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SENTIMENT ANALYSIS OF VIDEO EDITING APPLICATIONS USING SUPPORT VECTOR MACHINE ON GOOGLE COLAB Raharjo, Mugi; Putra, Jordy Lasmana; Heristian, Sujiliani; Napiah, Musriatun
Jurnal Informatika Vol 9, No 2 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i2.13699

Sentiment analysis is an important approach in understanding user opinions about an application. This study aims to analyze user reviews of the CapCut application using the Support Vector Machine (SVM) algorithm on the Google Colab platform. The preprocessing stages include data cleaning, word normalization using a dictionary from Kaggle, case folding, tokenization, stopword removal, and stemming. Furthermore, the data is converted into a numerical representation using the TF-IDF vectorization method. The labeling process is carried out using a sentiment lexicon obtained from GitHub. After performing data splitting, the SVM model is applied to classify sentiment into three categories: positive, negative, and neutral. The evaluation results show that the SVM model achieves the best accuracy of 90.12%. Based on the classification report, the model has high precision of 0.94 for positive and negative classes and 0.83 for the neutral class. Additionally, the confusion matrix indicates that the model can classify sentiment quite well, although there are still minor errors in predicting neutral sentiment. The findings of this study demonstrate that the SVM method can be effectively used to analyze user sentiment toward the CapCut application, providing valuable insights for improving user experience in the future.

IMPLEMENTASI FAILOVER CLUSTERING SERVER UNTUK MENGURANGI RESIKO DOWNTIME PADA WEB SERVER Abdul Hakim; Meirina Suci Ridha; Sujiliani Heristian; Arina Selawati; Pradnya Paramita
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 9 No. 3 (2024): Agustus
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

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

Penerapan web server dalam dunia perkantoran belakangan ini menjadi hal yang cukup penting, sebagai akibat adanya perkembangan aplikasi-aplikasi pendukung konvensional (Desktop-Based Application) yang sudah mulai bergeser menuju ke aplikasi berbasis web (Web-Based Applications). Dalam kenyataannya, penerapan web server di suatu perusahaan tidak cukup dengan hanya satu web server, untuk efektifitas kerja dari web server itu sendiri. Oleh karena itu, pada penelitian kali ini penulis mencoba untuk menerapkan teknik failover pada high availability clustering web server. Berdasarkan metode penelitian yang telah penulis lakukan dimulai dengan analisa kebutuhan, desain, testing hingga implementasi dan pengumpulan data dengan cara observasi, wawancara, dan studi pustaka, maka penerapan teknik failover pada high availability web server clustering adalah sebagai upaya untuk meminimalisir kegagalan akses yang mungkin terjadi kapan saja mengingat keadaan perangkat yang harus standby setiap saat, dimana nantinya setelah teknik ini diterapkan, maka saat server utama mengalami kegagalan akses pada aplikasi di dalam web server, akses tersebut secara otomatis akan di alihkan ke server ke-dua.

Klasifikasi Perilaku Pemain Game Online Menggunakan Naïve Bayes Berbasis Particle Swarm Optimization Heristian, Sujiliani; Anwar, Rian Septian; Kautsar, Hanggoro Aji Al; Sujiliani, Sujiliani Heristian; A
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.4433

Much research has been conducted to understand player behavior as a result of the rapid growth of online gaming. In this research, we use the Naive Bayes method optimized using Particle Swarm Optimization (PSO) to analyze the behavior classification of online game players. The classification accuracy value of the baseline method is 75.09% and the Area Under the Curve (AUC) value is 0.798. We use PSO-based optimization on Naïve Bayes to improve model performance. The results showed that the combination of Naïve Bayes and PSO increased classification accuracy to 95.28% with an AUC value of 0.990. This is a major advance that shows that combining the PSO algorithm with Naive Bayes can enable better classification of online game player behavior. These findings will make a significant contribution to the process of making plans that can improve the gaming experience.

