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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Majalah Ilmiah Teknologi Elektro Transmisi: Jurnal Ilmiah Teknik Elektro JURNAL SISTEM KOMPUTER JURNAL SISTEM INFORMASI BISNIS TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Rekayasa Mesin Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Scientific Journal of Informatics Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics Jurnal Ilmu Teknik dan Komputer Infotek : Jurnal Informatika dan Teknologi Journal of Applied Data Sciences SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Jurnal Profesi Insinyur Indonesia Jurnal Teknologi Majalah Ilmiah Teknologi Elektro Siber International Journal of Advanced Law
R Rizal Isnanto
Departemen Teknik Sistem Komputer, Universitas Diponegoro Jl. Prof. Sudharto, SH, Kampus UNDIP Tembalang, Semarang 50275, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Transportation

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Pengaruh Lebar Saluran pada Building Integrated Photovoltaic pada Performa PV Wijaya, Elang Pramudya; Yatim, Ardiyansyah Saad; Isnanto, R Rizal; Suprihanto, Agus
SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Vol 6 (2024)
Publisher : SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan)

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

Abstract

Salah satu contoh dalam mengkonversi energi matahari adalah dengan menggunakan solar photovoltaic atau yang biasa disebut solar PV untuk menghasilkan energi listrik dari sistem tersebut. Salah satu cara paling umum untuk mendapatkan energi dari matahari adalah melalui panel surya (PV). Namun, dalam beberapa kasus, kita dihadapkan pada tantangan keterbatasan lahan. Untuk mengatasi hal ini, integrasi panel PV ke dalam struktur bangunan dirancang guna mengoptimalkan potensi pemanfaatan energi matahari sekaligus menyelesaikan kendala ruang yang terbatas. Dengan mengintegrasikannya sebagai fasad atau menggantikan bahan bangunan tradisional, hal ini dapat mengurangi jejak karbon. Penelitian ini bertujuan untuk memodelkan pendinginan alami konvektif dan pendinginan pasif pada sistem BIPV dalam skala nyata serta menemukan pengaruh lebar saluran terhadap pendinginan pasif di permukaan modul PV. Selain itu, studi literatur juga dilakukan untuk mengetahui dampak pendinginan pasif terhadap kinerja PV dalam berbagai kondisi kecepatan angin. Simulasi CFD dilakukan dengan kondisi batas mengacu pada kondisi tes standar. Hasilnya menunjukkan bahwa kenaikan suhu PV yang paling rendah terjadi pada lebar celah yang lebih besar. Hal ini menunjukkan efisiensi produksi listrik yang lebih tinggi dari BIPV. Lebar celah udara menentukan perpindahan panas di dalam celah tersebut. Konduksi panas mendominasi proses perpindahan panas pada kecepatan rendah (1 m/s). Di sisi lain, suhu operasi sel dipengaruhi oleh apakah aliran mencapai kondisi aliran yang sepenuhnya berkembang atau tidak.
SKIN RASH CLASSIFICATION SYSTEM USING MODIFIED DENSENET201 THROUGH RANDOM SEARCH FOR HYPERPARAMETER TUNING Riyana Putri, Fayza Nayla; Isnanto, R.Rizal; Sugiharto, Aris
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.418

Abstract

Skin rashes caused by various diseases, such as monkeypox, cowpox, chickenpox, measles, and HFMD, often present similar symptoms, making accurate diagnosis challenging. This study aims to improve the classification of skin diseases through the application of a modified DenseNet-201 architecture combined with hyperparameter optimization using Random Search. The base DenseNet-201 model, with pre-trained weights, was first tested, achieving an accuracy of 63%, with the highest performance in the Healthy and HFMD classes. The proposed modified model, optimized using Random Search, improved overall accuracy to 80%, with enhanced precision, recall, and F1-score across most classes. The model’s performance was particularly notable in the HFMD and normal skin classes, although further improvements are needed for challenging classes like Cowpox and Measles. The findings highlight the potential of Random Search for hyperparameter tuning to enhance the performance of deep convolutional neural networks in the medical image classification domain, offering a promising tool for efficient and accurate skin disease detection.
Utilization of convolutional neural network in image interpretation techniques for detecting kidney disease Sulaksono, Nanang; Adi, Kusworo; Isnanto, Rizal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp602-613

