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All Journal Jurnal Ilmiah Informatika Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Transformatika Jurnas Nasional Teknologi dan Sistem Informasi E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal CoreIT JPM (Jurnal Pemberdayaan Masyarakat) Jurnal BALIRESO Dinamisia: Jurnal Pengabdian Kepada Masyarakat ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer Journal of Information System, Applied, Management, Accounting and Research Celebes Abdimas: Jurnal Pengabdian Kepada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Data and Science Indian Journal of Forensic Medicine & Toxicology International Journal of Engineering, Science and Information Technology Jurnal PkM (Pengabdian kepada Masyarakat) Caraka : Indonesia Journal of Communication Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Celebes Journal of Community Services KOPEMAS Jurnal Balireso: Jurnal Pengabdian pada Masyarakat Journal of Information Technology and its Utilization Linier: Literatur Informatika dan Komputer
Dolly Indra
Universitas Muslim Indonesia
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Analisis Pre-processing Sentimen Terhadap Komentar Layanan Indihome Pada Twitter Novanto, Achmad; Indra, Dolly; Astuti, Wistiani
LINIER: Literatur Informatika dan Komputer Vol 1, No 2 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v1i2.2491

Abstract

Dalam era globalisasi yang terus berkembang, peran teknologi informasi menjadi krusial dalam mengubah cara manusiaberinteraksi dan mengakses informasi. Perusahaan telekomunikasi, seperti PT Telkom Indonesia dengan layanannya,IndiHome, memanfaatkan kemajuan teknologi untuk menyediakan layanan digital berbasis Internet, Telepon Rumah, danTV Interaktif/IPTV. Meskipun sudah menjangkau seluruh Indonesia, pemahaman mengenai kepuasan pengguna terhadaplayanan IndiHome masih perlu diperdalam. Penelitian ini difokuskan pada analisis sentimen pengguna terhadap layananIndiHome melalui media sosial twitter. twitter menjadi platform yang signifikan dalam mengekspresikan pandangan, kritik,dan kepuasan pengguna. Pembatasan karakter dalam setiap cuitan memunculkan gaya bahasa baru, yang memicu kreativitaspengguna. Meski demikian, menganalisis sentimen dari tweet memiliki tantangan tersendiri, terutama karena penggunaankata-kata non-baku dan bahasa informal. Oleh karena itu, pentingnya preprocessing data dalam analisis sentimen menjadifokus utama penelitian ini. Langkah awal dalam penelitian ini bertujuan untuk meningkatkan keberhasilan klasifikasisentimen dengan membersihkan dan normalisasi data tweet. Hasil penelitian diharapkan dapat memberikan wawasan yanglebih akurat mengenai respons pengguna terhadap layanan IndiHome. Melalui langkah-langkah preprocessing yangdilibatkan, penelitian ini menyimpulkan bahwa data yang telah dipersiapkan menjadi lebih siap untuk tahap analisissentimen. Dengan demikian, analisis sentimen dapat memberikan hasil yang lebih relevan dan akurat, membuka peluanguntuk mengambil langkah-langkah strategis terkait dengan polarisasi sentimen yang teridentifikasi.
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.
Comparing Sentiment Labeling with RoBERTa and IndoBERTweet on Public Opinion of Program Makan Bergizi Gratis Putri Nur Rezky; Dolly Indra; Herdianti
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.v7i1.381

Abstract

The Program Makan Bergizi Gratis (MBG) is a flagship program of the Prabowo Subianto administration launched in 2024, triggering diverse public responses on social media. Sentiment analysis using deep learning models offers an effective approach to understanding public opinion at scale. However, selecting the appropriate model for Indonesian social media text remains challenging. This study aims to compare the performance of two pretrained transformer models, RoBERTa Base and IndoBERTweet Base, in conducting automatic sentiment labeling on Indonesian tweets related to the MBG program using a zero-shot labeling approach without human-annotated ground truth. A total of 1,831 tweets were collected from platform X and preprocessed using case folding, normalization, and stopword removal. Both models were applied in parallel to label each tweet with sentiment categories (positive, neutral, negative) along with confidence scores. The comparison was evaluated using agreement rate, Cohen's Kappa, and confidence score analysis. RoBERTa Base exhibits a conservative tendency with 75.20% neutral labels, while IndoBERTweet Base produces a more balanced distribution (68.16% neutral). The comparison shows 77.28% agreement with Cohen's Kappa of 0.490 (Moderate Agreement). RoBERTa Base achieves higher confidence (mean: 0.9559, 83.01% above 0.95) compared to IndoBERTweet Base (mean: 0.9236, 68.65% above 0.95). IndoBERTweet Base is more effective in detecting negative sentiment, identifying nearly twice as many negative tweets (13.54% vs. 7.48%). This study recommends IndoBERTweet Base for exploratory research requiring sensitive sentiment detection and RoBERTa Base for precision-critical applications. An ensemble approach combining both models is recommended for production-critical applications
Detection System of Strawberry Ripeness Using K-Means Indra, Dolly; Satra, Ramdan; Azis, Huzain; Manga, Abdul Rachman; L, Harlinda
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1054.25-31