Klasifikasi Perilaku Pemain Game Online Menggunakan Naïve Bayes Berbasis Particle Swarm Optimization Heristian, Sujiliani; Anwar, Rian Septian; Kautsar, Hanggoro Aji Al; Sujiliani, Sujiliani Heristian; A
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.4433

Much research has been conducted to understand player behavior as a result of the rapid growth of online gaming. In this research, we use the Naive Bayes method optimized using Particle Swarm Optimization (PSO) to analyze the behavior classification of online game players. The classification accuracy value of the baseline method is 75.09% and the Area Under the Curve (AUC) value is 0.798. We use PSO-based optimization on Naïve Bayes to improve model performance. The results showed that the combination of Naïve Bayes and PSO increased classification accuracy to 95.28% with an AUC value of 0.990. This is a major advance that shows that combining the PSO algorithm with Naive Bayes can enable better classification of online game player behavior. These findings will make a significant contribution to the process of making plans that can improve the gaming experience.

Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek Heristian, Sujiliani; Napiah, Musriatun; Erawati, Wati
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7775

Transportation is a means that a person uses to move from one place to another. One mode of transportation that is popular among the public is online motorcycle taxis, such as Gojek. Gojek continues to innovate to meet customer needs more effectively, as well as expand the scope of its services. This research aims to identify the number of positive and negative sentiments in the user review dataset, evaluate the performance of the algorithm used, and measure the level of customer satisfaction with Gojek services. Analysis was carried out on 6,485 customer reviews, which resulted in 4,387 positive sentiments and 2,098 negative sentiments. The classification model used, namely Naive Bayes, shows an accuracy of 88.5%, precision of 88.1%, and recall of 89.0%. The results of this research indicate that the Naive Bayes method provides good performance in analyzing the sentiment of user reviews of Gojek services

KLASIFIKASI KEMATANGAN PISANG BERDASARKAN CITRA WARNA KULIT MENGGUNAKAN DECISION TREE DAN SUPPORT VECTOR MACHINE DENGAN INTEGRASI YOLOV8 Gitisari, Deva; Nisrina, Restu Putri; Putri, Nayla Natania; Heristian, Sujiliani; Apriana, Veti; Santoso, Rame
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 2 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i2.6488

Di Indonesia, panen pisang sering dilakukan sebelum buah mencapai kematangan fisiologis. Akibatnya, seringkali pisang yang belum matang beredar di pasaran. Tujuan dari penelitian ini adalah untuk mengevaluasi akurasi dua algoritma Machine Learning, yaitu Decision Tree dan Support Vector Machine (SVM) untuk menentukan tingkat kematangan pisang dengan menggunakan dataset 6000 gambar pisang yang dikategorikan unripe, ripe, overripe, dan rotten. Dataset dipecah dalam rasio 80:20 untuk data latih dan data uji. Kemudian, metrik akurasi, presisi, recall, dan skor F1 digunakan untuk menguji. Hasil pengujian menunjukkan algoritma SVM memiliki akurasi tertinggi 92%, melampaui Decision Tree yang memiliki akurasi 82%. Model SVM Terbaik kemudian dikombinasikan dengan YOLOv8 untuk identifikasi kematangan pisang secara real-time menggunakan kamera. Penelitian ini memberikan kontribusi dengan menunjukkan efektivitas kombinasi HSV-SVM serta implementasi real-time menggunakan YOLOv8 menawarkan solusi praktis untuk pemantauan kualitas pisang secara otomatis.

Analyzing Public Sentiment Toward Makanan Bergizi Gratis Program Using Machine Learning Napiah, Musriatun; Heristian, Sujiliani; Raharjo, Mugi; Purnama, Rachmat Adi
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10445

Makanan Bergizi Gratis (MBG) program is a strategic initiative of the Indonesian government to improve the nutritional quality of schoolchildren. This research seeks to examine public sentiment regarding the MBG program by leveraging 10,000 tweets obtained from Kaggle. The method used combines Natural Language Processing (NLP) and Machine Learning approaches, several algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and LightGBM were tested to compare classification performance. The dataset contains a collection of public reviews categorized into three sentiment classes: positive, negative, and neutral. The analysis process includes text cleaning, tokenization, stopword removal, and stemming to obtain a cleaner text representation. Text features were then extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The results showed that the Logistic Regression 97% with an F1-score of 0.9552 models showed the most optimal performance. Sentiment analysis revealed 65% positive responses, 25% neutral, and 10% negative, with the dominant keywords being “nutrisi,” “sehat,” “anak sekolah,” and “gratis.” The results visualization, in the form of a Word Cloud and a bar chart, indicate that public opinion tends to be positive towards the implementation of the MBG program, particularly regarding improving the nutrition of schoolchildren. This research is expected to provide input for policymakers in evaluating public perceptions of the implementation of food-based social programs.

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