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All Journal International Journal of Artificial Intelligence Research Indonesian Journal on Software Engineering (IJSE) JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Kridatama Sains dan Teknologi Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Taroa : Jurnal Pengabdian Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pendidikan Terapan Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Journal of Excellence Humanities and Religiosity Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat Jurnal Pengabdian kepada Masyarakat (PEMAS) Scientific: Journal of Computer Science and Informatics Journix: Journal of Informatics and Computing Indonesian Applied Research Computing and Informatics Journal of Digital Technology and Computer Science
Siti Mutmainah
Universitas Muhammadiyah Bima
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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STRATEGI PENGEMBANGAN KARIER MAHASISWA ILMU KOMPUTER: MENENTUKAN PILIHAN KANTORAN, KERJA LEPAS, DAN WIRAUSAHA Mutmainah, Siti; Dahlan, Dahlan; Syarifuddin, Syarifuddin; Taufiqurahman, Taufiqurahman
Taroa: Jurnal Pengabdian Masyarakat Vol 5 No 1 (2026): Januari
Publisher : LPPM IAI Muhammadiyah Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52266/taroa.v5i1.4545

Abstract

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.
Improving the Accuracy of Social Media Sentiment Classification with the Combination of TF-IDF Method and Random Forest Algorithm Siti Mutmainah; Fathir; Erin Eka Citra
Journix: Journal of Informatics and Computing Vol. 1 No. 1 (2025): April
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i1.2

Abstract

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.
Genetic Algorithm Optimization for Solving the Traveling Salesman Problem in the Indonesian Business Environment Siti Mutmainah; Teguh Ansyor Lorosae; Erin Eka Citra
Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i2.14

Abstract

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.
Multi-Scale Convolutional Neural Network-Based Classification of Tuberculosis Chest X-ray Images M Ridwan; syafrudin _; Sahrul Fauzan Djiaulhaq; Siti Mutmainah; Teguh Ansyor Lorosae
Indonesian Applied Research Computing and Informatics Vol. 1 No. 2: December (2025)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64479/iarci.v1i2.60

Abstract

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.
MobileNetV2 Transfer Learning Implementation for Waste Classification Fifi Andriani; Ade Yuliati; Anis Yaturahmah; Siti Mutmainah
Indonesian Applied Research Computing and Informatics Vol. 1 No. 2: December (2025)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64479/iarci.v1i2.62

Abstract

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.
Student's Digital Literacy and an Evaluation of Awareness of Ethics, Privacy, and Online Safety Fathir Fathir; Siti Mutmainah; Teguh Ansyor Lorosae
Jurnal Pendidikan Terapan Vol 4, No 2 May (2026)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jupiter.v4i2.944

Abstract

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.
YOLOv9-Based Classification of Ganyong Plant Health for Early Detection of Leaf Spot Disease M Fikram; Siti Mutmainah; Dahlan
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.551

Abstract

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.
Development of an IoT-based Smart Farming System using ESP32 for Livestock Monitoring Rizki Fikriansyah; Siti Mutmainah; Dahlan
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.607

Abstract

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.
PELATIHAN GURU PAUD DALAM PENGGUNAAN APLIKASI EDUKASI INTERAKTIF UNTUK PENINGKATAN KUALITAS PEMBELAJARAN Zumhur Alamin; Lukman Lukman; Randitha Missouri; Nurfidianty Annafi; Siti Mutmainah; Khairunnas Khairunnas; Fathir Fathir
Taroa: Jurnal Pengabdian Masyarakat Vol. 2 No. 1 (2023): Januari
Publisher : LPPM IAI Muhammadiyah Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52266/taroa.v2i1.2616

Abstract

Program pelatihan untuk guru pendidikan anak usia dini (PAUD) dalam penggunaan aplikasi edukasi interaktif telah menunjukkan dampak positif yang signifikan pada kompetensi guru dan kualitas pembelajaran di tingkat PAUD. Melalui metode pelatihan yang interaktif dan praktis, guru-guru PAUD memperoleh pemahaman yang lebih baik tentang penggunaan aplikasi seperti Bandicam dan Canva untuk menciptakan pengalaman belajar yang menarik dan relevan bagi siswa-siswi mereka. Evaluasi terhadap umpan balik peserta pelatihan mengungkapkan kepuasan yang tinggi dan peningkatan yang signifikan dalam keterampilan teknologi guru-guru PAUD. Meskipun beberapa tantangan dihadapi selama pelaksanaan pelatihan, seperti batasan waktu dan tingkat keterampilan teknologi yang bervariasi di antara peserta, upaya mitigasi berhasil mengatasi kendala tersebut. Implikasi dari program pelatihan ini adalah peningkatan kualitas pembelajaran di tingkat PAUD melalui integrasi teknologi pendidikan interaktif. Program ini memiliki potensi untuk memberikan manfaat jangka panjang dalam meningkatkan pendidikan anak usia dini.
PENINGKATAN KESADARAN DAN PARTISIPASI MASYARAKAT MELALUI PELATIHAN PENGELOLAAN SAMPAH Randitha Missouri; Nurfidianty Annafi; Lukman Lukman; Khairunnas Khairunnas; Siti Mutmainah; Fathir Fathir; Zumhur Alamin
Taroa: Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2023): Juli
Publisher : LPPM IAI Muhammadiyah Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52266/taroa.v2i2.2617

Abstract

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.
Co-Authors Abel Ahmad Tasrif Ade Yuliati Anas Satria Anis Yaturahmah Dahlan Dhomas Hatta Fudholi Dikan , Noval Erin Eka Citra Erin Eka Citra Erin Eka Citra Fathir Fathir Fathir Fathurrahman Fathurrahman Fifi Andriani Fudholi, Dhomas Hatta Ghufran, Syed Muhammad Hayaty, Mardhiya Hilyatul Mustafidah Ilmiati Ilmiati Khairunnas Khairunnas Khairunnas Khairunnas Khairunnas, Khairunnas Khairunnisa Lisdiawati Lisdiawati Lukman Lukman LUKMAN, LUKMAN M Fikram M Ridwan M. Fikram Mechi Sakinatun Nufus Mentia, Dilla Puspita Miftahul Jannah Miftahul Jannah Muhammad Akbar Muhammad Amirul Mu'min Muhammad Amirul Mu'min Muhammad Hayun Mustafidah, Hilyatul Nur Hunul Khatimah Nurfadila Nurfidianty Annafi Nurfidianty Annafi, Nurfidianty Patryansya , Yoga Purwoko, Agus Qomar Rahmadhin Ramadhan, Sahrul Randitha Missouri Rizki Fikriansyah Rizki, Afsa Rukmiati, Rukmiati Sabar Aritonang Rajagukguk Sahrul Fauzan Djiaulhaq Sahrul Ramadhan Saifullah Setiawan, Muhammad Iwan Sri Mulyani St. Rahmah Sutriawan syafrudin _ Syarifuddin Syarifuddin Syarifuddin Syarifuddin Sya’Ban, Ahmad Taufiqurahman Taufiqurahman Taufiqurahman, Taufiqurahman Teguh Ansyor Lorosae Teguh Ansyor Lorosae Tri Stiyo Famuji Widayati, Ulfa Yuliyanti yuliyanti Zumhur Alamin