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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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SOSIALISASI DAN SIMULASI KEAMANAN SERVER KERBEROS DAN OPENLDAP DI SMK NEGERI 4 PALEMBANG Lindawati; Fadhli, Mohammad; Soim, Sopian; Deta Mediana, Salwa
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): Aptekmas Volume 7 Nomor 3 2024
Publisher : Politeknik Negeri Sriwijaya

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

Abstract

Computer networks are essential to daily life in the digital age because they link devices and provide distant information access. But technological progress also poses serious concerns to data security. Threats like viruses, malware, and hacker attacks put data's confidentiality and integrity in danger. For this reason, cybersecurity technicians must comprehend and use strong security frameworks. Kerberos, a security protocol that enables entities in a network to safely verify their identities, is one of the solutions that are presented. On the other side, directories are managed via Lightweight Directory Access Protocol (LDAP). In this context, teachers and students at the Vocational High School (SMK) with a focus on computer and network engineering receive instruction and training on network security. It is hoped that by doing this outreach, the community would see how important network security is and take the necessary precautions to safeguard data from intrusions. The outreach efforts were conducted at SMK Negeri 4 Palembang, which is the subject of this study. The findings demonstrate that students are able to test exploitation techniques on the framework used in the apache2 service and have a solid understanding of Kerberos, LDAP, and Firewalld.
Development of a Distributed Gradient Boosting Forest Algorithm with Residual Connections in Data Classification Respati, Rayhan Dhafir; Soim, Sopian; Fadhli, Mohammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4899

Abstract

The growing complexity and volume of data across various domains necessitate machine learning models that are scalable and robust for large-scale classification tasks. Ensemble methods such as Gradient Boosting Decision Trees (GBDT) demonstrate effectiveness; however, they encounter issues concerning scalability and training stability when applied to very deep architectures. This work presents a novel enhancement using residual connections derived from deep neural networks into the Distributed Gradient Boosting Forest (DGBF) algorithm. By enabling direct gradient propagation across layers, residual connections solve the vanishing gradient problem and so improve gradient flow, accelerate convergence, and stabilise the training process. The Residual DGBF model was assessed using seven distinct datasets across the domains of cybersecurity, financial fraud, phishing, and malware detection. The Residual DGBF consistently surpassed the baseline DGBF in terms of accuracy, precision, recall, and F1-score across all datasets. Particularly in datasets marked by imbalanced classes and complex feature interactions, this suggests improved generalisation and higher predictive accuracy. By proving more stable and strong gradients across the depth of the model, layer-wise gradient magnitude analysis supports these improvements and so confirms the effectiveness of residual connections in deep ensemble learning. This work improves ensemble techniques by combining the scalability and interpretability of decision tree ensembles with the residual architecture optimising benefits. The proposed Residual DGBF enables future research on enhanced deep boosting frameworks by offering a strong and scalable method to address challenging real-world classification tasks.
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.
Development of Convolutional Neural Network Models to Improve Facial Expression Recognition Accuracy Fatimatuzzahra, Fatimatuzzahra; Lindawati, Lindawati; Soim, Sopian
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28863

