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All Journal Teknika: Jurnal Sains dan Teknologi Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Ilmu Administrasi Negara (JUAN) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Community Development Journal: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JINAV: Journal of Information and Visualization KLIK: Kajian Ilmiah Informatika dan Komputer ARRUS Journal of Engineering and Technology JURNAL ILMIAH GLOBAL EDUCATION Brilliance: Research of Artificial Intelligence PENA ABDIMAS : Jurnal Pengabdian Masyarakat NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Pengabdian Masyarakat Waradin Sejahtera: Jurnal Inspirasi Mengabdi Untuk Negeri Indonesian Research Journal on Education Jurnal Manajemen Teknologi dan Sistem Informasi Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Pengabdian Multidisiplin Indonesia SISFOTENIKA INOVTEK Polbeng - Seri Informatika
Gagah Dwiki Putra Aryono
Universitas Bina Bangsa
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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

 1 
Decision-Making System for Determining Tuition Fees using the Simple Additive Weighting Method Gagah Dwiki Putra Aryono; Kenedi Kenedi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1324

Abstract

This study aims to develop a decision-making system for determining tuition fees using the Simple Additive Weighting (SAW) method. The SAW method is a multi-criteria decision-making technique that calculates the weighted sum of decision alternative attributes. This system is designed to assist school administration in making decisions related to the determination of tuition fees for students, which is a crucial source of funding for school operations. The system was developed using PHP programming language and MySQL database. This study utilized descriptive research methods and data collection techniques such as interviews, observations, and documentation. The collected data were then analyzed using the SAW method to determine the weight of each attribute and rank the decision alternatives. The system's performance was evaluated using black-box testing methods, and the results indicated that the system exhibited excellent accuracy, reliability, and efficiency. The testing results showed that the developed system can assist school administration in making decisions related to the determination of tuition fees for students. The use of this system can simplify the decision-making process and reduce errors in decision-making, thereby enhancing school operational activities
Enhanced Facial Expression Recognition Through a Hybrid Deep Learning Approach Combining ResNet50 and ResNet34 Models Auliana, Sigit; Mahrojah, Siti; Aryono, Gagah Dwiki Putra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1874

Abstract

Recognizing facial expressions is a critical aspect of computer vision and human-computer interaction. It facilitates the interpretation of human emotions from facial images, aiding in applications such as affective computing, social robotics, and psychological research. In this work, we propose using hybrid deep learning models, ResNet50 and ResNet34, for facial expression classification. These models, pre-trained on large-scale datasets, demonstrate exceptional feature extraction capabilities and have achieved excellent performance in various computer vision tasks. Our approach begins with the collection and preprocessing of a labeled facial expression dataset. The collected data undergoes face detection, alignment, and normalization to ensure consistency and reduce noise. After preprocessing, the dataset is divided into training, validation, and testing sets. We fine-tune the ResNet50 and ResNet34 models on the training set, employing transfer learning to adapt the pre-trained models specifically for the facial expression recognition task. Optimization techniques such as SGDM, ADAM, and RMSprop are used to update the models' parameters and minimize the categorical cross-entropy loss function. The trained models are evaluated on the validation set, achieving an accuracy of 98.19%. Subsequently, the models are tested on unseen facial images to assess their generalization capabilities. This proposed approach aims to deliver accurate and robust facial expression classification, thereby advancing emotion analysis and human-computer interaction systems.
Multi-Domain Medical Image Enhancement Through Fuzzy and Regression Neural Network Approach Auliana, Sigit; Nur Janah, Meishi; Gagah Dwiki Putra Aryono
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1875

Abstract

Medical image processing has heralded a significant transformation in contemporary medical science, offering the promise of diagnosing, treating, and curing patients while minimizing adverse effects. By leveraging medical imaging, physicians gain the ability to visualize internal structures without invasive procedures. Moreover, this technology contributes to our understanding of neurobiology and human behavior, with brain imaging aiding investigations into addiction mechanisms. Interdisciplinary collaboration among biologists, chemists, and physicists is facilitated by medical imaging, with resultant technologies finding applications across various fields. This study focuses on enhancing medical images in both frequency and time domains. Contrast enhancement is achieved through local transformation histogram techniques, followed by overall enhancement using a Fuzzy-Neural approach. The proposed methodology is implemented using MATLAB 2018b. The findings emphasize the efficacy of the proposed technique in improving image quality for both MR and Selenography images. Its outstanding performance, marked by a higher PSNR (32.96) and a lower MSE (20.04), indicates its potential for more precise and dependable image enhancement compared to current methods.
Co-Authors A Halim Abdul Kohar Abdul Kohar Achmad Dwi Saputro Aldi Setiawan, Aldi Aldiyanti, Fitri Aliffito, Sadam Nazar Andika Purnama Anggi Nur Aisah Anti Restiana April Laksana Ardiansyah, Rizqi Arfiansyah Putra Arief, Rahadian Asep Sapaatullah Audina, Alisa Auliana, Sigit Basuki Rakhim Setya Permana Chafid, Nurul DEWI FITRIANI Eightian, Vieri Ginola Elfrida Ratnawati Eris Dwi Purnama Eris Dwi Purnama Fachruroji, Fahmi Fadia Syifa Maulida Farhan Fauzan, Achmad Fasya, Nabiila Ferawati, Dede Fipi Andriani Gelard Untirtha Pratama Hamdan Hidayat, Amat Ilham Danuarta Ilham, Ilham Rizqi Kenedi, Kenedi Lukman David Mahrojah, Siti Maman Masyhuri Mamdu Mahsan Hawariz Marisa Azis Masyhuri, Maman Miftahul Huda Muhamad Ja’far Sodik Muhammad Saifulloh Naufal, Dody Noviyanti, Ayu Nur Janah, Meishi Nurul Chafid Nurul Chapid Purnama, Andika Putra, Arfiansyah Rahadian Arief Rahadian Arief Ramadhan, Adhitya Rizqa Ramadhian, Putri Rizqi Ardiansyah rohaimudin, iim Romayasari, Vera Setiawan, Ajis Siti Umaya Slamet Gunadi Sukmawan, Irwan Surya Wirana Rezky Umar Mansyuri Umar Mansyuri Untirtha Pratama, Gelard Wildan Saptanzani Wiwi Sulandari Yustiana, Sisy