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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik Informatika Journal of Sustainable Engineering: Proceedings Series
Agustinus Jacobus
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Sam Ratulangi, Jln. Kampus Unsrat, Bahu - Manado 95115, INDONESIA
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation Daniel Febrian Sengkey; Agustinus Jacobus; Fabian Johanes Manoppo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp734-743

Abstract

Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.
Ship-to-Shore Wireless Communication for Asynchronous Data Delivery to the Remote Islands Alwin M. Sambul; Sherwin R.U.A. Sompie; Daniel Febrian Sengkey; Agustinus Jacobus; Alicia A.E. Sinsuw
Journal of Sustainable Engineering: Proceedings Series Vol 1 No 1 (2019)
Publisher : Fakultas Teknik Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/joseps.v1i1.13

Abstract

Nowadays, many people who live in remote islands of Indonesia are still facing difficulties in terms of access to information. In the locations where end-to-end communication is not available, the asynchronous approach can be utilized to send information in the form of digital data. In some areas, we could utilize passenger ships or ferries as physical carriers to deliver digital data to the people in the remote islands which are located at a particular range of distance from the ship’s passing routes. This paper reports the channel performance of long-range WiFi connection oversea at 5 GHz using the real ship’s route at the North Sulawesi province‘s water in Indonesia as a sample scenario. The measurement results showed that the most stable ship-to-shore communication can be achieved in ±15 minutes at the maximum distance between the ship and shore of about 4 km. The maximum channel capacity was 120 Mbps for upload (from ship to shore) and 53 Mbps for download (from shore to ship), which is enough to deliver gigabytes of information to the people at the islands every time the ship passes by.
Implementing Support Vector Machine Sentiment Analysis to Students' Opinion toward Lecturer in an Indonesian Public University Daniel Febrian Sengkey; Agustinus Jacobus; Fabian Johanes Manoppo
Journal of Sustainable Engineering: Proceedings Series Vol 1 No 2 (2019)
Publisher : Fakultas Teknik Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/joseps.v1i2.27

Abstract

Student feedback is an important evaluation tool for quality improvement. Moreover, in Indonesian higher education system there is an assessment regulation that puts special attention to the availability of the student feedback system. However, parts of the questionnaire are in the form of descriptive text that requires more effort for analysis. This situation leads to a very tiresome work in case of the number of documents reaches several hundred or even thousands. There were some efforts to apply computer-assisted classification by utilizing machine learning, however, most of them only analyzed English documents. Only a handful that studied the classification of documents in Bahasa Indonesia. In reality, we found some cases where the students used mixed languages while filling the evaluation forms. Therefore, in this study, we expand the application of text classification by using Support Vector Machne (SVM) to cases of student feedback in mixed languages. The model was built computationally and from the test, we get 74% accuracy and 0.46 Kappa value.
Analisis Sentimen Berbasis Aspek Ulasan Produk Menggunakan CNN dan Bidirectional LSTM: Aspect-Based Sentiment Analysis Product Review Using CNN and Bidirectional LSTM Obedient Putro; Agustinus Jacobus; Feisy Kambey
Jurnal Teknik Informatika Vol. 20 No. 2 (2025): Jurnal Teknik Informatika
Publisher : Universitas Sam Ratulangi

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Abstract

 Abstract — The COVID-19 pandemic has transformed consumer lifestyles in Indonesia, notably increasing the use of e-commerce platforms due to social restrictions. This shift has influenced how consumers evaluate product quality, making consumer reviews a crucial element in purchasing decisions. Traditional sentiment analysis falls short in providing detailed insights into product aspects, making Aspect-Based Sentiment Analysis (ABSA) a promising solution. Deep learning models like Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) offer high accuracy in sentiment analysis. This study analyzes consumer sentiment towards e-commerce product aspects in Indonesia by applying ABSA, addressing the challenges of implementation in the Indonesian language, and measuring the accuracy and effectiveness of the hybrid CNN Bi-LSTM model. The methodology includes dataset preprocessing, aspect extraction and sentiment classification, data training and prediction, and model evaluation. The results show that the CNN Bi-LSTM model achieves an average accuracy of 90% for aspect extraction and 92% for sentiment classification. In conclusion, despite dataset limitations, optimal data preparation and the hybrid model effectively facilitate ABSA.   Key Word — ABSA; CNN; Bi-LSTM; Accuracy; Hybrid Model   Abstrak — Pandemi COVID-19 telah mengubah gaya hidup konsumen di Indonesia, terutama melalui peningkatan penggunaan platform e-commerce akibat pembatasan sosial. Perubahan ini mempengaruhi cara konsumen menilai kualitas produk, menjadikan ulasan konsumen elemen kunci dalam keputusan pembelian. Analisis sentimen tradisional tidak memadai untuk memberikan pemahaman mendetail tentang aspek produk, sehingga Aspect-Based Sentiment Analysis (ABSA) menjadi solusi yang menjanjikan. Model deep learning seperti Convolutional Neural Network (CNN) dan Bidirectional Long Short-Term Memory (Bi-LSTM) menawarkan akurasi tinggi dalam analisis sentimen. Penelitian ini menganalisis sentimen konsumen terhadap aspek produk e-commerce di Indonesia dengan menerapkan ABSA, menghadapi tantangan implementasi dalam bahasa Indonesia, dan mengukur akurasi serta efektivitas model hybrid CNN Bi-LSTM. Metodologi meliputi preprocessing dataset, aspect extraction dan sentiment classification, data training dan prediction, serta evaluasi model. Hasil menunjukkan model CNN Bi-LSTM memiliki akurasi rata-rata 90% untuk aspect extraction dan 92% untuk sentiment classification. Kesimpulannya, data preparation optimal meskipun ada keterbatasan dataset, dan model hybrid efektif untuk ABSA.   Kata kunci — ABSA; CNN; Bi-LSTM; Akurasi; Hybrid Model
Co-Authors Alicia A.E. Sinsuw Alwin M. Sambul Daniel Febrian Sengkey Dirko Gustaafiano Setyadharmaputra Ruindungan Fabian Johanes Manoppo Feisy Kambey Fenny Maria Takumansang Heilbert Armando Mapaly Kenneth Yosua R. Palilingan Monalisa Hana Runtulalo Obedient Putro Salaki, Reynaldo Joshua Sary Diane Ekawati Paturusi Sherwin R.U.A. Sompie