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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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Classification of Company Level Based on Student Competencies in Tracer Study 2022 using SVM and XGBoost Method Revandi, Tyo; Gunawan, Putu Harry
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7237

Abstract

Assessing the quality level of companies where graduates are employed is crucial for understanding the impact of academic programs on career placements. The use of methodologies that do not match the research objectives may lead to inaccurate or irrelevant analysis. When company classification methods are not aligned with the nature of the data collected in a tracking study, the risk of misinterpretation and the formulation of invalid generalizations becomes apparent. This study utilizes the 2022 Tracer Study Data from Telkom University, encompassing responses from 4306 graduates working across Local, National, and Multinational companies. The research employs support vector machine (SVM) and XGBoost algorithms to analyze and classify the company levels of the surveyed graduates. The primary objective is to enhance the accuracy of company level classification, thereby facilitating a more precise analysis of the Tracer Study dataset. The SVM and XGBoost algorithms are rigorously tested, and the results indicate an accuracy improvement with the XGBoost method, yielding a 2% increase over the SVM method. The evaluation is conducted with a data separation of 20% test data and 80% training data. This research not only contributes to the refinement of company level classification in the context of Tracer Studies but also underscores the potential of machine learning algorithms, specifically SVM and XGBoost, in providing valuable insights into graduates' professional trajectories. The findings of this study pave the way for more informed decision-making processes in academic and career development initiatives.
YouTube Viewership Increation Analysis and Prediction using Facebook Prophet Model Pratama, Rezqie Hardi; Gunawan, Putu Harry
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7240

Abstract

YouTube, a widely accessed video-sharing platform available through both mobile applications and web interfaces, serves as a medium for content creators, commonly referred to as YouTubers, to engage with their audience. The success of a YouTuber is intricately tied to their audience engagement, encompassing metrics such as total views, comments, and likes garnered by their videos. This study involves the analysis of 7,600 English-language videos uploaded on YouTube between August and September 2020. To assess the predictive success value of a video, the study employs the Facebook Prophet method. Focusing on the upload time as a primary parameter, this method forecasts the growth in the number of YouTube viewers using datasets obtained from the YouTube API. Leveraging Time Series modeling, Facebook Prophet processes data by considering audience interactions throughout a video broadcast. The results derived from the Facebook Prophet model indicate a predictive trend of increasing viewership on YouTube in the coming months. The evaluation of model linearity, measured using the R² score to gauge data reliability, reveals a score of 0.39 or 39% which indicates a positive linearity score. And using Pearson correlation it gives 75 accuracy score. This signifies the model's capability to reasonably predict the growth in the number of viewers, contributing valuable insights into the dynamics of YouTube audience engagement over time.
Sentiment Analysis of Public Responses Regarding The Use of Electric Cars in Indonesia with Support Vector Machine and Random Forest Methods Seraphina, Yessica Anglila; Gunawan, Putu Harry
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4649

Abstract

The diminishing use of fossil fuels has encouraged the search for alternative energy sources, one of which is the electric car. However, public acceptance of electric cars in Indonesia is not widely understood. This study aims to analyze public sentiment towards electric cars based on data from X social media. The dataset used consists of 3,450 data, which is analyzed using two machine learning methods, namely Support Vector Machine (SVM) and Random Forest. The research was conducted in three scenarios: SVM kernel comparison, Random Forest performance evaluation with various numbers of n-estimators (1, 10, 100), and performance comparison between the two methods. The experimental results show that Random Forest with 100 n-estimators produces the highest accuracy of 90.72% and F1-Score of 87.54%, while SVM with RBF kernel produces 89.35% accuracy and F1-Score of 85.15%. The performance difference of 1.37% shows that Random Forest is more effective in this sentiment analysis
Analysis of Stunting Prediction in Toddlers in Bekasi District Using Random Forest and Naïve Bayes Solin, Chintya Annisah; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6670

Abstract

This study aims to compare the performance of the Random Forest and Naïve Bayes algorithms in predicting stunting in toddlers using data from the Bekasi District Health Office. The analysis process begins with data cleaning, normalization, and sampling using the Adaptive Synthetic Sampling (ADASYN) method to handle data imbalance, followed by validation with Stratified K-Fold Cross Validation. The implementation of the algorithm shows that Random Forest has the highest accuracy of 89.62% and an F1-Score of 89.09%. Naïve Bayes Gaussian produces an accuracy of 88.72% and an F1-Score of 88.81%, while Naïve Bayes Bernoulli has a lower performance with an accuracy of 67.83% and an F1-Score of 69.72%. Random Forest shows advantages in overcoming noise and imbalanced data, making it an optimal choice for stunting prediction. Meanwhile, the performance of Naïve Bayes is influenced by the characteristics of the data, where the Gaussian variation is more suitable for continuous data. The results of this study provide insight that choosing the right algorithm, especially on imbalanced data, is very important to improve prediction accuracy. This study also recommends more attention to data preprocessing to ensure optimal prediction quality, especially for minority classes.
Public Political Sentiment Post 2024 Presidential Election: Comparison of Naïve Bayes and Support Vector Machine Patria, Widya Yudha; Gunawan, Putu Harry; Aquarini, Narita
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6734

