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All Journal Techno.Com: Jurnal Teknologi Informasi JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA JPM (Jurnal Pemberdayaan Masyarakat) ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Computer Science and Information Technologies Indonesian Journal of Data and Science TIN: TERAPAN INFORMATIKA NUSANTARA Buletin Sistem Informasi dan Teknologi Islam Linier: Literatur Informatika dan Komputer
Herdianti Darwis
Informatics Engineering, Faculty Of Computer Science, Universitas Muslim Indonesia
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Indonesian Cross-Platform Sentiment Analysis: DANN Transfer from General Applications to TradingView Muh. Rifqi Zulkifli; Purnawansyah; Herdianti Darwis
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.318

Abstract

Introduction: Cross-platform sentiment analysis for Indonesian language presents significant challenges when adapting models from general applications to specialized domains. Domain Adversarial Neural Networks (DANN) offer promising solutions for transfer learning, yet their effectiveness for Indonesian language remains largely unexplored, particularly under extreme class imbalance conditions common in trading platforms. Methods: This study investigates DANN effectiveness for transferring sentiment analysis knowledge from four strategically selected source domains to TradingView trading platform. The research utilizes 5,990 Indonesian reviews after preprocessing from an initial 6,000 samples, with source domains showing 66.5% positive sentiment while target domain exhibits 85.1% positive sentiment, creating an 18.7% distribution gap. Four experimental approaches were compared with statistical validation across multiple random initializations: Source-Only training, Multi-Domain training, Limited Target training, and DANN implementation. Results: DANN demonstrates stable cross-platform adaptation, achieving 87.77% ± 0.97% accuracy with consistent performance across initializations, outperforming Source-Only baseline (87.10% ± 0.84%) and Multi-Domain approach (86.98% ± 0.64%). While Limited Target baseline achieves higher accuracy (88.10% ± 2.23%), its high variance poses deployment risks. A-distance analysis reveals substantial domain gaps (193.00 ± 1.06), with DANN's adversarial training achieving modest domain separation reduction (72.90% ± 8.81% domain discrimination accuracy). Conclusions: This research contributes the first systematic evaluation of DANN for Indonesian cross-platform sentiment analysis, demonstrating that deployment consistency outweighs peak accuracy for production environments. The findings provide practical value for Indonesian fintech startups requiring robust sentiment analysis with limited labeled data. Future work should explore multi-target adaptation and optimization strategies for diverse Indonesian business domains
Smart Waste Bin Prototype for University Waste Management Fauzy Fathrurahman; Dolly Indra; Tasrif Hasanuddin; Herdianti Darwis; Tanaka Kazuaki
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.324

Abstract

Background: Waste mismanagement remains a critical issue in Indonesian campuses, where ineffective segregation and collection practices contribute to environmental pollution. Smart technologies offer opportunities to improve waste handling efficiency and monitoring in university environments. Methods: This study developed a smart waste bin prototype that integrates Internet of Things (IoT) sensors, machine learning–based image classification (MobileNetV2 with TensorFlow Lite), GPS tracking, and LoRa communication. The system was designed to classify three types of waste—plastic bottles, snack packaging, and cans—while enabling fill-level monitoring, automated sorting, and real-time location reporting. Results: Experimental results showed strong classification accuracy for plastic bottles (100%), but lower performance for snack packaging (53–80%) and cans (40–67%), especially in low-light conditions or with darker materials. The overall real-time testing accuracy reached 45.1%. LoRa communication provided long-range connectivity but was affected by electromagnetic interference, while GPS tracking was reliable in open areas but inconsistent indoors. Conclusions: The prototype demonstrates the feasibility of integrating AI and IoT for scalable campus waste management. Despite environmental and hardware limitations, it offers a modular framework that can be refined with improved lighting, EMI shielding, and enhanced datasets. This research contributes a practical model for smart campus initiatives and supports the adoption of sustainable waste management practices in higher education environments.
Drug Recommendation Using Multilabel Classification with Decision Tree Based on Patient Complaints and Diagnoses Muh Aristsyah Malik; Harlinda; Herdianti Darwis
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.397

Abstract

This study develops a drug recommendation system using multilabel classification with the Decision Tree algorithm based on patient complaint and diagnosis data from electronic medical records. The dataset consists of patient visit records from a community health center in Pangkajene and Kepulauan Regency and is transformed using multi-hot encoding. Model performance is evaluated under three dataset scenarios (N=500, N=800, and N=1000) using multilabel metrics, including Micro-F1, Samples-F1, Hamming Loss, Jaccard Similarity, Hit@5, Precision@K, and Recall@K. The best Decision Tree model achieved a Micro-F1 score of 0.292, Samples-F1 of 0.281, and Hit@5 of 0.690 on the N=1000 dataset scenario. Bootstrap validation with 1000 iterations indicates relatively stable performance, with narrow confidence intervals across evaluation metrics. These results show that the multilabel Decision Tree model is capable of capturing relationships between patient complaints, diagnoses, and drug therapies while maintaining an interpretable decision structure
Analisis Kepuasan dan Penggunaan Rekam Medis Elektronik Puskesmas Menggunakan Metode End User Computing Satisfaction dan Technology Acceptance Model Selvia Selvia; Dolly Indra; Herdianti Darwis
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9540

