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All Journal Desimal: Jurnal Matematika Jurnal Kridatama Sains dan Teknologi Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Media Informatika J-ESA (Jurnal Ekonomi Syariah) Indonesian Community Journal Taroa : Jurnal Pengabdian Masyarakat Jurnal Pendidikan Terapan Journal of Excellence Humanities and Religiosity Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat Scientific: Journal of Computer Science and Informatics Journix: Journal of Informatics and Computing Indonesian Applied Research Computing and Informatics
Fathir Fathir
Universitas Muhammadiyah Bima
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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INOVASI DAN TEKNOLOGI DALAM PERENCANAAN PEMBANGUNAN DAERAH YANG BERDAMPAK Dahlan Dahlan; Irma Putri; Fathir Fathir
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.v5i1.5794

Abstract

Perencanaan pembangunan daerah menuntut penggunaan data yang akurat, komprehensif, dan mudah dianalisis untuk mendukung pengambilan keputusan yang lebih efektif. Namun, sebagian besar pemerintah daerah masih menghadapi kendala dalam memanfaatkan teknologi digital untuk mendukung proses perencanaan. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kapasitas aparatur daerah dalam memanfaatkan inovasi dan teknologi, seperti Sistem Informasi Geospasial (GIS), dashboard analitik, dan aplikasi survei digital sebagai instrumen perencanaan pembangunan berbasis data. Metode yang digunakan meliputi sosialisasi, pelatihan teknis, praktik langsung, pendampingan, dan evaluasi kompetensi. Hasil kegiatan menunjukkan peningkatan signifikan dalam pemahaman dan kemampuan peserta dalam pemetaan wilayah, analisis data pembangunan, serta penyusunan rekomendasi pembangunan berbasis teknologi. Selain itu, peserta berhasil menghasilkan peta tematik, dataset pembangunan terstruktur, dan prototipe dashboard pembangunan daerah. Kegiatan ini memberikan kontribusi nyata dalam mendorong transformasi digital pemerintah daerah dan meningkatkan kualitas dokumen perencanaan. Kata Kunci: : inovasi digital, GIS, teknologi informasi
Forecasting the Consumer Price Index in Bima City Using Random Forest Regression Naafia, Suci Faaza; Fathir; mustafidah, Hilyatul
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

Accurate forecasting of the Consumer Price Index (CPI) is essential for supporting regional inflation monitoring and evidence-based economic decision-making. However, city-level CPI forecasting remains limited, particularly in smaller urban areas where local expenditure patterns may differ from national trends. This study aimed to develop a predictive model for the General CPI of Bima City, Indonesia, using Random Forest Regression based on monthly expenditure-group CPI data. A quantitative predictive research design was employed using secondary data published by the Central Statistics Agency (BPS) of Bima City from January 2013 to December 2025, comprising 156 monthly observations. The dataset was divided into training and testing subsets using an 80:20 ratio. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), while feature-importance analysis was performed to identify the most influential expenditure groups. The model achieved an MAE of 0.7626, an MSE of 1.4501, an RMSE of 1.2042, and an R² of 0.994, indicating strong predictive performance on the testing dataset. The actual–predicted comparison and scatter plot demonstrated close agreement between observed and predicted CPI values. Feature-importance analysis identified Finished Food, Health, and Foodstuffs as the dominant predictors contributing to model performance. These findings demonstrate that Random Forest Regression can effectively model the relationship between expenditure-group CPI variables and General CPI within the observed dataset while providing interpretable information regarding variable contribution. This study contributes to the growing application of interpretable machine learning in regional economic forecasting and offers a practical framework for supporting localized CPI monitoring and data-informed inflation analysis.
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.
Perbandingan Metode Elbow dan Silhouette Coefficient pada K-Means untuk Pengelompokan Wilayah Berdasarkan Indeks Pembangunan Manusia Shinta Zahira Hayathun Nufus; Fathir Fathir; Hilyatul Mustafidah
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.v8i1.10114