Abstract

This research is conducted with deep learning for kidney stone disease detection including cysts, stones, normal, and tumors using axial computerized tomography (CT) scan images. The author uses augmentation, generative adversarial networks (GANs), original, and synthetic minority over-sampling technique (SMOTE) to classify kidney disease (cyst, stone, normal, and tumor). This study uses the public dataset nazmul0087 and primary data/data from the hospital, using convolutional neural network (CNN) models, namely augmentation, GANs, original, and SMOTE by training and testing. The results of the accuracy value of the training model (dataset nazmul0087) in the detection of kidney cysts, stones, tumors, and normal. The results of augmentation value are 99.93%, GANs 100%, original 100%, and SMOTE 99.93%. In the results of the training model, a very high accuracy value is obtained, with perfect results. The testing model's accuracy value in detecting kidney cysts, stones, tumors, and normal kidney tissue in the original dataset and hospital data. The results of augmentation value are 11.48%, GANs 17.96%, original 21.76%, and SMOTE 20.41%. In the results of the training model, the highest accuracy value is obtained in the original model. For the testing model to automatically diagnose kidney illness and obtain a high accuracy value, which can enhance patient outcomes and save health care costs, we advise using it in conjunction with the original model.
Analisis Penguatan Jaringan Distribusi dalam Penyelenggaraan Event Internasional di Wilayah Kerja ULP Manahan-Surakarta Arianto, Mufid; Isnanto, R Rizal; Syakur, Abdul
Jurnal Profesi Insinyur Indonesia Vol 2, No 4 (2024): JPII
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpii.2024.24573

Abstract

Keandalan kelistrikan adalah hal yang wajib dipenuhi oleh PLN bagi kenyamanan konsumennya sesuai dengan service level agreement yang sudah ditetapkan oleh Pemerintah. Selain pelanggan VIP, VVIP dan pelanggan penting yang memerlukan perhatian khusus, ada juga event dengan skala nasional dan internasional seperti FIFA World Cup U-17 di Indonesia yang memerlukan tingkat keandalan yang sangat baik. Pada penyelenggaraan event tersebut seluruh petugas PLN disiagakan untuk memastikan pasokan listrik ke lokasi venue utama dengan kualitas baik. Venue utama yang memerlukan pengamanan ekstra adalah Jakarta International Stadium, Stadion Si Jalak Harupat, Stadion Manahan dan Stadion Gelora Bung Tomo. Seluruh venue utama telah dilakukan assessment dan penguatan jaringan kelistrikan. Pada pembahasan ini penulis akan fokus pada satu venue utama yang berada di wilayah kerja Unit Layanan Pelanggan (ULP) Manahan yaitu di Stadion Manahan Solo. Saat penyelenggaraan  tidak ditolerir terjadinya kedip dan dipastikan generator set (genset) dalam keadaan stanby dingin. Potensi gangguan distribusi perlu menjadi perhatian seperti penyebab internal (kegagalan relay proteksi, gangguan jointing dan terminating saluran kabel tegangan menengah) dan penyebab eksternal (cuaca ekstrim dan pekerjaan pihak lain). Untuk meningkatkan keandalan jaringan distribusi maka dilakukan penguatan seperti rekonfigurasi jaringan tegangan menengah (TM), penambahan backup suplai dan assessment peralatan dan instalasi milik langganan. Dengan penguatan jaringan distribusi diperoleh SAIDI sebesar 316,8 menit/pelanggan menjadi 172,8 menit/pelanggan, SAIFI sebesar 3,26 kali/pelanggan menjadi 2,2 kali/pelanggan dan diperoleh keandalan jaringan distribusi menjadi suplai layanan tanpa kedip disuplai melalui jaringan kelistrikan PLN. Kata kunci: Kedip Tegangan, SAIDI, SAIFI
High-Accuracy Stroke Detection System Using a CBAM-ResNet18 Deep Learning Model on Brain CT Images Tahyudin, Imam; Isnanto, R Rizal; Prabuwono, Anton Satria; Hariguna, Taqwa; Winarto, Eko; Nazwan, Nazwan; Tikaningsih, Ades; Lestari, Puji; Rozak, Rofik Abdul
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.569

