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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana IPTEK The Journal for Technology and Science Jurnal Ilmiah Kursor JOIV : International Journal on Informatics Visualization SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Pengabdian Informatika (JUPITA)
I Made Satria Bimantara
Universitas Udayana
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Other

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Sistem Pakar Untuk Diagnosis Fobia Menggunakan Metode Certainty Factor (CF) Bimantara, I Made Satria; Astuti, Luh Gede
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2021.v10.i01.p16

Abstract

A phobia is an excessive fear of a certain situation or object that can hinder the life of the sufferer. Phobias that are not immediately treated in individuals can lead to a state of frustration and even depression and the worst situation is the feeling of wanting to commit suicide. The earlier it is known that a person's phobic disorder is experienced, the faster treatment can be done by an expert. Expert systems can be used to diagnose a person's phobia and replace the role of an expert through a computer program. The expert system developed can diagnose nine phobias using 84 symptoms which are divided into three types of symptoms. Knowledge about phobias was obtained from a health online site in partnership with the Ministry of Health of the Republic of Indonesia. Certainty Factor (CF) method is used to overcome uncertainty in determining a disease based on its symptoms that usually occur in expert systems. The expert system is implemented based on a website using the PHP programming language and MySQL database. The CF method can be used to determine the percentage of a person's phobia based on symptoms by taking into account the weights of experts and users. System testing using Blackbox Testing shows that all the features that have been implemented in the expert system can function properly.
KEGIATAN BOOTCAMP PENGENALAN PYTHON UNTUK BIDANG DATA SCIENCE DAN MACHINE LEARNING DI PT HACKTIVATE TEKNOLOGI INDONESIA I Made Satria Bimantara; Luh Gede Astuti; I Wayan Supriana
Jurnal Pengabdian Informatika Vol. 1 No. 1 (2022): JUPITA Volume 1 Nomor 1, November 2022
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (526.822 KB) | DOI: 10.24843/JUPITA.2022.v01.i01.p28

Abstract

Magang Bersertifikat adalah salah satu program Kampus Merdeka yang diselenggarakan oleh Kementerian Pendidikan, Kebudayaan, Riset dan Teknologi RI. Salah satu perusahaan yang menjadi mitra magang di program ini adalah PT Hacktivate Teknologi Indonesia. Mahasiswa dapat belajar semua hal tentang statistik, machine learning (ML) dan visualisasi informasi yang dapat mempersiapkan kemampuan mahasiswa untuk berkarir sebagai data scientist pada program pengenalan Python untuk bidang data science (DS) yang telah disediakan perusahaan. Mahasiswa yang lolos magang diharuskan untuk mengikuti kegiatan bootcamp melalui daring mulai dari tanggal 23 Agustus 2021 sampai 14 Oktober 2021. Setelah bootcamp, mahasiswa melakukan magang sebagai Data Scientist di perusahaan mitra dari PT Hacktivate Teknologi Indonesia dari tanggal 15 Oktober 2021 sampai 23 Februari 2022. Mahasiswa telah memperoleh keterampilan coding untuk menerapkan DS dan ML dalam menyelesaikan beberapa permasalahan melalui serangkaian penugasan; bisa mengembangkan kemampuan softskill; dan dapat mempersiapkan diri untuk berkarir sebagai data scientist dengan mempelajari hal-hal tentang DS dan ML langsung dari instruktur yang berpengalaman di bidangnya melalui kegiatan ini. Oleh karena itu, kegiatan seperti ini penting untuk diadakan kembali ke depannya.
User-Centered Design-Based Approach in Scheduling Management Application Design and Development Darlis Herumurti; I Made Satria Bimantara; I Wayan Supriana
IPTEK The Journal for Technology and Science Vol 34, No 1 (2023)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v34i1.15088

