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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI JUTI: Jurnal Ilmiah Teknologi Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Smart Techno (Smart Technology, Informatic and Technopreneurship)
Supri Bin Hj Amir
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Computer Science & IT Dentistry Education Library & Information Science Mathematics

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Comparison of Elliptic Envelope Method and Isolation Forest Method on Imbalance Dataset Supri Bin Hj Amir; Bagas Prasetyo
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 1 (2020): JMSK, SEPTEMBER, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i1.10899

Abstract

The problem of unbalanced data is important in the field of Data Mining. Dataset with unbalanced classes is a dataset whose frequency of occurrence of certain classes is very much different from other classes. This imbalance problem will bias the classifier's performance. Many researchers have examined both the development of algorithms and modifications to the preprocessing stage to overcome this problem. This study discusses the comparison of One Class Classification algorithms, namely Elliptic Envelope and Isolation Forest on unbalanced data. From this study, the Elliptic Envelope Method showed better results compared to the Isolation Forest method with 80.28% recall testing and 80.28% precision while Isolation Forest showed 46.95% recall results and 46.95% precision.
Target prediction of compounds on jamu formula using nearest profile method Nur Hilal A Syahrir; Sumarheni Sumarheni; Supri Bin Hj Amir; Hedi Kuswanto
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11616

Abstract

Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known.
Deteksi Citra X-Ray Paru-Paru Terinfeksi COVID-19 dengan Algoritma CNN berbasis Aplikasi Web Supri Bin Hj Amir; Sitti Nur Azizah Fitriani Akbar; Hendra Hendra; Andi Muhammad Anwar; Sulfayanti Sulfayanti
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 17, No 1 (2022): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v17i1.6534

Abstract

Pada penelitian ini menggunakan algoritma Convolutional Neural Network (CNN) untuk mendeteksi COVID-19 berdasarkan citra X-ray Paru-paru. Arsitektur CNN yang digunakan adalah EfficientNetB7 dan Resnet152V2 dengan memanfaatkan teknik Transfer Learning. Penelitian ini berfokus pada membandingkan kinerja kedua model arsitektur dalam mengklasifikasikan citra X-ray Paru-paru terinfeksi COVID-19. Selanjutnya mengimplementasikan model CNN tersebut ke aplikasi deteksi Citra X-ray paru-paru berbasis web. Dari hasil evaluasi kedua model tersebut disimpulkan bahwa Resnet152-V2 mencapai kinerja lebih baik dibanding arsitektur CNN EfficientNetB7 dengan akurasi 97% sedangkan EfficientNetB7 dengan akurasi 95%.
Analisis Usability pada Aplikasi Mobile JKN (Studi Kasus: Dosen Universitas Hasanuddin) Ribal, Arjuna; Sampetoding, Eliyah Acantha Manapa; Hasbi, Muhammad; Amir, Supri Bin hj.
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 1: April 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i1.2592

Abstract

As part of the national social security program, BPJS Kesehatan plays a crucial role in providing accessible and affordable healthcare services for the community. The Mobile JKN application aims to enhance the efficiency and accessibility of healthcare services. This study aims to analyze and evaluate the user satisfaction level of the Mobile JKN application within the lecturer community at Hasanuddin University using the End-User Computing Satisfaction (EUCS) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) methods as its primary approaches. The EUCS method analysis revealed that, overall, Mobile JKN users feel "Satisfied," with variabel scores ranging from 3.58 (Timeliness) to 4.1 (Ease of Use). Additionally, the UTAUT2 method analysis found that the variabel with the most significant influence on user satisfaction with the BPJS Kesehatan Mobile JKN application is facilitating condition (89.81%), followed by behavioral intention (82.59%) and performance expectancy (82.41%).Keywords: Mobile JKN Application; End-User Computing Satisfaction; Unified Theory of Acceptance and Use of Technology 2; Snowball Sampling; Hasanuddin University Lecturers. AbstrakSebagai program jaminan sosial nasional, BPJS Kesehatan memiliki peran krusial dalam menyediakan akses layanan kesehatan yang mudah dan terjangkau bagi masyarakat. Aplikasi Mobile JKN bertujuan untuk meningkatkan efisiensi dan aksesibilitas layanan Kesehatan. Penelitian ini bertujuan untuk menganalisis dan mengevaluasi tingkat kepuasan pengguna aplikasi Mobile JKN pada lingkungan dosen Universitas Hasanuddin menggunakan metode End-User Computing Satisfaction (EUCS) dan Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) sebagai metode utama penelitian ini. Hasil analisis metode EUCS menunjukkan bahwa secara keseluruhan, pengguna aplikasi Mobile JKN merasa Puas dengan setiap variabelnya berkisar antara 3,58 (Timeliness) hingga 4,1 (Ease of Use). Selain itu, hasil analisis metode UTAUT2 menunjukkan bahwa faktor atau variabel yang memiliki pengaruh terbesar terhadap tingkat kepuasan pengguna aplikasi BPJS Kesehatan Mobile JKN merupakan facilitating condition (89,81%), diikuti oleh behavioral intention (82,59%), dan performance expectancy (82,41%). 
An Explainable Deep Learning for Malaria Blood Cell Classification Using DenseNet121 and Grad-CAM Octavian; Widjaja, Imelda; Amir, Supri
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.199