Abstract

One of the primary metrics for evaluating the effectiveness of efforts to improve people’s well-being through human development is the Human Development Index (HDI). Although Indonesia’s HDI has continued to improve, disparities in human development remain evident across regions, particularly in Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). Identifying regions with similar HDI characteristics is important for supporting more targeted development policies. However, the performance of K-Means clustering is highly influenced by the number of clusters used, making the selection of an appropriate cluster number essential. This study compares the Elbow Method and Silhouette Coefficient in determining the optimal number of clusters for 2024 HDI data covering 41 regencies and municipalities based on Life Expectancy, Expected Years of Schooling, Mean Years of Schooling, and Per Capita Expenditure. The results show that the Elbow Method produces three clusters, while the Silhouette Coefficient produces two clusters with a silhouette value of 0.5312. Evaluation using the Davies–Bouldin Index (DBI) indicates that the two-cluster solution achieves a lower DBI value (0.7350) than the three-cluster solution (1.0382). These findings suggest that the HDI structure in Bali, NTB, and NTT tends to form two major groups: regions with high human development and regions with medium-to-low human development. The results also indicate that the Silhouette Coefficient is more representative for determining the optimal number of clusters in HDI data with relatively similar regional characteristics. The clustering results may support policymakers in prioritizing development programs in education, health, and community welfare
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.
Klasifikasi Penyakit Daun Bawang Merah Menggunakan MobileNetV2 Convolutional Neural Network (CNN) Widia Ainun Arabiah; Fathir Fathir; Hilyatul Mustafidah
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10575

Abstract

Diseases affecting shallot plants are a primary cause of reduced crop quality and yield. Manual disease identification relies on visual observation, making it prone to error and time-consuming. This study aims to develop a classification model for shallot leaf diseases by combining Gray Level Co-occurrence Matrix (GLCM) feature extraction with MobileNetV2, classified using a Convolutional Neural Network (CNN). The dataset comprises 1,188 shallot leaf images categorized into five classes: downy mildew, healthy, leaf blight, *moler* (basal rot), and purple blotch. The research process involved dataset collection; pre-processing (image resizing to 224×224 pixels, grayscale conversion, normalization, and data augmentation); texture feature extraction using GLCM; and deep feature extraction using MobileNetV2. These features were then combined and used as input for the CNN classification model. Model evaluation was conducted using a confusion matrix, assessing accuracy, precision, recall, and F1-score. The results demonstrate that the proposed model achieved 93% accuracy, with balanced precision, recall, and F1-score values ​​across most classes. The contribution of this research is the integration of gray level co-occurrence matrix (GLCM) texture feature extraction with mobilenetv2 visual features within a convolutional neural network (CNN) model to improve the classification performance of shallot leaf diseases.
Pendampingan Penulisan Karya Ilmiah Mahasiswa Menggunakan Referensi Otomatis Berbasis Aplikasi Mendeley Luthfiyah Luthfiyah; Nurlaila Nurlaila; Zuhrah Zuhrah; Syarifuddin Syarifuddin; Fathir Fathir
Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat Vol. 2 No. 1 (2025): 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.v2i1.99

Abstract

Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kemampuan dosen dan mahasiswa dalam menulis karya ilmiah yang sesuai dengan standar akademik melalui pemanfaatan aplikasi manajemen referensi Mendeley. Permasalahan umum yang dihadapi dalam penulisan ilmiah, seperti ketidaktepatan dalam pengutipan, penulisan daftar pustaka, serta kurangnya pemahaman terhadap etika akademik, menjadi latar belakang utama pelaksanaan kegiatan ini. Kegiatan dilaksanakan di Kampus 1 Universitas Muhammadiyah Bima, bertempat di ruangan dosen, dengan pendekatan pelatihan yang terdiri dari tiga tahap: persiapan, pelaksanaan, dan evaluasi. Peserta diberikan materi teori penulisan ilmiah, dilanjutkan dengan praktik langsung penggunaan Mendeley, serta pendampingan dalam menyusun draft artikel ilmiah. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan peserta dalam menulis serta mengelola referensi secara sistematis dan bebas dari plagiarisme. Kegiatan ini diharapkan menjadi langkah awal dalam membangun budaya literasi ilmiah berbasis teknologi di lingkungan kampus.
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.
Pendampingan Penjaminan Mutu Sekolah/Madrasah Amal Usaha Muhammadiyah (AUM) di Wilayah Nusa Tenggara Barat Syarifuddin Syarifuddin; Fathurrahman Fathurrahman; Wahyu Mulyadi; Sukrin Sukrin; Ade S Anhar; Sri Jamilah; Fathir Fathir; Dewi Sartika
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.v3i1.212

