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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JurTI (JURNAL TEKNOLOGI INFORMASI) IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer IJISTECH Reputasi: Jurnal Rekayasa Perangkat Lunak INSANtek – Jurnal Inovasi dan Sains Teknik Elektro PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Digital Transformation Technology (Digitech) Jurnal Pengabdian Pada Masyarakat Journal of Technology Informatics and Engineering
Indra Riyana Rahadjeng
Universitas Bina Sarana Informatika
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Penerapan Pemilihan Model Arsitektur Terbaik pada Neural Network pada Prediksi Jumlah Siswa SD di Kecamatan Siantar Barat Ramadhani, Cerah Fitri; Siregar, Muhammad Noor Hasan; Rahadjeng, Indra Riyana; Windarto, Agus Perdana
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2676

Abstract

The use of the artificial neural network (Backpropagation) method can be used in determining the best architectural model for predicting the number of elementary school students in the Siantar Barat District. The dataset used is a dataset on the number of Elementary School (SD) students in West Siantar District, Pematang Siantar City in 2017-2021 obtained from the Website of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia (https://dapo.kemdikbud.go.id /pd/3/076303). The dataset is then divided into 2 parts, namely the training and testing dataset. In the training datasets, attribute X1 is a dataset for 2017, X2 is the dataset for 2018, X3 is a dataset for 2019, and attribute Y (target) is the dataset for 2020. For the test datasets, attribute X1 is the dataset for 2018, attribute X2 is a dataset for 2019, attribute X3 is a dataset for 2020 and attribute Y (target) is a dataset for 2021. The results obtained from the analysis of the Backpropagation and virtualization methods using the MatLab application can be generated with a valid dataset and produce an accuracy rate of 87.5% in architectural models 3-9-1. So that the Backpropagation method can be used as a prediction method that makes it very easy to find predictions.
PENERAPAN METODE TOPSIS DALAM PENILAIAN KINERJA GURU TETAP SD NEGERI KEBALEN 07 Susliansyah, Susliansyah; Rahadjeng, Indra Riyana; Sumarno, Heny; Deleaniara. M, Chyntia Marianna
Jurnal Pilar Nusa Mandiri Vol 15 No 1 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (979.127 KB) | DOI: 10.33480/pilar.v15i1.2

Abstract

To find out the problems faced in the teaching performance assessment process by utilizing the Technique For Order Preference method by Similiarity to Ideal Solution (TOPSIS), to manage the processing of Teacher data is a more optimal consideration. By using the (TOPSIS) method as a basis for processing teacher performance assessment data. This can allow the system to provide an assessment in accordance with the quality of each teacher and is expected to facilitate decision making in the assessment of Teacher's performance. The Technique For Order Preference by similiarity to Ideal Solution has been running well and can result in a weighting of assessment criteria and clear and fast information compared to manual calculations so SD Negeri Kebalen 07 can use it as a tool for making appropriate decisions.
Deep Learning to Extract Animal Images With the U-Net Model on the Use of Pet Images Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Alkhairi, Putrama
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7199

Abstract

This article explores the innovative application of deep learning techniques, specifically the U-Net model, in the realm of computer vision, focusing on the extraction of animal images from diverse pet datasets. As the digital landscape becomes increasingly saturated with pet imagery, the need for precise and efficient image extraction methods becomes paramount. The study delves into the challenges posed by varying animal poses and backgrounds, presenting a comprehensive analysis of the U-Net model's adaptability in handling these complexities. Through rigorous experimentation, this research refines existing methodologies, enhancing the accuracy of animal image extraction. The findings not only contribute to advancing the field of computer vision but also hold significant implications for wildlife monitoring, veterinary diagnostics, and the broader domain of image processing.
Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Yuhandri, Muhammad Habib
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7776

Abstract

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.
Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Yuhandri, Muhammad Habib
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7776

Abstract

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.
Optimisasi Fungsi Aktivasi pada Arsitektur LeNet untuk Meningkatkan Akurasi Klasifikasi Citra Tumor Otak Harliana, Harliana; Rahadjeng, Indra Riyana; Winanjaya, Riki
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7108

Abstract

Brain hemorrhage is a critical medical condition that requires early and accurate detection to improve patient recovery outcomes. However, conventional image classification methods for brain hemorrhage still face limitations in terms of accuracy and efficiency. To address this issue, this study proposes optimizing the LeNet model using various activation functions—ReLU, Sigmoid, Tanh, and Swish—to enhance classification performance. Several optimization strategies were applied, including data augmentation techniques (flipping, rotation, shearing, rescaling) and fine-tuning of hyperparameters, to improve model generalization. Experimental results indicate that the model utilizing the Swish activation function achieves the most stable overall performance, with an accuracy of 55%, recall of 54%, precision of 54%, F1-score of 54%, and a ROC AUC value of 0.45. Although this performance is still below clinical application standards, the findings serve as an initial step toward exploring activation function optimization in CNN architectures. Further research is needed to significantly enhance classification accuracy and enable clinical viability.
Design Framework of Expert System Program in Otolaryngology Disease Diagnosis use Extreme Programming (XP)Method(Case Study in THB Bekasi Hospital) melyani, Melyani; Prasetyo, Trisna Fajar; Rahadjeng, Indra Riyana; Mufid, Zainul; Rafik, Ahmad; Shaura, Rizkiana Karmelia; Daniel, Daniel; Emita, Isyana
Journal of Technology Informatics and Engineering Vol. 3 No. 3 (2024): December (Special Issue: Big Data Analytics) | JTIE: Journal of Technology Info
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i3.209

