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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Jurnal Matematika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) Visualita : Jurnal Online Desain Komunikasi Visual Surya Abdimas Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal eProceedings of Engineering Eduvest - Journal of Universal Studies Community Service Seminar and Community Engagement (COSECANT) The Indonesian Journal of Computer Science Jurnal Abdimas Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Putu Harry Gunawan
Universitas Telkom Bandung, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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Forecasting Number of New Cases Daily COVID-19 in Central Java Province Using Exponential Smoothing Holt-Winters Irandi, Dinda Fitri; Rohmawati, Aniq Atiqi; Gunawan, Putu Harry
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.565

Abstract

There is hard to mention how long the COVID-19 pandemic will discontinue. There are some factors, including the public’s efforts to slow spread and researchers’ work to observe more about this outbreak. From the beginning of the health crisis, particularly following the announcement of the first positive case In Indonesia due to the COVID-19 on March 2, 2020. Afterwards, the number of daily cases increase simultaneously in other regions in Indonesia until today. Due to the fact that the significant mobility of the people, Central Java has contributed the 3rd rank of potential number of COVID-19 positive cases in Indonesia. This study aims to forecast the number of COVID-19 daily new cases in Central Java to assist the government in preparing the necessary resources and controlling the spread of the COVID-19 virus in Central Java Province. We proposed Exponential Smoothing Holt-Winters with the Additive model with seasonal addition considering trend and seasonal factors. The dataset during March 14 to April 17, 2021, revealed fluctuation of trend and seasonal patterns. Our simulation studies indicate that Exponential Smoothing Holt-Winters provides sharp and well performance for forecasting daily new cases of COVID-19 in Central Java province with MAPE less than 10%.
An Exponential Smoothing Holt-Winters Based-Approach for Estimating Extreme Values of Covid-19 Cases Abi Rafdhi Hernandy; Rohmawati, Aniq Atiqi; Gunawan, Putu Harry
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.576

Abstract

Covid-19 is an ongoing outbreak across the world infecting millions, having significant fatality rate, and triggering economic disruption on a large scale. The demand of healthcare facility has been significantly affected by the increased Covid-19 cases. Many countries have been forced to do lockdown and physical distancing to avoid a crucial peak of novel Covid-19 pandemic that potentially overwhelms healthcare services. Central Java is the province with the third highest population density in Indonesia and predicted to be affected significantly over a particular period of this outbreak. Our paper aims to provide a modelling to estimate extreme values of daily Covid-19 cases in Central Java, between March and April 2021. We particularly capture seasonality during this period using Exponential Smoothing Holt-Winters. We employ that Value at Risk and mean excess function based-approaches for extreme value estimation. Our simulation studies indicate that Exponential Smoothing Holt-Winters and Value at Risk provide sharp and well prediction for extreme value with zero violation. Since a number of positive cases has resulted unprecedented volatility, estimating the extreme value of daily Covid-19 cases become a crucial matter to support maintain essential health services.
Arrhythmia Classification Using CNN-SVM from ECG Spectrogram Representation Fakhrudin, Abdul Daffa; Gunawan, Putu Harry
Eduvest - Journal of Universal Studies Vol. 4 No. 12 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i12.49993