Abstract

Advancements in information and computer technology, particularly in machine learning, have significantly alleviated human tasks. One of the current primary focuses is facial expression recognition using deep learning methods such as Convolutional Neural Network (CNN). Complex models like CNNs often encounter issues such as gradient vanishing and overfitting. This study aims to enhance the accuracy of CNN models in facial expression recognition by incorporating additional convolutional layers, dropout layers, and optimizing hyperparameters using Grid Search. The research utilizes the FER2013 public dataset sourced from the Kaggle website, trained and evaluated using CNN models, hyperparameter tuning, and downsampling methods. FER2013 comprises thousands of facial images representing various human expressions, with a specific focus on four facial expression categories (angry, happy, neutral, and sad). Through the addition of convolutional and dropout layers, as well as hyperparameter optimization, the developed model demonstrates a significant improvement in accuracy. Findings reveal that the refined CNN model achieves a highest accuracy of 98.89%, with testing accuracy at 89%, precision 78%, recall 78%, and F1-score 78%. This research contributes by enhancing facial expression recognition accuracy through optimized CNN models and providing a framework beneficial for the social-emotional development of children with special needs and aiding in the detection of mental health conditions. Additionally, it identifies avenues for future research, including exploring advanced data augmentation techniques and integrating multimodal information. Furthermore, this study paves the way for applications across diverse fields like human-computer interaction and mental health diagnostics.
Implementasi Layanan untuk Pesan Singkat Menggunakan Perangkat Universal Software Radio Pheripheral (USRP) B210 pada Standar Komunikasi 3GPP Berbasis 4G Anugraha, Nurhajar; Seliana, Imalda; Soim, Sopian
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41660

Abstract

Communication technology continues to develop rapidly, now 4G has become a network standard used in various countries. 4G networks offer higher data speeds, lower latency, and greater capacity compared to previous networks. Now, we can exchange information and establish relationships with others without having to meet in person. However, in remote areas and areas with limited networks, there are still problems in using these celluler phones that are poorly understood by people living in remote areas or areas that are not covered by celluler phone networks. For example, the use of very simple cellular phone facilities such as Short Message Service (SMS). OpenBTS is a new part of BTS technology that is very economical both in terms of funds and resources because openBTS is based on opensource software so that it is easily available and anyone can implement this openBTS, and can provide services to communicate such as Short Message Service (SMS) or short messages and can be used as a substitute for telephone facilities in the event of signal interference. For this reason, in this study, the implementation of services for short messages using the Universal Software Radio Peripheral (USRP) B210 device on 4G-based 3GPP communication standards. The hardware used is USRP B210.
Desain dan Pengembangan Website untuk Mendeteksi Malware Menggunakan Framework Flask yang Diintegrasikan dengan Machine Learning Ciksadan, Ciksadan; Soim, Sopian; Jami, Nurlita
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.42003

Abstract

One of the most widely used media for information dissemination is the website. A dynamic and informative website will make it easier for users to access information. Web development often requires complex technologies. One method that can simplify the development process is using the Flask framework, which offers flexibility and freedom to developers. A website must also have functionality to be useful; one current issue is the increasing number of malware file cases. Therefore, there is a need for a medium that can analyze a file. However, currently, there are limited services available for this purpose. This research aims to build a website that detects malware files using the Flask framework integrated with machine learning for malware file detection. Through this research, a website with five informative menus has been developed, featuring a dynamic and easily accessible interface with a malware file detection capability reaching 99% accuracy.
Pengembangan Model Support Vector Machine untuk Meningkatkan Akurasi Klasifikasi Diagnosis Penyakit Jantung Fahrudin, Gantar Fitra; Suroso, Suroso; Soim, Sopian
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.42254

Abstract

Heart disease is a serious health issue that leads to high mortality risk worldwide. Contributing factors include high cholesterol, diabetes, and high blood pressure. Therefore, early prediction of heart disease is a crucial initial step to reduce mortality risk. This paper proposes a new heart disease classification model based on the Support Vector Machine (SVM) algorithm to enhance disease detection performance. To improve diagnostic accuracy, we apply feature selection techniques and grid search. The performance of the enhanced model is validated by comparing it with a simple model using a confusion matrix. The enhanced model achieves an accuracy of 96.56%, showing an improvement of 8.91% over the previous model, which had an accuracy rate of only 87.65%. Additionally, the number of features used is reduced from 14 to 8, decreasing the computational load from 100% to about 32%. These results indicate that the enhanced SVM provides better and more efficient performance compared to other methods in heart disease classification
Monitoring Kapal Menggunakan Automatic Identification System(AIS) Dengan RTL-SDR dan Low Noise Amplifier (LNA) Sari, Rani Purnama; Lindawati, Lindawati; Soim, Sopian
PROtek : Jurnal Ilmiah Teknik Elektro Vol 9, No 2 (2022): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v9i2.4691

