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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 Smatika Jurnal : STIKI Informatika Jurnal
Sopian Soim, Sopian
Politeknik Negeri Sriwijaya
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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..
Implementasi Convolutional Neural Network Pada Alat Klasifikasi Kematangan dan Ukuran Buah Nanas Berbasis Android Salamah, Irma; Humairoh, Sherina; Soim, Sopian
Jurnal Inovtek Polbeng Seri Informatika Vol 8, No 2 (2023)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v8i2.3413

Abstract

Sumatera Selatan merupakan wilayah produksi buah nanas paling tinggi di tahun 2021. Dalam proses penjualan buah nanas bergantung pada ukuran dan kematangan. Para petani mengklasifikasikan buah nanas secara subjektif dengan kedua mata, sehingga menyebabkan proses klasifikasi tidak efektif. Teknologi machine learning berkembang sangat pesat, salah satunya deep learning yang menggunakan syaraf tiruan (neural network) yang sangat dalam (deep) untuk mempelajari representasi fitur dari data secara otomatis. Penelitian ini bertujuan untuk mengimplementasikan algoritma Convolutional Neural Network (CNN) untuk mengklasifikasi kematangan dan ukuran buah nanas agar proses pemilahan hasil produksi buah nanas menjadi efektif dan akurat. Terdapat 6 label klasifikasi yaitu, nanas besar matang, besar setengah matang, sedang matang, sedang setengah matang, kecil matang dan kecil setengah matang. Digunakan Raspberry pi 3B+ dan kamera pi sebagai alat pengambilan citra buah. Didapatkan hasil akurasi proses training sebesar 99,4 % dan akurasi proses validasi sebesar 92,4% dengan dataset sebanyak 275 data untuk setiap label. Dataset digunakan 80% sebagai data training dan 20% data validasi. Sedangkan untuk pengujian testing pada alat digunakan 90 data uji dengan hasil akurasi sebesar 90,83%. Dan hasil klasifikasi akan tampil pada aplikasi android termasuk jumlah stok nanas yang telah dideteksi, sehingga dapat mempermudah pekerjaan petani dalam menyortir buah nanas.
Implementasi Algoritma Support Vector Machine (SVM) untuk Deteksi Banjir Frenica, Agnes; Lindawati, Lindawati; Soim, Sopian
Jurnal Inovtek Polbeng Seri Informatika Vol 8, No 2 (2023)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v8i2.3443

Abstract

Banjir merupakan bencana yang paling banyak terjadi pada tahun 2022 di Indonesia. Berdasarkan laporan Badan Nasional Penanggulangan Bencana (BNPB), jumlah bencana banjir setara 43,1% dari total kejadian bencana nasional sekitar 1.524 kejadian. Salah satu wilayah yang secara berulang terkena dampak banjir selama periode 2022 hingga 2023 adalah Sumatera Selatan sebanyak 99 kali. Dalam upaya penanggulangan masalah ini, teknologi machine learning dapat digunakan sebagai alat pendeteksi dan peringatan dini terhadap banjir, salah satunya Algoritma SVM. Dalam penelitian ini, kinerja berbagai kernel SVM dinilai, dan kernel dengan kinerja terbaik dipilih untuk digunakan dalam sistem deteksi banjir. Penelitian ini mengkategorikan status banjir dengan tiga label klasifikasi: "aman," "waspada," dan "bahaya". Dalam upaya membangun model prediksi status banjir, berbagai algoritma SVM seperti linear, polynomial, RBF, dan sigmoid diimplementasikan menggunakan dataset publik. Lalu akan diuji dengan alat deteksi banjir yang terdiri dari dari arduino uno, nodemcu, dan berbagai sensor seperti sensor ultrasonik, sensor debit air, sensor curah hujan, sensor suhu dan kelembaban. Evaluasi dilakukan melalui pengukuran akurasi, presisi, recall, F1-Score, kurva ROC, dan Cross-Validation. Hasil evaluasi menunjukkan bahwa model SVM dengan kernel polynomial terpilih sebagai model deteksi yang optimal dibandingkan dengan kernel lainnya. Model ini mencapai akurasi pelatihan dan pengujian sebesar 1.0, waktu pelatihan hanya 0.0012 detik, waktu prediksi 0.0002 detik, serta presisi, recall, dan F1-score sebesar 1.0. Selain itu, cross-validation juga mencapai 1.0 dalam mengklasifikasikan data banjir. Pengujian pada alat digunakan 131 data uji dengan hasil akurasi sebesar 1.0. Hasil klasifikasi dan data sensor disajikan melalui aplikasi Android, memudahkan dalam pemantauan banjir.
Prediksi Pengguna Broadband di Indonesia sampai Tahun 2024 Menggunakan Pemodelan Crisp-DM dengan Metode Decision Tree dan Algoritma ID3 Studi Kasus di BPS dan APJII Novianda, Nabila Rizqia; Soim, Sopian; Lindawati, Lindawati
Patria Artha Technological Journal Vol 5, No 2 (2021): Patria Artha Technological Journal
Publisher : Department of Electrical Engineering, University of Patria Artha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33857/patj.v5i2.453

