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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Biomedical Science and Bioengineering
Adi Wibowo
Departement Of Computer Science, Informatics, Universitas Diponegoro
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER Indra Waspada; Adi Wibowo; Noel Segura Meraz
Jurnal Ilmu Komputer dan Informasi Vol 10, No 2 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (774.143 KB) | DOI: 10.21609/jiki.v10i2.481

Abstract

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.
Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning Retno Kusumaningrum; Iffa Zainan Nisa; Rizka Putri Nawangsari; Adi Wibowo
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.737

Abstract

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.
Android skin cancer detection and classification based on MobileNet v2 model Adi Wibowo; Cahyo Adhi Hartanto; Panji Wisnu Wirawan
International Journal of Advances in Intelligent Informatics Vol 6, No 2 (2020): July 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i2.492

Abstract

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.
Pembinaan Pola Pikir Komputasi dan Informatika pada Siswa Sekolah Dasar Sukmawati Nur Endah; Eko Adi Sarwoko; Nurdin Bahtiar; Adi Wibowo; Kabul Kurniawan
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 11, No 1 (2020): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v11i1.2317

Abstract

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.
MoFlus: An Open-Source Android Software for Fluorescence-Based Point of Care Panji Wisnu Wirawan; Adi Wibowo
Journal of Biomedical Science and Bioengineering Vol 1, No 2 (2021)
Publisher : Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (80.137 KB) | DOI: 10.14710/jbiomes.2021.v1i2.39-48

Abstract

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.
Modifikasi Metode Fuzzy C-Means untuk Klasifikasi Citra Daun Padi Fra Siskus Dian Arianto; Adi Wibowo; Bayu Surarso
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 17, No 1 (2022): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v17i1.6068

Abstract

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%.
Modifikasi Pattern Informatics untuk Prediksi Hotspot Aktivitas Seismik pada Gempa di Pulau Jawa Adi Wibowo; Asep Insani; Boko Nurdiyanto S.
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 2: Mei 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (990.482 KB)

Abstract

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.
Combination of K-NN and PCA Algorithms on Image Classification of Fish Species Rini Nuraini; Adi Wibowo; Budi Warsito; Wahyul Amien Syafei; Indra Jaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5178

Abstract

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.
Sistem Penilaian Jawaban Singkat Otomatis pada Ujian Online Berbasis Komputer Menggunakan Algoritma Cosine Similarity Dedy Kurniadi; Rahmat Gernowo; Bayu Surarso; Adi Wibowo; Budi Warsito
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 2 (2023): Volume 9 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i2.66934

Abstract

Penggunaan teknologi di bidang pendidikan sekarang ini sedang trending ke arah penilaian secara otomatis, namun penilaian secara otomatis ini memiliki permasalahan yaitu belum bisa mengkoreksi jawaban teks singkat secara otomatis, selain itu pada saat ini juga belum tersedia platform yang bisa mengkoreksi jawaban singkat secara otomatis, penilaian jawaban teks singkat ini membutuhkan waktu koreksi yang lama dan hasil penilaian yang tidak konsisten jika koreksi dilakukan oleh manusia, pada penelitian ini diusulkan sistem yang mampu mengkoreksi ujian peserta didik pada bagian jawaban singkat secara otomatis atau disebut dengan Automated Short Answer Grading (ASAG) dengan menggunakan metode cosine similarity, tahapan yang dilakukan adalah melakukan ekstraksi pada dua variabel inputan yaitu teks pada jawaban peserta didik dan teks pada kunci jawaban yang dilakukan dengan ekstraksi teks casefolding, tokenizing, stopword removal, setelah tahapan tersebut dilakukan kemudian dihitung nilai similarity antara kunci jawaban ujian dengan jawaban peserta didik apakah jawaban peserta didik sama dengan kunci jawaban atau tidak, dengan menggunakan skor yang dinilai otomatis menggunakan sistem, dihasilkan similarity antara jawaban peserta didik dengan kunci jawaban rata-rata sebesar 85,4%, untuk menguji korelasi koreksi jawaban peserta didik dengan sistem dan koreksi yang dilakukan oleh manusia maka dilakukan uji korelasi antara hasil penilian yang dilakukan oleh sistem dengan hasil penilaian yang dilakukan oleh manusia (instruktur) dengan menggunakan kendall’s w value menghasilkan nilai w antara instruktur 1 dengan sistem sebesar 0,885 dan instruktur 2 dengan sistem sebesar 0,883 dengan nilai chi square sebesar 135,4 dan 133,8 dengan p sebesar 0,0001, hasil tersebut menunjukkan ASAG memiliki korelasi yang tinggi dan sistem ASAG ini bisa melakukan penilaian secara otomatis.
Diabetes Mellitus Early Detection Simulation using The K-Nearest Neighbors Algorithm with Cloud-Based Runtime (COLAB) Mohamad Jamil; Budi Warsito; Adi Wibowo; Kiswanto Kiswanto
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1510.215-221

Abstract

Diabetes Mellitus is a genetically and clinically heterogeneous metabolic disorder with manifestations of loss of carbohydrate tolerance characterized by high blood glucose levels as a result of insulin insufficiency. Public knowledge of diabetes mellitus 39.30% is influenced by public health education and information about diabetes mellitus that the public has ever received. Early detection of diabetes mellitus can prevent the development of chronic complications and allow timely and rapid treatment. The aim of this study is to simulate the early detection of diabetes mellitus with the K-Nearest Neighbors (K-NN) algorithm using Cloud-Base Runtime (COLAB). The highest accuracy is 76% in K=3, the highest precision is 68% in K=3 and the highest recall is 60% in K=3.  The researchers used K-NN as a method to classify data from the Pima Indians Diabetes Database and obtained a fairly good accuracy value of 76% with a value of k = 3.
Co-Authors Adiyono, Soni Asep Insani Bayu Surarso Beta Noranita Beta Noranita Boko Nurdiyanto S. Budi Warsito Budi Warsito Cahyo Adhi Hartanto Dedy Kurniadi Djalal Er Riyanto Eko Adi Sarwoko Ferda Ernawan Fra Siskus Dian Arianto Guruh Aryotejo Iffa Zainan Nisa Indra Jaya Indra Waspada Kabul Kurniawan Kiswanto Kiswanto mohamad jamil Muh. Abdur Rohman Muh. Abdur Rohman Noel Segura Meraz Nurdin Bahtiar Prajanto Wahyu Adi Rahmat Gernowo Retno Kusumaningrum Rini Nuraini Rizka Putri Nawangsari Sukmawati Nur Endah Wahyul Amien Syafei Wirawan, Panji Wisnu