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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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PEMBERDAYAAN DESA MELALUI PENGENALAN DAN WORKSHOP SISTEM INFORMASI DESA BERBASIS OPENSID Zumhur Alamin; Randitha Missouri; Siti Mutmainah; Fathir Fathir; Sutriawan Sutriawan; Muhammad Amirul Mu'min
Taroa: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2025): Juli
Publisher : LPPM IAI Muhammadiyah Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52266/taroa.v4i2.4080

Abstract

Teknologi informasi memberikan dampak signifikan termasuk dalam penyelenggaraan pelayanan publik tingkat desa. Kegiatan pengabdian kepada masyarakat ini mendalami pengaruh transformasi desa digital melalui pengenalan dan implementasi Sistem Informasi Desa (SID) di desa Kalajena Kecamatan Wera Kabupaten Bima. Tujuan kegiatan ini adalah meningkatkan literasi digital komponen desa, mengidentifikasi potensi perubahan dalam efisiensi administratif desa, transparansi pemerintahan desa, dan partisipasi aktif masyarakat dalam pengambilan keputusan melalui adopsi teknologi informasi. Dalam pelaksanaan kegiatan, digunakan metode pendekatan partisipatif yang melibatkan interaksi secara intensif bersama perangkat desa dan masyarakat. Tahapan di mulai dengan sesi pengenalan konsep SID sebagai landasan teoritis, dilanjutkan dengan proses implementasi openSID secara offline atau pada jaringan komputer lokal dan uji coba implementasi secara online. Tahapan implementasi online berkaitan dengan penyewaan hosting dan melakukan pendaftaran domain. Selanjutnya, pelatihan praktis pengoperasian SID yang bertindak sebagai admin untuk perangkat desa, kemudian pengoperasian SID sebagai pengunjung kepada masyarakat desa. Hasil dari kegiatan ini menggambarkan peningkatan pemahaman serta kesadaran perangkat desa dan masyarakat akan potensi transformasi melalui penggunaan SID. Penerapan SID mampu menghasilkan efisiensi dalam proses administratif desa, meningkatkan transparansi dalam penyampaian informasi pemerintahan desa, serta mendorong partisipasi aktif masyarakat dalam berbagai proses pengambilan keputusan dalam penerapan kebijakan desa.
STRATEGI PENGEMBANGAN KARIER MAHASISWA ILMU KOMPUTER: MENENTUKAN PILIHAN KANTORAN, KERJA LEPAS, DAN WIRAUSAHA Siti Mutmainah; 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.
Classification of Swiftlet Nest Quality Based on SNI 8998:2021 Using Deep Learning Ilmiati Ilmiati; Siti Mutmainah; Khairunnas Khairunnas
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9901

Abstract

The quality of swiftlet nests is a key factor in determining the market value and quality standards of this commodity in both domestic and international markets. The quality classification process, which is currently dominated by manual methods, has fundamental weaknesses, namely high subjectivity and inconsistency in sorting results. This study aims to evaluate the performance of deep learning architectures in automatically classifying the quality of swiftlet nests based on visual characteristics. The main contribution of this study is to address the research gap in previous publications by strictly aligning quality class labels with the formal document of the Indonesian National Standard (SNI) 8998:2021, as well as presenting a cross-architecture comparative analysis to map model performance trade-offs. Evaluations were conducted on the MobileNetV2, and presents a cross-architecture comparative analysis to map model performance trade-offs. Evaluations were conducted on the MobileNetV2, ResNet50, and YOLOv8n-cls architectures using accuracy, precision, recall, and F1-score metrics. The research dataset includes visual images of swiftlet nests grouped into three quality classes (good, moderate, and poor) through self-documentation and augmentation techniques. Test results show that YOLOv8n-cls achieved the highest performance in this scenario with an accuracy of 99.5%, precision of 98.78%, recall of 98.72%, and an F1-score of 98.71%. Meanwhile, MobileNetV2 achieved a competitive accuracy of 98.37% with good computational efficiency, while ResNet50 demonstrated the lowest performance (66% accuracy) due to network complexity on the limited dataset. This research indicates that lightweight architectures exhibit good stability for limited-size visual datasets; however, external validation using larger datasets remains necessary to test the models’ generalization capabilities more broadly.
Komparasi Kinerja Arsitektur MobileNetV2 dan EfficientNetB0 Untuk Klasifikasi Penyakit Daun Tanaman Kedelai Lisdiawati Lisdiawati; Siti Mutmainah; Khairunnas Khairunnas
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10006