Abstract

Arrhythmia, a critical subset of cardiovascular diseases and a leading cause of morbidity and mortality, is caused by irregular heartbeats that disrupt the normal rhythm of the heart. Detecting arrhythmias accurately is essential for timely diagnosis and treatment, which can be achieved through electrocardiogram (ECG) signals. This study presents a hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model for arrhythmia classification, leveraging spectrogram representations of ECG signals. The CNN extracts spatial and temporal features from the spectrograms, while the SVM classifies five arrhythmia classes: Normal (N), Supra-ventricular premature (S), Ventricular escape (V), Fusion of ventricular and normal (F), and Unclassified (Q). Preprocessing techniques such as wavelet denoising and Short-Time Fourier Transform (STFT) were applied to improve signal quality and facilitate robust feature extraction. The proposed model was trained and evaluated on the MIT-BIH Arrhythmia Database, achieving a weighted F1-score of 0.985, demonstrating its ability to handle the imbalanced dataset effectively. Class-wise metrics highlighted high precision, recall, and F1-scores for majority classes and commendable performance for underrepresented classes, despite the inherent imbalance. These findings underscore the hybrid model's potential for arrhythmia classification by integrating the feature extraction strengths of CNNs with the precise classification capabilities of SVMs. Future research could address dataset imbalance through augmentation techniques and explore the model’s generalizability by testing on larger and more diverse datasets, paving the way for its application in real-world clinical scenarios.
Modelling of COVID-19 Disease Spread in Yogyakarta City Using the Fourth-Order Runge Kutta Method and SIR model Pratama, Aditya Nur; Gunawan, Putu Harry
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5804

Abstract

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has created enormous worldwide health issues, particularly in Yogyakarta, Indonesia, a city with distinct socio-cultural dynamics and a crucial role in national education. Understanding how the virus spreads in this particular milieu is critical for successful public health responses. To simulate and investigate COVID-19 transmission dynamics in Yogyakarta, this work uses the Susceptible-Infected-Recovered (SIR) epidemiology model, enhanced by the Fourth Order Runge-Kutta (RK4) numerical approach. The RK4 technique improves the model's accuracy by providing precise numerical solutions to the differential equations governing disease transmission. The study identifies the optimal infection rate parameter (β = 0.2037) that minimizes the Root Mean Squared Error (RMSE) between the model's predictions and actual data. These findings offer critical insights into the local pandemic trajectory, which can directly support the government in tailoring public health strategies, assist researchers in refining epidemiological models, and guide the general public in understanding transmission risks. The methodologies and results from this study can also serve as a reference for similar epidemiological assessments in other regions.
Improving Children's Computational Thinking Through a Combination of Unplugged and Plugged-in CT Techniques Tangible with Robot Games Gunawan, Putu Harry; Indwiarti, Indwiarti; Wirayuda, Tjokorda Agung Budi
Jurnal Abdimas Vol. 29 No. 1 (2025): June 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/j1j9cc66

Abstract

In today's digital era, introducing the concept of computational thinking ( CT ) from an early age is very important. Binekas School, as an educational partner at the Playgroup, Kindergarten (TK), and Elementary School (SD) levels, is committed to introducing CT to children starting at the age of 4. Currently, Binekas School only offers optional extracurricular coding activities at the elementary school level and uses a hard coding approach, which may be too challenging for most students. This school wants to prepare students with an introduction to the basics of coding from kindergarten using a more child-friendly approach, namely plugged-in with tangible robotics. The proposed solution includes an introduction to CT knowledge with a focus on algorithm development and CT training through tangible plugged-in techniques using robots for children aged 4 years and above. This training will not only improve children's understanding of the CT concept in an interactive and fun way, but will also prepare them for future educational challenges. The tools that will be used in this training are the Robotic Gigo Smart Brick, a robotic system designed for children so that they can learn basic programming concepts and computational thinking through interactive games. This outreach activity uses a combination of unplugged CT through card media and physical maps to train problem-solving mindsets accompanied by the opportunity to test proposed solutions with plugged-in CT using the Robotic Gigo Smart Brick. From the results of the activity evaluation through a questionnaire, it was found that 92% of students agreed that the application of the combination of unplugged CT and plugged-in CT was fun, the material was easy to understand and they were interested in getting further material.
Pengenalan Konsep Computational Thinking Menggunakan Robot Edukasi Untuk Anak Usia Dini Gunawan, Putu Harry; Indwiarti, Indwiarti; Wirayudha, Tjokorda Agung Budi
Surya Abdimas Vol. 9 No. 4 (2025)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/abdimas.v9i4.6503

