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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Journal of the Medical Sciences (Berkala Ilmu Kedokteran) Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Eksplora Informatika Communications in Science and Technology Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JAREE (Journal on Advanced Research in Electrical Engineering) Communications in Humanities and Social Sciences Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal FORTEI-JEERI Prosiding SNPBS (Seminar Nasional Pendidikan Biologi dan Saintek) Pancaran Pendidikan Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Teknomatika: Jurnal Informatika dan Komputer
Noor Akhmad Setiawan
Department Of Electrical Engineering And Information Technology, Faculty Of Engineering, Universitas Gadjah Mada, Yogyakarta
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Neuroscience Physics Social Sciences

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A review of convolutional neural network-based computer-aided lung nodule detection system Sekar Sari; Tole Sutikno; Indah Soesanti; Noor Akhmad Setiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1044-1061

Abstract

Worldwide, lung cancer is the major cause of death and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, naïve bayes, k-nearest neighbor (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We hope that our research may help professional researchers and radiologists design more effective CAD systems for lung cancer detection.
Predicting The Quality of Red Wine and White Wine Using Data Mining Ni Wayan Priscila Yuni Praditya; Noor Akhmad Setiawan; Fery Antony
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v4i2.15095

Abstract

In business intelligence or artificial intelligence, data mining is a technique that can classify and cluster data based on the nature and correlation of the data set used. in data mining, several methods can be used, such as C45, K-Means, Apriori Decision Tree, KNN, LSTM, Naive Bayesian, etc. This research utilizes the Decision Tree method which aims to classify the quality of red wine and white wine. The results of this study indicate that the prediction of red wine has a precision of 61.1%, recall of 60.7%, f-measure of 60.3%, and an average accuracy of 60.7% while white wine has a precision of 58.2%, recall of 58.7%, f-measure 58.4%, and 58.7% accuracy. The method used in this study also shows that Decision Tree can outperform other methods such as Lib-SVM, BayesNet, and Multi Perceptron.
KONVERSI DATA KE FORMAT DNA DAN PERBANDINGAN HASIL KOMPRESINYA MENGGUNAKAN GENCOMPRESS TERHADAP WINRAR Heilbert Armando Mapaly; Teguh Bharata Adji; Noor Akhmad Setiawan
Jurnal Teknomatika Vol 6 No 1 (2013): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

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

Abstract

Teknik kompresi data saat ini merupakan hal yang penting dalam menyimpan media dalam bentuk digital. Secara umum, teknik kompresi terbagi menjadi lossy compression dan loseless compression. Penelitian ini mencoba untuk menerapkan konsep kompresi DNA yang bersifat loseless. Karena DNA hanya terdiri dari huruf a, c, g, dan t, maka kompresi ini hanya dapat diaplikasikan pada media teks, padahal, media yang ada saat ini tidak hanya berupa teks saja. Oleh sebab itu penelitian ini bertujuan untuk menemukan cara agar kompresi tersebut dapat diaplikasikan pada media lain serta membandingkan hasil kompresi tersebut dengan aplikasi WinRAR. Untuk dapat dikompresi dengan teknik kompresi DNA, semua file tersebut harus dikonversikan ke dalam bentuk DNA. Proses konversi tersebut dilakukan dengan mengubah kode binary yang ada pada file menjadi untaian rantai DNA. 00 diubah menjadi ‘a’, 01 diubah menjadi ‘c’, 10 diubah menjadi ‘g’ dan 11 diubah menjadi ‘t’. Walaupun pada konversi data ke format DNA ukuran file DNA menjadi lebih besar dari file awal, namun ukuran file hasil kompresi menjadi lebih kecil dari ukuran file awal karena memanfaatkan kelebihan teknik kompresi DNA. Ukuran file hasil kompresi dengan GenCompress menunjukkan hasil yang lebih baik dari WinRAR ketika terdapat pengulangan dengan jumlah yang sama pada isi file.
COMPARISON OF DATA MINING CLASSIFICATION TECHNIQUES FOR HEART DISEASE PREDICTION SYSTEM Rezty Amalia Aras; Noor Akhmad Setiawan
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 2 No. 2 (2022): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (789.232 KB) | DOI: 10.55606/teknik.v2i2.672