Abstract

Soybean leaf diseases can reduce the quality and productivity of plants, so an accurate and efficient detection method is needed. This study aims to compare the performance of the MobileNetV2 and EfficientNetB0 architectures in classifying soybean leaf diseases using a deep learning-based transfer learning approach. The dataset used consists of soybean leaf images grouped into several disease classes, then divided into training (80%), validation (10%), and testing (10%) data. The pre-processing stage includes resizing the images to 224 × 224 pixels, normalizing pixel values, and data augmentation in the form of rotation, shifting, zooming, and horizontal flipping. The training process is carried out using the Adam optimizer with a learning rate and applying Early Stopping to reduce the risk of overfitting. Model evaluation is carried out using a confusion matrix, accuracy, precision, recall, and F1-score. The results show that MobileNetV2 obtains an accuracy of 81%, higher than EfficientNetB0 which obtains an accuracy of 70%. The contribution of this study is to provide a comparative analysis of the effectiveness of both architectures in classifying soybean leaf diseases and to show that MobileNetV2 is more optimal for application to the dataset used.
Multiclass Herbal Plant Classification Using CNN Architectures: A Comparative Study of MobileNetV2, EfficientNetV2B0, NASNetMobile, and InceptionV3 Mechi Sakinatun Nufus; Siti Mutmainah; Fathir Fathir
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10048

Abstract

Indonesia is a country with an exceptionally rich biodiversity; herbal plants offer a wide range of benefits in the fields of health and traditional medicine. However, the process of identifying herbal leaves is still done manually and is often prone to errors due to similarities in shape, color, and texture among leaves. This study aims to develop a multi-class herbal plant leaf image classification system based on a Convolutional Neural Network (CNN) by comparing four transfer learning architectures: MobileNetV2, EfficientNetV2B0, NASNetMobile, and InceptionV3. The dataset used consists of 10 classes of herbal plant leaves. The contributions of this study include a comparative analysis of four CNN architectures for multi-class classification, an evaluation of the effectiveness of preprocessing and data augmentation on a limited dataset, and recommendations for the most optimal model based on accuracy and computational efficiency. The experimental results show that all models achieved validation accuracy above 98%. InceptionV3 delivered the best performance with a test accuracy of 97%, precision of 90%, and accuracy, recall, and F1-score of 89% respectively, demonstrating good generalization ability. Meanwhile, MobileNetV2 offers the best balance between accuracy and computational efficiency, making it a promising candidate for herbal plant identification systems based on mobile devices or in environments with limited computational resources.
Peningkatan Literasi Digital Masyarakat Melalui Pelatihan Pemanfaatan Teknologi Informasi di Desa Ntonggu Fathurrahman Fathurrahman; Fathir Fathir; Siti Mutmainah
Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat Vol. 3 No. 2 (2026): Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat (JP3M)
Publisher : Yayasan Assyifa Assyaka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71301/jp3m.v3i2.253

Abstract

Perkembangan teknologi informasi yang semakin pesat menuntut masyarakat untuk memiliki kemampuan literasi digital yang memadai agar mampu beradaptasi dan memanfaatkan teknologi secara optimal dalam kehidupan sehari-hari. Namun, masih terdapat kesenjangan literasi digital di wilayah pedesaan, termasuk di Desa Ntonggu, yang disebabkan oleh keterbatasan pengetahuan, keterampilan, serta pemahaman masyarakat dalam penggunaan teknologi informasi. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi digital masyarakat melalui pelatihan pemanfaatan teknologi informasi yang bersifat edukatif dan aplikatif. Metode yang digunakan meliputi sosialisasi, pelatihan praktik langsung, serta pendampingan penggunaan perangkat teknologi seperti smartphone dan aplikasi digital yang mendukung aktivitas pendidikan, ekonomi, dan administrasi desa. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan masyarakat dalam menggunakan teknologi informasi secara bijak dan produktif, terutama dalam mengakses informasi, komunikasi digital, serta pemanfaatan layanan berbasis daring. Pelatihan ini juga mendorong kesadaran masyarakat akan pentingnya keamanan digital dan etika dalam penggunaan teknologi. Dengan demikian, kegiatan ini diharapkan dapat berkontribusi terhadap peningkatan kualitas sumber daya manusia dan mendukung pembangunan desa berbasis teknologi informasi secara berkelanjutan.
Prediksi Pengangguran di Nusa Tenggara Barat Menggunakan Regresi Linier Berganda Berbasis Supervised Learning: Predicting Unemployment in West Nusa Tenggara Using Supervised Machine Learning-Based Multiple Linear Regression Rukmiati, Rukmiati; Mutmainah, Siti; Khairunnas, Khairunnas
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2860