Abstract

One nation with a democratic political system is Indonesia. The public is able to express themselves freely. The public's use of social media is expanding quickly, particularly among users of platform ‘X’. The now trending tweets concern the 2024 presidential election. The reaction to the results of the 2024 presidential election has ranged from positive to negative to neutral. Large numbers of tweets can be used as a source of information to do their sentiments analysis. It is possible to know if people, in general, are satisfied or unsatisfied with the outcome of the presidential election thanks to the emotion categorization. This study aims to analyze public sentiment regarding the election result utilizing machine learning methods which will provide insights into public opinion that can be useful in political strategy as well as in public discourse assessment. In this paper, we will compare the Naïve Bayes Classifier (NBC) and the Support Vector Machine (SVM) algorithms for tweet classification of platform ‘X’ sentiment. This study presents the performed data analysis on 2193 data points (from platform X) that have been classified into neutral, positive, and negative categories using the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) techniques. Balancing SMOTE is used to address data imbalance, and TF-IDF is applied for feature extraction. Results depicts that Naïve Bayes Classifier (NBC) gives an accuracy of 62.41% whereas Support Vector Machine (SVM) gives 62.19% accuracy. This accuracy on these creations demonstrates how able models can be when classifying varying public sentiments between political events, highlighting the abilities, but also weaknesses of such efforts in sentiment classification. This paper contributes to the further development of sentiment analysis by providing an assessment of how effective these algorithms are, and by stressing the need for unbalance data treatment on research utilizing social media.
Sentiment Analysis of SiKasep Application Reviews on the Play Store Using the Naïve Bayes Approach Afrahtama, Ariiq; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7777

Abstract

The Ministry of Public Works and Public Housing (PUPR) launched the SiKasep application (Subsidized Housing Mortgage Information System) to streamline subsidized housing loan applications. This research analyzes user sentiment toward SiKasep using 3,416 Google Play Store reviews through Naïve Bayes classification to provide actionable insights for government digital service improvement. The methodology encompasses data scraping, comprehensive preprocessing addressing Indonesian language challenges (slang normalization and morphological complexity), TF-IDF feature extraction, and Complement Naïve Bayes classification with hyperparameter optimization. The preprocessing pipeline reduced vocabulary sparsity by 47%, while RandomOverSampler addressed significant class imbalance. The Complement Naïve Bayes classifier achieved 75.98% accuracy with balanced performance across sentiment classes (precision: 79%, recall: 76%, F1-score: 76%). Analysis revealed predominantly negative sentiment (52.4%), primarily related to registration and authentication difficulties, including document verification, login functionality, and KTP integration issues. Positive sentiment highlighted user appreciation for core housing services when technical barriers were absent. The findings emphasize the importance of streamlined registration processes and robust technical infrastructure for government digital services. This research contributes to understanding Indonesian e-government user experiences and provides a replicable sentiment analysis framework supporting evidence-based policy development for enhanced digital service delivery.
Public Sentiment Classification on Megathrust Issues in Social Media Using BERT Algorithm Wicaksono, Candra Kus Khoiri; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8016

