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All Journal ComEngApp : Computer Engineering and Applications Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal technoscientia Prosiding SNATIF Teknika: Jurnal Sains dan Teknologi Prosiding Semnastek Scientific Journal of Informatics Proceeding SENDI_U SMATIKA Jurnal Ampere JURNAL NASIONAL TEKNIK ELEKTRO PROtek : Jurnal Ilmiah Teknik Elektro ITEj (Information Technology Engineering Journals) JETT (Jurnal Elektro dan Telekomunikasi Terapan) JURNAL MEDIA INFORMATIKA BUDIDARMA VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal RESISTOR (Rekayasa Sistem Komputer) Patria Artha Technological Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Qua Teknika Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik JURNAL TEKNOLOGI TECHNOSCIENTIA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Pendidikan dan Teknologi Indonesia Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Teknik Elektro Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Emitor: Jurnal Teknik Elektro INOVTEK Polbeng - Seri Informatika
Sopian Soim, Sopian
Politeknik Negeri Sriwijaya
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Journal : Building of Informatics, Technology and Science

 1 
Implementasi Algoritma SVM Non-Linear Pada Klasifikasi Analisis Sentimen Perkembangan AI di Sektor Pendidikan Putri, Alda Nabila; Aryanti, Aryanti; Soim, Sopian
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

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

Abstract

As technology advances, the utilization of the X platform or formerly Twitter is expanding, allowing users to exchange opinions on various topics including the transformative impact of AI in the Education sector. While AI has great potential in revolutionizing the quality and accessibility of education, it can also bring potential challenges, such as over-reliance on technology. Sentiment analysis is a computational approach to identify, extract, and classify sentiments, opinions, and emotions expressed in text. To examine the problem, this research implements a Non-Linear Support Vector Machine model to analyze sentiment about AI in the education sector. This study built four SVM models with different kernel functions, namely linear, RBF, Polynomial, and Sigmoid kernels. By utilizing 3,000 tweet data collected from platform X by scraping technique, the SVM model with polynomial kernel succeeded in becoming the best model, with accuracy, precision, recall and f1-score values of 92%. This model was able to classify 52.9% of the tweet data with positive sentiment and 47.1% of the tweet data with negative sentiment, which shows that in general, users of platform X tend to have a positive sentiment towards the development of AI in the education sector.
Pengembangan Algoritma Convolutional Neural Networks (CNN) untuk Klasifikasi Objek dalam Gambar Sampah Putri Vandalis, Yoke Annisa; Soim, Sopian; Lindawati, Lindawati
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5585

Abstract

Waste is a serious issue facing the world today, with increasing human activity and global economic growth. One important step in waste management is the classification process, which aims to separate types of waste based on their characteristics so they can be recycled, processed, or disposed of properly. Previous research has shown that Convolutional Neural Networks (CNN) are effective algorithms for multi-class classification. Therefore, this study develops an optimized CNN model for automatic waste classification, with a primary focus on improving accuracy through modifications to the CNN architecture. The dataset used consists of 17,366 waste images from various sources, which are then divided into training and testing data after undergoing preprocessing to ensure good data quality before training the model. However, one of the main challenges in developing a CNN model for multi-class classification is the risk of difficulty in learning class features, especially when the model is faced with too many classes. To address this issue, this study implements a strategy by adding convolutional layers to the CNN architecture. This method aims to deepen the network to capture more complex features from the given data, which in turn can improve the model's generalization to new data. Evaluation results show that the modified CNN model achieved a training accuracy of 88% after 40 epochs, with a testing accuracy of around 83%. This research not only contributes to the development of more advanced automatic waste classification technology but also provides a strong foundation for further research in this field. With increased waste management effectiveness, it is hoped to have a positive impact on the environment and public health as a whole..
A Comparative Study of Machine Learning Classifiers with SMOTE for Predicting Purchase Intention Khairunnisa, Khairunnisa; Soim, Sopian; Lindawati, Lindawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7615

