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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Algoritma International Journal of Artificial Intelligence Research Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Informasi dan Komputer Aisyah Journal of Informatics and Electrical Engineering SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Yumary: Jurnal Pengabdian kepada Masyarakat Jurnal Abdimas Bina Bangsa Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Algoritma IJOEM: Indonesian Journal of Elearning and Multimedia Jurnal Rekayasa Perangkat Lunak
Bintoro, Panji
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Lampung Script Recognition Using Convolutional Neural Network Panji Bintoro; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 1 (2022): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70041

Abstract

The Lampung script is often used in writing words in Lampung language. The Lampung language itself is used by native Lampung people and people who learn Lampung language. The Lampung script is difficult to learn because there are many combinations of parent characters and subletters. CNN is a method in the field of object recognition that has a specific layer, namely a convolution layer and a pooling layer that allows the feature learning process well. Handwriting recognition as in character recognition in MNIST, CNN produces better performance compared to other methods. From the advantages of CNN, the CNN method with DenseNet architecture was chosen as the best architecture to recognize each Lampung script. In this study, there are 2 main processes, namely preprocessing, and recognition. This study succeeded in applying the CNN method which can recognize Lampung script. The dataset is divided into 4 groups of characters that have different sounds. First, the parent character data get 98% accuracy. Second, the parent letter data with the above letters get 98% accuracy. Third, the parent character data with the sub-letters on the side get 98% accuracy. Fourth, the parent letter data with the lower letters get 97% accuracy.
SOSIALISASI PENGGUNAAN APLIKASI ADUAN ELEKTRONIK MAKSIMUM KOMPLAIN (EMAK) PADA DINAS SOSIAL KABUPATEN PRINGSEWU Dwi Yana Ayu Andini; Zulkifli Zulkifli; Andi Mulyono; Panji Bintoro; M. Galih Ramaputra; Joko Triloka
Jurnal Abdimas Bina Bangsa Vol. 4 No. 1 (2023): Jurnal Abdimas Bina Bangsa
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/jabb.v4i1.361

Abstract

The social service is a government agency tasked with improving the quality of social welfare for individuals, groups and communities. One of the mandates of the social service is the service of social complaints in the regions, especially in Pringsewu District. Currently the complaint system still uses a manual system. This caused an obstacle, namely the slow service of complaints, so EMAK (Maximum Complaint Electronic Application) was created to speed up and improve the handling of complaints related to the need for social services, especially in the administrative area of the Pringsewu social service. The methods used in the EMAK application are interview methods, observation methods and literature reviews. This application is designed with a simple layout to facilitate the use of social services manager, just enter according to the section, users can directly access the relevant application. The Emak application was developed to make it easier for social services to process information on public complaints
Application of Lung Diseases Detection based on CSLNet Panji Bintoro; Zulkifli Zulkifli; Fitriana Fitriana; Sukarni Sukarni; Abdullah Abdullah
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.68815

Abstract

Lung diseases caused by fungal or bacterial infections can lead to inflammation in lung and even death when not detected early. A standard method for diagnosing lung diseases is the use of chest X-ray, which require careful examination of X-ray images by a radiology expert. Therefore, this study proposes several new architecture models, namely CSLNet, to classify chest X-ray images for diagnosing whether patients suffer from COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, and normal. The experimental results show that the model has an 0.99 average Accuracy, 0.98 Precision, 0.98 Recall, and 0.98 f1-score. Meanwhile, the Receiver Operating Characteristic (ROC) for bacterial pneumonia, COVID-19, normal, tuberculosis, and viral pneumonia are 0.97, 0.99, 0.99, 0.94, and 0.97 respectively. This study is based on a deep learning with a new model, CSLNet, which can work well on the dataset of chest X-ray images used for diagnosing lung diseases.
"Perbandingan Kinerja Rendering EEVEE dan Siklus di Blender 3.5 dalam Konteks Visual Interaktif untuk Animasi 3D" Aviv Fitria Yulia; Zulkifli; Panji Bintoro; Dwi Yana Ayu Andini; Joko Triloka
Jurnal Penelitian Pendidikan IPA Vol 10 No 7 (2024): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i7.5910