Abstract

Automatic Identification System (AIS) is a ship transponder that uses MMSI data, speed, position, destination, ship type, and size to locate, track, and monitor ships. Only Vessel Traffic Services (VTS) and a few other agencies can be supervised presently. This is one of the issues that must be resolved. To resolve this issue, hardware that can receive AIS signals at 161.975 MHz and 162.025 MHz and convert them into information signals is required. RTL-SDR is hardware capable of receiving signals in the frequency range of 25-1700 MHz. Its antenna is used to achieve maximum signal reception by establishing a direct line of sight to the AIS data source. Yagi antennas can only receive signals from a single direction, the front. Low Noise Amplifier (LNA) is also used in order to optimize the signal received by the antenna. The signal can be processed and decoded using SDR-Sharp and AISMon to provide data that can be plotted on OpenCPN. The performance of the AIS data decoding process is controlled by the strength and weakness of the signal that the RTL-SDR receiver can receive. Therefore, the antenna and receiver must be placed in a clear line of sight (LOS) with the ship's AIS transponder emitting source. It is envisaged that this monitoring system would make it easier to monitor ships in real-time
Rancang Bangun Monitoring Lokasi Pesawat Menggunakan ADS-B dengan RTL-SDR dan Raspberry Pi Diraputra, M Yoga Azto; Soim, Sopian; Sarjana, Sarjana
PROtek : Jurnal Ilmiah Teknik Elektro Vol 8, No 2 (2021): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v8i2.3233

Abstract

Abstract — Automatic Dependent Surveillance Broadcast (ADS-B) is a surveillance technology that provides information on aircraft in the air in the form of 24 bit ICAO aircraft address, ident or squawk, massage, altitude, nationality, speed, longitude, track and heading. The problem faced now is that surveillance can only be done with a web and android-based application on FlightRadar24 so that if the user wants to display more aircraft information, the user is required to pay a subscription. To overcome this problem, hardware is needed that can receive ADS-B signals with a frequency of 1090 MHz and can translate them into information signals. RTL-SDR is hardware that can receive signals with a frequency range from 25 MHz - 1700 MHz, by applying the Raspberry Pi it is used to configure RTL-SDR as a receiver capable of receiving information from ADS-B signals. To get the maximum reception, an omnidirectional antenna is needed that can receive signals from all directions. With this system, it is expected to make it easier to monitor aircraft in real time and processing ADS-B signal data is determined by the strength and weakness of the signal that can be received by RTL-SDR.
Performance Improvement of Fake News Detection Models Using Long Short-Term Memory Hyperparameter Optimization Lindawati, Lindawati; Ramadhan, Muhammad Fadli; Soim, Sopian; Novianda, Nabila Rizqi
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v%vi%i.45420

Abstract

Purpose: The proposed model was developed based on prior research that distinguished between fake and real news using a deep learning-based methodology and an LSTM neural network, with a model accuracy of 99.88%. This study uses hyperparameter tuning techniques on a Long Short-Term Long Memory (LSTM) neural network architecture to improve the accuracy of a fake news detection model.Methods: To improve the accuracy of the fake news detection model and optimize the model from previous research, this study uses the hyperparameter tuning technique on models with Long Short-Term Memory (LSTM) neural network architecture. For this technique, three different types of experiments, hyperparameter tuning on the LSTM layer, Dense layer, and Optimizer, were conducted to obtain the best hyperparameters in each layer of the model architecture and the model parameters proposed. The fake and real news dataset, which has also been used in earlier studies, was used in this study.Results: The proposed model could detect fake news with a high accuracy of 99.97%, surpassing the previous research models with an accuracy of 99.88%.Novelty: The novelty of this study was the hyperparameter tuning technique on different layers of the LSTM neural network to optimize the fake news detection model. The research aims to improve upon previous approaches and increase the accuracy of the model. 
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