Abstract

The rapid growth of e-commerce has made it increasingly important for online platforms to understand user behavior, particularly in predicting purchasing intention. This study examines the implementation of three machine learning models: Logistic Regression, Random Forest, and Gradient Boosting, to classify purchase intention using real transaction session data. One of the primary obstacles confronted in this investigation is the matter of class imbalance found in the dataset, where 10422 records indicate no purchase while only 1908 indicate a completed purchase. This disparity may result in a biased model performance that prioritizes the dominant class and limits the ability to accurately detect minority class behavior, which in this case is the actual purchase. To resolve this matter, During the data preprocessing phase, the Synthetic Minority Over-sampling Technique (SMOTE) was implemented. Accuracy, precision, recall, and F1-score metrics were implemented to assess each model's functionality. The results indicate that following the implementation of SMOTE, the Random Forest model attained the best accuracy of 93%, succeeded by Gradient Boosting at 90% and Logistic Regression with 84%. These findings demonstrate that the use of SMOTE significantly improves model sensitivity and balance. This study provides useful insights into designing fairer and more effective predictive systems in the field of e-commerce.
Co-Authors Abu Hasan Ade Silvia Handayani Adewasti Adewasti Adewasti, Adewasti Agung, Muhammad Zakuan Ahmad Adriansyah Ahmad Jazuli Ahmad Taqwa Ali Nurdin Alpharisy, Kevin Farid Alqhaniyyu, Faris Amiza, Ibel Dwi Amperawan Amperawan Amperawan Amperawan, Amperawan Anisah, Masayu Aryanti Aryanti . Aryanti Aryanti Ciksadan, Ciksadan Damsi, Faisal Deta Mediana, Salwa Diraputra, M Yoga Azto Dody Novriansyah Fadhli, Mohammad Fahrudin, Gantar Fitra Faisal Damsi, Faisal Farhan, Novendra Fathria Nurul Fadillah Fatimatuzzahra Fatimatuzzahra Fistania Ade Putri Maharani Frenica, Agnes Garnis, Aishah Garnis, Aishah Gusni Amini Siagian Hafizh Ulwan Handayani, Kurnia Wati Pascitra Hj. Lindawati Humairoh, Sherina Husni, Nyayu Latifah Ihsan Mustaqiim Irawan Hadi Irawan Hadi Irma Salamah Irma Salamah Jami, Nurlita Joni, Bahri Joni, Bahri Junaidi Junaidi Junaidi Junaidi Junaidi, Junaidi Khairunnisa Khairunnisa L. Lindawati LINDAWATI Lindawati Lindawati Lindawati Lindawati Lindawati Lindawati M Yoga Azto Diraputra Maharani, Fistania Ade Putri Martinus Mujur Rose Mohammad Fadhli Mujur Rose Nabila, Puspita Aliya Nadiah Nadiah Nakiatun Niswah Nasron Nasron Nisa, Suci Lutfia Novianda, Nabila Rizqi Novianda, Nabila Rizqia Novriansyah, Dody Nurhajar Anugraha Nurul Fadhilah Oktariani Oktariani Oktariani, Oktariani Oktavia Manalu, Ria Pipit Wulandari Putri Andela Putri Vandalis, Yoke Annisa Putri, Alda Nabila Raihanah, Adinda Ramadhan, Muhammad Fadli Rani Purnama Sari Repi, Intan Putri Ayu Agita Respati, Rayhan Dhafir Riona Alpeni Rivaldo Arviando Rizky, Putri Alifia Rodicky, Nadio Rose, Mujur Rumiasih Rumiasih Salsabila Dina Sari Sari, Rani Purnama Sarjana Sarjana Sarjana, Sarjana Savitri, Yulivia Rhadita Seliana, Imalda Septiani, Dinda Sholihin Sholihin Sholihin Sholihin Subianto, Cahyo Bayu Sudirman Yahya Suroso Suroso Suroso Suroso suzan zefi Tarnita Rizky Prihandhita Tely, Aristo Theresia Enim Agusdi Trisa Azahra Wulandari, Pipit Yanziah, Asma Zakuan Agung, Muhammad