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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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Journal : Building of Informatics, Technology and Science

 1 
Comparative Assessment of Low Job Competitiveness Among University Graduates Using Naïve Bayes and KNN Algorithms Hamonangan, Ricardo; Palupi, Irma; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5406

Abstract

Tracer studies investigate the career outcomes of graduates, encompassing job search experiences, employment conditions, and the application of acquired skills post-graduation. These studies are pivotal for universities and colleges to assess graduate success and shape educational policies. This study aims to elucidate the factors contributing to low job competitiveness through the application of classification models like KNN and Naïve Bayes. It also evaluates how competencies developed during university studies impact this scenario. Key issues addressed include the identification of factors causing low job competitiveness and the assessment of competencies trained during university education. Utilizing a dataset comprising two classes and seven features, the KNN method achieved an accuracy of 71.00%, while Naïve Bayes achieved 70.00%. The data set size is 1853 (around 20% of the survey sample) of unemployed alumni. The results indicate that the lack of specific competencies, particularly those related to practical skills and real-world application, is a major factor contributing to low job competitiveness. The results highlight a specific competency as most crucial in the KNN model, whereas different competencies play significant roles in the Naïve Bayes model. Despite variations in competency importance across models, all features significantly contribute to predictions. This research enhances the classification of workforce competitiveness levels within tracer studies and underscores the potential of KNN and Naïve Bayes algorithms to identify factors influencing low job competitiveness. These findings support informed decision-making in academic and career development initiatives, emphasizing the critical influence of university-trained competencies on job market readiness.
Decision Tree Algorithm for Predicting Alumni Job Competitiveness Through Waiting Time Working Panuluh, Bagus; Palupi, Irma; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5647

Abstract

The absorption of alumni from universities into the world of work is an essential indicator that universities must pay attention to. One-way universities can pay attention to their alums is through tracer studies, where they can evaluate their curriculum's relevance to what is needed in today's world of work. One aspect that can be seen from the tracer study to assess the competitiveness of alums is the waiting time for alums to get their first job. This is because the sooner alums get jobs, the better the curriculum the university provides to students. This research aims to apply machine learning to predict the waiting time for alums from Telkom University to get their first job and find out what factors influence the waiting time for work. The algorithm used in the research is the Decision Tree with hyperparameter tuning using Grid Search and feature selection application. There are 3 methods of feature selection used for comparison: Spearman's Rank Correlation, Chi-square, and Principal Component Analysis. This research produces the best prediction model in applying Chi-square and hyperparameter tuning with an accuracy of 0.79, recall of 0.79, precision of 0.80, and F1-Score 0.75. Several features, such as the number of companies registered, how to find and get work, internship and practicum experience, ethical competency, discussion, and IT skills, have the biggest effects on the model.
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.
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