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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika JURNAL SISTEM INFORMASI BISNIS Bulletin of Electrical Engineering and Informatics Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) InComTech: Jurnal Telekomunikasi dan Komputer Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Biomedical Science and Bioengineering Jurnal Sistem Informasi Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Informatika

Adi Wibowo

Departement Of Computer Science, Informatics, Universitas Diponegoro

Author-ID : 5285625

Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

Published : 29 Documents Claim Missing Document

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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

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

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

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

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)

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.

Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory Rahmat Jayanto; Retno Kusumaningrum; Adi Wibowo
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.691

Advances in information technology have given rise to online hotel reservation options. The user review feature is an important factor during the online booking of hotels. Generally, most online hotel booking service providers provide review and rating features for assessing hotels. However, not all service providers provide rating features or recap reviews for every aspect of the hotel services offered. Therefore, we propose a method to summarise reviews based on multiple aspects, including food, room, service, and location. This method uses long short-term memory (LSTM), together with hidden layers and automation of the optimal number of hidden neurons. The F1-measure value of 75.28% for the best model was based on the fact that (i) the size of the first hidden layer is 1,200 neurons with the tanh activation function, and (ii) the size of the second hidden layer is 600 neurons with the ReLU activation function. The proposed model outperforms the baseline model (also known as standard LSTM) by 10.16%. It is anticipated that the model developed through this study can be accessed by users of online hotel booking services to acquire a review recap on more specific aspects of services offered by hotels

Resampling Technique for Imbalanced Class Handling on Educational Dataset Anief Fauzan Rozi; Adi Wibowo; Budi Warsito
JUITA: Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.15498

Educational data mining is an emerging field in data mining. The need for accurate in identifying student accomplishment on a course or maybe an upcoming course can help the institution to build technology-aided education better. Educational data mining becoming a more important field to be studied because of its potential to produce a knowledge base model to help even the teacher or lecturer. Like another classification task, educational data mining has a common and frequently discovered problem. The problem that occurred in educational data mining specifically and classification tasks generally is an imbalanced class problem. An imbalanced class is a condition where the distribution of each class is not in the same proportion. In this research, it is found that the class distribution is severely imbalanced and it is a multiclass dataset that consists of more than two class labels. According to the problem stated beforehand, this paper will focus on the imbalanced class handling and classification with several methods on both of it such as Linear Regression, Random Forest and Stacking for classification and SMOTE, ADASYN, and SMOTE-ENN for the resampling algorithm. The methods are being evaluated using a 10-fold cross-validation and an 80-20 splitting ratio. The result shows that the best performance coming from the Stacking classification on ADASYN resampled dataset evaluated using an 80-20 splitting ratio with a 0.97 F1 score. The result of this study also shows that the resampling technique improves classification performance. Even though the no-resampling classification result produced a decent result too, it can be caused by several things such as the general pattern of the data for each class is already been good from the start. Thus, there is no real drawbacks if the original data is processed.

PEMBANGUNAN PROTOTYPE SISTEM INFORMASI ADMINISTRASI KEPENDUDUKAN BERBASIS DATA TERDISTRIBUSI Adi Wibowo; Muh. Abdur Rohman; Beta Noranita; Djalal Er Riyanto
Jurnal Sistem Informasi Vol. 6 No. 1 (2010): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Data kependudukan merupakan suatu hal yang harus dikelola oleh pemerintah, baik daerah maupun pusat. Mekanisme pendataan yang disimpan pada masing-masing daerah dan tidak adanya komunikasi yang menyinkronkan data menyebabkan pencatatan ganda NIK. Sistem basis data terdistribusi merupakan kumpulan basis data yang tersebar di dua komputer atau lebih yang terhubung dalam jaringan komputer. Sistem basis data terdistribusi memberikan keuntungan ketersediaan data serta otonomi dalam pengelolaan data pada masing-masing lokal. Metode basis data terdistribusi yang digunakan adalah metode fragmentasi horizontal. Rancangan basis data terdistribusi data kependudukan dapat digunakan untuk mencegah pencatatan ganda NIK. Prototype Sistem Informasi Administrasi Kependudukan Berbasis Data Terdistribusi (SIAK BDT) yang dibuat digunakan untuk melakukan manajemen data kependudukan seperti menambah, mengubah, dan menampilkan data kependudukan dari berbagai lokasi yang berbeda, serta ketersediaan data. Pengujian terhadap prototype SIAK BDT dilakukan dengan metode simulasi. Dari hasil pengujiannya, prototype SIAK BDT mampu melakukan manajemen data penduduk dari berbagai lokasi yang berbeda dan ketersediaan data. Population data is a matter that must be managed should the government, either local or central. Data collection mechanism that is stored in each region and the lack of communication led to record multiple data sync NIK. Distributed database system is a collection of databases that are spread across two or more computers connected in a network computer. Distributed database systems provide the advantage of data availability as well as autonomy in the management of data on each local. Distributed database methods used are horizontal fragmentation method. The design of distributed database population data can be used to prevent double registration of NIK. Prototype of Sistem Informasi Administrasi Kependudukan Berbasis Data Terdistribusi (SIAK BDT) made is used to perform demographic data management such as adding, changing, and displaying population data from a variety of different locations, and availability of data. Prototype testing of SIAK BDT performed by the method of simulation. From the test results, prototype SIAK BDT able to perform data management people from many different locations and availability of data.

