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All Journal International Journal of Artificial Intelligence Research Innovative: Journal Of Social Science Research BULETIN FISIKA
Hardiansyah, Deni
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Humanities Astronomy Computer Science & IT Electrical & Electronics Engineering Energy Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health Other

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The Classification Method is Used for Sentiment Analysis in My Telkomsel Hardiansyah, Deni; Aziz, RZ Abdul; Hasibuan, Muhammad Said
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1229

Abstract

User reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews.
Analisis Dan Perancangan Sistem Informasi Kepegawaian Di PT. Halim Industri Beton Ringan Berbasis Desktop Hardiansyah, Deni; Putra, Aldi Pratama; Karsono, Karsono
Innovative: Journal Of Social Science Research Vol. 3 No. 5 (2023): Innovative: Journal of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

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

Abstract

Teknologi komputer saat ini telah berkembang pesat sesuai dengan perkembangan zaman. Sehingga kegunaan komputer juga berkembang pada berbagai bidang kehidupan manusia mulai dari pendidikan, hiburan, sampai kepada bidang bisnis. Pada saat ini, Sistem kepegawaian yang berjalan masih dilakukan secara manual, dengan melibatkan arsip pegawai untuk input data dan laporan yang harus disimpan pada saat proses penerimaannya, sehingga membutuhkan waktu dalam prosesnya. Melihat kenyataan itu, penulis mencoba membantu untuk mengatasi masalah tersebut dengan cara membuat sistem yang masih manual menjadi sistem yang sudah terkomputerisasi untuk mempermudah proses input, edit, maupun penyimpanan data. PT. Halim Industri Beton Ringan khususnya pada bagian personalia dan umum membutuhkan suatu sistem informasi yang menunjang dan memberikan kemudahan bagi user. Untuk itulah penulis mencoba membuat Skripsi tentang Perancangan Sistem Informasi Kepegawaian di PT. Halim Industri Beton Ringan Berbasis Dekstop dengan Menggunakan Microsoft Visual Studio 2008. Maka dari itu dengan perancangan program ini tentu akan lebih baik dari sistem manual dan berjalan lebih efektif dan efisien.
Impact of Training Dataset Size on the Accuracy of L-SVR Single-Time-Point Renal Dosimetry for [¹⁷⁷Lu]Lu-PSMA-617 Therapy Wicaksono, Abdurrahman Aziz; Jabar, Jaja Muhammad; Siregar, Syahril; Hardiansyah, Deni
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p07

Abstract

Radiopharmaceutical therapy (RPT) using [¹⁷⁷Lu]Lu-PSMA-617 requires accurate dosimetry to evaluate organs-at-risk (OAR), specifically the kidneys. Single-time-point (STP) dosimetry simplifies clinical workflows by reducing SPECT/CT acquisition. Machine learning (ML) offers a potential solution, yet clinical implementation is hindered by the scarcity of sufficient training datasets for ML-based studies. This study investigated the relationship between training dataset size and time-integrated activity (TIA) estimation accuracy. A Linear Support Vector Regression (L-SVR) model was trained on synthetic virtual patients (VPs, 5,000 total) simulated from a published PBMS NLMEM renal biokinetics at five imaging times (t=1.8 h, 18.7 h, 42.6 h, 66.2 h, and 160.3 h). Time-activity-curve (TAC) and reference TIA (rTIA) were calculated for each VP. Random sampling was performed in increasing dataset sizes. Sample sizes were sub-sampled to training (80%) and testing (20%) datasets. L-SVR was trained on STP data at 42.6 h post-injection (best-time-point of PBMS NLMEM study) from the training dataset and tested by generating estimated TIA (eTIA) with input from the testing dataset. Performance was evaluated by calculating root-mean-square-error (RMSE) and mean-absolute-percentage-error (MAPE) of the eTIA to rTIA. Results showed that the accuracy of eTIA from ML STP dosimetry depends on training size: small samples (n=10) yielded poor performance (RMSE>85.98%, MAPE>89.1%). Accuracy improved significantly at n=500 (RMSE=14.07%) and plateaued beyond n=1,000 (peak RMSE=13.07%). Results indicate that the L-SVR model of the study requires sample sizes of n>200, with optimal gains up to n=2,000. This study suggests synthetic data as a methodological bridge between limited clinical datasets and data-intensive ML approaches.
Co-Authors Aziz, RZ. Abdul Jabar, Jaja Muhammad Karsono Karsono M Said Hasibuan Putra, Aldi Pratama Siregar, Syahril Wicaksono, Abdurrahman Aziz