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All Journal Jurnal Ilmu Komputer dan Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Ramadhan, Mgs. M. Luthfi
Universitas Sriwijaya
Religion Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences

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Encoder-Decoder with Atrous Spatial Pyramid Pooling for Left Ventricle Segmentation in Echocardiography Fityan Azizi; Mgs M Luthfi Ramadhan; Wisnu Jatmiko
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 2 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i2.1165

Abstract

Assessment of cardiac function using echocardiography is an essential and widely used method. Assessment by manually labeling the left ventricle area can generally be time-consuming, error-prone, and has interobserver variability. Thus, automatic delineation of the left ventricle area is necessary so that the assessment can be carried out effectively and efficiently. In this study, encoder-decoder based deep learning model for left ventricle segmentation in echocardiography was developed using the effective CNN U-Net encoder and combined with the deeplabv3+ decoder which has efficient performance and is able to produce sharper and more accurate segmentation results. Furthermore, the Atrous Spatial Pyramid Pooling module were added to the encoder to improve feature extraction. Tested on the Echonet-Dynamic dataset, the proposed model gives better results than the U-Net, DeeplabV3+, and DeeplabV3 models by producing a dice similarity coefficient of 92.87%. The experimental results show that combining the U-Net encoder and DeeplabV3+ decoder is able to provide increased performance compared to previous studies.
An alternative for kernel SVM when stacked with a neural network Ramadhan, Mgs M Luthfi
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i1.1172

Abstract

Many studies stack SVM and neural network by utilzing SVM as an output layer of the neural network. However, those studies use kernel before the SVM which is unnecessary. In this study, we proposed an alternative to kernel SVM and proved why kernel is unnecessary when the SVM is stacked on top of neural network. The experiments is done on Dublin City LiDAR data. In this study, we stack PointNet and SVM but instead of using kernel, we simply utilize the last hidden layer of the PointNet. As an alternative to the SVM kernel, this study performs dimension expansion by increasing the number of neurons in the last hidden layer. We proved that expanding the dimension by increasing the number of neurons in the last hidden layer can increase the F-Measure score and it performs better than RBF kernel both in term of F-Measure score and computation time.
Improving Remote Sensing Change Detection Via Locality Induction on Feed-forward Vision Transformer Fazry, Lhuqita; Mgs M Luthfi Ramadhan; Jatmiko, Wisnu
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i1.1188

Abstract

The main objective of Change Detection (CD) is to gather change information from bi-temporal remote sensing images. The recent development of the CD method makes use of the recently proposed Vision Transformer (ViT) backbone. Despite ViT being superior to Convolutional Neural Networks (CNN) at modeling long-range dependencies, ViT lacks a locality mechanism, a critical property of pixels that comprise natural images, including remote sensing images. This issue leads to segmentation artifacts such as imperfect changed region boundaries on the predicted change map. To address this problem, we propose LocalCD, a novel CD method that imposes the locality mechanism into the Transformer encoder. Particularly, it replaces the Transformer's feed-forward network using an efficient depth-wise convolution between two $1 \times 1$ convolutions. LocalCD outperforms ChangeFormer by a significant margin. Specifically, it achieves an F1-score of 0.9548 and 0.9243 on CDD and LEVIR-CD datasets.
Enhancing Assault Maneuvers in Simulated Scenarios of Multiple Invader Kamikaze Drones through the Utilization of a Modified Adaptive Elforce Algorithm Triditya, Gregory; Ramadhan, Mgs M Luthfi; Jatmiko, Wisnu
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i1.1202

