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<![CDATA[SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER ]]>
<![CDATA[Indra Waspada]]>;
<![CDATA[Adi Wibowo]]>;
<![CDATA[Noel Segura Meraz]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 10, No 2 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v10i2.481
<![CDATA[The cancer cell gene expression data in general has a very large feature and requires analysis to find out which genes are strongly influencing the specific disease for diagnosis and drug discovery. In this paper several methods of supervised learning (decisien tree, naïve bayes, neural network, and deep learning) are used to classify cancer cells based on the expression of the microRNA gene to obtain the best method that can be used for gene analysis. In this study there is no optimization and tuning of the algorithm to test the ability of general algorithms. There are 1881 features of microRNA gene epresi on 25 cancer classes based on tissue location. A simple feature selection method is used to test the comparison of the algorithm. Expreriments were conducted with various scenarios to test the accuracy of the classification.]]>
<![CDATA[Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning ]]>
<![CDATA[Retno Kusumaningrum]]>;
<![CDATA[Iffa Zainan Nisa]]>;
<![CDATA[Rizka Putri Nawangsari]]>;
<![CDATA[Adi Wibowo]]>
<![CDATA[ International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/ijain.v7i3.737
<![CDATA[Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.]]>
<![CDATA[Android skin cancer detection and classification based on MobileNet v2 model ]]>
<![CDATA[Adi Wibowo]]>;
<![CDATA[Cahyo Adhi Hartanto]]>;
<![CDATA[Panji Wisnu Wirawan]]>
<![CDATA[ International Journal of Advances in Intelligent Informatics Vol 6, No 2 (2020): July 2020 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/ijain.v6i2.492
<![CDATA[The latest developments in the smartphone-based skin cancer diagnosis application allow simple ways for portable melanoma risk assessment and diagnosis for early skin cancer detection. Due to the trade-off problem (time complexity and error rate) on using a smartphone to run a machine learning algorithm for image analysis, most of the skin cancer diagnosis apps execute the image analysis on the server. In this study, we investigate the performance of skin cancer images detection and classification on android devices using the MobileNet v2 deep learning model. We compare the performance of several aspects; object detection and classification method, computer and android based image analysis, image acquisition method, and setting parameter. Skin cancer actinic Keratosis and Melanoma are used to test the performance of the proposed method. Accuracy, sensitivity, specificity, and running time of the testing methods are used for the measurement. Based on the experiment results, the best parameter for the MobileNet v2 model on android using images from the smartphone camera produces 95% accuracy for object detection and 70% accuracy for classification. The performance of the android app for object detection and classification model was feasible for the skin cancer analysis. Android-based image analysis remains within the threshold of computing time that denotes convenience for the user and has the same performance accuracy with the computer for the high-quality images. These findings motivated the development of disease detection processing on android using a smartphone camera, which aims to achieve real-time detection and classification with high accuracy.]]>
<![CDATA[Pembinaan Pola Pikir Komputasi dan Informatika pada Siswa Sekolah Dasar ]]>
<![CDATA[Sukmawati Nur Endah]]>;
<![CDATA[Eko Adi Sarwoko]]>;
<![CDATA[Nurdin Bahtiar]]>;
<![CDATA[Adi Wibowo]]>;
<![CDATA[Kabul Kurniawan]]>
<![CDATA[ E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 11, No 1 (2020): E-DIMAS ]]>
Publisher : <![CDATA[Universitas PGRI Semarang ]]>
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DOI: 10.26877/e-dimas.v11i1.2317
<![CDATA[Bebras adalah sebuah inisiatif internasional yang tujuannya adalah untuk mempromosikan Computational Thinking (Berpikir dengan landasan Komputasi atau Informatika), di kalangan guru dan murid mulai kelas 3 SD, serta untuk masyarakat luas. Berpikir komputasional (Computational Thinking) adalah metode menyelesaikan persoalan dengan menerapkan teknik ilmu komputer (informatika). Tantangan bebras menyajikan soal-soal yang mendorong siswa untuk berpikir kreatif dan kritis dalam menyelesaikan persoalan dengan menerapkan konsep-konsep berpikir komputasional. Cara untuk mempromosikan computational thinking adalah dengan menyelenggarakan kegiatan kompetisi secara daring (on line), yang disebut sebagai "Tantangan Bebras" (Bebras Challenge). Tantangan Bebras bukan hanya sekedar untuk menang. Selain untuk berlomba, tantangan Bebras juga bertujuan agar siswa belajar Computational Thinking selama maupun setelah lomba. Pengabdian ini berupaya untuk mensosialisasikan dan melakukan pembinaan ke sekolah-sekolah mengenai bebras task sehingga harapannya siswanya mampu bersaing untuk ikut dalam Bebras Challenge Indonesia di tahun mendatang. Kegiatan ini meliputi pre test, pembahasan dan post-test terkait soal-soal Bebras (Bebras Task). Hasil kegiatan menunjukkan bahwa adanya peningkatan rata-rata pemahaman pola pikir komputasi dan informatika pada SD Ummul Quro’ sebesar 13,74% untuk siswa kelas IV dan V serta sebesar 10% untuk siswa kelas III.]]>
