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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) POROS TEKNIK JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) CYBERNETICS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Perempuan & Anak Jurnal Impresi Indonesia Makara Journal of Technology Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Repositor
Chandranegara, Didih Rizki
Universitas Muhammadiyah Malang
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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IMPLEMENTASI METODE PERSONAL EXTREME PROGRAMMING DALAM PERACANGAN APLIKASI PEMESANAN RUANG RAPAT BERBASIS ANDROID DISKOMINFO JAWA TIMUR Akbar, Muhammad Sulthoni; Nuryasin, Ilyas; Chandranegara, Didih Rizki
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 6 No 1 (2024): March 2024
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v6i1.348

Abstract

The advancement of information technology greatly enhances human activities, including the introduction of information management and meeting room reservation systems. In the East Java Provincial Office of Communication and Informatics, manual room reservation processes pose challenges, especially when administrators are unavailable. To address this, a research project aims to develop an IT-based application for efficient meeting room management and reservation. This will simplify the process for employees, enabling systematic and effective room bookings. The application allows users to check room availability online in real-time, eliminating the need for direct interaction with administrators. This implementation is expected to significantly enhance the overall efficiency of the East Java Provincial Office of Communication and Informatics. The application development will follow Personal Extreme Programming (PXP), known for its adaptability in software development. PXP involves stages like requirements, planning, design, implementation, and testing, iterated as needed. Successful application of PXP relies on skilled developers, requiring clear requirement identification and accurate work estimation. This research builds upon prior studies demonstrating accelerated project completion using PXP. It aims to provide insights into applying PXP for flexible software development, emphasizing information technology's role in enhancing organizational efficiency.
Pengolahan Korpus Dataset Audio Bacaan Al-Qur"™an Menggunakan Metode Wav2Vec 2.0 Aminudin, Aminudin; Nuryasin, Ilyas; Amien, Saiful; Wicaksono, Galih Wasis; Chandranegara, Didih Rizki; Thoifah, I'anatut; Rizky, Wahyu; Ferdiansyah, Danny; Azzahra, Kiara; Lathifah, Fildzah; Aulyah, Khairunnisa
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 1 (2024): Volume 10 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i1.71576

Abstract

Pengembangan sistem otomasi pengenalan ucapan (Automatic Speech Recognition/ASR) di dalam membaca Al-Qur'an dibutuhkan korpus data audio bacaan Al-Qur'an dan beranotasi dengan transkripsi tekstual agar dapat diproses oleh algoritma machine learning. Pemrosesan Korpus dataset ini dibangun mengingat belum adanya dataset beserta pemrosesanya menggunakan metode tertentu untuk keperluan riset di dalam pengembangan ASR. Paper ini menyajikan kumpulan corpus dataset dan pengolahannya menggunakan metode Wav2Vec 2.0 dengan total 24 ribuan dataset hasil dari rekaman dari 170 santri dengan jenjang umur 4 sampai dengan 16 tahun. Pemrosesan korpus dataset dibuat mengikuti standar metode Wav2Vec 2.0 agar dapat digunakan sebagai data latih pada pemrosesan machine learning. Wav2Vec merupakan model yang dapat mempelajari representasi vektor dari masukan sinyal suara dengan proses pembelajaran self-supervised learning. Wav2Vec juga mampu menangani perbedaan aksen dan karakteristik pembaca Al-Qur'an yang bervariasi dan lebih akurat karena menggunakan deep learning. Dari hasil pengujian menggunakan parameter Precision didapatkan hasil accuracy sebesar 65.52%, precision dengan nilai 0.83 Recall dengan nilai 0.66 dan F1-Score dengan nilai 0.73 serta Word Error Rate (WER) dengan nilai 0.5. Diharapkan dengan adanya pemrosesan korpus dataset ini dapat membantu pengembangan dan riset terkait automasi sistem bacaan Al-Qur'an dengan teknik deep learning dan meningkatkan minat generasi milenial untuk belajar Al-Qur'an dengan memanfaatkan teknologi terkini.
Classification of Dermoscopic Images Using CNN-SVM Minarno, Agus Eko; Fadhlan, Muhammad; Munarko, Yuda; Chandranegara, Didih Rizki
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2153

