Articles
<![CDATA[PENINGKATAN KESADARAN DAN PARTISIPASI MASYARAKAT MELALUI PELATIHAN PENGELOLAAN SAMPAH ]]>
<![CDATA[Missouri, Randitha]]>;
<![CDATA[Annafi, Nurfidianty]]>;
<![CDATA[Lukman, Lukman]]>;
<![CDATA[Khairunnas, Khairunnas]]>;
<![CDATA[Mutmainah, Siti]]>;
<![CDATA[Fathir, Fathir]]>;
<![CDATA[Alamin, Zumhur]]>
<![CDATA[ Taroa: Jurnal Pengabdian Masyarakat Vol 2 No 2 (2023): Juli ]]>
Publisher : <![CDATA[LPPM IAI Muhammadiyah Bima ]]>
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DOI: 10.52266/taroa.v2i2.2617
<![CDATA[Krisis pengelolaan sampah global membutuhkan solusi berkelanjutan yang melibatkan partisipasi masyarakat. Penelitian ini mengevaluasi dampak positif metode pelatihan berbasis masyarakat terhadap peningkatan kesadaran dan partisipasi dalam pengelolaan sampah. Kegiatan ini dilakukan di Kelurahan Mande, Kecamatan Mpunda, Kota Bima. Survei awal menunjukkan pemahaman dan partisipasi yang rendah, dengan hanya 40% responden memiliki pemahaman memadai. Setelah pelatihan, pemahaman meningkat signifikan menjadi 85%, menunjukkan efektivitas pelatihan dalam meningkatkan kesadaran masyarakat terkait pengelolaan sampah. Meskipun partisipasi di tingkat rumah tangga menunjukkan penurunan yang tidak signifikan, perubahan positif dalam praktik pengelolaan sampah dapat diukur dari hasil survei. Analisis kualitatif menyoroti perubahan sikap mendalam pada masyarakat, yang merespons pelatihan dengan menerapkan konsep-konsep pengelolaan sampah secara aktif. Evaluasi juga menegaskan pelatihan berhasil menciptakan keterlibatan masyarakat yang lebih luas di tingkat komunitas. Hasil survei menunjukkan peningkatan pesat dalam pemahaman dampak lingkungan pengelolaan sampah berkelanjutan (90%). Partisipasi dalam kegiatan komunitas terkait sampah melonjak dari 30% menjadi 80%, menunjukkan dampak pelatihan dalam merangsang keterlibatan masyarakat.]]>
<![CDATA[STRATEGI PENGEMBANGAN KARIER MAHASISWA ILMU KOMPUTER: MENENTUKAN PILIHAN KANTORAN, KERJA LEPAS, DAN WIRAUSAHA ]]>
<![CDATA[Mutmainah, Siti]]>;
<![CDATA[Dahlan, Dahlan]]>;
<![CDATA[Syarifuddin, Syarifuddin]]>;
<![CDATA[Taufiqurahman, Taufiqurahman]]>
<![CDATA[ Taroa: Jurnal Pengabdian Masyarakat Vol 5 No 1 (2026): Januari ]]>
Publisher : <![CDATA[LPPM IAI Muhammadiyah Bima ]]>
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DOI: 10.52266/taroa.v5i1.4545
<![CDATA[Kegiatan kuliah umum ini bertema “Kenali Potensi IT Anda: Temukan Jalur yang Tepat antara Kantoran, Freelance, atau Wirausaha” merupakan salah satu bagian dari pengabdian kepada masyarakat yang bertujuan untuk memberikan pemahaman kepada mahasiswa Ilmu Komputer mengenai berbagai jalur karier di bidang teknologi informasi. Kegiatan yang diselenggarakan oleh Program Studi Ilmu Komputer Universitas Muhammadiyah Bima dan diikuti oleh 110 mahasiswa ini menghadirkan dua pembicara dari kalangan akademisi dan praktisi IT. Beberapa materi yang disampaikan antara lain pengenalan profesi karier bidang IT, serta jalur pilihan kerja sebagai karyawan, freelancer, dan entrepreneur digital, berikut kelebihan dan tantangannya. Kegiatan berlangsung secara interaktif dengan melibatkan mahasiswa sebagai moderator dan pemateri, serta didampingi oleh dosen pendamping. Berdasarkan pengamatan, kegiatan ini meningkatkan pemahaman peserta mengenai karier di bidang TI dan mendorong refleksi diri terhadap potensi dan minat mahasiswa. Hasil pengabdian menunjukkan pentingnya sinergi antara kampus dan dunia industri dalam mempersiapkan lulusan yang adaptif dan kompeten. Kegiatan ini diharapkan dapat menjadi langkah awal dalam membangun strategi pengembangan karier mahasiswa yang lebih terarah.]]>
<![CDATA[Pelatihan Penulisan Proposal Skripsi Dan Tips Penggunaan Aplikasi Mendeley ]]>
<![CDATA[Mutmainah, Siti]]>;
<![CDATA[Ansor Lorosae, Teguh]]>;
<![CDATA[Fathir, Fathir]]>
<![CDATA[ Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat Vol. 3 No. 1 (2026): Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat (JP3M) ]]>
Publisher : <![CDATA[Yayasan Assyifa Assyaka ]]>
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DOI: 10.71301/jp3m.v3i1.197