<![CDATA[Analisis Perbandingan SAW dan TOPSIS pada Sistem Pendukung Keputusan Karyawan Terbaik ]]>
<![CDATA[Arizona Firdonsyah]]>;
<![CDATA[Budi Warsito]]>;
<![CDATA[Adi Wibowo]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v7i3.11475
<![CDATA[The decision-making process has many assessment criteria needed as the basis for its assessment. A large number of problems regarding the length of time required in the decision-making process require decision-makers to find solutions. Decision Support System is one option that can be developed by decision makers because it can help improve efficiency and accuracy in the decision-making process. The process of developing decision support requires certain calculation methods as part of the processing. The methods that are quite widely used to build a decision support system include the Simple Additive Weighting (SAW) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. This research aims to analyze the accuracy of the cases raised as solutions to decision-making problems. A dynamic decision support system has been successfully created to design dynamics in the calculation of the SAW method and the TOPSIS method. The system is evaluated and analyzed for its accuracy level based on manual calculations. The results obtained are the SAW system has an accuracy value of 65% and the TOPSIS system is 100%. Furthermore, the calculation of the accuracy value of the SAW and TOPSIS methods in order to find out the best method to use by taking parameters in the form of the same value results generated from the calculations of the two methods. The results obtained are the accuracy value of the SAW method of 40% and the TOPSIS method of 100% based on testing using 60 employee data and 8 criteria used.]]>
<![CDATA[MoFlus: An Open-Source Android Software for Fluorescence-Based Point of Care ]]>
<![CDATA[Panji Wisnu Wirawan]]>;
<![CDATA[Adi Wibowo]]>
<![CDATA[ Journal of Biomedical Science and Bioengineering Vol 1, No 2 (2021) ]]>
Publisher : <![CDATA[Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S) ]]>
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DOI: 10.14710/jbiomes.2021.v1i2.39-48
<![CDATA[High-sensitivity fluorescence-based tests are utilized to monitor various activities in life science research. These tests are specifically used as health monitoring tools to detect diseases. Fluorescence-based test facilities in rural areas and developing countries, however, remain limited. Point-of-care (POC) tests based on fluorescence detection have become a solution to the limitations of fluorescence-based tools in developing countries. POC software for smartphone cameras was generally developed for specific devices and tools, and it ability to select the desired region of interest (ROI) is limited. In this work, we developed Mobile Fluorescence Spectroscopy (MoFlus), an open-source Android software for camera-based POC. We mainly aimed to develop camera-based POC software that can be used for the dynamic selection of ROI; the number of samples; and the types of detection, color, data, and for communication with servers. MoFlus facilitated the use of touch screens and data given that it was developed on the basis of the SurfaceView library in Android and Javascript object notation applications. Moreover, the function and endurance of the app when used multiple times and with different numbers of images were tested.]]>
<![CDATA[Implementation of Support Vector Machine - Recursive Feature Elimination for MicroRNA Selection in Breast Cancer Classification ]]>
<![CDATA[Ratih Permatasari]]>;
<![CDATA[Adi Wibowo]]>
<![CDATA[ Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 14 No. 1 (2020) ]]>
Publisher : <![CDATA[Fakultas Teknik, Universitas Brawijaya ]]>
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DOI: 10.21776/jeeccis.v14i1.602