Abstract

Di era digital saat ini pengenalan konsep berpikir komputasi (Computational Thinking - CT) sejak usia dini menjadi sangat penting. Pelatihan computational thinking (CT) untuk anak usia PAUD/TK bertujuan untuk memperkenalkan konsep dasar CT melalui kegiatan bermain yang menyenangkan dan interaktif. CT bukan tentang pemrograman, melainkan tentang mengembangkan kemampuan berpikir logis, sistematis, dan kreatif dalam memecahkan masalah sejak dini. Saat ini TK Telkom Buah Batu berkomitmen untuk memperkenalkan CT kepada anak-anak mulai usia 4 tahun. Sementara itu, TK Telkom Buah Batu belum pernah mengadakan kegiatan yang berkaitan dengan coding. Sehingga tujuan dari kegiatan ini adalah mengenalkan konsep CT bagi siswa TK Telkom menggunakan pendekatan yang menyenangkan dan mudah dipahami anak, yaitu plugged-in dengan tangible robotics atau robot edukasi. Kegiatan pengmas ini memiliki bidang fokus TIK dengan TKT 1 (dasar) dan mendukung pendidikan berkualitas (SDG 4) dan inovasi (SDG 9). Metode atau tahapan kegiatan yang dilakukan dalam PKM ini dibagi menjadi empat tahapan besar yakni, presnetasi, praktikum atau demo, eval__uasi dan feedback atau masukan. Hasil kegiatan menunjukkan keberhasilan tim PkM dalam menyelesaikan kebutuhan mitra. Dari survey, rata-rata aspek respon diperoleh skor 4,75 dari 5,00 yang masuk kedalam kategori sangat baik.
Classification of Acne Severity Using K-Nearest Neighbor (KNN) and Random Forest Method Gloria Flourin Maitimu; Putu Harry Gunawan; Muhammad Ilyas
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p06

Abstract

The development of machine learning technology, especially in dermatology, offers excellent opportunities for classifying and diagnosing skin conditions such as acne. This study aims to apply and compare two machine learning methods, K-Nearest Neighbors (KNN) and Random Forest methods, to classify acne severity into three levels: mild, moderate, and severe. The acne density and average confidence features were extracted from facial images using the YOLOv8 model based on acne bounding boxes. While the KNN model achieves 95% accuracy, the Random Forest model reaches 97%, indicating superior performance with excellent precision, recall, and F1-score values. With its level of accuracy, the integration of the Random Forest model and the features extracted using the YOLOv8 model appear to be a promising tool in dermatology for classifying acne severity in a more accurate and effective way.
Breaking Class Imbalance: Machine Learning Solutions for Stunting Detection Hasna Aqila Raihana; Putu Harry Gunawan; Narita Aquarini
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p03

Abstract

Stunting is a critical public health issue primarily caused by malnutrition, which hampers the growth of children. This study evaluates the performance of two machine learning models, K-Nearest Neighbors (KNN) and Decision Tree, in classifying stunting status in toddlers. Three strategies for handling class imbalance—no sampling, Synthetic Minority Over-sampling Technique (SMOTE), and random undersampling-are compared to enhance the detection of the minority class (stunting). The results show that KNN with SMOTE achieved the best performance, with an accuracy of 99.17% and an F1-Score of 99.17%, highlighting the model’s effectiveness in balancing sensitivity to the minority class. In contrast, although Decision Tree achieved an accuracy of 99.11% without sampling technique, it faced challenges in detecting stunting, which were addressed with the use of SMOTE, improving its accuracy to 97.41%. The application of random undersampling caused a significant decline in performance for both models. These findings underscore the effectiveness of SMOTE in handling class imbalance for stunting detection and provide valuable insights into the application of machine learning techniques in addressing public health issues.
Computational Parallel on Simulation of Wave Attenuation by Mangrove Forest Putu Harry Gunawan; Irma Palupi; Nurul Ikhsan; Iryanto Iryanto; Naila Al Mahmuda
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p02