<![CDATA[Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kemampuan mahasiswa dalam menulis proposal skripsi secara sistematis serta memberikan pemahaman dan keterampilan dalam penggunaan aplikasi Mendeley sebagai alat manajemen referensi. Permasalahan yang dihadapi mahasiswa antara lain rendahnya pemahaman terhadap struktur penulisan proposal skripsi dan kurangnya kemampuan dalam mengelola sitasi serta daftar pustaka secara benar. Metode yang digunakan dalam kegiatan ini adalah pelatihan dan pendampingan yang dilaksanakan melalui penyampaian materi, praktik langsung, serta diskusi interaktif. Subjek kegiatan adalah mahasiswa Universitas Muhammadiyah Bima yang sedang atau akan menyusun proposal skripsi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman mahasiswa terhadap sistematika penulisan proposal skripsi serta kemampuan dalam menggunakan aplikasi Mendeley untuk pengelolaan referensi. Pelatihan ini memberikan dampak positif dalam meningkatkan kesiapan mahasiswa dalam menyusun proposal skripsi yang sesuai dengan kaidah penulisan ilmiah dan etika akademik.]]>
<![CDATA[Improving the Accuracy of Social Media Sentiment Classification with the Combination of TF-IDF Method and Random Forest Algorithm ]]>
<![CDATA[Siti Mutmainah]]>;
<![CDATA[Fathir]]>;
<![CDATA[Erin Eka Citra]]>
<![CDATA[ Journix: Journal of Informatics and Computing Vol. 1 No. 1 (2025): April ]]>
Publisher : <![CDATA[Ran Edu Center ]]>
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DOI: 10.63866/journix.v1i1.2
<![CDATA[Sentiment classification on social media text data is one of the main challenges in public opinion analysis. The large volume of data and the diversity of informal languages make sentiment analysis a challenge in itself, especially in the context of Indonesian. This research aims to improve the accuracy of social media sentiment classification by combining Term Frequency-Inverse Document Frequency (TF-IDF) method as a text representation technique and Random Forest algorithm as a classification model. The dataset used consists of 20,000 Indonesian opinion data collected from Twitter and Instagram, and has been labeled into three sentiment categories: positive, negative, and neutral. This data went through a preprocessing stage, including text cleaning, tokenization, stopword removal, stemming, and normalization. Experimental results show that the combination of TF-IDF and Random Forest yields an accuracy of 91.2% with average precision, recall, and F1-score values above 0.90. The confusion matrix analysis revealed that the model was highly effective in classifying positive and negative sentiments, although there were challenges in distinguishing neutral sentiments. These findings indicate that the approach used is quite reliable and can be used as a foundation for the development of sentiment analysis systems on an industrial scale as well as further research.]]>
<![CDATA[Genetic Algorithm Optimization for Solving the Traveling Salesman Problem in the Indonesian Business Environment ]]>
<![CDATA[Siti Mutmainah]]>;
<![CDATA[Teguh Ansyor Lorosae]]>;
<![CDATA[Erin Eka Citra]]>
<![CDATA[ Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August ]]>
Publisher : <![CDATA[Ran Edu Center ]]>
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DOI: 10.63866/journix.v1i2.14
<![CDATA[The Traveling Salesman Problem (TSP) is one of the combinatorial optimization problems that is highly relevant in distribution and logistics route planning. This study aims to optimize the Genetic Algorithm (GA) for solving TSP in the Indonesian business environment, which has complex geographical characteristics and diverse logistics infrastructure. The proposed approach combines dynamic parameter adaptation and regional clustering to improve convergence efficiency and solution quality. Experiments were conducted on the distribution route data of an Indonesian logistics company with three scenarios: conventional GA, adaptive GA, and clustering-based GA. Performance evaluation was based on total travel distance, computation time, solution stability, and convergence rate. The results show that adaptive AG produces the best performance, with a reduction in total travel distance of up to 20% more efficient, faster convergence time (95 iterations compared to 120 iterations in conventional AG), and solution stability reaching 90.6%. These findings indicate that parameter adaptation in AG can significantly improve the effectiveness of TSP optimization in the Indonesian business context. The contribution of this research not only strengthens the development of adaptive metaheuristic algorithms but also provides practical benefits for the logistics industry in designing more efficient, cost-effective, and sustainable distribution routes.]]>
<![CDATA[Multi-Scale Convolutional Neural Network-Based Classification of Tuberculosis Chest X-ray Images ]]>
<![CDATA[M Ridwan]]>;
<![CDATA[syafrudin _]]>;
<![CDATA[Sahrul Fauzan Djiaulhaq]]>;
<![CDATA[Siti Mutmainah]]>;
<![CDATA[Teguh Ansyor Lorosae]]>
<![CDATA[ Indonesian Applied Research Computing and Informatics Vol. 1 No. 2: December (2025) ]]>