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

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%.

K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals Emigawaty Emigawaty; Kusworo Adi; Adian Fatchur Rochim; Budi Warsito; Adi Wibowo
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1761

More and more local governments in Indonesia are making their data available to the public. This benefits data scientists, researchers, business owners, and other potential users seeking datasets for empirical research and business innovation. However, just because Open Government Data (OGD) portals are accessible does not mean that they necessarily adhere to the established rules and principles of data openness. To evaluate the level of openness of 24 OGD portals in Indonesia, this study used the K-means Clustering algorithm to partition them into three levels: Leaders, Followers, and Beginners. A group of 30 participants, including researchers, data scientists, business enablers, and graduate students, rated the portals on 32 sub-questions related to the eight main principles of data disclosure, focusing on health, population, and education datasets. The study found that eight portals were categorized as Leaders, ten as Followers, and seven as Beginners regarding their level of openness. The study demonstrated that the K-means Clustering algorithm can be effectively used to assess the degree of openness of OGD portals in Indonesia based on eight main principles of data openness. The study recommends increasing the number of OGD portals in eastern territories to supplement the existing case studies in the western and central regions.

Co-Authors Abraham Timotius Asmoro Putro Adian Fatchur Rochim Agus Winarno Alfania Sarah Handayani Andys Sandra Kurniawan Anief Fauzan Rozi Aris Puji Widodo Asep Insani Aziz Jihadian Barid Bayu Surarso Beta Noranita Beta Noranita Beta Noranita Boko Nurdiyanto S. Bramono Triprastowo Budi Warsito Budi Warsito Budi Warsito Budi Warsito Budi Warsito Budi Warsito Cahyo Adhi Hartanto Dandi Indra Wijaya Dedy Kurniadi Djalal Er Riyanto Djalal Er Riyanto Eka Rahmawati Eko Adi Sarwoko Emigawaty Ervin Gubin Moung Ferda Ernawan Fra Siskus Dian Arianto Gregorius Anung Hanindito Guruh Aryotejo H Hadiyanto Helmie Arif Wibawa Henri Tantyoko Iffa Zainan Nisa Indra Jaya Indra Waspada Junta Zeniarja Kabul Kurniawan Kusworo Adi Marsani Asfi Mohd Hanafi Ahmad Hijazi Mohd Suhairi Md Suhaimin Muh. Abdur Rohman Muh. Abdur Rohman Muh. Abdur Rohman Noel Segura Meraz Nurdin Bahtiar Patricia Anthony Prajanto Wahyu Adi Rahmat Gernowo Rahmat Jayanto Ratih Titi Komala Sari Retno Kusumaningrum Rini Nuraini Riza Arifudin Rizka Putri Nawangsari Satriawan Rasyid Purnama Soni Adiyono Sukmawati Nur Endah Sutikno Sutikno Wahyul Amien Syafei Whisnumurti Adhiwibowo Widowati Wirawan, Panji Wisnu Yesaya Rudolf Susanto Widyanto

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