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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Journal of Sustainable Engineering: Proceedings Series
Fabian Johanes Manoppo
Universitas Sam Ratulangi
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering 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.
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.
Optimalisasi Kebutuhan Pengangkutan Sampah Dan Potensi Reduksi Timbulan Sampah Dengan Metode Mass Balance Di Kecamatan Malalayang Kota Manado Sharon Victorya Rori; Steeva Gaily Rondonuwu; Fabian Johanes Manoppo
Jurnal Teknik Vol 20 No 2 (2022): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v20i2.244

Abstract

Sampah merupakan permasalahan yang semakin sulit untuk diatasi saat ini karena banyaknya faktor pendukung yang terkait yaitu antara lain tingginya gaya hidup masyarakat perkotaan serta kurangnya kemampuan masyarakat dalam memahami pengolahan pereduksian sampah. Penelitian ini bertujuan untuk mengetahui berapa besar jumlah persebaran timbulan dan komposisi sampah yang dihasilkan dan potensi reduksi timbulan sampah di Kecamatan Malalayang. Penelitian ini dilaksanakan di Kecamatan Malalayang selama 6 hari kerja dengan pengumpulan data yang dilakukan adalah dengan melakukan survey primer dengan data hasil dari observasi dan wawancara dengan pihak terkait dan dengan data survey sekunder. Hasil dari data yang dikumpulkan akan digunakan dalam pemecahan rumusan masalah penelitian dengan menggunakan metode mass balance. Berdasarkan data, bahwa timbulan Sampah per jiwa di Kecamatan Malalayang adalah 2.4 kg/hari.jiwa. Dan dalam presentase keseluruhan komposisi sampah Kecamatan Malalayang terbesar yaitu sampah anorganik senilai 56,52% dan untuk sampah organik senilai 43,48%. Total berat sampah sangat dipengaruhi oleh jumlah penduduk setempat dengan prosentasenya 99% dan total berat residu sampah tidak terlalu berpengaruh pada total keseluruhan komposisi sampah dengan prosentasenya 30%. Sehingga perlu adanya optimalisasi dalam hal pereduksian persampahan.
Attention-based Residual Long Short-Term Memory for Earthquake Return Period Prediction in the Sulawesi Region Bachmid, Muhdad; Sengkey, Daniel; Manoppo, Fabian
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1506

Abstract

Indonesia, particularly the Sulawesi region, experiences significant seismic activity due to its position at the convergence of three major tectonic plates. This study seeks to construct a model for predicting earthquake return periods in the Sulawesi area by employing the Residual Long Short-Term Memory (Residual LSTM) architecture integrated with an attention mechanism. The dataset utilized originates from the United States Geological Survey (USGS), focusing on the Sulawesi Island region within the coordinates of latitude -6.184° to 2.021° and longitude 118.433° to 125.552°, spanning the years 1975 to 2024. The research methodology is structured into three primary phases: (1) data collection and preprocessing, including data cleaning, missing value handling, and normalization, (2) exploratory data analysis to understand seismic data characteristics, and (3) development of the Residual LSTM model with an attention mechanism. The evaluation results show excellent model performance with Train Loss 0.0090, Test Loss 0.0091, Training MAE 0.0698, Testing MAE 0.0717, Training RMSE 0.0947, Testing RMSE 0.0951, and stable Huber Loss of 0.0045 for both training and testing data. The implementation of residual connections successfully addressed the vanishing gradient problem, while the attention mechanism enhanced prediction interpretability. The small discrepancy between the training and testing metrics confirms the model's robust generalization ability, indicating its strong potential for applications in predicting earthquake return periods.
Co-Authors Agustinus Jacobus Bachmid, Muhdad Daniel Febrian Sengkey Rondonuwu, Steeva G. Sharon Victorya Rori