Abstract

In recent years, the threat of megathrust earthquakes has intensified concern among scientists and the public, especially in seismically active countries like Indonesia. As people increasingly turn to social media to express fears and opinions about such disasters, these platforms offer a rich, real-time resource for gauging public sentiment. This study introduces a sentiment-classification system built on IndoBERT, an Indonesian-language adaptation of the renowned BERT architecture. Our model was trained on a custom-labeled dataset of social-media posts categorized as positive, negative, or neutral. Preprocessing involved tokenizing the text, truncating or padding inputs to 64 tokens, and converting sentiment labels into PyTorch tensor format to facilitate efficient training. We fine-tuned the IndoBERT model using the AdamW optimizer with a learning rate of 1e-5, a dropout rate of 0.1, and early stopping criteria to guard against overfitting, training for a maximum of seven epochs. Notably, the IndoBERT classifier achieved a validation accuracy of 93.33% on a hold-out test set representing 20% of the data, with this peak occurring in the very first epoch. This rapid convergence likely reflects both the strong pretrained language representations inherent in IndoBERT and the specific characteristics of the dataset. While early stopping effectively prevented overfitting, the immediate peak suggests that the model required minimal additional fine-tuning to adapt to this sentiment classification task. These findings demonstrate that advanced natural-language-processing tools like IndoBERT can reliably interpret sentiment in the context of sensitive topics and have the potential to be integrated into disaster-response frameworks, equipping officials with timely, data-driven insights into public opinion and concerns during emergencies.
Pembekalan Berpikir Komputasional Untuk Guru-Guru Homeschooling Sahabat Anak Terang Pengajar Anak Special Needs Gunawan, Putu Harry; Pudjoadmojo, Bambang; Rachmawati, Ema; Purnama, Bedy; Sujana, Aprianti Putri; Rudawan, Rikman Aherliwan
Charity : Jurnal Pengabdian Masyarakat Vol. 7 No. 1 (2024): Charity - Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sekolah Homeschooling Sahabat Anak Terang adalah layanan sekolah inklusi yang terbuka melayani anak-anak jenjang SD dan SMP melalui pendekatan multisensori. Setiap anak dibuatkan kurikulum individu dalam bentuk Individual Lesson Plan (ILP). Sekolah ini memiliki kurikulum khusus untuk menangani satu anak yang memiliki kebutuhan khusus. Area kurikulum Homeschooling Sahabat Anak Terang meliputi tiga (3) area yaitu Literasi, Matematika, dan Project-based Learning. Kurikulum ini disusun secara terstruktur dan konseptual melalui pengalaman-pengalaman multisensori. Berpikir Komputasional (BK) merupakan konsep berpikir secara Informatika melalui beberapa konsep seperti logika, abstraksi, dekomposisi, algoritma, dan pengenalan pola. Konsep ini sangat penting untuk diberikan pada semua aspek mata pelajaran yang ada di sekolah. Tujuan diadakan kegiatan Pengabdian Kepada Masyarakat (PKM) ini adalah untuk memberikan pengetahuan kepada guru-guru di Homeschooling Sahabat Anak Terang terkait pola berpikir komputasional. Pola berpikir komputasional ini belum sepenuhnya dimengerti oleh guru-guru di sekolah tersebut sehingga mereka sangat tertarik untuk menerapkan konsep ini. Hasil dari kegiatan ini berupa pelatihan dan sosialisasi pengembangan konsep BK dalam beberapa tahapan, seperti pemaparan, latihan soal, permainan dan bedah BK ke mata pelajaran. Berdasarkan umpan balik masyarakat atau guru-guru dalam kegiatan PKM ini, didapatkan sebesar 97,1% peserta dari 35 orang sangat setuju dan setuju bahwasannya kegiatan yang dimaksud sesuai dengan kebutuhan mitra. Selain itu, 100% peserta sangat setuju dan setuju kegiatan PKM dilanjutkan di masa yang akan datang.
Long Short-Term Memory Approach for Predicting Air Temperature In Indonesia Gunawan, Putu Harry; Munandar, Devi; Farabiba, Anis Zainia
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.551

Abstract

Air temperature is one of the main factors for describing the weather behaviour in the earth. Since Indonesia is located on and near equator, then monitoring the air temperature is needed to determine either global climate change occurs or not. Climate change can have an impact on biological growth in various fields. For instance, climate change can affect the quality of production and growth of animal and plants. Therefore, air temperature prediction is important to meteorologists and Indonesian government to provide information in many sectors. Various prediction algorithms have been used to predict temperature and produce different accuracy. In this study, the deep learning method with Long Short-Term Memory (LSTM) model is used to predict air temperature. Here, the results show that LSTM model with one layer and Adaptive Moment Estimation (ADAM) optimizer produce accuracy which is 32% of , 0.068 of MAE and 0.99 of RMSE. Moreover, here, ADAM optimizer is found better than Stochastic Gradient Descent (SGD) optimizer.
Enhancement of White Blood Cells Images using Shock Filtering Equation for Classification Problem Vito, Gregorius; Gunawan, Putu Harry
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.739

Abstract

Medical image processing has developed rapidly in the last decade. The autodetection and classification of white blood cells (WBC) is one of the medical image processing applications. The analysis of WBC images has engaged researchers from medical also technology fields. Since WBC detection plays an essential role in the medical field, this paper presents a system for distinguishing and classifying WBC types: eosinophils, neutrophils, lymphocytes, and monocytes, using K-Nearest Neighbor (K-NN) and Logistic Regression (LR). This study aims to find the best accuracy of pre-processing images using original grayscale, shock filtering, and thresholding grayscale. The highest average accuracy in classifying WBC images in the conducting research is 43.54% using the LR algorithm from 2103 images. It is obtained from the combination of thresholding grayscale image and shock filtering equation to enhance the quality of an image. Overall, using two algorithms, KNN and LR, the classification accuracy can increase up to 12%.
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