Abstract

Traditional machine learning methods like GLCM and ABCD rules have long been employed for image classification tasks. However, they come with inherent limitations, primarily the need for manual feature extraction. This manual feature extraction process is time-consuming and relies on expert domain knowledge, making it challenging for non-experts to use effectively. Deep learning methods, specifically Convolutional Neural Networks (CNN), have revolutionized image classification by automating the feature extraction. CNNs can learn hierarchical features directly from the raw pixel values, eliminating the need for manual feature engineering. Despite their powerful capabilities, CNNs have limitations, mainly when working with small image datasets. They may overfit the data or struggle to generalize effectively. In light of these considerations, this study adopts a hybrid approach that leverages the strengths of both deep learning and traditional machine learning. CNNs are automatic feature extractors, allowing the model to capture meaningful image patterns. These extracted features are then fed into a Support Vector Machine (SVM) classifier, known for its efficiency and effectiveness in handling small datasets. The results of this study are encouraging, with an accuracy of 0.94 and an AUC score of 0.94. Notably, these metrics outperform Abbas' previous research by a significant margin, underscoring the effectiveness of the hybrid CNN-SVM approach. This research reinforces that SVM classifiers are well-suited for tasks involving limited image data, yielding improved classification accuracy and highlighting the potential for broader applications in image analysis.
Implementation of Generative Adversarial Network (GAN) Method for Pneumonia Dataset Augmentation Chandranegara, Didih Rizki; Sari, Zamah; Dewantoro, Muhammad Bagas; Wibowo, Hardianto; Suharso, Wildan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1675

Abstract

As a communicable disease, the majority of pneumonia cases are brought on by bacteria or viruses, which cause the lungs' alveoli to swell with fluid or mucus. Pneumonia may arise from this and further making breathing challenging since the lungs' air sacs are unable to contain enough oxygen for the body. Pneumonia may generally be diagnosed clinically (by a physician based on physical symptoms) as well as through a photo chest radiograph, CT scan, and MRI. In this case, the lower cost of a chest radiograph examination making it as one of the most popular medical imaging tests. However, chest radiograph photo readings have a disadvantage, where it takes a long time for medical staff or physicians to identify the patient's illness since it is difficult to detect the condition. Therefore, an identification of chest radiograph imagery into various forms using machine learning becomes one way to address this issue. This research focuses on building a deep neural network model using techniques from the Generative Adversarial Network algorithm. GAN is a category of machine learning techniques using two models to be trained simultaneously, one is a generator model to generated fake data and the other is a discriminator model used to separate the raw data from the real data set images. The dataset used is Chest X-Ray images obtained from repo GitHub and repo Kaggle totaling 5,863 with normal data 1583 images and pneumonia data 4273 imagesThe results showed that the use of the Generative Adevrsarial Network method as augmentation data proved to be more effective in improving the generalization of neural networks, this can be seen from the results the result of the accuracy value obtained is 97%.
Comparison of Transfer Learning Models in Classification Dental and Tongue Disease Images Azhar, Yufis; Setiono, Fauzan Adrivano; Chandranegara, Didih Rizki
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.487

Abstract

According to the Global Burden of Disease Study, dental caries is the most prevalent oral health ailment, affecting around 3.5 billion individuals globally. According to the Ministry of Health of the Republic of Indonesia, 93% of children in the country suffer from oral health issues, making poor oral health a serious public health concern. The tongue and teeth in the mouth are particularly vulnerable to a wide range of illnesses, and the condition of the mouth is a key sign of the health of the body as a whole. The CNN algorithm has been utilized in numerous studies to classify disorders of the tongue and teeth. Nevertheless, no study has classified tongue and dental diseases using merged datasets as of yet. This research addresses this gap by focusing on the classification of dental and tongue diseases using transfer learning techniques with CNN architecture models VGG16, VGG19, and ResNet50. The primary aim is to compare these three models to identify the one with the most optimal performance in handling related cases. Based on the results, the best accuracy was achieved with data augmentation and models trained for 75 epochs. The VGG16 model attained 94% accuracy, VGG19 achieved 93% accuracy, and ResNet50 also reached 94% accuracy. These findings suggest that transfer learning with CNN architectures can effectively classify dental and tongue diseases. The implications are significant for developing automated diagnostic tools that can aid in the early detection and treatment of oral health issues globally.
Analysis of the Combination of Naïve Bayes and MHR (Mean of Horner’s Rule) for Classification of Keystroke Dynamic Authentication Sari, Zamah; Chandranegara, Didih Rizki; Khasanah, Rahayu Nurul; Wibowo, Hardianto; Suharso, Wildan
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.839