Abstract

Indonesia is a country with a population of 270.20 million people recorded at the Central Statistics Agency in September 2020. Based on the International Telecommunication Union (ITU) reported, the United Nations (UN) body, the number of world internet users in 2018 was 3.9 billion, exceeding half the world's population. Today the position of the internet continues to be significant in social, economic, and political life in a globalized world. Every year the internet continues to influence human life. Therefore we need research on forecasting the number of broadband users in the future to find out the number of users will increase or decrease as well as planning for Indonesian broadband. This forecasting research uses, using a comparison, namely the double exponential smoothing method with two different alphas and two betas, then the single exponential smoothing method with two different alphas. The double exponential smoothing method with alpha 0.2 betas 0.4 with the result that the MAD, MSE, MAPE values are smaller than single exponential smoothing with the first part alpha 0.8 and the second part alpha 0.5 and smaller than the double method exponential smoothing with alpha 0.6 betas 0.2. After getting the best value from the double exponential smoothing forecasting method, the prediction process is continued using the decision tree method, namely the ID3 algorithm for decision making every year from 2000 to 2024 to expand bandwidth or by not expanding bandwidth with the result that there are 19 years to expand bandwidth and there are 6 years for the decision not to expand the bandwidth.
Implementasi Port Knocking, Port Blocking Pada Keamanan Jaringan Komputer Berbasis Mikrotik Repi, Intan Putri Ayu Agita; Soim, Sopian
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 6 No. 3 (2023): Jurnal RESISTOR Edisi Desember 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v6i3.1463

Abstract

Perkembangan Internet pada zaman modern seperti sekarang sangatlah canggih, akan tetapi semakin canggih perkembangan zaman maka akan banyak pula yang harus diwaspadai karena banyaknya serangan yang bisa dilakukan oleh para hacker terutama di bagian keamanan jaringan. Keamanan jaringan sangat penting untuk lebih diperhatikan. Untuk itu harus dipersiapkan keamanan yang sulit ditembus untuk mengamankan dan meminimalisir resiko ancaman pada jaringa. Oleh karena itu dalam penelitian ini penulis melakukan eksperimen dengan judul "Implementasi Port Knocking, Port Blocking Pada Keamanan Jaringan Komputer Berbasis Mikrotik". Variable yang digunakan ialah berbasis mikrotik dengen tujuan untuk meningkatkan keamanan akses pengguna komputer dengan mencegah pencurian data dan informasi, serta menjaga data tetap aman. Hasil akhir penelitian menunjukkan bahwa pengguna harus melakukan autentikasi pada port tertentu, yang membuat server menjadi lebih aman. Selain itu, beberapa port juga ditutup untuk mencegah serangan akses dari pihak yang tidak sah. Dengan menggabungkan metode port knocking dan penutupan port (port blocking), serangan dari pihak yang tidak berwenang menjadi lebih sulit.
Implementasi Algoritma Kriptografi AES CBC Untuk Keamanan Komunikasi Data Pada Hardware Rizky, Putri Alifia; Soim, Sopian; Sholihin, Sholihin
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 7 No. 2 (2024): Jurnal RESISTOR Edisi Agustus 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v7i2.1650