Abstract

This research aims to compare the performance of two rendering engines, namely EEVEE and Cycles, available in Blender 3.5, in the context of developing interactive visual 3D animation. The rendering engine is a key element in 3D animation production, and the right choice between EEVEE and Cycles can have a significant impact on the final animation result. In this research, we conducted a series of experiments and analyzes to evaluate rendering speed, the quality of the resulting images, and the ability to achieve the visual effects desired by the animator. The results of this research provide deep insight into the strengths and limitations of each rendering engine in interactive 3D animation scenarios. These findings can help animators, game developers, and similar creative professionals make more informed choices when choosing a rendering engine that suits their project needs. Thus, this research contributes to the development of rendering techniques in the growing 3D animation industry.
APLIKASI DIAGNOSIS PENYAKIT PADA TUMBUHAN TOMAT BERBASIS WEBSITE Niawati, Mirna; Ardhy, Ferly; Andika, Tahta Herdian; Bintoro, Panji
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.541

Abstract

Tomato fruit as a seasonal crop that is very popular in Indonesia, often faces obstacles in its production due to disease attacks that are often not identified by farmers, which ultimately has an impact on decreasing crop yields for farmers. In this study, researchers apply the if-then function in which it determines the type of disease based on data and facts from an expert. This research resulted in an application of diagnosing tomato plant diseases using the method of developing an extreme proggraming system, with the aim that this application can help farmers to diagnose the early stages of the type of disease based on the symptoms experienced.
Analisis Perbandingan Klasifikasi Virus Cacar Monyet Dengan Pendekatan Algoritma Machine Learning Bintoro, Panji; Zulkifli, Zulkifli; Putri, Nopi Anggista
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2024: SNESTIK IV
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2024.5849

Abstract

Monkeypox Virus (MPXV) is part of the Orthopoxvirus (OPXV) group within the Poxviridae family. This virus is contagious when someone has direct contact with infected individuals, animals, or contaminated materials. Transmission can occur through direct bodily contact, animal bites, respiratory droplets, or mucus in the eyes, nose, or mouth. However, since the emergence of the recent outbreak in May 2022, this disease has spread to various countries, posing a threat to develop into a global pandemic. Many machine learning algorithm approaches, including for classifying monkeypox disease, are proposed. This research suggests a system that can be used for Comparative Analysis of monkeypox virus classification with several machine learning algorithm approaches. From the work that has been done, it states that the neural network algorithm model outperforms other algorithm models. Testing the neural network algorithm model obtained accuracy of 1.0, precision of 1.0, recall of 1.0, f1-score of 1.0, and ROC-AUC of 1.00 for Monkeypox Positive and Monkeypox Negative.
Komparasi Layanan Video Live Streaming Menggunakan Metode Quality of Service Hardiyanti, Fitri; Bintoro, Panji; Ratnasari, Ratnasari; Herdian Andika, Tahta; Ardhy, Ferly; Eko Setiawan, Agustinus
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1610

Abstract

Video streaming is growing very rapidly nowadays, many people use it in their daily lives, especially since the pandemic, video streaming has become an important need. Video conferencing applications such as Zoom, Google Meet, and Cisco Webex have become increasingly important in facilitating remote communications, allowing people to communicate, collaborate, and hold virtual meetings without geographic restrictions. This research uses the QoS method to analyze the comparison of service quality from video streaming such as Zoom, Cisco Webex and Google Meet. From the results of the research carried out, each video stream was assessed with QoS. The research carried out was by testing 3 applications, namely Zoom, Cisco Webex and Google Meet, the best results were obtained, namely Zoom with a Throughput value of 6685842, a Delay value of 47ms, a Jitter value of 47 ms and a Packet Loss value of 0.004%. The QoS method can be applied in comparative quality analysis in testing video streaming service applications.
Deep convolutional neural network for Lampung character recognition Bintoro, Panji; Zulkifli, Zulkifli; Fitriana, Fitriana; Sukarni, Sukarni
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6734

Abstract

Recognition of document based, and handwritten characters has recently emerged as highly relevant field of study in the field of digital image processing. The ability to read and write Lampung script is a crucial competency as it helps preserve the language, which is a part of Indonesian culture. This research utilizes data obtained from classified documents and handwritten samples, categorized into eight types. To recognize Lampung characters, deep convolutional neural network (DCNN) architecture is proposed. The novelty of this architecture lies in optimizing document-based and handwritten character recognition to achieve the best performance in terms of accuracy and execution time. The proposed architecture will be compared to principal component analysis (PCA) combined with support vector machine (SVM) to evaluate its results. Experimental results using the DCNN architecture show an average accuracy of 99.3% and an execution time of 283 seconds for all data, while PCA and SVM exhibit an average accuracy of 92.9%. Furthermore, the recognition results for all data from documents and handwritten samples yield satisfactory accuracy of 98.6%. These results make the DCNN architecture suitable for use in recognizing Lampung characters and are expected to make it easier for Lampung people to recognize Lampung character.
Machine Learning Untuk Klasifikasi Penyakit Jantung: Machine Learning Untuk Klasifikasi Penyakit Jantung Ratnasari; Jurnaidi Wahidin, Ahmad; Eko Setiawan, Agustinus; Bintoro, Panji
Aisyah Journal Of Informatics and Electrical Engineering (A.J.I.E.E) Vol. 6 No. 1 (2024): Aisyah Journal Of Informatics and Electrical Engineering
Publisher : Aisyah Journal Of Informatics and Electrical Engineering (A.J.I.E.E)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30604/jti.v6i1.272

