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All Journal Katalogis Jurnal Penelitian Komunikasi dan Opini Publik JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Journal of Technopreneurship and Information System (JTIS) Building of Informatics, Technology and Science TEPIAN Buletin Poltanesa Bulletin of Computer Science Research Bulletin of Information Technology (BIT) DiJITAC : Digital Journal of Information Technology and Communication Informatika Wicida Research in Education, Technology, and Multiculture
Salmon Salmon
STMIK Widya Cipta Dharma
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Transportation Other

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Rancang Bangun Aplikasi Edukasi Interaktif Pengenalan Pahlawan Indonesia Menggunakan Algoritma Fisher-Yates Nadhif Nandana Kartomo; Salmon Salmon; Rizky Zakariyya Rasyad
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.672

Abstract

This research was conducted to develop an interactive media application for introducing the names of Indonesian national heroes. If successful, this research is expected to provide users with an easier way to learn and recognize Indonesian national heroes. The study was carried out at SMP 21 Samarinda. The data collection methods used were observation, in which direct observations were conducted at SMP 21 Samarinda, and interviews, involving direct question-and-answer sessions related to the objectives of the research. The system development tools used in this study were Adobe Flash CS6 and Adobe Photoshop CS6. The final result of this research is an interactive media application for introducing the names of Indonesian national heroes, implementing the Fisher-Yates shuffle algorithm to make the learning experience more engaging and easier to understand for users.
Analisis Komentar Youtube Terhadap Polemik Ijazah Presiden Ke 7 Indonesia Menggunakan Support Vector Machine Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.883

Abstract

This study aims to analyze public sentiment toward the controversy surrounding President Joko Widodo’s academic credentials by examining user comments on YouTube. A total of 20,294 comments were collected and processed through text cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labels were assigned using a lexicon-based approach, producing positive, negative, and neutral categories. The experimental results indicate that the combination of SVM, TF-IDF, and SMOTE achieved strong classification performance, with an accuracy of 86.87%. The model demonstrated better performance in identifying negative and neutral sentiments, while some positive sentiments tended to be misclassified as neutral. Overall, this study shows that sentiment analysis based on YouTube comments can serve as an effective approach for mapping public opinion on socio-political issues in an automated and large-scale manner. Feature extraction utilized Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification was performed using a Support Vector Machine (SVM). The model achieved an accuracy of 86.87% and a macro F1-score of 0.87, indicating that the integration of TF-IDF, SMOTE, and SVM is effective for large-scale sentiment classification of YouTube comments related to socio-political issues.
Analisis Komentar Youtube Terhadap Kebijakan Bebas Impor Oleh Pemerintah Pusat Menggunakan Support Vector Machine Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.995

Abstract

YouTube has become an important platform for expressing public opinion on government policies, including the free import policy. This study aims to analyze the sentiment of YouTube user comments regarding the free import policy using the Support Vector Machine (SVM) algorithm. The data were collected through web scraping using the YouTube Data API v3 from a Kompas.com video, resulting in 3,267 raw comments. The research stages include text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), lexicon-based sentiment labeling, and sentiment classification using SVM. To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM model achieved an accuracy of 77.00% without tuning and 75.15% after hyperparameter optimization, with improved balance across sentiment classes. These findings indicate that SVM is effective for sentiment classification of YouTube comments.
Design and Construction of Teacher and Student Attendance Using Website-Based Radio Frequency Indentification: Case Study of SDN 011 Tenggarong Seberang Muhammad Rezha Nur Hakiki; Salmon Salmon; Ivan Haristyawan
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2697

Abstract

This study aims to design and develop a teacher and student attendance system using Radio Frequency Indentification (RFID) technology integrated with a web-based platform as a solution to challenges encountered in manual attendance recording at SDN 011 Tenggarong Seberang. Conventional methods often result in delayed recapitulation, data entry errors, and difficulties in presenting attendance information quickly and accurately. By utilizing RFID technology, the attendance process is automated when the card is detected by the system, allowing data to be stored directly in the database without manual input. Integration with a web-based application enables real-time access to attendance data, presentation in structured reports, and analysis according to school administrative needs. The system development involved stages of requirements analysis, system architecture design, RFID device implementation, web interface creation, and functional testing to ensure accuracy and speed of data recording. The results indicate that the digital attendance system significantly improves administrative efficiency, reduces recording errors, and supports the monitoring of teacher and student discipline. Additionally, the web-based system provides flexibility for operators to manage and review attendance records. Therefore, the implementation of an RFID-based web attendance system is proven effective in supporting the modernization of administrative management in elementary schools.
Clustering Academic Data of Junior High School Students to Identify Learning Groups Using The DBSCAN Algorithm at SMP Muhammadiyah 5 Samarinda Mini H; Siti Lailiyah; Salmon
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2293

Abstract

The formation of study groups at the junior high school level plays an important role in improving the quality of learning and promoting equality in student learning outcomes. However, the process of grouping students is still largely carried out manually based on teachers’ intuition, subjective observations, or attendance data, which may lead to mismatches in students’ abilities and hinder the optimal achievement of learning objectives within the school environment. This study aims to identify study groups based on students’ academic data at SMP Muhammadiyah 5 Samarinda. The data used include scores in science (exact) and non-science (non-exact) subjects, exam results, assignment scores, attendance records, and parents’ educational backgrounds. The research stages consist of data cleaning, feature engineering, standardization, the application of the DBSCAN algorithm, and evaluation using the Silhouette Score. The analysis results reveal three main clusters: cluster 0 with 89 students (medium achievement), cluster 1 with 50 students (high achievement), and cluster 2 with 5 students (low achievement). In addition, 14 students (8.9%) were identified as noise. The Silhouette Score value of 0.217 indicates that the cluster separation quality is relatively weak; however, DBSCAN successfully detected outliers that may not be identified by other algorithms. These findings suggest that, although the cluster quality is not yet optimal, the applied algorithm remains useful for exploring students’ learning patterns and can serve as a basis for more targeted learning interventions.
Investment Decision-Making for High-Potential Startups in the Digital Economy Using AHP and VIKOR Salmon Salmon; Rizki Galang Rahmadani; Bartolomius Harpad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8633

