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All Journal Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) TIN: TERAPAN INFORMATIKA NUSANTARA
Ce, Win
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STUDI KOMPARATIF ALGORITMA MACHINE LEARNING PADA ANALISIS SENTIMEN MEDIA SOSIAL Panjaitan, Febriyanti; Ce, Win; Oktafiandy, Hery; Kanugrahan, Ghanim; Ramdhani, Yudi; Hafizh Cahaya Putra, Vito; Permai, Antika
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13277

Abstract

Analisis sentimen di Twitter telah menjadi salah satu topik utama dalam penelitian terkait opini publik di bidang ekonomi, politik, dan isu sosial. Penggunaan machine learning dalam analisis sentimen memungkinkan untuk memproses data teks secara efisien. Penelitian ini bertujuan untuk mengeksplorasi literatur terkait analisis sentimen menggunakan metode machine learning pada Twitter dalam konteks ekonomi, politik, dan isu sosial. Metode yang digunakan adalah Systematic Literature Review (SLR), dengan pengumpulan artikel dari tiga database utama: IEEE Xplore, Google Scholar, dan Scopus. Setelah menerapkan kriteria inklusi dan eksklusi, 45 artikel relevan terpilih untuk dianalisis. Hasil penelitian menunjukkan bahwa Support Vector Machine (SVM) memiliki performa terbaik dengan akurasi rata-rata 85.3%, diikuti oleh Random Forest (83.7%) dan Naïve Bayes (81.5%). KNN dan Decision Tree menunjukkan performa lebih rendah, kemungkinan karena sensitivitas terhadap data yang tidak seimbang. Tren penelitian mengindikasikan bahwa analisis sentimen di bidang ekonomi lebih banyak berkaitan dengan dampak kebijakan ekonomi, di bidang politik fokus pada opini publik terkait pemilu dan kebijakan pemerintah, sementara di bidang isu sosial berkaitan dengan gerakan sosial dan kebijakan kesehatan.
Evaluation of Machine Learning Models for Sentiment Analysis in the South Sumatra Governor Election Using Data Balancing Techniques Panjaitan, Febriyanti; Ce, Win; Oktafiandi, Hery; Kanugrahan, Ghanim; Ramdhani, Yudi; Putra, Vito Hafizh Cahaya
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1019

Abstract

Sentiment analysis is crucial for understanding public opinion, especially in political contexts like the 2024 South Sumatra gubernatorial election. Social media platforms such as Twitter and YouTube provide key sources of public sentiment, which can be analyzed using machine learning to classify opinions as positive, neutral, or negative. However, challenges such as data imbalance and selecting the right model to improve classification accuracy remain significant. This study compares five machine learning algorithms (SVM, Naïve Bayes, KNN, Decision Tree, and Random Forest) and examines the impact of data balancing on their performance. Data was collected via Twitter crawling (140 entries) and YouTube scraping (384 entries), and text features were extracted using CountVectorizer. The models were then evaluated on imbalanced and balanced datasets using accuracy, precision, recall, and F1-score. The Decision Tree and Random Forest models achieved the highest accuracies of 79.22% and 75.32% on imbalanced data, respectively. However, they also exhibited overfitting, as indicated by their near-perfect training performance. Naïve Bayes, on the other hand, demonstrated the lowest accuracy at 54.55% despite achieving high precision, suggesting frequent misclassification, particularly for the minority class. SVM and KNN also struggled with imbalanced data, recording accuracies of 58.44% and 63.64%, respectively. Significant improvements were observed after applying data balancing techniques. The accuracy of SVM increased to 71.43%, and KNN improved to 66.23%, indicating that these models are more stable and effective when class distributions are even. These findings highlight the substantial impact of data balancing on model performance, particularly for methods sensitive to class distribution. While tree-based models achieved high accuracy on imbalanced data, their tendency to overfit underscores the importance of balancing techniques to enhance model generalization.
Evaluasi Strategi Peningkatan Daya Saing Usaha Mikro di Sektor Kuliner melalui Implementasi Modul CRM pada Sistem ERP Open Source Odoo Andini, Novia; Haerul, Haerul; Ce, Win; Ramdhani, Yudi
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.8785

Abstract

Micro-enterprises play a crucial role in driving Indonesia’s national economic growth. To survive in an increasingly competitive environment and continue to develop, the adoption of technology has become an unavoidable necessity for business actors. This study aims to analyze the effectiveness of implementing the Customer Relationship Management (CRM) module within the Odoo Enterprise Resource Planning (ERP) system in supporting strategies to enhance the competitiveness of micro-enterprises. The research employs the Prototype method, which includes stages of requirements gathering, system design and configuration, implementation, User Acceptance Testing (UAT), as well as system deployment and strategic evaluation. The results indicate that the implementation of the CRM module provides measurable positive impacts on operational performance and customer relationship management. The frequency of recorded customer interactions increased from an average of 3 to 10 interactions per day, the number of new customers rose from 5 to 12 customers per week, and the number of repeat customers increased from 8 to 18 customers per week. In terms of operational efficiency, the time required for transaction recording was reduced from approximately 10 minutes to 4 minutes per transaction through process automation within the ERP system. In addition, access to data and reports that was previously manual and unstructured is now available automatically and in real time, thereby supporting faster and data-driven decision-making by business owners.Strategically, the implementation of ERP through the CRM module has proven to be an effective tool in strengthening the competitive position of micro-enterprises. This is reflected in improved quality of customer interactions, increased acquisition of new customers, and growing customer loyalty. These improvements were achieved through the optimal utilization of features available in the Odoo system, enabling the CRM module to be maximized in supporting the sustainability and development of micro-enterprises.
Co-Authors Andini, Novia Febriyanti Panjaitan Haerul, Haerul Hafizh Cahaya Putra, Vito Hery Oktafiandi Kanugrahan, Ghanim Oktafiandy, Hery Permai, Antika Yudi Ramdhani