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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Teknik Informatika (JUTIF) PARAHITA : Jurnal Pengabdian kepada Masyarakat Sevanam: Jurnal Pengabdian Masyarakat AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Pengabdian Masyarakat Inovasi Indonesia
Sasongko, Theopilus Bayu
Informatics, Faculty Of Computer Science, Universitas Amikom Yogyakarta, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences

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Komparasi dan Analisis Kinerja Model Algoritma SVM dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA) Theopilus Bayu Sasongko
Jurnal Teknik Informatika dan Sistem Informasi Vol 2 No 2 (2016): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v2i2.627

Abstract

Attribute Selection is very important for classification process. This research has been done by doing attribute selection using PSO method (Particle Swarm Optimization) on SVM algorithm (Support Vector Machine). The development of the classification model uses three parameters especially data attribute, influence of the transformation of various kernel function and penalty factor (C) toward the performance of SVM and PSO-SVM classification.  The analysis uses five kernels in mySVM library that existed in Rapidminer application namely dot, radial, polynomial, neural, and anova kernel. The training data used in the first model classification development is student interest data at ABC high school on 2013-2014 year academic.  The first model is evaluated using accuracy, precision, recall, and auc value test. The first result shows that the anova kernel on PSO-SVM is able to work with accuracy level 99.30% using penalty factor 0.1. The second model has been developed to predict student interest in XYZ high school. The second result shows that PSO-SVM with kernel anova is able to classify students interest with 99.29% accuracy level.  Keywords— Optimization, SVM, PSO-SVM, Student Interest. 
Sentiment Analysis on BRImo Application Reviews Using IndoBERT Simarmata, Asyer Aprinando Pratama; Sasongko, Theopilus Bayu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.8162

Abstract

The advancement of information technology has significantly impacted various sectors, including digital banking. BRImo, a mobile banking application from Bank Rakyat Indonesia (BRI), has been widely used, generating numerous user reviews that reflect their experiences. This study applies IndoBERT, a transformer-based model specifically designed for the Indonesian language, to analyze sentiment in BRImo user reviews. IndoBERT excels in handling the unique characteristics of the Indonesian language, such as informal and mixed-language usage. The dataset was collected from Kaggle and processed through labeling, data balancing, and splitting into 80% training, 10% validation, and 10% testing data. The IndoBERT model was evaluated using a confusion matrix and achieved 90% accuracy, with F1-scores of 0.89 for negative, 0.91 for neutral, and 0.90 for positive sentiments. Sentiment analysis results indicate that a significant portion of negative reviews highlight issues related to login difficulties, transaction failures, and slow customer service response times. These insights can help BRI enhance application reliability and customer support efficiency. This study demonstrates that IndoBERT is effective in sentiment analysis for Indonesian text and can be utilized to enhance BRImo services by providing deeper insights into user feedback.
Implementasi Metode Forward Selection pada Algoritma Support Vector Machine (SVM) dan Naive Bayes Classifier Kernel Density (Studi Kasus Klasifikasi Jalur Minat SMA) Sasongko, Theopilus Bayu; Arifin, Oki
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 4: Agustus 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1093.895 KB)

Abstract

Peminatan merupakan kegiatan yang disediakan oleh pihak sekolah yang berguna untuk mengakomodasi pilihan minat, bakat, atau kemampuan peserta didik dengan orientasi pemusatan. Penentuan jalur minat umumnya melibatkan banyak attribute. Klasifikasi maupun prediksi pada data mining menggunakan fitur seleksi sangat penting untuk pemilihan attribute yang tepat, karena berpengaruh pada performansi model, oleh sebab itu perlu metode untuk melakukan seleksi atribut. Penelitian ini membandingkan implementasi metode forward selection pada Algoritma SVM dan Naïve Bayes Kernel Density. Studi kasus yang digunakan adalah jalur minat pada siswa SMA pada dua sekolah yang berbeda. Proses pembentukan model klasifikasi dengan menganalisa perubahan kernel, faktor pinalti (C) SVM, number of kernel Naïve bayes kernel density, dan hasil feature subset forward selection. Digunakan lima buah eksperimen kernel SVM yaitu dot (linear), radial (RBF), polynomial, neural, dan anova. Proses uji coba perubahan parameter menggunakan rentang 0.0-100.0. Hasil dari penelitian ini diantaranya adalah feature subset dataset SMA ABC yang terpilih yaitu nilai IPA, tes akademik, abstrak konseptual, analisa sintesa, dan logika numerik, sedangkan feature subset SMA XYZ yaitu nilai IPA, logika numerik, dan analisa sintesa. Hasil pengujian dataset SMA ABC menggunakan algoritma FS-SVM berbasis kernel anova parameter C=10.0 sebesar 99.29%. Sedangkan hasil pengujian dataset SMA XYZ menggunakan algoritma FS-SVM berbasis kernel anova parameter C=10.0 sebesar 95.17%. AbstractSpecialization is an activity provided by the school that is useful to accommodate the choice of interests, talents, or abilities of students with a concentration of orientation. The determination of interest generally involved many attributes. The classification and prediction on the data mining that use the selection feature is very important for the selection of the right attribute, because it affects the performance of the model, therefore a method is needed to select attributes. This study compares the implementation of the forward selection method in the SVM Algorithm and Naïve Bayes Kernel Density. The case study that is used is the interest of students in high school and compared with two different schools. The process of modelling by studying kernel changes, penalty factors (C) SVM, number of kernel Naïve bayes kernels, and the results of features from subset forward selection. Five SVM kernel experiments ared used, namely dot (linear), radial (RBF), polynomial, neural, and anova. The trial process of changes parameters uses the range 0.0-100.0. The results of this study include features of selected ABC SMA subset datas, which are IPA values, academic tests, conceptual abstracts, synthesis analysis, and numerical logic, while the XYZ SMA subset features are IPA values, numerical logic, and synthesis analysis. The test results of the ABC High School dataset that use the kernel-based FS-SVM algorithm parameter C = 10.0 is 99.29%. While the results of testing the XYZ SMA dataset that use the kernel-based FS-SVM algorithm parameter C = 10.0 for 95.17%.
Analisis Efek Augmentasi Dataset dan Fine Tune pada Algoritma Pre-Trained Convolutional Neural Network (CNN) Sasongko, Theopilus Bayu; Haryoko, Haryoko; Amrullah, Agit
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106583

