. However, the skin is also susceptible to diseases, one of which is skin cancer. Skin cancer can be extremely dangerous if not treated promptly, as it can lead to death. Therefore, early detection is crucial. This study proposes a technology-based solution by classifying skin cancer using a convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST API using Flask. The HAM10000 dataset, consisting of 10,015 skin lesion images across seven classes, was used for model training. Various testing scenarios were conducted to determine the optimal parameter combination. The best results were achieved with an accuracy of 83.84%, precision and recall of 83%, and an F1-score of 83%, using a training data configuration of 70%, dropout of 0.4, and a batch size of 64. The model implemented in this Android application can perform early detection of skin cancer quickly, practically, and easily accessible to the general public, though healthcare professionals must still supervise it. However, although this model can assist users in making early predictions, the prediction results from this model are only a tool for early detection and do not replace clinical diagnosis by professional medical personnel.2) Figure 8 shows the display for taking pictures through the gallery or camera. Users can choose the image they want to upload from the gallery or the camera to be analysed and predicted by the model.
Co-Authors A.Y. Erwin Dodu A.Y. Erwin Dodu A.Y. Erwin Dodu Abdul Mahatir Najar Agustinus Kali Ahmad Ilham, Amil Amil Ahmad Ilham Aminuyati Amriana Amriana Amriana Amriana Andani Achmad Andi Hendra Andipa Batara Putra Angraeni, Dwi Shinta Ardiyansyah, Rizka Arief Pratomo Arief, Ardiaty Asminar Asminar Asri Arif Asriani Asriani, Asriani Asrul Sani Ayu Hernita Ayyub, Mohammad Azhar Baso Mukhlis Candriasih, Ni Kadek Chandra, Ferri Rama Dessy Santi Dharmakirti, Dharmakirti Djohari, Riyandi Dwitama Dodu, A. Y. Erwin Dodu, A.Y Erwin Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwimanhendra, Muhammad Rifaldi Dwiwijaya, Kadek Agus Erwin Dodu, Albrecht Yordanus Fajriyah, Nurul Fanny Astria, Fanny Hajra Rasmita Ngemba Hamid, Odai Amer Hasanuddin Hasanuddin Ihalauw, Sahron Angelina Imat Rahmat Hidayat Isminarti, Isminarti Jeprianto Rurungan, Jeprianto K. Julianto, K. Kalatiku, Protus P Krisna Rendi Awalludin Lamasitudju, Chairunnisa Landusa, Natalia Anastasya Luh Putu Ratna Sundari Maharani, Wulan Mery Subito Mohamad Ilyas Abas Mohamad Irfan, Mohamad Muhsin, Abid Narke, I Made Reyvinno Dirga Nouval Trezandy Lapatta Novilia Chandra Paloloang, Muhammad Fairus B. Priska, Salsa Dilah Protus Pieter Kalatiku Putra, Subkhan Dinda Rahma Tanti Rahmah Laila Raivandy, I Made Randhy Rieska Setiawaty Rinianty, Rinianty Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar Septiana, Stevi Septiano Anggun Pratama Setiawan, Dita Widayanti Sri Khaerawati Nur Sunandar, Aisyah Syahskiah Syahrullah Syahrullah Syaiful Hendra Thia Wydia Astuti Usama Wawagalang, A. Nolly Sandra Wirdayanti Wisanti, Widya Yuli Asmi Rahman Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli