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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Teknik Elektro dan Komputer TRIAC PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) JHCLS SmartComp Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Christian Sri kusuma Aditya, Christian Sri kusuma
Teknik Informatika, Institut Teknologi Sepuluh Nopember
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Social Sciences

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MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture Ujilast, Novia Adelia; Firdausita, Nuris Sabila; Aditya, Christian Sri Kusuma; Azhar, Yufis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5457

Abstract

Alzheimer's disease is a neurodegenerative disorder or a condition characterized by degeneration and damage to the nervous system. This leads to a decline in cognitive abilities such as memory, thinking, and focus, which can impact daily activities. In the medical field, a technology called Magnetic Resonance Imaging (MRI) can be used for the initial diagnosis of Alzheimer's disease through image procedures-based recognition methods. The development of this detection system aims to assist medical professionals, including doctors and radiologists, in diagnosing, treating, and monitoring patients with Alzheimer's disease. This study also aims to classify different types of Alzheimer's disease into four distinct classes using the convolutional neural network method with the EfficientNet-B0 and EfficientNet-B3 architectures. This study used 6400 images that encompass four classes, namely mild demented, moderate demented, non-demented, and very mild demented. After conducting testing for both scenarios, the exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the exactness was 97.00%.
Optimizing Indonesian Banking Stock Predictions with DBSCAN and LSTM Purwandhani, Septiannisa Alya Shinta; Sajiatmoko, Aletta Agigia Novta; Aditya, Christian Sri Kusuma
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4439

Abstract

Investing in the stock market is challenged by high volatility, which often leads to inaccurate price predictions. Prediction models often struggle to handle the fluctuation phenomenon and produce unstable forecasts. This study aims to predict stock prices in three banks, namely PT Bank Central Asia Tbk (BBCA), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), and PT Bank Mandiri (Persero) Tbk (BMRI) using Long Short-Term Memory (LSTM) with the integration of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for anomaly detection. DBSCAN is applied with an epsilon (ε) of 0.5 and a minimum of 5 samples using Euclidean distance. The LSTM model consists of two hidden layers with 50 units, optimized using Adam, and applying the Mean Squared Error (MSE) loss function. The results show that DBSCAN improves prediction accuracy under several conditions. For BBCA stock, the lowest MSE was 0.003 at the 2nd fold with DBSCAN compared to 0.006 without DBSCAN. For BMRI stock achieved an MSE of 0.003 at the 4th fold with DBSCAN, while the 5th fold without DBSCAN obtained 0.000. For BBRI stock showed the best MSE of 0.003 at the 2nd fold with DBSCAN and the 5th fold without DBSCAN. These results show that the integration of DBSCAN can improve prediction especially when extreme price fluctuations occur. This research contributes to the development of stock price prediction methods that can be one of the benchmarks for investors before making decisions so that they do not experience losses.
Optimalisasi Model CNN dengan Teknik Kontras Lokal CLAHE untuk Klasifikasi Pneumonia pada Citra X-Ray Salma, Rania Alfita; Aditya, Christian Sri Kusuma
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.v7i2.8271

Abstract

Pneumonia is a lung infection that has a widespread impact on public health, particularly in areas with limited access to healthcare services. Chest X-ray imaging plays an important role in diagnosing this disease; however, low contrast quality often becomes an obstacle to automated classification using deep learning methods. This study aims to evaluate the effectiveness of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method in enhancing the visual quality of chest X-ray images and to analyze its impact on the performance of a Convolutional Neural Network (CNN) model in detecting pneumonia. CLAHE enhances the visibility of radiographic details through local contrast redistribution with a clip limit, allowing previously indistinct pathological structures to be more clearly recognized by the CNN. The dataset used consists of 2,623 X-ray images that are divided into two classes, namely Normal and Pneumonia. The training process was conducted under two scenarios, without and with the application of CLAHE. The evaluation results show that the CNN model without CLAHE achieved an accuracy of 96.18%, while the model with CLAHE improved to 99.69%. This improvement is significant as it reduced the classification error rate from approximately 3.8% in the model without CLAHE to only 0.3% in the model with CLAHE, while also increasing precision, recall, and f1-score across all classes. Therefore, combination of CLAHE and CNN can be applied as an effective approach for pneumonia detection that is accurate, consistent, and efficient, especially in environments with limited computational resources.
Students Final Academic Score Prediction Using Boosting Regression Algorithms Muhammady, Dignifo Nauval; Nugraha, Haidar Aldy Eka; Nastiti, Vinna Rahmayanti Setyaning; Aditya, Christian Sri Kusuma
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28352

Abstract

Academic grades are crucial in education because they assist students in acquiring the knowledge and skills necessary to succeed in school and their future. Accurately predicting students' final academic performance grade score is important for educational decision-makers. However, creating precise prediction models based on students' historical data can be challenging due to the complex nature of academic data. This research analyzes student academic data totaling 649 Portuguese language course student data that has been processed according to data requirements which are then predicted using XGBoost Regressor, Light Gradient Boosting Machine (LGBM), and CatBoost. This research aims to develop a robust prediction model that can effectively predict students' final academic performance. This research offers valuable insights into the factors that influence academic success and provides practical implications for educational institutions looking to improve their decision-making processes. The prediction requires identifying key predictors of academic performance, such as previous grades, attendance records, and socio-economic background. The research makes a contribution by improving the matrix MAE in this research is less than the previous research from 2.2 average each algorithm to 0.22 average, this less MAE means the better model. The research achieved MAE score of 0.22 average. In conclusion, this research is expected to address the challenge of predicting student academic performance through the application of advanced machine learning techniques. The results provide valuable insights for decision-makers in education and highlight the importance of a data-driven approach to improving academic performance. By utilizing machine learning algorithms, educational institutions can effectively support student learning and success.
Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking Atsqalani, Hasbi; Hayatin, Nur; Aditya, Christian Sri Kusuma
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.669

Abstract

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively.
PENGEMBANGAN EKSTRAKSI FITUR MEDIA BERITA ONLINE UNTUK KALEIDOSKOP BERITA TAHUNAN Aditya, Christian Sri Kusuma; Wiyono, Briansyah Setio
Jurnal Likhitaprajna Vol 9 No 1 (2025)
Publisher : FKIP Universitas Wisnuwardhana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37303/peduli.v9i1.715

Abstract

Informasi menjadi suatu hal yang dibutuhkan seiring dengan perkembangan teknologi informasi dan komunikasi. Salah satu sumber informasi tersebut adalah situs berita daring yang berisi artikel berita dengan topik yang berbeda. Dengan banyaknya jumlah artikel berita dengan berbagai macam topik maka proses pengelompokan tersebut menjadi sulit dilakukan dan membutuhkan waktu yang lama. Oleh karena itu, dibutuhkan sistem yang dapat mengelompokkan artikel berita secara otomatis agar proses pengelompokkan lebih mudah dan cepat. Ekstraksi fitur situs berita online bertujuan mengelempokkan artikel berita secara otomatis dan mendapatkan artikel yang populer dalam jangka waktu tertentu. Kegiatan dari program pengabdian ini akan mengembangkan sebuah rancangan fitur sistem kaleidoskop berita secara otomatis. Artikel berita yang didapatkan dari situs berita kemudian akan dikelompokkan sesuai dengan topik beritanya masing-masing. Jumlah topik berita yang paling banyak akan terpilih pada kaleidoskop berita. Kaleidoskop sebagai suatu rangkuman dari aneka peristiwa yang telah terjadi baik dalam bentuk artikel maupun video, yang biasa diterbitkan di tiap periode yang ditentukan. Kaleidoskop juga dapat menjadi sarana untuk memberikan gambaran tentang program apa saja yang telah berjalan, serta target apa saja yang telah dicapai.
Diabetes Detection Using Extreme Gradient Boosting (XGBoost) with Hyperparameter Tuning Dinanthi, Devi Aprilya; Ramadanti, Elisa; Aditya, Christian Sri Kusuma; Chandranegara, Didih Rizki
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 2 (2024): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/qr3hw926

Abstract

Diabetes is a serious condition that can lead to fatal complications and death due to metabolic disorders caused by a lack of insulin production in the body. This study aims to find the best classification performance on diabetes dataset using Extreme Gradient Boosting (XGBoost) method. The dataset used has 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE, and hyperparameter optimization is performed using GridSearchCV and RandomSearchCV. Model evaluation was performed using confusion matrix as well as metrics such as accuracy, precision, recall, and F1-score. The test results show that the use of GridSearchCV and RandomSearchCV for hyperparameter tuning provides good results. The application of data resampling also managed to improve the overall model performance, especially in the XGBoost method that has been optimized using GridSearchCV, which achieved the highest accuracy of 85%, while XGBoost with RandomSearchCV optimization showed 83% accuracy performance.
Implementasi Transfer Learning pada Convolutional Neural Network dengan Arsitektur VGG dalam Klasifikasi Down Syndrome di Asia Uwar, Tarissa Rizky Salsabiila; Aditya, Christian Sri Kusuma
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7150