Abstract

Kemajuan teknologi deep learning seringkali berbanding lurus dengan keterkaitan metode yang dapat diandalkan dalam penggunaan jumlah data yang besar. Convolutional Neural Network (CNN) adalah salah satu algoritma deep learning yang paling popular saat ini guna pengolahan citra. Pada era deep learning model CNN yang kompleks seperti saat ini memiliki tantangan-tantangan yang baru baik gradient vanishing, overfitting yang dikarenakan keterbatasan dataset, optimasi parameter hingga keterbatasan perangkat keras. Penelitian ini bertujuan mengukur pengaruh teknik fine tuning dan augmentasi dataset pada model transfer learning CNN Mobilenet, Efficientnet, dan Nasnetmobile dengan dataset yang variasi jumlah dataset yang memiliki jumlah yang terbatas. Pada hasil dari penelitian ini, dari ketiga dataset yang digunakan sebagai dalam melakukan training pada model efisien transfer learning baik MobileNet, EfficientNet, dan NasNetmobile, teknik augmentasi zoom range ataupun random erase dapat meningkatkan akurasi pada dataset dengan jumlah 56 citra dan 222 citra, sedangkan pada dataset dengan jumlah 500 data citra, semua teknik augmentasi terbukti dapat meningkatkan akurasi pada model arsitektur MobileNetV2 dan NasNetMobile. Sedangkan teknik fine tuning terbukti efektif dalam meningkatkan akurasi pada semua skala data yang kecil. AbstractToday deep learning technology is often associated with reliable processes (methods) when we have large amounts of data. In deep learning CNN (Convolutional Neural Network) plays a very important role which is often used to analyze (classify or recognize) visual images. In the era of deep learning models such as the complex Convolutional Neural Network (CNN) as it is today, it has new challenges such as gradient vanishing, overfitting due to dataset limitations, parameter optimization to hardware limitations. The MobileNet architecture was coined in 2017 by Howards, et al, which is one of the convolutional neural networks (CNN) architectures that can be used to overcome the need for excessive computing resources. This study aims to measure the effect of fine tune and dataset augmentation techniques on CNN mobilenet, efficientnet, and nasnetmobile transfer learning models with very small datasets. The results of this study are that of the three datasets used as the basis for training in efficient transfer learning models (mobilenet, efficientnet, and nasnetmobile), random erase and zoom range augmentation techniques dominate the increase in model accuracy. The amount of increase in accuracy after random erase or zoom range augmentation that occurs is about 0.03% to 0.1%.
SENTIMENT ANALYSIS OF CYBERBULLYING USING BIDIRECTIONAL LONG SHORT TERM MEMORY ALGORITHM ON TWITTER Safitri, Anisa Ika; Bayu Sasongko, Theopilus
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1922