Abstract

Malaria diagnosis based on microscopic examination of blood smears is time-consuming and highly dependent on skilled laboratory personnel, which limits its scalability in resource-constrained environments. This study investigated whether an explainable deep learning approach could provide reliable and interpretable malaria blood cell classification using a convolutional neural network based on the DenseNet121 architecture combined with Gradient-weighted Class Activation Mapping to visualize the image regions influencing model predictions. Five-fold cross-validation was applied to ensure a stable and unbiased performance evaluation. The model achieved a mean classification accuracy of 0.8285 with low variation across folds, and the precision, recall, and F1-score values were balanced between the parasitized and uninfected classes. Visual explanations consistently highlighted intracellular regions associated with parasite presence in infected cells and more uniform cytoplasmic regions in uninfected samples, indicating that the network learned the biologically meaningful features of the cells. The results demonstrated that DenseNet121 provided a stable and interpretable solution for malaria blood cell classification when supported by a visual explanation, thereby enabling transparent automated screening. The proposed framework is suitable for integration into smart healthcare and medical informatics systems, where both predictive reliability and interpretability are required.
Comparative analysis of deep learning models for multi-horizon rainfall forecasting in flood-prone tropical highlands Supri Amir; Amran Rahim; Fitrah Ramadhan; Edy Saputra; Octavian
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1518

Abstract

Accurate rainfall forecasting is critically important for the effective mitigation of floods and the management of water resources in tropical highland regions that serve as principal upstream catchment areas for major reservoirs. Heavy periods of rainfall in the upper watershed can exceed a reservoir’s capacity for storage and release, thus contributing to the recurrence of flooding in downstream urban areas and their surrounding regions. This study addresses this practical model-selection problem by systematically comparing Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Fusion Transformer  (TFT), and AutoTFT with multiple daily forecasting horizons for rainfall prediction. Historical rainfall records were combined with meteorological variables to develop the forecasting models. Model performance was evaluated across short- and medium-term forecasting horizons using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that AutoTFT consistently achieved the lowest MAE across all forecasting horizons, ranging from 6.75 mm (1-day) to 7.79 mm (14-day). At the 3-day and 9-day horizons, AutoTFT also produced the lowest MSE (201.36 and 215.91 mm²) and RMSE (14.19 and 14.69 mm), demonstrating superior predictive accuracy. At the 7-day and 14-day horizons, TFT slightly outperformed AutoTFT in terms of RMSE (14.66 and 14.88 mm, respectively), although AutoTFT maintained the lowest MAE. Meanwhile, LSTM achieved the lowest RMSE (12.79 mm) at the 1-day horizon, indicating competitive performance for very short-term forecasting. Overall, the transformer-based models, particularly AutoTFT and TFT, consistently outperformed the recurrent architectures at medium- and long-range forecasting horizons, highlighting their potential as reliable and generalizable approaches for rainfall forecasting in flood-prone tropical highlands and supporting more effective early warning systems and water resource management.
Co-Authors Amran Rahim Andi Muhammad Anwar Bagas Prasetyo Edy Saputra Eliyah Acantha Manapa Sampetoding Fitrah Ramadhan Hasbi, Muhammad Hedi Kuswanto Hendra Hendra Octavian Octavian Ribal, Arjuna Sitti Nur Azizah Fitriani Akbar Sulfayanti Sumarheni Sumarheni Syahrir, Nur Hilal A. Widjaja, Imelda