Publisher : <![CDATA[PT. Teras Digital Nusantara ]]>
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DOI: 10.64479/iarci.v1i2.60
<![CDATA[Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis, which mainly attacks the lung organs. One of the most commonly used methods of TB diagnosis is thorax X-ray imaging. The images of the examination results are visually analyzed by medical personnel to identify certain patterns or characteristics that indicate TB disease. However, the manual analysis process takes time and depends on the doctor's experience. Therefore, this study utilizes Artificial Intelligence (AI) technology as a diagnostic tool to provide alternative solutions that are faster and more efficient in determining TB status in patients. This study proposes the use of the Multi-Scale Convolutional Neural Network (CNN) method to classify tuberculosis disease based on thorax X-ray images. The data used was in the form of lung X-ray images that acted as inputs at the image processing stage. The dataset collected consisted of 790 images divided into two classes, namely normal lungs and lungs indicated by tuberculosis. The CNN architecture includes three convolutional layers with a kernel size of 3×3, three max pooling layers of 2×2, and one fully connected layer with a softmax activation function. Each convolutional layer uses 128 filters, and the model learning process is optimized using the Adam Optimizer algorithm. The training process was carried out for 15 epochs and resulted in an accuracy rate of 81%. Furthermore, at the model evaluation stage, an accuracy of 79% was obtained, indicating that the proposed method has sufficient performance in classifying tuberculosis disease.]]>
<![CDATA[MobileNetV2 Transfer Learning Implementation for Waste Classification ]]>
<![CDATA[Fifi Andriani]]>;
<![CDATA[Ade Yuliati]]>;
<![CDATA[Anis Yaturahmah]]>;
<![CDATA[Siti Mutmainah]]>
<![CDATA[ Indonesian Applied Research Computing and Informatics Vol. 1 No. 2: December (2025) ]]>
Publisher : <![CDATA[PT. Teras Digital Nusantara ]]>
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DOI: 10.64479/iarci.v1i2.62
<![CDATA[Waste management issues represent one of the major challenges in maintaining environmental sustainability, as the waste sorting process is still largely performed manually, requiring significant time and effort and relying heavily on human accuracy, which makes it inefficient and prone to errors. Therefore, this study utilizes Artificial Intelligence (AI) technology as a solution to support more effective and sustainable environmental management by proposing the use of the Convolutional Neural Network (CNN) algorithm to classify waste types based on digital images. The data used consist of waste images as inputs in the image processing stage, which are then classified into several waste categories. The CNN architecture applied consists of multiple convolutional layers with a kernel size of 3×3, max pooling layers for feature extraction, and a fully connected layer with a softmax activation function to determine the output class, while the model training process is optimized using the Adam Optimizer algorithm. The experimental results demonstrate that the proposed CNN model is capable of classifying waste types with a good level of accuracy, indicating that this AI-based approach can serve as an effective supporting solution for intelligent, efficient, and sustainable waste management systems and contribute to environmental conservation efforts.]]>
<![CDATA[Student's Digital Literacy and an Evaluation of Awareness of Ethics, Privacy, and Online Safety ]]>
<![CDATA[Fathir Fathir]]>;
<![CDATA[Siti Mutmainah]]>;
<![CDATA[Teguh Ansyor Lorosae]]>
<![CDATA[ Jurnal Pendidikan Terapan Vol 4, No 2 May (2026) ]]>
Publisher : <![CDATA[Sakura Digital Nusantara ]]>
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DOI: 10.61255/jupiter.v4i2.944
<![CDATA[Purpose The development of digital technology has become an integral part of teenagers’ lives; digital technology not only presents opportunities but also poses challenges. Digital literacy is a crucial and key aspect in ensuring that students can use technology wisely and responsibly. This study aims to identify digital literacy among teenagers, with a focus on online ethics, privacy, and security. Methods The method used was a quantitative approach to measure the correlation between frequency and digital literacy among adolescents. Data was collected using a Google Forms questionnaire, which yielded responses from 105 participants. Findings The analysis results show that the majority of students have a moderate level of digital literacy (79%), while only a small proportion have a high level of digital literacy (18%). Research Implications The analysis results indicate a significant positive correlation between the frequency of internet use and the level of digital literacy, as well as between the frequency of internet use and the ability to distinguish between valid and invalid content. Originality There was also a relationship between frequency of internet use and difficulty distinguishing between true and false information.]]>
