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All Journal Buletin Poltanesa Journal of Advanced Health Informatics Research International Journal of Informatics and Computing
Rajiansyah
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health

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A Bibliometric Analysis of Knowledge Distillation in Medical Image Segmentation Muntiari, Novita Ranti; Rania Majdoubi; Rajiansyah
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.297

Abstract

This study conducts a bibliometric analysis and systematic review to examine research trends in the application of knowledge distillation for medical image segmentation. A total of 806 studies from 343 distinct sources, published between 2019 and 2023, were analyzed using Publish or Perish and VOSviewer, with data retrieved from Scopus and Google Scholar. The findings indicate a rising trend in publications indexed in Scopus, whereas a decline was observed in Google Scholar. Citation analysis revealed that the United States and China emerged as the leading contributors in terms of both publication volume and citation impact. Previous research predominantly focused on optimizing knowledge distillation techniques and their implementation in resource-constrained devices. Keyword analysis demonstrated that medical image segmentation appeared most frequently with 144 occurrences, followed by medical imaging with 110 occurrences. This study highlights emerging research opportunities, particularly in leveraging knowledge distillation for U-Net architectures with large-scale datasets and integrating transformer models to enhance medical image segmentation performance
Pengembangan Aplikasi Pendataan Keluar-Masuk STNK pada Sistem Kredit PT Indeks Media Finance Arfyanti, Ita; Salmon; Rajiansyah
Poltanesa Vol 23 No 1 (2022): Juni 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i1.1211

Abstract

Dalam proses pengajuan pembiayaan kredit motor dan mobil bekas, kelengkapan surat-surat dalam hal ini STNK menjadi salah satu syarat yang harus dipenuhi. Jika STNK sudah tidak berlaku maka PT Indeks Media Finance melakukan pengurusan melalui biro jasa. Penelitian ini berfokus pada membangun sebuah aplikasi pendataan keluar masuk STNK. Metode Pengembangan sistem yang digunakan penelitian yaitu metode System Development Life Cycle (SDLC) atau yang lebih dikenal siklus hidup pengembangan sistem mulai dari tahapan analisis, desain sampai implementasi dan pengujian. Harapan sistem yang dibangun tidak hanya dapat mendata keluar-masuk STNK tetapi juga dalam hal pembuatan laporan untuk pimpinan.
Support Vector Machine Based Machine Learning for Sentiment Analysis of User Reviews of the Bibit Application on Google Play Store Ega Shela Marsiani; Natsir, Fauzan; Redo Abeputra Sihombing; Millati Izzatillah; Rajiansyah
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

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

Abstract

The increasing use of financial technology (fintech) applications has changed the investment patterns of users in Indonesia. Bibit, as one of the popular fintech investment platforms, receives many user reviews through the Google Play Store that reflect user perceptions and satisfaction levels. Although the volume of user reviews continues to increase, systematic analysis of user sentiment is still limited, making it difficult for developers to understand the needs and experiences of users. Therefore, an artificial intelligence-based approach is needed to efficiently and objectively extract and analyze user opinions. This study aims to conduct sentiment analysis of user reviews of the Bibit application using a Machine Vector Machine (SVM) based machine learning model. The research methodology includes data collection, pre-processing of texts, extraction of features using TF-IDF, as well as classification of sentiment into positive, negative, and neutral categories. Of the total review data, 7,801 data (79.99%) were used as training data, and 1,561 data (20.01%) were used as test data with a division ratio of 80:20 according to general standards in machine learning. The purpose of this study was to identify the dominant user sentiment and evaluate the classification performance of the SVM algorithm. The results of the experiment showed that the SVM model achieved high accuracy and was able to capture user opinions effectively, thus providing valuable input for developers in improving the quality of applications and user engagement on fintech platforms.
Co-Authors ega shela marsiani Fauzan Natsir Ita Arfyanti Millati Izzatillah Muntiari, Novita Ranti Rania Majdoubi Redo Abeputra Sihombing Salmon