Abstract

Cyberbullying on social media such as Twitter is becoming an increasing social problem in today's society. Cyberbullying has a negative influence on mental health, increasing the risk of anxiety, sadness, and even suicide. The purpose of this research is to develop a model to classify tweets that contain or do not contain cyberbullying by applying the BiLSTM technique to sentiment analysis on Twitter. In this research, Word2Vec is used to weight each word in a tweet. The initial stage in this research is data collection with a total dataset of 47,692 tweets generated by Kaggle, preprocessing which consists of data cleaning, removing duplicates, case folding, tokenizing, stopword removal and lemmatization, classification and evaluation. This research uses the Bidirectional Long Short-Term Memory (Bi-LSTM) method and identifies patterns associated with bullying on social media. Testing uses Confusion Matrix and the results on classification show accuracy of 82.29%, precision of 82,04%, recall of 81,95% and F1-Score 81,89%. This sentiment analysis technique is expected to be the first step to combat and avoid cyberbullying on the Twitter platform. From several tests of existing reference algorithms, the classification accuracy performed includes having good performance.
Early Detection of Alzheimer's Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization) Rosyida, Anistya; Sasongko, Theopilus Bayu
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1716

Abstract

Alzheimer's disease is a degenerative disease associated with memory loss, communication difficulties, mental health, thinking skills, and other psychological disorders that affect a person's daily activities. Alzheimer's disease is a disease that causes disability for people aged 70 years and over and is the seventh highest contributor to death in the world. However, until now there has not been found an effective treatment to cure Alzheimer's disease. Thus, early detection of Alzheimer's disease is very important so that sufferers of Alzheimer's disease can immediately receive intensive medical care so as to reduce the death rate from Alzheimer's disease. One method that can be used to detect Alzheimer's disease is by utilizing a machine learning algorithm model. The machine learning model in this study was carried out using the Decision Tree C4.5 algorithm classification method based on Binary Particle Swarm Optimization (BPSO). The C4.5 Decision Tree algorithm is used to classify Alzheimer's disease, while the BPSO algorithm is used to perform feature selection. By performing feature selection with the BPSO algorithm, the results show that the BPSO algorithm can improve accuracy and can increase the performance of the C4.5 algorithm in the Alzheimer's disease classification process. The results of the accuracy of the C4.5 algorithm using the BPSO feature selection are greater, namely 98.2% compared to the C4.5 algorithm without BPSO feature selection, which is only 96.4%. 
Peningkatan Manajemen E-Learning dan Keterlibatan Pengguna dalam Implementasi Moodle di SMK Nasional Berbah, Yogyakarta Hadinegoro, Arifiyanto; Sasongko, Theopilus Bayu; Ningrum, Fauzia Anis Sekar; Rahim, Abd. Mizwar A.; Fikri, Muhammad Ainul; Huda, Amirudin Khorul
Jurnal Pengabdian Masyarakat Inovasi Indonesia Vol 1 No 2 (2023): JPMII - Desember 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jpmii.290

Abstract

Dokumen ini membahas implementasi aplikasi Moodle dalam manajemen pembelajaran dan administrasi di SMK Nasional Berbah, Yogyakarta. Tujuan dari penggunaan Moodle adalah untuk meningkatkan kreativitas dan produktivitas di lingkungan sekolah melalui platform e-learning. Meskipun SMK Nasional Berbah telah berusaha menciptakan media e-learning yang bermanfaat, mereka masih menghadapi beberapa kendala dalam pembuatan konten dan optimalisasi penggunaan e-learning. Untuk mengatasi kendala tersebut, dilakukan kegiatan sosialisasi dan pelatihan manajemen pengguna bagi karyawan dan staf khusus IT SMK Nasional Berbah. Kegiatan ini melibatkan 16 karyawan, tim staf e-learning, 2 pemateri, dan 3 pendamping pemateri dari Universitas Amikom. Fokus utama kegiatan ini adalah memberikan bimbingan teknis dalam pengelolaan pengguna LMS Moodle. Dalam pengabdian kepada masyarakat, dilakukan kolaborasi antara SMK Nasional Berbah dan Program Studi Informatika Universitas Amikom Yogyakarta. Kendala yang dihadapi dalam pelaksanaan pengabdian meliputi keterbatasan bandwidth server LMS Moodle, ketersediaan koneksi internet yang kurang memadai, dan kurangnya keterlibatan tim teknis dalam proses sosialisasi. Sebagai tindak lanjut, direncanakan langkah-langkah peningkatan yang melibatkan guru-guru sebagai pengguna utama Moodle. Hasil akhir dari kegiatan ini adalah para guru dan siswa mampu menggunakan Moodle sebagai wadah dalam kegiatan belajar mengajar.  Dengan demikian, implementasi Moodle di SMK Nasional Berbah dapat lebih efektif dan memberikan manfaat yang maksimal dalam proses pembelajaran dan administrasi di sekolah tersebut.
Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation Abd Mizwar A. Rahim; Theopilus Bayu Sasongko
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4035