Abstract

The development of autonomous drone technology has led in their widespread deployment, especially in combat scenarios. One instance of this is the utilization of kamikaze drones, as seen in the Ukraine war. Autonomous defense drones have been used to counter these invading kamikaze drones. This study focuses on simulating scenarios involving invader vs. defender drones, primarily exploring invader drone maneuver motions to maximize damage inflicted on chosen targets. The work we conducted presents an enhanced el-force algorithm that employs Coulomb's Law-based maneuver techniques to improve the effectiveness of multiple kamikaze invader drones when engaging target defended by defender drones. We aim to improve traditional el-force by addressing key challenges such as siege tendencies and unproductive conduct. In addition, we explore various attacking formations to determine the most effective formation. To evaluate the performance of our proposed algorithm, we conducted simulation in a dynamic 3D environment, employing damage inflicted as the evaluation metric. Through rigorous testing, we conclusively demonstrate that our proposed method combining with a circular formation, outperforms alternative attacking maneuvers and formations. Our findings provide insights into optimal maneuver movements and attacking formations, improving the effectiveness of invader drones in engaging and damaging designated targets.
Machine Learning Classification of Insomnia Using Multidimensional Features and SMOTE Novanza, Trional; Ramadhan, Mgs. M. Luthfi; Caryn, Femilia Hardina; Passa, Rahma Satila; Iqrom, Redho Aidil; Seftianto, Ferlian; Hardoni, Andre; Baturohmah, Habi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3372

Abstract

Sleep disorders, especially insomnia, are common among adolescents and negatively affect health. Early detection is crucial for appropriate treatment. This study aims to classify insomnia severity in adolescents using a Machine Learning (ML) model and multidimensional features derived from 19 questionnaire instruments. The dataset consists of 95 adolescents aged 16–19 years, categorized into Insomnia, Subclinical Insomnia, and Control classes. The modeling process includes reducing multicollinearity, class balancing with SMOTE, and hyperparameter optimization using GridSearchCV and StratifiedKFold. Feature importance analysis was conducted using decision tree-based methods and permutation importance. The results show that SMOTE improves SVM performance from 0.690 to 0.793 and positively affects Random Forest. Logistic Regression performs best without SMOTE (accuracy 0.759), while XGBoost shows the lowest accuracy (0.614) even with SMOTE. A total of 11 features consistently contribute to all models. In conclusion, ML models, particularly SVM, are effective for classifying insomnia severity in adolescents. 
Pelatihan dan Pendampingan Transformasi Digital untuk Penguatan Pemasaran Produk UMKM Melalui Website M Fachrurrozi; Ferlian Seftianto; Andre Hardoni; Habi Baturohmah; Trional Novanza; Redho Aidil Iqrom; Femilia Hardina Caryn; Mgs. M. Luthfi Ramadhan; Rahma Satila Passa
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 2 (2026): Maret 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i2.1094

Abstract

Banyak UMKM di Sumatera Selatan belum mampu memanfaatkan teknologi digital untuk pemasaran karena rendahnya literasi digital, tidak adanya platform promosi online, serta minimnya pendampingan teknologi. Hal ini membuat jangkauan pasar terbatas dan menghambat peningkatan daya saing. Untuk meningkatkan daya saing UMKM dilakukan pendekatan Participatory Action Approach melalui empat tahap: (1) sosialisasi dan pemetaan kebutuhan (2) pelatihan literasi digital dan pembuatan website (3) pendampingan pengelolaan website (4) evaluasi serta perencanaan keberlanjutan. Kegiatan berlangsung Juli–November 2025 di UMKM Bingkai Kaca Anugrah Jaya, Palembang. Mitra berhasil membuat dan mengelola website https://tokobingkaiplg.com/. Website menampilkan profil usaha, katalog produk, serta integrasi dengan media sosial. Terdapat peningkatan kemampuan literasi digital mitra, peningkatan interaksi pengunjung website rata-rata sekitar 20% dalam dua bulan awal. Program Transformasi digital melalui website ini berhasil meningkatkan jangkauan pemasaran serta memperkuat daya saing produk lokal. Model pelatihan dan pendampingan terbukti efektif dan dapat direplikasi pada UMKM lain.
Co-Authors andre hardoni Caryn, Femilia Hardina Fazry, Lhuqita Femilia Hardina Caryn Ferlian Seftianto Fityan Azizi Habi Baturohmah Habi Baturohmah Hardoni, Andre Iqrom, Redho Aidil M Fachrurrozi Novanza, Trional Passa, Rahma Satila Rahma Satila Passa Redho Aidil Iqrom Seftianto, Ferlian Triditya, Gregory Trional Novanza Wisnu Jatmiko