Abstract

Stroke is a brain dysfunction that occurs suddenly as a result of local or overarching damage to the brain, lasts for at least 24 hours, and causes about 15 million deaths each year globally. Immediate medical treatment is essential to reduce the potential for further brain damage in stroke patients. Medical imaging, especially computed tomography (CT scan), plays a crucial role in the diagnosis of stroke. This study aims to develop and evaluate a deep learning architecture based on Convolutional Block Attention Module (CBAM) and ResNet18 for stroke classification in CT images. This model is designed through data preprocessing, training, and evaluation stages using a cross-validation approach. The results showed that the CBAM-ResNet18 integration resulted in a high accuracy of 95% in distinguishing stroke and non-stroke cases. The accuracy rate reached 96% for nonstroke identification (class 0) and 94% for stroke (class 1), with recall rates of 96% and 93%, respectively. Outstanding classification ability is demonstrated by an Area Under the Curve (AUC) value of 0.99. In comparison, the standard ResNet18 model shows significant fluctuations in validation loss and difficulty in generalization, with training accuracy only reaching 64-68%. On the other hand, CBAM-ResNet18 showed a significant performance improvement with a validation accuracy of 95%, a validation loss of 0.0888, and good generalization on new data. However, the limitations of the dataset and the interpretation of the results indicate the need for further validation to ensure the generalization of the model. These results show the great potential of the CBAM-ResNet18 architecture as an innovative tool in stroke diagnostic technology based on CT imaging analysis. This technology can support faster and more accurate clinical decision-making, as well as open up opportunities for further research related to the development of artificial intelligence-based systems in the medical field.
A Proposed Model for Detecting Learning Styles Based on the Felder-Silverman Model Using KNN and LR with Electroencephalography (EEG) Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Yeh, Ming-Lang; Wijaya, Adi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.659

Abstract

The identification of learning styles plays a crucial role in enhancing personalized education and optimizing learning outcomes. This research proposes a model for detecting learning styles based on the Felder-Silverman model using two machine learning algorithms: K-Nearest Neighbors (KNN) and Linear Regression (LR). Electroencephalography (EEG) data, known for its ability to capture cognitive and neural activity, serves as the primary dataset for this study. The proposed model was tested on a dataset comprising EEG signals collected during various learning tasks. Feature extraction and preprocessing techniques were employed to ensure high-quality input for the learning algorithms. The experimental results revealed that the LR-based model achieved an accuracy of 96.4%, significantly outperforming the KNN-based model, which obtained an accuracy of 89.9%. These findings highlight the potential of EEG-based models for accurately identifying learning styles, offering valuable insights for educators and researchers aiming to implement adaptive learning systems. This study demonstrates the feasibility and effectiveness of combining EEG data with machine learning techniques for learning style detection, paving the way for more personalized and efficient educational approaches. Future research will explore the integration of additional physiological data and advanced machine learning methods to further improve model accuracy and applicability.
Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach Widayati, Qoriani; Adi, Kusworo; Isnanto, R Rizal; Agustini, Eka Puji; Julianto, Dewa Rizki Rahmat; Prakasa, Fawwaz Bimo
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.977

Abstract

Student loyalty is a crucial factor supporting the sustainability of higher education institutions. The aim of this study is to predict student loyalty using a machine learning approach, specifically the random forest algorithm. The data for this research were collected through a questionnaire that included variables such as service quality, emotional attachment, brand satisfaction, brand trust, and socio-economic conditions, distributed to 107 students in Palembang. The resulting dataset was processed through preprocessing, model training, and performance evaluation, employing metrics such as accuracy, precision, recall, and F1-score. The analysis using the random forest algorithm achieved an accuracy of 90.9%. These findings are expected to provide valuable insights for higher education institutions in developing more effective strategies to enhance student loyalty.
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Triloka, Joko; Aziz, RZ Abdul; Kurniawan, Tri Basuki; Maizary, Ary; Wibaselppa, Anggawidia
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.660

Abstract

This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Review of Systematic Literature about Sentiment Analysis Techniques Sasongko, Cornelius Damar; Isnanto, Rizal; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp227-236

Abstract

Sentiment analysis, also known as opinion mining, is an important task in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment or opinion expressed by the author. With the advancement of technology and the widespread use of social media and online review platforms, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. The purpose of this research is to present a comprehensive literature review on sentiment analysis techniques. This research utilizes the systematic literature review method. This method involves systematic steps in searching, evaluating, and analyzing relevant literature in the field of sentiment analysis. The literature search was conducted through scientific databases and other reliable sources. Relevant articles were then selected based on pre-determined inclusion and exclusion criteria. The data from the selected articles were then comprehensively analyzed to identify the sentiment analysis techniques used and the key findings in the research. The results show that there are various techniques and approaches that have been developed and tested in sentiment analysis, some of the commonly used techniques include rule-based methods, classification-based methods, and machine learning-based methods.
Co-training pseudo-labeling for text classification with support vector machine and long short-term memory Handayani, Sri; Isnanto, Rizal; Warsito, Budi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2158-2168