Abstract

Coastal ecosystems, specifically mangrove trees, safeguard coastal regions against natural disasters like erosion, floods, and tsunamis. Numerical simulations employing the Shallow Water Equation (SWE), encompassing mass and momentum conservation equations, are used to comprehend how mangroves attenuate wave energy. The SWE incorporates Manning's friction term, which is directly influenced by mangrove forests. However, the SWE's complexity and sensitivity to initial conditions hinder analytical solutions. Despite its increasing computational demands, we utilize the robust staggered grid method to address this challenge. Our study examines mangroves' wave-attenuating effects and introduces a parallel computational model using OpenMP to expedite computations. Findings reveal that mangroves can reduce wave amplitudes by up to 33% when employing a Manning's coefficient of 0.3 within confined basin simulations. Furthermore, our parallel computing experiments demonstrate substantial computation speed enhancements; the speedup improves up to a point, with a notable 7.26-fold acceleration observed when utilizing eight threads compared to a single line. Moreover, more than a 10-fold acceleration is observed when the number of threads is greater than 16. This underscores the significance of parallelization in exploring mangrove contributions to coastal protection.
Co-Authors Abi Rafdhi Hernandy Abi Rafdhi Hernandy Ade Romadhony Aditya Firman Ihsan Adrin, Athaya Fatharani Afrahtama, Ariiq Agung Ferdiana Agung Toto Wibowo Ahmad Lubis Ghozali Aniq Atiqi Rohmawati Anis Zainia Farabiba Annisa Aditsania Aprianti Putri Sujana Aquarini, Narita Ardhito Utomo Ardhito Utomo Ari Satrio Arnanti Primiana Yuniati Bagus Gigih Adisalam Bambang Ari Wahyudi Bambang Pudjoatmodjo Bambang Pudjotatmodjo Bedy Purnama Conny Tria Shafira Dede Tarwidi Deni Saepudin Devi Munandar Devi Munandar, Devi Didit Adytia Dinda Fitri Irandi Djoko Murdowo Dodi Wisaksono Sudiharto Eka Ismantohadi Ema Rachmawati Ema Rachmawati Ema Rachmawati Fadhil Lobma Fakhrudin, Abdul Daffa Farabiba, Anis Zainia Fat'hah Noor Prawira Fat’hah Noor Prawira Fat’hah Noor Prawira Fazmah Arif Yulianto Fenty Alia Fityanul Akhyar Friska Fristella Friska Fristella Gloria Flourin Maitimu Gregorius Vito Hamonangan, Ricardo Hasbi Rabbani Hasna Aqila Raihana I Gde Made Bagus Nurseta Wijaya Indwiarti Irandi, Dinda Fitri Irma Palupi Iryanto Iryanto Jondri Jondri Lazuardy Azhari Bacharuddin Noor Ledya Novamizanti Lukman Nurwahidin M. Sofyan Bahrum Juniardi Mahmud Imrona Muhammad Arzaki Muhammad Daffa Dhiyaulhaq Muhammad Hablul Barri Muhammad Ilyas Muhtar, Na'il Muta'aly Muthi, Muhammad Ariq Naila Al Mahmuda Narita Aquarini Nur Nining Aulia Nurul Ikhsan Panuluh, Bagus Patria, Widya Yudha Prabasworo, Bhanu Pratama, Aditya Nur Pratama, Rezqie Hardi Prawita, Fat’hah Noor Pudjoadmojo, Bambang Rachmad Ryan Feryal Rajib Sainan Zulkifli Ratri Wulandari Revandi, Tyo Rifki Wijaya Rikman Aherliwan Rudawan Rimba Whidiana Ciptasari Rita Purnamasari Satria Mandala Selly Meliana Seraphina, Yessica Anglila Siti Fitria Yonalia Solin, Chintya Annisah Sri Soedewi Tb Dzulfiqar Alhafidh Tjokorda Agung Budi Wirayuda Tora Fahrudin Vina Putri Damartya Vito, Gregorius Wicaksono, Candra Kus Khoiri Wirayudha, Tjokorda Agung Budi Yoreza Mandala Putra ZK Abdurahman Baizal