Abstract

The process of manually making and setting course schedules using Microsoft Excel is ineffective, time-consuming, and still prone to errors. This research develops a website-based scheduling management application with a case study at SMK Pariwisata Margarana so that it can solve scheduling problems manually. The UserCentered Design (UCD) method is applied in the application prototype design stage. Open interviews, field observations, simulations, and questionnaires were used as research data collection methods. Three iterations were carried out at the prototype design stage to fulfill all user needs. The high-fidelity prototype in the last iteration is then implemented into an application. Application quality is measured using ISO/IEC 25010 with five characteristics. The test results on usability characteristics show that the scheduling management application obtains an average usability score of 91.2%. The appropriateness recognizability sub-characteristic obtained the highest usability score of 93.53%. UCD can help produce an application that can meet all the user’s needs when implemented in the application design phase.
CASE BASED REASONING (CBR) FOR OBESITY LEVEL ESTIMATION USING K-MEANS INDEXING METHOD I Made Satria Bimantara; I Wayan Supriana
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.268

Abstract

As many as 600 million of the 1.9 billion adults who are overweight are obese. Obesity that is not treated immediately will be a risk factor for increasing cardiovascular, metabolic, degenerative diseases, and even death at a young age. Case Based Reasoning (CBR) can be used to estimate a person's obesity level using previous cases. The old case with the highest similarity will be the solution for the new case. Indexing methods such as the K-Means Algorithm are needed so that the search for similar cases does not involve all cases on a case base so that it can shorten the computation time at the retrieve stage and still produce optimal solutions. Cosine similarity is used to find relevant clusters of new cases and Euclidean distance similarity is used to calculate similarity between cases. Random subsampling method was used to validate the CBR system. The test results with K=2 indicate that the CBR is better than the CBR-K-Means, each of which produces an average accuracy of 88.365% and 88.270% at a threshold of 0.8. CBR-K-Means produces an average computation time at the retrieve stage of 33.55 seconds and is faster than the CBR of 35.5 seconds.
OPTIMIZATION OF K-MEANS CLUSTERING USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GROUPING TRAVELER REVIEWS DATA ON TRIPADVISOR SITES I Made Satria Bimantara; I Made Widiartha
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.269

Abstract

K-Means Algorithm can be used to group tourists based on reviews on tourist destination objects. This algorithm has a weakness that is sensitive to the determination of the initial centroid. The initial centroid that is determined at random will decreasing the level accuracy, often gets stuck at the local optimum, and gets a random solution. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The optimal number of clusters (K) will be determined using the Elbow method by calculating the SSE value of the resulting cluster. The average Silhouette Coefficient (SC) is used to measure the quality of the clusters produced by the K-Means Algorithm with and without the PSO Algorithm. This study uses secondary data obtained from the UCI Machine Learning Repository with the name Travel Reviews Data Set which consists of 980 records and 10 attributes. The test results show that K=2 is the optimal number of clusters. The K-Means and PSO Algorithm gives an average SC value of 0.300358 which is better than without the PSO Algorithm of 0.300076. The optimal PSO hyperparameter generated is the number of particles=30, \varphi_1=2.2, and {\ \varphi}_2=3 at maximum iteration of 100.
Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy I Made Satria Bimantara; Anny Yuniarti
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62874

Abstract

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.
Pengembangan Sistem Prediksi Bantuan Program Keluarga Harapan (PKH) Berbasis Machine Learning I Wayan Supriana Supriana; Made Agung Raharja; I Made Satria Bimantara
SINTECH (Science and Information Technology) Journal Vol. 6 No. 1 (2023): SINTECH Journal Edition April 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i1.1297

Abstract

The Family Hope Program (PKH) is a poverty alleviation program which is one of the government's strategies in reducing the poverty line. This program provides cash social assistance to poor families who are included in the list of beneficiary families with a focus on education and health. The purpose of implementing the PKH program is not only to reduce poverty and increase human resources but to break the poverty chain. The implementation of PKH in its realization experienced many obstacles that caused the program not to be on target, this was because the data verification process was not yet effective and was still carried out manually. A process is needed to digitize the distribution and realization of the family of hope program. Through this research, a system was developed that can predict the value of PKH beneficiary assistance. The system developed is based on machine learning with a prediction model using Artificial Neural Network (ANN) and Backpropagation learning algorithm. Parameters in the learning system using PKH assessment as many as 8 indicators from the data of PKH beneficiaries in Tabanan Regency. Based on the prediction model testing using two data treatments, namely with and without preprocessing data. Parameters treated with data on numeric attributes and categories provide optimal values with an R2 Score of 0.695824 with a number of hidden layers of 500 and a max epoch of 375
Implementasi Sistem Cerdas Menggunakan Case Base Reasoning Sebagai Rujukan Terpadu Penerima Bantuan Kemiskinan di Kabupaten Tabanan Supriana, I Wayan; Giri, Gst. Ayu Vida Mastrika; Bimantara, I Made Satria
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 5 No. 2 (2022): Jurnal RESISTOR Edisi Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v5i2.1097