<![CDATA[YOLOv9-Based Classification of Ganyong Plant Health for Early Detection of Leaf Spot Disease ]]>
<![CDATA[M Fikram]]>;
<![CDATA[Siti Mutmainah]]>; Dahlan
<![CDATA[ Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026 ]]>
Publisher : <![CDATA[Academic Bright Collaboration ]]>
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DOI: 10.61220/dtcs.v3i2.551
<![CDATA[Purpose – This study implements the YOLOv9 architecture to automatically classify the health condition of ganyong leaves (Canna edulis Kerr.) as an early-detection tool for leaf spot disease. The study addresses the limitations of subjective manual identification and supports farmers in Bumipajo Village, Bima Regency, in reducing potential crop failure. Methods – A primary field dataset consisting of 1,383 image objects was collected and divided into training, validation, and testing sets using a 70:20:10 ratio. YOLOv9 was implemented by integrating Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). Model training was conducted in Google Colab using GPU acceleration, a batch size of 4, and 50 epochs. Findings – Evaluation on independent test data showed strong detection performance, with mAP@50 of 99%, Precision of 99%, Recall of 100%, and an average inference speed of 58.4 ms per image. These results indicate that YOLOv9 can effectively preserve disease-related morphological features in visually complex biological objects. Research implications – The findings are limited to the environmental conditions of the data collection site and one disease type. The reported time efficiency also depends on GPU-based hardware and requires further validation on mobile devices. Originality – This study contributes a field-based primary dataset of ganyong leaves and validates YOLOv9 for a local agricultural commodity that remains underexplored.]]>
<![CDATA[Development of an IoT-based Smart Farming System using ESP32 for Livestock Monitoring ]]>
<![CDATA[Rizki Fikriansyah]]>;
<![CDATA[Siti Mutmainah]]>; Dahlan
<![CDATA[ Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026 ]]>
Publisher : <![CDATA[Academic Bright Collaboration ]]>
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DOI: 10.61220/dtcs.v3i2.607
<![CDATA[Purpose – Livestock farming is a vital sector of the Indonesian economy, yet the common practice of allowing livestock to roam freely renders manual monitoring inefficient and exposes farmers to risks of loss, accidents, and theft. This study presents an IoT-based livestock monitoring prototype designed to enable real-time location tracking and automated boundary violation alerts, addressing the lack of affordable and practical smart monitoring solutions for smallholder farmers. Methods – The system was developed using an ESP32 microcontroller integrated with a Neo-6M GPS module and a Telegram bot for automatic notifications. A geofencing boundary of 50 meters was configured from a fixed reference point. Twelve trials were conducted across morning, afternoon, and evening sessions to evaluate system performance under varying conditions. Findings – The system delivered location alerts every ten minutes with Google Maps links and coordinates. Under normal conditions, livestock positions were detected within 5.0–12.7 meters of the reference point. Boundary violations exceeding 50 meters triggered immediate alerts, with notification latency ranging from 3 to 8 seconds under stable network conditions. GPS baseline error was approximately 5.0–5.5 meters, with an accuracy variation of ±2–3 meters. Research Implications – System performance is constrained by Wi-Fi network stability and environmental factors affecting GPS accuracy, limiting its generalizability to areas with reliable connectivity. Further field testing is required before broader implementation. Originality – This study contributes a low-cost, ESP32-based geofencing solution integrated with Telegram, offering a practical and scalable approach to smart livestock monitoring in developing agricultural contexts.]]>