Abstract

Penyakit jantung disebabkan oleh kondisi abnormal jantung dan pembuluh darah, secara luas dianggap sebagai ancaman langsung terhadap kehidupan dan kesehatan manusia. Diagnosa yang tepat pada fase awal merupakan tugas yang sangat menantang karena adanya ketergantungan yang kompleks yang harus dipertimbangkan pada berbagai faktor. Oleh karena itu dibutuhkan pengembangan sistem diagnosis medis sedemikian rupa sehingga dapat membantu dalam mengambil keputusan pada proses diagnostik. Penelitian ini bertujuan untuk mencari algoritma mechine learning yang memiliki akurasi yang paling tinggi untuk menprediksi apakah seseorang mengidap penyakit jantung atau tidak berdasarkan database medis. Penelitian kami membandingkan enam metode klasifikasi mechine learning yaitu Naïve Bayes, kNN, Random Forest, Logistic Regression, SVM, Decision Tree dan AdaBoost dengan dataset Cleveland Clinic Foundation yang tersedia di “UCI Machine Learning Repository”. Hasil dari penelitian ini menunjukan bahwa algoritma Naive Bayes memiliki akurasi paling tinggi yaitu sebesar 84.67%, lalu Logistic Regression diurutan kedua dengan akurasi 84.30%, Kemudian Random Forest 81.70%, SVM 81%, Tree 74%, kNN 73%, AdaBoost 71.30%.
Expert System for Diagnosis of Lung Disease from X-Ray Using CNN and SVM Zulkifli, Zulkifli; Soeprihatini, Retno Ariza; Sfenrianto, Sfenrianto; Wiyanti, Zulvi; Bintoro, Panji; Fitriana, Fitriana; Sukarni, Sukarni; Putri, Nopi Anggista; Andini, Dwi Yana Ayu
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.870

Abstract

The lung disease diagnosis expert system utilizes human knowledge to diagnose various conditions affecting the lung. Diseases caused by fungal or bacterial infection in the organ can cause inflammation as well as death when it is not detected on time. A standard method to diagnose these conditions is the use of a chest X-ray (CXR), which requires careful examination of the image by an expert. In this study, several CNN and SVM architectural models were proposed to classify CXR images to diagnose whether a person has COVID-19, Viral Pneumonia, Bacterial Pneumonia, Tuberculosis (TB), and Normal. The experiment showed that InceptionV3 had the best results compared to other CNN architectures and SVM. Classification accuracy, precision, recall, and f1-score of CXR images for COVID-19, Viral Pneumonia, Bacterial Pneumonia, TB, and Normal were 0.86, 0.91, 0.91, and 0.91, respectively. This study was based on a deep learning system with different CNN and SVM architectures that can work well on the CXR images dataset for diagnosing lung disease.
Co-Authors Abdullah Abdullah Adamu Abubakar Muhammad Agus Harjoko Ahmad Jurnaidi Wahidin Andi Mulyono Andini, Dwi Yana Ayu Aras, Irsan Aviv Fitria Yulia Dwi Yana Ayu Andini Dwi Yana Ayu Andini Eko Setiawan, Agustinus Fandi Ahmad Ferly Ardhy Fitri Hardiyanti, Fitri Fitriana Fitriana Heni Pujiastuti Herdia Andika, Tahta Joko Triloka M. Galih Ramaputra Niawati, Mirna Nopi Anggista Putri Nopi Anggista Putri, Nopi Anggista Nur Aminudin Ratnasari Ratnasari Ratnasari Ratnasari Setiawan, Agustinus Eko Sfenrianto, Sfenrianto Soeprihatini, Retno Ariza Sukarni Sukarni Sukarni Sukarni Tahta Herdian Andika Widiandana, Panggah Wiyanti, Zulvi Yana Ayu Andini, Dwi Zulkifli