Abstract

Electronic medical records (EMR) are health information systems used to improve the efficiency of patient data management and the quality of healthcare services in healthcare facilities. However, several problems have been found, such as network disruptions or errors, double workloads, and suboptimal user satisfaction levels. This study aims to analyze the level of satisfaction and use of electronic medical records and to determine the relationship between satisfaction and system use at the Tamamaung Community Health Center in Makassar City and the Community Health Center Technical Implementation Unit (UPTD) in North Kolaka Regency. The study used a quantitative approach with a cross-sectional design. Respondents in this study were 55 healthcare workers who used EMR selected using a full sampling technique (Full Sampling). Satisfaction analysis was conducted using the End User Computing Satisfaction (EUCS) and Technology Acceptance Model (TAM) methods. Analysis of the relationship between satisfaction and system use was conducted using the Spearman Rank Correlation test. The results showed that all EUCS and TAM variables were in the "High" category. The correlation test results showed a correlation coefficient of 0.463 with a significance value of 0.000 (p <0.05), which indicates a significant and very strong relationship between satisfaction and use of the EMR system. The conclusion of this study shows that the higher the level of satisfaction, the higher the level of use of the RME system.
Implementasi Sistem Layanan Mandiri untuk Efisiensi Administrasi Desa Biji Nangka Kabupaten Sinjai Purnawansyah; Rahma Puspitasari; Abdul Rachman Manga&#039;; Herdianti Darwis; Sitti Nurhalimah
Jurnal Pemberdayaan Masyarakat Vol 11 No 1 (2026): Mei
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jpm.v11i1.13200

Abstract

This community service program aims to improve administrative efficiency in Biji Nangka Village, which previously used manual processes and was prone to delays, inconsistencies, and the risk of archive loss. This activity implemented a website-based self-service system and provided training to village officials on the use of key features such as digital letter management, automatic numbering, and electronic archive storage. A total of 17 participants participated in the training and all successfully operated the system. Evaluation results showed that the time to create letters was reduced from 10–15 minutes to 3–5 minutes. Furthermore, the results of the pre-test and post-test comparison showed a 9.412% increase in participant understanding, indicating the effectiveness of the training in improving the digital competence of village officials. Overall, this program has had a positive impact on improving the quality of administrative services and supporting the realization of digital-based village governance.
Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset Poetri Lestari Lokapitasari Belluano; Reyna Aprilia Rahma; Herdianti Darwis; Abdul Rachman Manga
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p235-242

Abstract

This study aims to analyze the performance of various ensemble machine learning methods, such as Adaboost, Bagging, and Stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that Adaboost is effective in addressing data imbalance, while imbalanced data methods can significantly improve accuracy. However, the selection of imbalanced data methods should be carefully tailored to the dataset characteristics and clinical objectives. In conclusion, addressing data imbalance can enhance skin cancer classification accuracy, with Adaboost being an exception that shows a decrease in accuracy after applying imbalanced data methods.
Co-Authors - Nurhikma A. Muh. Syafei Emil Ma’arif A. Nurjulianty Abdul Rachman Manga&#039; Adela Regita Azzahra Alfitriana Riska Andi Ranreng Sombeng Annisa Febrianti Arif Kurnia Dedy Atmajaya Dewi Widyawati Dian Dolly Indra Dolly Indra Erick Irawadi Alwi Farniwati Fattah Fauzy Fathrurahman Fina Febriana Fitrah Wahyu Ningsih Harlinda Harlinda L Harlinda Lahuddin Haviluddin Haviluddin Hilma Aszahrah Huzain Azis Ihwana As’ad Ikmar Mawardi Irawati Irawati Jumrayanti Arfah lilis nurhayati Lokapitasari Belluano, Poetri Lestari M. Dimas Taufiqurahman Manga, Abdul Rachman Masita Balla Muh Aristsyah Malik Muh Firman Abd Rahman Muh. Arsyil Karimi Muh. Rifqi Zulkifli Muhammad Faiq Rahmatullah Nur Afra Dimitri Pratiwi Nur Almar&#039; Atussaliha Nur Chaerunnisa Purnawansyah Purnawansyah Putri Regina Prayoga Rahma Puspitasari Ramdan Sastra Reyna Aprilia Rahma Selvia Selvia Siska Anraeni Sitti Nurhalimah Sunarti Passura Backar Tanaka Kazuaki Tasrif Hasanuddin Tasrif Hasanuddin Wistiani Astuti Wistiani Astuti Yulita Salim Zahrizhal Ali