Abstract

DM is the process of analyzing data from different perspectives and gathering knowledge that can be used for different applications. Classification as one of the data mining techniques used to predict group membership. For example, the healthcare industry. DM provides a set of techniques for discovering hidden patterns from data. In this paper, we examine the heart disease dataset in order to obtain information or patterns that can be useful for making a decision. The test in this paper is a prediction of heart disease using three classification methods, namely OneR, decision tree and naive bayes. The results of this experiment show predictions from each experiment with different levels of prediction accuracy in each method used with 91.48% accuracy for the decision tree, 85.18% for naive Bayes and 76.3% for OneR.
Internal content classification of ultrasound thyroid nodules based on textural features Nugroho, Anan; Nugroho, Hanung Adi; Setiawan, Noor Akhmad; Choridah, Lina
Communications in Science and Technology Vol 1 No 2 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.2.2016.25

Abstract

Ultrasound (US) is one of the best imaging modalities on thyroid identification. The suspicious thyroid is indicated in the existence of palpable nodules whose solid or cystic composition. Solid nodules have high possibility to be malignant than cystic. An effort to detect and classify the internal content of thyroid nodule has become challenge problem in radiology area. Operator dependence of ultrasound imaging makes it complicated due to missing interpretation among radiologists. Objective Computer Aided Diagnosis (CAD) was designed to solve it which works on texture analysis of histogram statistic, gray level co-occurrence matrice (GLCM) and gray level run length matrices (GLRLM). The fine-needle aspiration cytology (FNAC) is not needed because the textural pattern is significantly different between solid and cystic nodules.  Multi-layer perceptron (MLP) was adopted to do classification process for 72 US thyroid images yield an accuracy of 90.28%, the sensitivity of 87.80%, specificity of 93.55% and precision of 94.74%.
Interval type-2 fuzzy logic system for diagnosis coronary artery disease Sajiah, Adha Mashur; Setiawan, Noor Akhmad; Wahyunggoro, Oyas
Communications in Science and Technology Vol 1 No 2 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.2.2016.26

Abstract

Coronary artery disease (CAD) is a disease that has been the deadliest disease in Indonesia. The ratio of cardiologists over potential patients is not appropriate either. Intelligent system which can help doctors or patients for cheap and efficient diagnosing CAD is needed. Medical record data, acquisition of cardiologist knowledge and computing technology can be utilized for developing fuzzy logic based intelligent system. Type-1 fuzzy logic system (T1 FLS) has been widely used in various fields. T1 FS has limitation in representing and modelling uncertainty and minimize the impact. Whereas, type-2 fuzzy set (T2 FS) was also introduced as fuzzy set that can model uncertainty more sophisticated. T2 FLS does have a higher degree of freedom when modeling uncertainty but it is quite difficult to make the membership function. An interval T2 FS is a T2 FS in which the membership grade on third dimension is the same everywhere so it is simpler than T2 FS. This paper aims to clarify the better capability of IT2 FLS over T1 FLS on the development of CAD diagnosis system. Rules and membership function were formulated with the help of fuzzy c-means. This study illustrated the causes of CAD risk factors, fuzzification, type reduction and defuzzification. The resulted system was tested with percentage split method (50%-50%) to produce training data and testing data. This test is performed ten times with random seed to separate the data set. The resulted system generates an average of 73.78% accuracy, 71.94% sensitivity and 76.52% specificity.
Comparison of Distributed K-Means and Distributed Fuzzy C-Means Algorithms for Text Clustering Agastya, I Made Artha; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.46

Abstract

Text clustering has been developed in distributed system due to increasing data. The popular algorithms like K-Means (KM) and Fuzzy C-Means (FCM) are combined with MapReduce algorithm in Hadoop Environment to be distributable and parallelizable. The problem is performance comparison between Distributed KM (DKM) and Distributed FCM (DFCM) that use Tanimoto Distance Measure (TDM) has not been studied yet. It is important because TDM’s characteristics are scale invariant while allowing discrimination collinear vectors. This work compared the combination of TDM with DKM (DKM-T) and TDM with DFCM (DFCM-T) to acquire performance of both algorithms. The result shows that DFCM-T has better intra-cluster and inter-cluster densities than those of DKM-T. Moreover, DFCM-T has lower processing time than that of DKM-T when total nodes used are 4 and 8. DFCM-T and DKM-T could perform clustering of 1,400,000 text files in 16.18 and 9.74 minutes but the preprocessing times take hours.
Optimasi Deteksi Kebocoran dengan Menggunakan Phase Stretch Transform pada Retina Fluorescein Angiography Images untuk Penyakit Malaria Rochim, Febry Putra; Nugroho, Hanung Adi; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 3 No 2 (2018)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.523 KB) | DOI: 10.21924/cst.3.2.2018.82