Abstract

The scarcity of labeled data may hamper training text-processing models. In response to this issue, a novel and intriguing strategy that combines the co-training method and pseudo-labeling design is applied to enhance the model's performance. This method, a component of an efficient semi-supervised learning paradigm for processing and comprehending text, is a fresh perspective in the field. The model, which combines a support vector machine (SVM) for classification and long short-term memory (LSTM) for text sequence interpretation, is a unique approach. By introducing samples that may be marginalized in the labeled data, the co-training approach could help solve the class imbalance problem by using a small amount of labeled data and the rest unlabeled. This study assesses the model's performance using a student dataset from higher education institutions to establish a threshold for each model's degree of confidence and ascertain how much the model can be generalized depending on the threshold. The SVM threshold was calculated as >=0.88, and the LSTM threshold was calculated as >=0.5 using a mixture of confidence metrics.
Co-Authors Abdul Syakur Achmad Chaerodin Achmad Hidayatno Achmad Hidayatno Ade Riyantika Dewi Adhi Susanto Adi Mora Tunggul Adi Wijaya Adian Fatchur Rochim Adrian Putranda Rispurwadi Agus Suprihanto Agustini, Eka Puji Ahmad Ramdhani Ajub Ajulian Zahra Macrina Al Iman, Yusraka Dimas Alan Prasetyo Rantelino Albert Ginting An'im Almiktad Andhika Dewanta Andhika Hanifa Naufaliawan Andino Maseleno Anton Satria Prabuwono Ardianto Eskaprianda Ari Muhardono Arianto, Mufid Aris Puji Widodo Aris Sugiharto Aris Triwiyatno Astrid Aprillini Aulia Nastiti Aziz, RZ. Abdul Basuki Rahmat Masdi Siduppa Bhutra, Yuvraj Budi Warsito Chauhan, Rahul Damar Wicaksono Danang Respati Setyabudi Deddy Sucipta Syahril Dewi, Deshinta Arrova Dewi, Rany Puspita Dhody Kurniawan Dian Kurnia Widya Buana Dilan Arya Sujati Dimas Robby Firmanda Dini Indriyani Putri Donni Widagdo Dwi Novianto Eko Didik Widianto Eko Winarto Erizco Satya Wicaksono Ervin Adhi Cahyanugraha Fatima Setyani Ferry Dwi Setiyawan Firdaus Aditya GALIH WICAKSONO Gilang Aditya Pamungkas Handayani, Sri Hardiyanto Hardiyanto Hayu Andarwati Hefmi Fauzan Imron Hendy Cahya Lesmana Hilal Afrih Juhad Ike Pertiwi Windasari Imaduddin Amrullah Muslim Imam Tahyudin Irham Fa'idh Faiztyan Iwan Purwanto Jatmiko Endro Suseno Julianto, Dewa Rizki Rahmat Kataria, Yachi Kurniawan Teguh Martono Kurniawan, Tri Basuki Kusworo Adi Lia Lidya Roza Liga Filosa M Said Hasibuan Maizary, Ary Maman Somantri Martin Clinton Tosima Manullang Maulana Muhammad Iqbal Misik Puspajati Nurmadjid Saputri Mona Pradipta Hardiyanti Muh. Udka Muhamad Taopik Gibran Muhammad Fahmi Awaj Muhammad Kautsar Muhammad Nur Hadi Munawar Agus Riyadi Mustafa, Mustafa Mustafid Mustafid Nahdi Saubari Nanang Sulaksono, Nanang Nazwan, Nazwan Neneng Neneng Nugroho, Waluyo Nur Setyo Permatasasi Putri W Nurhayati, Oki Dwi Nurul Arifa Oky Dwi Nurhayati Pertiwi, Rahayu Putri Prakasa, Fawwaz Bimo Pramuko Tri Prastowo Prima Widyaningrum PUJI LESTARI Qoriani Widayati Rachel Chrysilla Tijono Refika Khoirunnisa Reza Najib Hidayat Rinta Kridalukmana Rinta Kridalukmana Rivaldi MHS Riyana Putri, Fayza Nayla Rizaldi Habibie Rizaldy Khair Rizky Gelar Maliq Rizky Parlika, Rizky Rosdelima Hutahaean Roza, Lia Lidya Rozak, Rofik Abdul Ruli Handrio Santoso, Imam Saptian, Fiega Adhi Sapto Nisworo Sasongko, Cornelius Damar Satya Arisena Hendrawan Setiawan, Annas Sri Lestari Sri Sumiyati Sri Widodo, Thomas Suhardjo Suhardjo Sumardi . Talitha Almira Taqwa Hariguna Teguh Dwi Prihartono Tikaningsih, Ades Toni Wijanarko Adi Putra Triloka, Joko Tyas Panorama Nan Cerah Ufan Alfianto Unaliya, Maitri Wandri Okki Saputra Wibaselppa, Anggawidia Widi Puji Atmojo Widiasmoro, Andi Wijaya, Elang Pramudya Yatim, Ardiyansyah Saad Yeh, Ming-Lang Yenita Dwi Setiyawati Yessy Kurniasari Yongki Yonatan Marbun Yudi Eko Windarto Yunus Anis, Yunus