Abstract

Strategi dan inovasi mempercepat penanggulangan kemiskinan pemerintah Kabupaten Tabanan semakin digalakkan, tahun 2020 diperkirakan persentase kemiskinan mengalami peningkatan karena banyak sektor parisiwata dan sektor industri lainnya terdampak covid-19. Sampai saat ini distribusi program-program pengentasan kemiskinan berpusat pada database terpadu, sementara dilapangan terdapat banyak kendala. Identifikasi rumah tangga miskin perlu ditingkatkan sehingga dapat menentukan jenis bantuan utama yang dibutuhkan berdasarkan komponen kriteria yang sudah dipenuhi. Melalui penelitian ini dikembangkan aplikasi berupa sistem cerdas yang dapat menentukan bantuan prioritas rumah tangga miskin. Sistem yang dikembangkan menggunakan metode case base reasoning yaitu identifikasi rumah tangga sasaran didasari oleh penalaran berbasis kasus. Model penilaian menggunakan 23 fitur identifikasi rumah tangga miskin dan 18 fitur bantuan kemiskinan. Berdasarkan penelitian yang sudah dilakukan, model CBR dengan kluster K-Means lebih baik dibandingkan CBR tanpa kluster. Komposisi data training 80% dan data testing 20%, sistem CBR dengan indexing K-mean memiliki akurasi sebesar 0.48% dan tanpa indexing sebesar 0.46%
Content and Network Feature in Attention-based Neural Network for Stance Detection on COVID-19 Vaccination Tweets Bimantara, I Made Satria; Irdayanti, Marina; Nisa, Chilyatun; Purwitasari, Diana
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2671

Abstract

Stance detection in COVID-19 vaccination utilizing tweets is crucial for several reasons, such as public health communication, monitoring vaccine sentiment, and identifying misinformation. This research aims to explore the use of attention-based neural networks for stance detection in Indonesian COVID-19 vaccination tweets. The research focuses on enhancing accuracy by integrating content and network features. The content features represent the tweet's text, while network features define the user account's following or unfollowing. The primary contribution of this research is the development of an Attention Long Short-Term Memory (AttLSTM) model for stance detection in Indonesian tweets related to the COVID-19 vaccination. This model combines content and network features to improve accuracy in classifying user attitudes. We also highlight the performance differences between Word2Vec and FastText for numerical text representation in the AttLSTM model. The research used the Indonesian COVID-19 vaccination-related tweet dataset from prior research. The dataset is extracted using user metadata to obtain content and network features necessary to represent users' interest in tweets. Our research method involves data preparation, preprocessing, extraction of content and network features, and the development of an AttLSTM model. By integrating content and network features into the AttLSTM model with Word2Vec text representation, the study demonstrates superior performance compared to the LSTM baseline model and FastText. Adding attention mechanisms to the baseline LSTM model can capture crucial information, such as the minority class inside a tweet's text. Future research will involve exploring advanced data processing methods and ensemble learning techniques to further improve the model's performance.
Co-Authors Anny Yuniarti Chilyatun Nisa, Chilyatun Darlis Herumurti Devan Bramantya Diana Purwitasari Giri, Gst. Ayu Vida Mastrika I Made Widiartha I Wayan Supriana I Wayan Supriana I Wayan Supriana Supriana Irdayanti, Marina Luh Gede Astuti Luh Gede Astuti Raharja, Made Agung