Abstract

Malarial Retinopathy (MR) is indicated by retina alteration such as white dots occurrence which is caused by malaria. Leak detection is a key factor of MR’s early diagnosis. Inconsistent size and shape of the leakages with the colour contrast that relatively similar with the background. Leak detection’s algorithm is one of the most complex algorithms on the fundus image analysis field. Therefore, improving performance in the leakage detection is essential. This study focuses on automated leakage detection on fluorescein angiography (FA) images. The methods used in this study are vessel segmentation, saliency detection, phase stretch transform (PST), optic disk removal and leak detection to extract some features which then classified to correctly validate the leak. From 20 patient data large focal leak images with 31 leak points, 28 of them have been correctly detected. So, the experiment produced the accuracy and specificity of 0.98 and 0.9, respectively. With the proposed method of this study, there is a potential to enhance the knowledge on MR field in the future.
Wart treatment method selection using AdaBoost with random forests as a weak learner Putra, M. Azka; Setiawan, Noor Akhmad; Wibirama, Sunu
Communications in Science and Technology Vol 3 No 2 (2018)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.523 KB) | DOI: 10.21924/cst.3.2.2018.96

Abstract

Selection of wart treatment method using machine learning is being a concern to researchers. Machine learning is expected to select the treatment of warts such as cryotherapy and immunotherapy to patients appropriately. In this study, the data used were cryotherapy and immunotherapy datasets. This study aims to improve the accuracy of wart treatment selection with machine learning. Previously, there are several algorithms have been proposed which were able to provide good accuracy in this case. However, the existing results still need improvement to achieve better level of accuracy so that treatment selection can satisfy the patients. The purpose of this study is to increase the accuracy by improving the performance of weak learner algorithm of ensemble machine learning. AdaBoost is used in this study as a strong learner and Random Forest (RF) is used as a weak learner. Furthermore, stratified 10-fold cross validation is used to evaluate the proposed algorithm. The experimental results show accuracy of 96.6% and 91.1% in cryotherapy and immunotherapy respectively.
Machine learning algorithm for improving performance on 3 AQ-screening classification Pratama, Taftazani Ghazi; Hartanto, Rudy; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 4 No 2 (2019)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (293.516 KB) | DOI: 10.21924/cst.4.2.2019.118

Abstract

Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.
Co-Authors Adhistya Erna Permanasari Adi Nugroho Adi Wijaya Adi Wijaya Agastya, I Made Artha Ahmad Fauzi Mabrur Aji, Marcus Nurtiantara Anggrahini, Dyah Wulan Anugrah Galang Persada Anugrah Galang Persada, Anugrah Galang Aras, Rezty Amalia Auliya, Syafira Baehaqi Bagus Kurniawan Bambang Sugiyantoro Berbudi Bowo Laksono Brahmantya Aji Pramudita Daru Hagni Setyadi Desyandri Desyandri Dewi, Sri Kusuma Dwi Retno Puspita Sari E. Elsa Herdiana Murhandarwati Eko Nugroho Eko Nugroho Fery Antony Fityah, Farhatul Galuh Indah Zatadini Gilang Adityasakti Hairani Hairani Hanung Adi Nugroho Haried Novriando Heilbert Armando Mapaly I Made Yulistya Negara I Md. Dendi Maysanjaya Igi Ardiyanto Indah Soesanti Ipin Prasojo Ipin Prasojo, Ipin Irma Yuliana Julianto Lemantara Kadek Dwi Pradnyani Novianti Kadek Dwi Pradnyani Novianti, Kadek Dwi Pradnyani Kharisma Adi Utama Lina Choridah Lukito Edi Nugroho Luthfi Ardi Made Satria Wibawa Maghfirah Maghfirah Maghfirah Maghfirah Maghfirah Marcus Nurtiantara Aji Mochammad Wahyudi Muhammad Arzanul Manhar Muhammad Fawaz Saputra Murnani, Suatmi Ni Wayan Priscila Yuni Praditya Nugraha, Anggit Ferdita Nugroho, Adi Nugroho, Anan Oyas Wahyunggoro Paulus Insap Santosa Persada, Anugrah Galang Prasojo, Ipin Putra, M. Azka Rahmadi, Ridho Ratna Lestari Budiani Buana Ridho Rahmadi Ridho Rahmadi Rifkie Primartha Rochim, Febry Putra Rudy Hartanto Sajiah, Adha Mashur Sekar Sari Siti Helmyati Sri Kusuma Dewi Sri Kusuma Dewi, Sri Kusuma SRI RAHAYU Sri Suning Kusumawardani Subhan Afifi Sunu Wibirama Surjono Surjono Teguh Bharata Adji Tito Yuwono, Tito Tole Sutikno Utama, Kharisma Adi Widhi Hartanto Widhia K.Z Oktoeberza Wijaya, Adi Zatadini, Galuh Indah