Abstract

This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the dataset, and training was carried out on the Backpropagation ANN model to classify the condition of the corn plants. After training the model, the next model evaluation is carried out using the confusion matrix method. The research results show that the method used can produce quite high accuracy when identifying the condition of corn plants, with an accuracy of 99%. This shows that the use of GLCM and ANN Backpropagation can be a good alternative in identifying the condition of corn plants. This research provides benefits in making it easier to accurately identify the condition of corn plants.
Implementation of SSL-Vision Transformer (ViT) for Multi-Lung Disease Classification on X-Ray Images Baasith, Rafi Haqul; Sasongko, Theopilus Bayu; Hadinegoro, Arifiyanto; Saputro, Uyock Anggoro
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11844

Abstract

Chest X-ray imaging is one of the most widely used modalities for lung disease screening; however, manual interpretation remains challenging due to overlapping pathological patterns and the frequent presence of multiple coexisting abnormalities. In recent years, Vision Transformer (ViT) models have demonstrated strong potential for medical image analysis by capturing global contextual relationships. Nevertheless, their performance is highly dependent on large-scale labeled datasets, which are costly and difficult to obtain in clinical settings. To address this limitation, this study proposes a Self-Supervised Learning Vision Transformer (SSL-ViT) framework for multi-label lung disease classification using the CheXpert-v1.0-small dataset. The proposed approach leverages self-supervised pretraining to learn robust and transferable visual representations from unlabeled chest X-ray images prior to supervised fine-tuning. A total of twelve clinically relevant thoracic disease labels are retained, while non-disease labels are excluded to enhance interpretability and reduce confounding effects. Experimental results demonstrate that SSL-ViT achieves a high recall of 0.73 and a peak AUC of 0.75 on the test set, indicating strong sensitivity in detecting pathological cases. Compared to the baseline ViT model, SSL-ViT exhibits a recall-oriented performance profile that is particularly suitable for screening applications, where minimizing false negatives is critical. Furthermore, Grad-CAM visualizations confirm that the model focuses on anatomically meaningful lung regions, supporting its clinical relevance. These findings suggest that SSL-enhanced Vision Transformers provide a robust and effective solution for multi-label chest X-ray screening tasks.
Implementasi Algoritma MFCC dan CNN dalam Klasifikasi Makna Tangisan Bayi Yusdiantoro, Senli Yusdiantoro; Sasongko, Theopilus Bayu
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3243

Abstract

Menangis merupakan salah satu usaha bayi dalam berkomunikasi untuk menyampaikan suatu kondisi yang sedang dialaminya, baik itu sedang lelah, sakit perut, rasa tidak nyaman maupun lapar. Bagi sebagian orang tua yang baru memiliki anak tentu tidak selalu mampu untuk memahami apa yang dikehendaki oleh bayi ketika dia menangis, karena suara tangisan yang dihasilkan terdengar hampir sama. Maka, pada penelitian ini dibuat sebuah sistem klasifikasi makna tangisan bayi dengan mengimplementasikan deep learning. Untuk memahami arti tangisan bayi berdasarkan penyebabnya dengan mengimplementasikan metode Mel-Frequency Cepstral (MFCC) sebagai fitur ekstraksi ciri dan CNN sebagai metode klasifikasi. Diantara proses pelatihan dan pengujian yang telah berhasil dilakukan dalam penelitian ini diperoleh hasil akurasi tertinggi terhadap pelatihan yang dilakukan dengan 50 epoch sebesar 93,84% dan model mampu mengklasifikasikan makna tangisan bayi berdasarkan penyebabnya terhadap data baru dengan rata-rata akurasi 88.04%.
Co-Authors Abd Mizwar A. Rahim Abd Mizwar A. Rahim Abdul Mizwar A. Rahim AGIT AMRULLAH Agus Fatkhurohman Agus Fatkhurohman Alifqi Radjavani Amrullah, Yusuf Amri Anas Fikri Hanif Anas Fikri Hanif Arif Dwi Laksito Arifin, Oki Arifiyanto Hadinegoro, Arifiyanto Arnandito, Seno Baasith, Rafi Haqul Dewantoro, Agus Erfina Nurussa’adah Fajar, Rizqi Al Fikri, Muhammad Ainul Ginanjar Wiro Sasmito Hannan Asrawi Haryoko, Haryoko Hasbi Huda, Amirudin Khorul Joshua Agung Nurcahyo Joshua Agung Nurcahyo Khairul Rohman Kusuma, Satya Tegar Muhammad Fikri Hidayatullah Muhammad Taqiyuddin MUhammad Yusrizal Ningrum, Fauzia Anis Sekar Radjavani, Alifqi Rahim, Abd. Mizwar A. Restu Bumi Apit Rivi Neritarani Rivi Neritarani Rosyida, Anistya Safitri, Anisa Ika Saputro, Uyock Anggoro Satya Tegar Kusuma Senli Yusdiantoro Yusdiantoro Setyanto, Joko Simarmata, Asyer Aprinando Pratama Subagyo, Ageng Ramdhan Yusdiantoro, Senli Yusdiantoro