Abstract

The prevalence of Ear, Nose, and Throat (ENT) diseases presents diagnostic challenges, especially in resource-limited settings. At THB Bekasi Hospital, constrained specialist availability and long consultation queues highlight the need for an accessible diagnostic solution. This study aims to develop an expert system for diagnosing ENT diseases using the Extreme Programming (XP) methodology, incorporating the forward chaining technique for inference. The research includes assessment, knowledge acquisition through expert consultations, system design, and rigorous testing. The system was developed as a mobile application using Android Studio, enabling users to input symptoms and receive real-time diagnostic insights. The knowledge base integrates data from medical experts, synthesizing 11 diseases and 35 symptoms into a robust decision-making framework. The diagnostic process applies predefined rules to ensure accuracy in identifying conditions such as sinusitis, laryngitis, and otitis. Evaluation results demonstrate a 100% match accuracy during testing with 15 test cases, confirming the system’s reliability. The application offers users rapid diagnostic assistance, promoting timely treatment for ENT issues, although it does not substitute medical professionals. By leveraging ubiquitous smartphone access, this system addresses gaps in healthcare accessibility and enhances patient autonomy. This research contributes a scalable framework for deploying expert systems in other medical domains. Future improvements include integrating geolocation services for nearby specialist referrals and adopting backward chaining for more complex diagnoses, thereby broadening its applicability and utility
Manajemen Data Karyawan Berbasis Web Untuk Meningkatkan Efisiensi Dan Akurasi Informasi Hendrian, Yayan; Rahadjeng, Indra Riyana; Said, Muhammad Arifin
INSANtek Vol. 6 No. 1 (2025): Mei 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/insantek.v6i1.8166

Abstract

Manajemen data karyawan yang baik sangat dibutuhkan untuk menunjang kinerja perusahaan dan efisiensi karena informasi yang diperlukan sifatnya up to date dan akurat. Yang menjadi objek dalam penelitian ini adalah PT. Astana Kinaya Mitratama. Saat ini sistem manajemen data karyawan di perusahaan tersebut meskipun sudah terkomputerisasi tetapi masih bersifat manual, belum bisa diakses dari tempat lain sehingga kurang praktis dan kurang fleksibel.  Oleh karena itu diperlukan terobosan yang lebih modern dan praktis. Penelitian ini berusaha memberikan solusi tersebut dengan cara merancang dan mengimplementasikan sistem manajemen data karyawan berbasis web di PT Astana Kinaya Mitratama guna meningkatkan efisiensi dan akurasi informasi agar mudah di update kapanpun tidak mesti di kantor. Sistem informasi ini dibangun menggunakan Laravel, PHP dan My SQl. Sistem ini diharapkan dapat menggantikan metode konvensional yang masih manual, sehingga proses pengelolaan data karyawan menjadi lebih terstruktur, cepat, dan akurat. Penulis mengunakan metode penelitian PPDIOO. Hasil yang diharapkan, website ini bisa menjadi solusi yang efektif dalam pengelolaan data karyawan di PT Astana Kinaya Mitratama sehingga dapat meningkatkan efisiensi operasional, dan memastikan akurasi informasi yang lebih baik, serta mempermudah akses dan pengolahan data bagi manajemen dan karyawan.
Klasifikasi Tumor Otak Berbasis Magnetic Resonance Imaging Menggunakan Algoritma Convolutional Neural Network Citra R, Fatmah; Indriyani, Fintri; Rahadjeng, Indra Riyana
Digital Transformation Technology Vol. 3 No. 2 (2023): Artikel Periode September 2023
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v3i2.3469

Abstract

Tumor otak adalah kondisi yang ditandai dengan tumbuhnya sel-sel abnormal dan ganas di dalam otak. Citra digital melalui Magnetic Resonance Imaging (MRI) merupakan salah satu metode untuk membantu dokter dalam mengklasifikasikan jenis tumor otak. Namun, klasifikasi secara manual membutuhkan waktu yang lama dan memiliki risiko kesalahan yang besar, untuk itu diperlukan cara otomatis dan akurat dalam melakukan klasifikasi citra MRI. Pada penelitian ini, dirancang suatu sistem berbasis deep learning, yaitu Convolutional Neural network (CNN) dengan arsitektur Alexnet. Jumlah dataset yang digunakan dalam penelitian ini sebanyak 7.022 citra MRI dengan empat kelas yaitu glioma, meningioma, pituitary, dan no tumor yang bersumber dari kaggle.com. Para peneliti terdahulu telah mendapatkan hasil yang memuaskan di semua sub bidang analisis citra medis, seperti klasifikasi, deteksi, segmentasi, dan peningkatan citra. Dengan keberhasilan ini, para peneliti menemukan sebuah model yang dapat mendiagnosis dan mendeteksi secara otomatis, yang dapat membantu para ahli di bidangnya dan menunjukkan bahwa teknologi deep learning akan sangat bermanfaat bagi kemajuan analisis citra medis, hasil penelitian diperoleh akurasi sebesar 0,9642.
Co-Authors Achmad Ihsan Fajrin Adi Supriyatna Agus Perdana Windarto Alkhairi, Putrama Citra R, Fatmah Daniel Daniel Deleaniara. M, Chyntia Marianna Endang Wahyudi Fatmah Citra R Fintri Indriyani Harliana Harliana Heny Sumarno, Heny Herdian, Barlin Isyana Emita Lydia Salvina Helling Melyani Melyani, Melyani Mufid, Zainul Muhammad Noor Hasan Siregar Rafik, Ahmad Ramadhani, Cerah Fitri Ririn Restu Aria Ritapuspitasari Ritapuspitasari Rizkiana Karmelia Shaura Said, Muhammad Arifin Sidik Sidik Susi Susilowati Susliansyah, Susliansyah Trisna Fajar Prasetyo Winanjaya, Riki Yasmin, Ghaisani Yayan Hendrian Yuhandri, Muhammad Habib