Abstract

The rapid growth of the digital economy has driven the emergence of numerous startup companies that play a vital role as catalysts for innovation and business transformation in the modern era. However, the increasing number of startups poses a major challenge for investors in selecting the most potential and profitable investment opportunities. The main problem lies in the multi-criteria evaluation process, which involves various aspects such as market potential, product innovation, business model, team performance, and financial stability. To address this complexity, this study applies a combination of the Analytical Hierarchy Process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods as an objective and measurable multi-criteria decision-making approach. The AHP method is utilized to determine the priority weights of each criterion through a pairwise comparison process. The results show that market potential (C1) is the most dominant criterion with a weight of 0.458, followed by product innovation (C2) with a weight of 0.247, and business model (C3) with a weight of 0.144. Meanwhile, team performance (C4) and financial stability (C5) have relatively lower weights of 0.105 and 0.046, respectively. These findings indicate that market and innovation aspects are the primary factors influencing startup investment feasibility. Furthermore, the VIKOR method is employed to rank the alternatives based on compromise solutions toward the ideal outcome. The results reveal that startup A17 has the lowest compromise value (Q = 0.0000), making it the most optimal investment alternative, followed by A4 (Q = 0.0303) and A19 (Q = 0.0586). This study demonstrates that the combination of AHP and VIKOR methods provides a comprehensive, objective, and consistent analysis in the decision-making process for digital startup investments. The proposed approach assists investors in evaluating startups more systematically and accurately based on the priority of relevant criteria in the context of the dynamic digital economy. Therefore, a decision support system based on the AHP-VIKOR method can serve as an effective solution for decision-makers to identify and select the most promising startups for future development.
Customer Sentiment Analysis of E-Commerce Products Using the Naïve Bayes Method and Word Embedding Bartolomius Harpad; Azahari Azahari; Salmon Salmon
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8879

Abstract

This study discusses customer sentiment analysis toward e-commerce products using the Naïve Bayes method combined with Word Embedding techniques to enhance the semantic understanding of Indonesian-language customer reviews. The research background is based on the rapid growth of e-commerce, which has created a strong need to understand consumer opinions through online reviews. The main challenge in sentiment analysis lies in the complexity of natural language, such as the use of informal words, abbreviations, and diverse emotional expressions. This study utilizes 40,607 Tokopedia customer reviews across five product categories with three sentiment labels (positive, neutral, and negative). The research stages include data collection, text preprocessing (case folding, tokenization, stopword removal, stemming, and slang normalization), feature representation using Word2Vec and FastText, and classification using Multinomial Naïve Bayes. Experimental results show that the combination of Word2Vec and Naïve Bayes achieved an accuracy of 87.92%, while FastText and Naïve Bayes improved accuracy to 91.52%. The FastText-based model proved superior in handling morphological variations and non-standard spellings, making it more effective for Indonesian customer review texts. The WordCloud visualization reveals the dominance of positive words such as “sesuai” (appropriate), “barang” (item), and “cepat” (fast), indicating customer satisfaction regarding product conformity and service speed. The Confusion Matrix results indicate a bias toward the positive class due to data imbalance, where the model still struggles to recognize neutral and negative classes. Overall, this study demonstrates that integrating Word Embedding with Naïve Bayes enhances classification performance and provides richer semantic representations compared to traditional Bag of Words approaches. This approach has the potential to be applied in developing data-driven recommendation systems and marketing strategies within Indonesia’s e-commerce ecosystem.
Co-Authors Abil Firnanda Achmad Sadzali Muftisjar Ahmad Rofiq Hakim Alfan Oktaviansyah Maulana Putra Amelia Yusnita Andi Yusika Andi Yusika Rangan Andi Yusika Rangan Andriawan Angel Crystia Nadia Arsita Aulia Abdul Rahman Awang H. Kridalaksana Azahari Azahari, Azahari Bagus Ari Ramadhan Bartolomius Harpad Christianus Pakembongan Dewi Rosita Fachry Abda El Rahman Haeruddin Haeruddin Harianto, Kusno Harpad, Bartolomius Heny Pratiwi Ignasius Aditya Anggoro Putra Imran Imran Ita Arfyanti Ivan Haristyawan Kusnandar Kusnandar Kusnandar Kusnandar Kusnandar Kusnandar Mardianus Maulita Maulita Mini H Muhamad Risky Ramadhani Muhammad Beny Hafis Muhammad Fahmi Muhammad Jumal Sandika Muhammad Rezha Nur Hakiki Nadhif Nandana Kartomo Nur Haeda Nurhasanah Nurhasanah Nurhuda, Asep Nurlaili Nurlaili Nursobah, Nursobah Pitrasacha Adytia Puji Astuti Amalia Rachmad Fauzi Rajiansyah Reza Andrea Rizki Galang Rahmadani Rizky Meizal Saputra Rizky Zakariyya Rasyad Roffi Maulidi Ronny Sadya Winda Sari Siti Lailiyah Siti Lailiyah Sri Rakhmawati Sugeng Suryani Sugih Niansyah Suryani, Sugeng Tino Purniawan Widyatmike Gede Mulawarman Yohanes Rombe Paran Yunita Yunita