Abstract

  Kegiatan Pendampingan Penjaminan Mutu Sekolah Amal Usaha Muhammadiyah (AUM) Wilayah Nusa Tenggara Barat oleh Fasilitator Pimpinan Pusat merupakan upaya strategis untuk memperkuat tata kelola dan kualitas layanan pendidikan Muhammadiyah melalui penguatan sistem penjaminan mutu internal sekolah. Kegiatan ini dilatarbelakangi oleh masih beragamnya tingkat pemahaman dan praktik penjaminan mutu di sekolah sekolah AUM, baik pada jenjang Sekolah Dasar, Sekolah Menengah Pertama, Sekolah Menengah Atas, maupun Madrasah Aliyah, khususnya dalam menghadapi tuntutan Standar Nasional Pendidikan dan implementasi Kurikulum Merdeka. Tujuan kegiatan ini adalah meningkatkan kapasitas kepala sekolah dan guru dalam memahami, menerapkan, serta menginternalisasikan sistem penjaminan mutu Muhammadiyah secara sistematis, berkelanjutan, dan berbasis data, Metode pelaksanaan kegiatan menggunakan pendekatan pendampingan partisipatif dan berbasis praktik yang memadukan sosialisasi, workshop, coaching, serta supervisi akademik dan manajerial. Kegiatan dilaksanakan di tiga lokasi strategis, yaitu Kota Mataram, Sumbawa Besar, dan Kota Bima, yang berfungsi sebagai pusat klaster bagi sekolah sekolah AUM di seluruh wilayah Nusa Tenggara Barat. Tahapan kegiatan meliputi pemetaan mutu awal melalui evaluasi diri sekolah, penguatan konseptual tentang siklus penjaminan mutu (PPEPP), pendampingan penyusunan dokumen mutu dan rencana tindak lanjut, serta monitoring dan refleksi bersama.Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan komitmen kepala sekolah serta guru terhadap pentingnya penjaminan mutu sebagai bagian dari budaya kerja sekolah. Antusiasme dan partisipasi aktif peserta dari berbagai jenjang pendidikan mencerminkan tumbuhnya kesadaran kolektif untuk melakukan perbaikan berkelanjutan dalam pengelolaan dan pembelajaran. Selain itu, kegiatan ini juga memperkuat jejaring kolaborasi antar sekolah AUM lintas wilayah dan jenjang sebagai komunitas praktik yang saling belajar dan mendukung peningkatan mutu.
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.
Co-Authors Ade S. Anhar Andriadin, Andriadin Chandra Wisnu Nugroho Dahlan Dewi Sartika Erin Eka Citra Erin Eka Citra Fathurrahman Fathurrahman Fathurrahman Fathurrahman Hilyatul Mustafidah Hilyatul Mustafidah Ika Novianti Ilham Ilham Irawansah, Didik Irma Eryanti Putri Irma Putri Khairunnas Khairunnas Khairunnas, Khairunnas Leonard Maramis Lukman Lukman LUKMAN, LUKMAN Luthfiyah Luthfiyah Luthfiyah Luthfiyah Mantafani, Ana Mechi Sakinatun Nufus Miftahul Jannah Mokhamad Amin Hariyadi Muarif Muarif Muhammad Amirul Mu'min Muhammad Amirul Mu'min Muhammad Amirul Mu'min Mustafidah, Hilyatul Naafia, Suci Faaza Nabila Melani Putri Nur Husnul Khatimah Nurfidianty Annafi Nurfidianty Annafi, Nurfidianty Nurlaila Nurlaila Nurlaila Nurlaila Nursani Nursani Putri, Irma Ramadhani, Intan Randitha Missouri Ridwan, Ridwan Rizki, Afsa Sahrul Ramadhan Sa’adati, Yuan Shinta Zahira Hayathun Nufus Siti Mutmainah Sri Jamilah Suci Faaza Naafia Sukrin Sukrin Sutriawan Syarifuddin Syarifuddin Syarifuddin Syarifuddin Teguh Ansyor Lorosae Teguh Ansyor Lorosae Wahyu Mulyadi Widia Ainun Arabiah Yana Safitri Yana Safitri, Yana Yuan Sa’adati Yuliyanti yuliyanti Yuniati Yunifa Miftachul Arif Zuhrah Zumhur Alamin