Abstract

Tingkat Pengangguran Terbuka (TPT) merupakan salah satu indikator penting dalam mengukur kondisi ketenagakerjaan dan kesejahteraan masyarakat. Penelitian ini bertujuan untuk menganalisis pengaruh Indeks Pembangunan Manusia (IPM), Tingkat Kemiskinan, dan Inflasi terhadap TPT di Provinsi Nusa Tenggara Barat serta membangun model prediksi menggunakan Regresi Linear Berganda. Data yang digunakan merupakan data sekunder yang diperoleh dari Badan Pusat Statistik (BPS) Provinsi Nusa Tenggara Barat. Tahapan penelitian meliputi pengumpulan data, preprocessing, analisis data eksploratif (EDA), pengujian asumsi klasik, pemodelan, dan evaluasi model. Hasil pengujian asumsi klasik menunjukkan bahwa model telah memenuhi asumsi normalitas, multikolinearitas, heteroskedastisitas, dan autokorelasi. Untuk memperkuat kontribusi machine learning, dilakukan perbandingan antara Regresi Linear Berganda dan Decision Tree Regression. Hasil evaluasi menunjukkan bahwa Regresi Linear Berganda memperoleh nilai MSE sebesar 0,4357, RMSE sebesar 0,6601, dan R² sebesar 0,3226, sedangkan Decision Tree Regression memperoleh nilai MSE sebesar 0,5442, RMSE sebesar 0,7377, dan R² sebesar 0,1540. Berdasarkan hasil tersebut, Regresi Linear Berganda menghasilkan tingkat kesalahan yang lebih rendah dan kemampuan prediksi yang lebih baik dibandingkan dengan Decision Tree Regression. Dengan demikian, Regresi Linear Berganda dipilih sebagai model terbaik untuk memprediksi Tingkat Pengangguran Terbuka di Provinsi Nusa Tenggara Barat.
Pelatihan Penulisan Proposal Skripsi Dan Tips Penggunaan Aplikasi Mendeley Siti Mutmainah; Teguh Ansor Lorosae; Fathir Fathir
Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat Vol. 3 No. 1 (2026): Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat (JP3M)
Publisher : Yayasan Assyifa Assyaka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71301/jp3m.v3i1.197

Abstract

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.
nalisis Sentimen Berbasis Aspek Ulasan Aplikasi Ruangguru Pada Platform Android dan iOS menggunakan BiLSTM Siti Mutmainah; Erin Eka Citra; Teguh Ansyor Lorosae; Fathir Fathir
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7475

Abstract

Analysis of user reviews can provide valuable insights for app developers in improving quality, but conventional sentiment analysis only categorizes sentiment in general terms. Aspect-based Sentiment Analysis (ABSA) is a method that can be used to extract specific opinions from various aspects of user reviews. This study compares ABSA on user reviews of Ruangguru app on Android and iOS platforms. Review data was collected from Google Play Store and Apple App Store, processed, and classified into sentiment polarity using deep learning models such as BiLSTM with Word2Vec. The analysis was conducted to find out the aspects talked about by users and the sentiment associated with each aspect. The evaluation results show that the BiLSTM model with Word2Vec features performs well on the sentiment analysis task achieving 84% accuracy. In the aspect extraction task, the model performs very well with accuracy, precision, recall, and F1 Score values of 97%. These results show that the combination of BiLSTM and Word2Vec is an effective approach in understanding user opinions and preferences from Ruangguru app review text data and has the potential to be applied in the development of automated opinion analysis systems. Price aspect extraction results are the most dominant topic discussed on both platforms, followed by features and materials. Positive sentiment towards the price aspect dominates, but there is also a significant proportion of negative sentiment, especially on the Android platform.
Integration of Fuzzy Logic and Neural Networks for Explainable Early Diagnosis of Rice Plant Diseases Teguh Ansyor Lorosae; Miftahul Jannah; Siti Mutmainah; Fathir; Hilyatul Mustafidah
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.21

Abstract

Early diagnosis of rice leaf diseases remains challenging due to subtle symptom manifestation, uncontrolled illumination, heterogeneous backgrounds, and the limited interpretability of purely data-driven models. This study proposes an explainable hybrid framework integrating a Mamdani Fuzzy Inference System (FIS) with an Artificial Neural Network (ANN) for early rice leaf disease diagnosis under real-field conditions. The framework combines engineered symptom descriptors extracted from segmented leaf regions (GLCM texture and HSV color features), acquisition-time environmental measurements, and a fuzzy-derived disease severity cue to mitigate symptom ambiguity while preserving rule-based interpretability. Experiments were conducted on 8,000 field-acquired rice leaf images collected from multiple locations, covering Healthy, bacterial leaf blight, brown spot, and leaf smut classes. Evaluation followed a leakage-controlled, location-disjoint protocol. Across five independent runs, the proposed FIS–ANN achieved an average accuracy of 91.3 ± 0.6% and a macro-F1 score of 90.8 ± 0.7%, significantly outperforming a feature-based ANN and a fine-tuned ResNet-18 baseline (paired McNemar test, p < 0.05). Per-class analysis shows consistent recall improvements for visually overlapping diseases, and additional evaluation on mild-severity samples confirms maintained sensitivity at early disease stages. Field deployment experiments using smartphone-acquired images from unseen locations further demonstrate robust generalization with low on-device inference latency. These results indicate that integrating fuzzy severity reasoning into a lightweight neural classifier provides a practical balance between performance, interpretability, and computational efficiency, supporting early disease screening and mobile decision-support applications in precision agriculture.
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&#039;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