Abstract

Perkembangan teknologi informasi semakin berkembang dengan pesat, keamanan komunikasi data menjadi sangat penting. Penelitian ini mengembangkan algoritma kriptografi Advanced Encryption Standard (AES) dengan mode operasi Cipher Block Chaining (CBC) untuk meningkatkan keamanan komunikasi data. Tujuannya adalah menghasilkan algoritma AES-CBC yang lebih efektif dan efisien dalam melindungi data. Metode penelitian meliputi studi literatur, perancangan perangkat keras dan lunak, integrasi sistem, pengujian, pengambilan data, dan analisis. Implementasi dilakukan pada perangkat keras berbasis Arduino dan LoRa sebagai perantara komunikasi. Hasil pengujian menunjukkan bahwa algoritma AES-CBC berhasil mengenkripsi dan mendekripsi berbagai jenis plaintext menjadi ciphertext yang aman. Penggunaan mode CBC terbukti meningkatkan keamanan dibandingkan AES standar dengan mengenkripsi setiap blok data berdasarkan blok sebelumnya. Implementasi pada perangkat keras juga berhasil, menambah variasi dalam penelitian kriptografi. Kesimpulannya, pengembangan algoritma AES-CBC dan implementasinya pada perangkat keras memberikan kontribusi signifikan dalam meningkatkan keamanan komunikasi data, baik dalam lingkungan akademis maupun praktis.
Vehicle Class Prediction at Toll Gate Using Deep Learning Nisa, Suci Lutfia; Soim, Sopian; Agung, Muhammad Zakuan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9833

Abstract

In the era of digitalization and automation, efficiency in the traffic management system at toll gates is very important. One of the efforts to improve this efficiency is to develop an automatic vehicle class detection system using deep learning technology, especially Convolutional Neural Network (CNN). This research aims to design and implement a CNN model that can identify and classify the types of vehicles passing through toll gates. The model development process includes collecting and annotating vehicle image data, data pre-processing, and CNN model training and testing. The evaluation results show that the developed model can achieve an accuracy of about 96% in detecting vehicle classes, so it can be integrated with the toll gate system to increase the speed and accuracy in the vehicle classification process. Thus, this solution is expected to reduce the waiting time of toll users and improve operational efficiency.
Klasifikasi Kondisi Kendaraan Berpotensi Kecelakaan Berbasis Android Menggunakan Long Short Term Memory Nabila, Puspita Aliya; Soim, Sopian; Handayani, Ade Silvia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7005

Abstract

Traffic accidents are a severe problem that often results in loss of life and property damage. Efforts to overcome this situation require real-time vehicle monitoring with the capture and collection of relevant data to provide information about the driver and family at home to reduce the risk of accidents by identifying potentially dangerous vehicle conditions automatically and quickly. This research utilizes Long Short Term Memory technology to analyze sensor data installed in the vehicle to an android device to be classified according to three conditions that recognize vehicle conditions as safe, alert, or dangerous. The Long Short Term Memory model used achieved a high level of accuracy with a value of 99.96% when training on data. After testing, this model still has a good level of accuracy with a value of 93.3%. In the test, the precision value of each class is 83.33% for the safe class, 80% for the danger class, and 100% for the alert class. In indicating that Long Short Term Memory in this study is very efficient in identifying and classifying vehicle conditions to reduce potential accidents. The information processed by Long Short Term Memory will be transmitted to an Android application capable of delivering up-to-date insights into the vehicle's condition. This app incorporates cautionary alerts in the presence of potential accident indicators to aid in vigilance and accident prevention. The integration of this system aims to enhance road safety and diminish the occurrence of accidents resulting from suboptimal vehicle conditions or hazardous driver conduct. This application can provide convenience for vehicle owners to know the state of the vehicle in real-time remotely in optimal conditions.
Optimizing Performance Random Forest Algorithm Using Correlation-Based Feature Selection (CFS) Method to Improve Distributed Denial of Service (DDoS) Attack Detection Accuracy Soim, Sopian; Sholihin, Sholihin; Subianto, Cahyo Bayu
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.24783

Abstract

In the ever-evolving digital era, Distributed Denial of Service (DDoS) attacks have become a major threat to the security of networks and online services, making it important to develop effective strategies to detect and overcome such attacks.This research aims to improve the performance of Random Forest algorithm in dealing with DDoS attacks by using Correlation-Based Feature Selection method. This method can identify and select the most relevant features from the dataset used, in this case the CIC-DDoS2019 dataset, with respect to accuracy, precision, recall, and F1-score as evaluation metrics, so that this research achieves the best results in effectively detecting and preventing DDoS attacks, making an important contribution in strengthening the security of networks and online services.The results show that the application of the Correlation-Based Feature Selection method is able to improve DDoS attack detection in a complex network context using the Random Forest algorithm, increasing the detection accuracy rate to 99.89%. These findings highlight the potential of using the Random Forest algorithm with the CFS method in improving DDoS attack detection in complex network environments.This study recorded a significant improvement compared to the previous study, which only achieved an accuracy rate of 99.7% using the feature importance method. 
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 APRILIANI, DEFINA 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 Rabbaniansyah, Kgs Muhammad Farhan 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