Abstract

Keystroke Dynamics Authentication (KDA) is a technique used to recognize somebody dependent on typing pattern or typing rhythm in a system. Everyone's typing behavior is considered unique. One of the numerous approaches to secure private information is by utilizing a password. The development of technology is trailed by the human requirement for security concerning information and protection since hacker ability of information burglary has gotten further developed (hack the password). So that hackers can use this information for their benefit and can disadvantage others. Hence, for better security, for example, fingerprint, retina scan, et cetera are enthusiastically suggested. But these techniques are considered costly. The advantage of KDA is the user would not realize that the system is using KDA. Accordingly, we proposed the combination of Naïve Bayes and MHR (Mean of Horner’s Rule) to classify the individual as an attacker or a nonattacker. We use Naïve Bayes because it is better for classification and simple to implement than another. Furthermore, MHR is better for KDA if combined with the classification method which is based on previous research. This research showed that False Acceptance Rate (FAR) and Accuracy are improving than the previous research.
Diabetes Detection Using Extreme Gradient Boosting (XGBoost) with Hyperparameter Tuning Dinanthi, Devi Aprilya; Ramadanti, Elisa; Aditya, Christian Sri Kusuma; Chandranegara, Didih Rizki
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 2 (2024): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/qr3hw926

Abstract

Diabetes is a serious condition that can lead to fatal complications and death due to metabolic disorders caused by a lack of insulin production in the body. This study aims to find the best classification performance on diabetes dataset using Extreme Gradient Boosting (XGBoost) method. The dataset used has 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE, and hyperparameter optimization is performed using GridSearchCV and RandomSearchCV. Model evaluation was performed using confusion matrix as well as metrics such as accuracy, precision, recall, and F1-score. The test results show that the use of GridSearchCV and RandomSearchCV for hyperparameter tuning provides good results. The application of data resampling also managed to improve the overall model performance, especially in the XGBoost method that has been optimized using GridSearchCV, which achieved the highest accuracy of 85%, while XGBoost with RandomSearchCV optimization showed 83% accuracy performance.
User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor Chandranegara, Didih Rizki; Ashari, Anzilludin; Sari, Zamah; Wibowo, Hardianto; Suharso, Wildan
Makara Journal of Technology Vol. 27, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mouse dynamics authentication is a method for identifying a person by analyzing the unique pattern or rhythm of their mouse movement. Owing to its distinctive properties, such mouse movements can be used as the basis for security. The development of technology is followed by the urge to keep private data safe from hackers. Therefore, increasing the accuracy of user classification and reducing the false acceptance rate (FAR) are necessary to improve data security. In this study, we propose to combine the K-nearest neighbor method and simple random sampling and obtain a sample from a dataset to improve the classification of users and attackers. The results show that our proposed method has high accuracy for implement to practical system and reports the best results than previous research with a FAR of 0.037. Therefore, this method can be implemented in a real login system. The high false rejection rate of our proposed method will not be a problem because the most important thing in the login system is denying the attacker system access.
Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS Chandranegara, Didih Rizki; Vitanti, Vizza Dwi; Suharso, Wildan; Wibowo, Hardianto; Arifianto, Sofyan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1728

Abstract

Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.
Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication Didih Rizki Chandranegara; Hardianto Wibowo; Agus Eko Minarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14815

Abstract

Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner’s Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method.
Co-Authors Agus Eko Minarno Agus Zainal Arifin Ahmad Hanif Nurfauzi Akbar, Muhammad Sulthoni Akbi, Denar Regata Aminudin, Aminudin Aprilya Dinathi, Devi Arolina Rinardi Ashari, Anzilludin Aulyah, Khairunnisa Azzahra, Kiara christian s.k.aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma christianskaditya Dewantoro, Muhammad Bagas Dinanthi, Devi Aprilya Ekanita Rakhmah Eko Budi Cahyono Elan Cahya Niswary Elsandy Wirahadi Kusumah Fachry Abda El Rahman Ferdiansyah, Danny Ferdy Yoga Permana Hanif, Lathifah Hardianto Wibowo Heni Pujiastuti Ianatut Thoifah Imron, Moch Khasanah, Rahayu Nurul L. Yasril Imam Lathifah Hanif Lathifah, Fildzah M. Ridho Abror Moch Imron Moch. Rosid Noviansyah Ningsih Djamsi Novidianto, Raditya Nuryasin, Ilyas Raffi Ainul Afif Rahayu Nurul Khasanah Rakhmah, Ekanita Ramadanti, Elisa Rista Azizah Arilya Rizaldi, Muhammad Ikhwananda Rizky, Wahyu Robin Nusantara Haming Saiful Amien Sari, Zamah Setiono, Fauzan Adrivano Sofyan Arifianto Sumadi, Fauzi Dwi Setiawan Vallent Austin Theasar Kurniawan Vita Amalia Herlinda Vitanti, Vizza Dwi Wanvy Arifha Saputra Wicaksono, Galih Wasis Wildan Suharso Wildan Suharso Yuda Munarko Yufis Azhar Zamah Sari