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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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Prediksi Cuaca Harian Menggunakan Algoritma Long Short-Term Memory (LSTM) Berdasarkan Data Meteorologi Tahun 2025 Ika Novianti; Fathir Fathir; Irma Eryanti Putri
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.v7i4.10689

Abstract

Daily weather prediction is crucial in supporting decision-making in the agriculture, transportation, disaster mitigation, and community activities influenced by atmospheric conditions. The weather in Bima City is dynamic, requiring a predictive model capable of learning sequential meteorological data patterns. This study aims to build a daily average temperature prediction model using the Long Short-Term Memory (LSTM) algorithm based on 2025 meteorological data. The contribution of this study is to develop an LSTM-based daily average temperature prediction model using multivariate meteorological data from Bima City combined with pre-processing steps in the form of missing value handling using moving averages, MinMaxScaler normalization, and time series data formation using a 30-day sliding window. This study also provides an initial evaluation of the application of LSTM to local meteorological data from Bima City, which has been studied only limitedly, as a basis for developing a deep learning-based weather prediction system. The variables used include minimum temperature (TN), maximum temperature (TX), average temperature (TAVG), average air humidity (RH_AVG), rainfall (RR), sunshine duration (SS), and average wind speed (FF_AVG). The test results show that the model produces a Root Mean Square Error (RMSE) value of 0.7242 and is able to follow the daily temperature change pattern in the actual data. The prediction results on the test data also show that most of the predicted values ​​have a relatively small difference compared to the actual values, so the model is able to describe the daily temperature change pattern quite well. Based on the predicted weather parameters, the model is able to provide information about daily weather conditions, namely sunny, cloudy, and rainy, according to the values ​​of rainfall, air humidity, and sunshine duration produced. This predicted information is expected to help the community as an initial picture of future weather conditions so that it can support the planning of various daily activities. However, the results of this study are still limited to one prediction method and have not been compared with other methods. Therefore, further research can conduct comparisons with other algorithms to improve the accuracy of weather predictions in Bima City.
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.
LAYANAN PERPUSTAKAAN BERBASIS APLIKASI SLIMS (SENAYAN LIBRARY MANAGEMENT SYISTEM) DALAM MENINGKATKAN KUALITAS PENGGUNA Muarif Muarif; Fathir Fathir; Chandra Wisnu Nugroho; Leonard Maramis; Jufrin Jufrin
Jurnal Media Informatika Vol. 6 No. 3 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i3.5834

Abstract

Perpustakaan tradisional dengan koleksi besar akan menghadapi kesulitan dalam mengelola dan mengendalikan koleksi seiring berjalannya waktu, karena jumlah koleksi cenderung terus bertambah. Hal ini menyebabkan pengguna memerlukan waktu yang lebih lama untuk mencari sumber informasi yang mereka butuhkan. Selain itu, akses terhadap perpustakaan terbatas hanya pada lokasi fisik perpustakaan dan pada waktu operasional tertentu. Oleh karena itu, layanan perpustakaan yang inklusif bagi masyarakat memerlukan dukungan dari sistem otomasi terkomputerisasi yang siap digunakan kapan saja, memudahkan akses ke sumber informasi yang diperlukan. perkembangan teknologi informasi, perpustakaan sebagai media pengumpul, pengolah, dan pendistribusi informasi harus beradaptasi dengan teknologi ini. Tanpa sentuhan teknologi informasi, perpustakaan dianggap ketinggalan zaman, kuno, dan tidak berkembang. Senayan Library Management System (SliMS) adalah salah satu aplikasi manajemen perpustakaan yang luas penggunaannya di Indonesia. Aplikasi ini dibuat untuk mendukung pengelolaan perpustakaan, termasuk katalogisasi, sirkulasi, dan pelaporan. SliMS menyediakan berbagai fitur yang memudahkan pengelolaan dan akses informasi di perpustakaan, sehingga dapat meningkatkan efisiensi operasional dan kualitas layanan bagi pengguna. Tempat pelaksanaan penelitian ini dilakukan di Perpustakaan Universitas Muhammadiyah Bima, tujuan penelitian Untuk mengetahui bagaimana Layanan berbasis aplikasi SliMS dalam meningkatkan kualitas pengguna di Perpustakaan Universitas Muhammadiyah Bima, Penelitian ini menggunakan desain kuantitatif deskriptif, Responden dalam penelitian ini yaitu Mahasiswa dan Civitas Akademika Universitas Muhammadiyah Bima sebagai Anggota Perpustakaan yang berjumlah 155 Orang, Teknik pengambilan Sample dalam penelitian ini menggunakan Random sampling, Teknik pengumpulan data dalam penelitian ini yaitu dengan melakukan observasi, menyebarkan kuesioner dan studi dokumentasi. Hasil Penelitian yang dilakukan dengan pendekatan kuantitatif Deskriptif, Berdasarkan Hasil Penelitian bahwa inovasi layanan berbasis aplikasi SLiMS dalam meningkatkan kualitas pengguna di perpustakaan Universitas Muhammadiyah Bima sebagian besarnya adalah positif. Aplikasi Sistem Informasi Manajemen Perpustakaan (SLiMS) telah menjadi solusi populer untuk mengelola koleksi perpustakaan secara digital. penggunaan SLiMS di Perpustakaan Universitas Muhammadiyah Bima merupakan langkah strategis untuk meningkatkan efisiensi dan efektivitas layanan perpustakaan. Penerapan aplikasi SLiMS memiliki peluang yang signifikan terhadap kualitas pengguna yang diberikan oleh perpustakaan.
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&#039;min Muhammad Amirul Mu&#039;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