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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 Jurnal Indonesia Sosial Teknologi eProceedings of Engineering Eduvest - Journal of Universal Studies The Indonesian Journal of Computer Science Jurnal Abdimas Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Proceeding of Community Service and Engagement
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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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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.
Modeling and Simulation of Vehicle Velocity-Density on Buah Batu Road Using Decision Tree Regression Ramadhan Aditya; Putu Harry Gunawan
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 10 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i10.1343

Abstract

This study aims to explore and simulate the traffic flow model on Buah Batu Road using the velocity-density function generated by the Decision Tree Regression method. The model utilizes a macroscopic approach, specifically the Lightill, Whitham, and Richards (LWR) model, which considers vehicle interactions. Observational data were collected directly from Buah Batu Road and processed to produce a velocity-density function, which shows that vehicle speed decreases as density increases, following a non-linear but step-like pattern. The velocity function generated by the Decision Tree Regression indicates that for low density (ρ < 0.102), the average speed is predicted to be around 3.681 to 4.551, while at high density (ρ > 0.273), the speed drops to around 1.411 or lower. The simulation was conducted on a 60-meter road segment with a total simulation time of 5 minutes and a grid resolution of 300 points. At the beginning of the simulation, a peak density of 0.70 was recorded in the 15-25 meter segment, which then shifted and decreased to 0.50 in the 30-50 meter segment by the end. The results indicate that vehicle movement reduces density and improves traffic flow. Thus, the Decision Tree Regression method has proven effective in modelling and simulating the velocity-density relationship to understand traffic dynamics on Buah Batu Road.
Linear Regression-Based Traffic Flow Simulation: Vehicle Density and Speed Analysis on Buah Batu Road Adam Ichwanul Ichsan; Putu Harry Gunawan
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 10 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i10.1345

Abstract

Traffic congestion has become an increasingly severe problem in many major cities around the world, including in the city of Bandung. Population growth and increased vehicle use exacerbate congestion. Jalan Buah Batu, one of the main roads in the city of Bandung, often experiences congestion due to high density. This study explains the traffic flow simulation using the Lighthill-Whitham-Richards (LWR) model with a speed-density function obtained from observation data on Jalan Buah Batu, Bandung. The data included the relationship between vehicle density and speed which was then analyzed using the linear regression method. The approximation of the velocity-density function obtained from linear regression is v(ρ)= -6.904+4.302. Traffic flow simulations were carried out with a road length of 60 meters, a total time of 5 minutes, and high resolution with 300 grid points. At the beginning of the simulation, a peak density of 0.70 occurred in a 15-25 meter road segment. Over time, the peak density shifted and decreased: 0.65 at 20-30 meters at 1.25 minutes, 0.60 at 25-35 meters at 2.5 minutes, and 0.50 at 30-50 meters at the end of the simulation (5 minutes). These results show the movement of vehicles that reduce congestion and improve the smooth flow of traffic. In conclusion, linear regression is effective in determining the velocity-density function.
PELATIHAN PENGENALAN BERPIKIR KOMPUTASIONAL UNTUK GURU DAN SISWA SMA TELKOM Indwiarti Indwiarti; Putu Harry Gunawan; Jondri Jondri
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 3 No. 1 (2023): Prosiding COSECANT : Community Service and Engagement Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v3i1.7128

Abstract

Berpikir komputasional merupakan proses yang memposisikan diri untuk berpikir menyerupai sebuah mesin yang bergerak secara dinamis. Sehingga berpikir komputasional merupakan sebuah konsep atau metode dalam mengamati masalah, dan mencari solusi dengan menggunakan teknologi komputer. Berdasarkan keputusan kepala Badan Standar, Kurikulum, dan Asesmen Pendidikan (BSKAP) No. 008/H/KR/2022 tentang Capaian Pembelajaran pada PAUD, jenjang Pendidikan Dasar dan jenjang Pendidikan Menengah Pada Kurikulum Merdeka, bahwa berpikir komputasional merupakan kemampuan problem solving yaitu keterampilan generik yang penting sejalan dengan perkembangan teknologi digital yang pesat. Sehingga dalam penerapan kurikulum Merdeka, diperlukan kemampuan guru dan siswa dalam menggunakan dan mengembangkan cara Berpikir Komputasional dalam kegiatan belajar mengajar. Saat ini SMA Telkom sudah menggunakan Kurikulum Merdeka, yang menekankan cara berpikir secara komputasional. Guru dan siswa merasakan kebutuhan untuk meningkatkan pengetahuan dan wawasan dalam berpikir komputasi yang bermanfaat dalam membantu dalam memecahkan masalah melalui cara-cara yang sederhana, dan melatih pikiran agar terbiasa berpikir secara logis, kreatif, dan terstruktur. Tujuan diadakan kegiatan pengabdian masyarakat ini adalah untuk memberikan pengetahuan kepada siswa dan guru di SMA Telkom Bandung tentang konsep berpikir komputasional. Pelatihan ini dilaksanakan pada tanggal 9 November 2023 bertempat di Telkom University Landmark Tower (TULT). Untuk mengukur apakah materi pelatihan ini dapat dipahami oleh peserta, maka dilakukan pre-test dan post-test. Kemudian dilakukan pengujian hipotesis terhadap rataan banyaknya pertanyaan yang dijawab benar oleh peserta. Menggunakan tingkat kepercayaan ? = 0.05, diperoleh hasil bahwa hasil post-test lebih bagus dibandingkan hasil pre-test. Hal ini menunjukkan bahwa peserta dapat mengikuti dan memahami materi pelatihan. Selain analisis terhadap pemahaman materi, juga dilakukan survey penilaian peserta terhadap pelatihan, dan mendapatkan nilai kepuasan sebesar 94.38%. Melalui pelatihan ini, diharapkan guru dan siswa SMA Telkom Bandung dapat menerapkan cara berpikir komputasional dalam kegiatan pembelajaran sehingga memacu penalaran kritis, kreatif, dan mandiri pada siswa.
Co-Authors Abi Rafdhi Hernandy Abi Rafdhi Hernandy Adam Ichwanul Ichsan 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 Ramadhan Aditya 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