Abstract

Strawberry is one type of fruit that is favored by the people of Indonesia. The detection process to identify strawberries can be done by utilizing advances in computer technology, One of them is in the field of digital image processing. In this study, we made a strawberry ripeness detection system using the values of Red, Green and Blue as the reference values, while for identification in determining the type of classification using the K-Means algorithm that uses the Euclidean distance difference as the reference. Based on the results of testing using the K-Means algorithm on 51 strawberry images consisting of ripe, semi ripe and raw fruit yielding an accuracy rate of 82.14%, we also conducted tests other than strawberry images as many as 8 images yielded an accuracy rate of 100%.
Co-Authors Abdul Rachman Manga’ Abdul Rauf Tuasikal Agung, Riski Dewa Ahmad Toni, Ahmad Alda, A. Nurul Aisya Aldri Frinaldi Amir, Nur Hikmah Andi Apryan Mallarangen andi Widya Mufila Gafar Anik Nur Handayani Anwar, Faudiah Ardhiansya Yusuf Arfan Zainuddin Armind Mauldi Kurniawan As'ad, Ihwana Astuti, Wistiani Damanhuri, Nor Salwa Daris, Mega Asfirawati Djamereng, Asdar Erick Irawadi Alwi Erick Irawadi Alwi Erick Irawadi Alwi, Erick Irawadi Erick, Erick Irawadi Alwi Fadly Achmad Farniwati Fattah Fauzy Fathrurahman Fery Setyo Aji Firman Shantya Budi, Firman Shantya Fitriyani Umar Hadyan Mardhi Fadlillah Haerdiansyah Syahnur, Muhammad Harlinda Lahuddin Hayudin Hasnanda Maila Herdianti Herdianti Darwis Herman Herman Hi. Talib, Juraiz Hidayat, Muh Wahyu Huzain Azis Ihwana As’ad Irawati Irawati Irawati Irawati Irja, Mulianty Cipta jabir, sitti rahmah Jufriadif Na`am, Jufriadif Juita Mandasari Julius Santony Kadri Rahmat Suat, Wahyu Kasman Kasman L, Harlinda Lilis Nur Hayati lilis nurhayati Lisna Ariani Lukman Syafie Lutfi Budi Ilmawan Lutfi Budi Ilmawan, Lutfi Budi Manga, Abdul Rachman Manga, Abdul Rachman Mansyur, St. Hajrah Mardiyyah Hasnawi Muh Fadlan Risqullah Dwitama N Muh Yeyen Dwi Suherman Muh. Fachrisyam Muh. Ridwan Rahim Muhammad Al Mubarak Muhammad Arfah Asis Muhammad Farhan Hermansyah Mukarramah, Rifqatul Musdalifah Musdalifah Mustika, Mustika Octavia Novanto, Achmad Nur Hayati, Lilis Nurhalima Nurhalima Prihandani, St. Nadya Kurnia Purnawansyah Purnawansyah Putri Nur Rezky Rahma, Dewi Ernita Rahmat Suat, Wahyu Kadri Rahmayani, Nurul Ramadan, Syahril Ramdan Satra Ramdaniah Ramdaniah Rezky Anugrah Rifky Saputra Scania, Muhammad Rosa Andrie Asmara Salsa, Salsabila Aurelia Saputra Scania, Muhammad Rifky Satma, Satma St. Hajrah Mansyur Subhan Ardhiman Sugiarti, Sugiarti Sukur, Widianti Syahnur, Muh. Haerdiansyah Syahrul Mubarak Abdullah Tanaka Kazuaki Tasmil Tasmil Tasrif Hasanuddin Tasrif Hasanuddin Taufik, Muhammad Asrai Tenri Sa'nah Umar Mansyur Umar, Fitriyani Veithzal Rivai Zainal Wahyu Sakti Gunawan Irianto Yuhandri Yuhandri, Yuhandri Yundari, Yundari Zahra, Andi Fathimatuz Zahra