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All Journal CogITo Smart Journal Jurnal SOLMA ILKOM Jurnal Ilmiah JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Rambideun : Jurnal Pengabdian Kepada Masyarakat Digital Transformation Technology (Digitech)
Suryani Suryani
Universitas Dipa Makassar
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Library & Information Science Public Health Social Sciences Other

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Application of General Regression Neural Network Algorithm in Data Mining for Predicting Glass Sales and Inventory Quantity Suryani, Suryani; Intan, Indo; Mochtar Yunus, Farhan; Haris, Adammas; Faizal, Faizal; Nurdiansah, Nurdiansah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1562.229-239

Abstract

FF Jaya Glass is a shop that supplies and installs 3 mm to 12 mm glass. The store obtained glass from suppliers to be processed in shape and size according to customers’ order. After completing the customer's order, the shop worker will install the glass at the requested location. Unfortunately, currently stores do not utilize sales data to predict sales either manually or by utilizing technology. As a result, the store cannot predict when the number of glass orders will increase or decrease. In addition, errors often occur when ordering glass for the next period. As a result, stores often run out of glass supplies due to the large number of glass orders so that the achievement of profits is not optimal. This study aims to identify sales variables in glass sales data and build a general regression neural network model as a data mining method. In addition, this study aims to iterate to find the best value in the sales data training process, design and create applications according to user needs, and conduct system validation tests. The general regression neural network method is used to predict sales. The results of this study indicate that the application of general regression neural networks can be used to predict sales. This will make it easier for the store to provide glass supplies in the coming months with an accuracy of 98.1%.
Optimizing a Fire and Smoke Detection System Model with Hyperparameter Tuning and Callback on Forest Fire Images Using ConvNet Algorithm Suryani Suryani; Suryani, Suryani; Syahlan Natsir, Muhammad
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1937.46-58

Abstract

Forest fire is a significant issue, especially for tropical countries like Indonesia. One of the impacts of forest fires is environmental pollution and damage, such as damage to flora and fauna, water, and soil. Fire detection technology is crucial as a preventive measure before the spread or expansion of fire points. Several forest fire detection systems have been developed by various research studies, with detection targets varying. Objects in the form of images are usually detected using the RGB color filtering method, but this method still results in false detections in image processing. Therefore, a classification model is built to detect fire and smoke in images using the Convolutional Neural Network (ConvNet) algorithm. In the development of the ConvNet model, a comparison of models is also conducted to assess the influence of Hyperparameter Tuning and Callbacks in optimizing the model's classification performance. The research results indicate that out of the six comparison scenarios created, the best model is obtained with 90% training data and 10% testing data, which is also optimized with Hyperparameter Tuning and Callbacks, with a Validation Accuracy of 98.18% and Validation Loss of 4.97%. This model is then implemented in the interface system.
Co-Authors Abdul Ibrahim Ahyuna Ahyuna Ahyuna Akbar Bahtiar Akbar Bahtiar Andrew Ridow Johanis Andrew Ridow Johanis M Annah Annah, Annah Arham Arifin Arkjun Yudistira Pratama Arwansyah Arwansyah Asmah Akhriana Asrul Syam Asrul Syam Atnasius Alan Pabembe Baharuddin Baharuddin Bahtiar, Akbar Djafar, Imran Erni Marlina Faizal Faizal Faizal Faizal, Faizal Fatmasari Fatmasari Fransiska Sagita Patulak Hardi Hardi Hardi Hardi Hardi Hardi Haris, Adammas Harlina, Sitti Hasriani Hasriani Hasriani Hasriani Hasriani Hasriani, Hasriani Hasyrif SY Herlinda Herlinda Husain Husain Husain T Indo Intan Iqbal Gunawan Joseph Tumiwa Madyana Patasik Magfirah Magfirah Marsa Marsa Michael Octavianus Michael Oktavianus Mochtar Yunus, Farhan Moh. Rifkan Muh. Khaddafi Muh. Syahlan Natsir Muhammad Agus Muljanto Nirwana Nirwana Novita Sambo Layuk Nurdiansah Nurdiansah, Nurdiansah Nurlina Nurlina Nurlindasari Tamsir Nurul Aini Rauf, Abdul Riki Taruk Bua Sadly Samsuddin Sadly Syamsuddin Sri Wahyuni ST. Aminah Dinayati Ghani Suci Ramadhani Arifin Suryani Suryani Syamsul Bahri Thabrani R Usman Usman W Soetikno, Y Johny