<![CDATA[Breast cancer is the most frequent cancer caused death among women. An attempt to reduce death cases caused by breast cancer, was to detect cancer cells when it still in early stage. MicroRNA is one of the biomarker for cancer that can be used to detect cancer cell even in its early stage. However, MicroRNA data tends to have thousand types of expression which required a lot of costs if it examined one by one thoroughly. Feature selection method can be used to extract important MicroRNAs that support clasification process between normal people and people with breast cancer. Support Vector Recursive Feature Elimination (SVM-RFE) is one of the feature selection method that can be used to select MicroRNA data. This research aims to produce the best smallest subset that contains selected MicroRNA expressions using the SVM-RFE as feature selection method. This experiment result showed that the best selected subset was able to provide 99% classification accuracy with only 3 MicroRNA expressions, where 2 from 3 selected MicroRNA hold potential as a biomarker of breast cancer.]]>
<![CDATA[Modifikasi Metode Fuzzy C-Means untuk Klasifikasi Citra Daun Padi ]]>
<![CDATA[Fra Siskus Dian Arianto]]>;
<![CDATA[Adi Wibowo]]>;
<![CDATA[Bayu Surarso]]>
<![CDATA[ Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 17, No 1 (2022): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer ]]>
Publisher : <![CDATA[Mulawarman University ]]>
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DOI: 10.30872/jim.v17i1.6068
<![CDATA[Metode Fuzzy C-means merupakan algoritma pembelajaran tidak terawasi yang menggunakan derajat keanggotaan untuk menentukan cluster tiap-tiap titik data. Proses pembelajaran yang tidak terawasi menjadi keunggulan untuk dapat diterapkan pada gambar yang terdapat noise. Dilakukan modifikasi terhadap metode Fuzzy C-means yaitu dengan melakukan penentuan dan perubahan matriks partisi menggunakan fungsi keanggotaan fuzzy untuk mendapatkan proses pembelajaran dan akurasi cluster. Penelitian ini bertujuan untuk mendapatkan model terbaik klasifikasi warna daun padi (Oryza Sativa) berdasarkan citra digital dengan menggunakan modifikasi metode fuzzy c-means yang diterapkan untuk klasifikasi. Data citra daun padi yang digunakan sebanyak citra dengan ukuran dimana data dibagi menjadi data latih citra untuk mendapatkan model dan 160 citra digunakan untuk pengujian model klasifikasi. Data citra diubah menjadi matriks Red, Green, Blue (RGB) yang kemudian ditransformasi menjadi matriks fuzzy. Penetapan nilai elemen-elemen matriks partisi dilakukan dengan membangkitkan bilangan random berdistribusi Uniform yang kemudian diubah menjadi matriks fuzzy. Model fuzzy c-means terbaik untuk klasifikasi diperoleh dengan menggunakan pusat cluster dari proses pembelajaran pada 9 percobaan terhadap parameter pangkat (). Diperoleh model terbaik modifikasi metode fuzzy c-means untuk klasifikasi pada percobaan parameter pangkat () sama dengan 2 dengan accuracy (ACC) 71%, specificity (SPC) 76%, sensitivity (TPR) 54%, positive predictive value (PPV) 51%, dan negative predictive value (NPV) 85%.]]>
<![CDATA[Modifikasi Pattern Informatics untuk Prediksi Hotspot Aktivitas Seismik pada Gempa di Pulau Jawa ]]>
<![CDATA[Adi Wibowo]]>;
<![CDATA[Asep Insani]]>;
<![CDATA[Boko Nurdiyanto S.]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 2: Mei 2017 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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<![CDATA[Earthquake is a serious problem in economic, social, and cultural point of view. The forecasting and prediction can be one way solution in reducing the effects of earthquakes in a region. In this paper, pattern informatics method was modified with time parameters to conduct hotspot prediction of seismic activity for the earthquake forecasting in Java. The experiment using seismic activity and earthquake data in Java were conducted to examine the perfomance of proposed method with several period prediction scenarios. The prediction results show an improvement of prediction result and shorten the prediction period.]]>
<![CDATA[Combination of K-NN and PCA Algorithms on Image Classification of Fish Species ]]>
<![CDATA[Rini Nuraini]]>;
<![CDATA[Adi Wibowo]]>;
<![CDATA[Budi Warsito]]>;
<![CDATA[Wahyul Amien Syafei]]>;
<![CDATA[Indra Jaya]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v7i5.5178
<![CDATA[To do fish farming, you need to know the types of fish to be cultivated. This is because the type of fish will affect how it is handled and managed. Therefore, this study aims to develop an image processing system for classifying fish species, especially cultivated fish, with a combination of the K-Nearest Neighbor (K-NN) algorithm and Principal Component Analysis (PCA). The feature extraction used is feature extraction based on its color and shape. The K-NN algorithm can group certain objects considering the shortest distance from the object. According to the best criteria, the PCA method is employed in the meanwhile to decrease and keep the majority of the relevant data from the original characteristics. On the basis of the test results, the accuracy value obtained is 85%. The use of a combination of the K-NN and PCA algorithms in the image classification of fish species in the research that has been done has been shown to be capable of increasing accuracy by 7.5% compared to only using the K-NN algorithm.]]>