Abstract

Early detection of Down syndrome is crucial for enabling early intervention and providing healthcare education for children. Down syndrome is associated with specific facial features, such as distinct characteristics of the eyes, nose, lips, face shape, hair, and skin color, which can be analyzed using computer vision techniques. This study aims to classify Down syndrome, especially in the Asian Region, which includes countries with medium/low SDI. The study proposed a CNN based on the VGG16 and VGG19 architectures by implementing transfer learning and augmentation. Augmentation is performed to balance the number of images between classes, while transfer learning is used to train the model first on ImageNet data. The dataset used consists of two categories, Down syndrome and Healthy. The results indicate that the VGG16 model has higher sensitivity and is able to classify more cases of Down syndrome, but has a fairly large prediction error. However, VGG19 model has a better specificity value and has a smaller potential for prediction error. The best model in this study was selected based on the highest validation accuracy value, where VGG19 achieved an accuracy of 93% in its best iteration, and VGG16 achieved an accuracy of 91%. These findings suggest that the proposed models, particularly VGG19, exhibit optimal performance in classifying Down syndrome, especially in the Asian region, with a lower prediction error rate.
Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree Nadya, Fhara Elvina Pingky; Ferdiansyah, M.Firdaus Ibadi; Nastiti, Vinna Rahmayanti Setyaning; Aditya, Christian Sri Kusuma
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48145

Abstract

Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models.Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%.Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.
Hybrid Video Transcription Summarization with a BERT-Based Clustering and BART Darmawan, Fathul Agit; Mauludin, Muhammad Bima; Aditya, Christian Sri Kusuma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7066

Abstract

The use of video as a medium for information and education is rapidly increasing across online platforms. However, long durations and unstructured delivery often hinder audiences from grasping the core message, presenting challenges for the development of automatic summarization methods for monologues, interviews, and podcasts. Extractive methods often yield less coherent summaries, while abstractive methods may overlook important details. To address this issue, this study proposes a hybrid approach combining extractive and abstractive techniques. In the extractive stage, sentences are represented using BERT embeddings and clustered using two methods, namely K-Means Clustering and Hierarchical Clustering (agglomerative). The abstractive stage then employs the BART model to generate summaries that are more coherent and informative. Experimental evaluations on 20 Human Metapneumovirus (HMPV) videos indicate the strongest performance on monologues, with ROUGE-1 of 57%, ROUGE-2 of 30%, and ROUGE-L of 32%. Although lower performance was observed for interviews and podcasts due to dynamic interactions and frequent speaker shifts, the hybrid approach consistently surpassed extractive-only and abstractive-only baselines. These results highlight the effectiveness of the hybrid approach and its potential for developing more adaptive video summarization in the future.
Co-Authors Agus Zainal Arifin AINA, LISTYA NUR Aji, Bayu Puspito Alif Akbar Fitrawan, Alif Akbar ASHURI, PUTRI INTAN Atsqalani, Hasbi CAHYANI, INDAH ARDHIA Chandranegara, Didih Rizki Darmawan, Fathul Agit Diana Purwitasari Dinanthi, Devi Aprilya Evi Dwi Wahyuni Ferdiansyah, Danny Ferdiansyah, M.Firdaus Ibadi Firdausita, Nuris Sabila Handoko, Chanavaro Bayu K., Halif Fachrizal Erliawan Kusumo, Wisnu Prayogo Lailiyah, Asmaul Mamluatul Hani’ah Mauludin, Muhammad Bima Muhammady, Dignifo Nauval Nadya, Fhara Elvina Pingky Nugraha, Haidar Aldy Eka Nur Hayatin Nur Putri Hidayah Purwandhani, Septiannisa Alya Shinta Ramadanti, Elisa S, Vinna Rahmayanti Sajiatmoko, Aletta Agigia Novta Salma, Rania Alfita Sunan, Rohid Aji Ujilast, Novia Adelia Uwar, Tarissa Rizky Salsabiila Wicaksono, Galih Wasis Wiyono, Briansyah Setio Yuda Munarko Yufis Azhar