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All Journal Al Ibtida: Jurnal Pendidikan Guru MI Eduma : Mathematics Education Learning and Teaching Diya Al-Afkar: Jurnal Studi al-Quran dan al-Hadis ITEj (Information Technology Engineering Journals) Procediamath al-Aulad: Journal of Islamic Primary Education Dimasejati: Jurnal Pengabdian Kepada Masyarakat Nama jurnal : International Journal of Education and Humanities Journal of Mathematics Instruction, Social Research and Opinion NJMMS Smart Techno (Smart Technology, Informatic and Technopreneurship) International Journal of Smart Systems Educational Insights Cendikia Pendidikan Jurnal Informatika: Jurnal Pengembangan IT TERAS: Jurnal Pengabdian Masyarakat Sosial Budaya International Journal of Technology and Modeling Perspectives in Mathematics and Applications (PERMATA)
Saluky
IAIN Syekh Nurjati Cirebon
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV Saluky, Saluky; Marine, Yoni; Bahiyah, Nurul
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4929

Abstract

Low Contrast can cause low image quality and make it difficult for proper image analysis. One technique to improve image quality is to increase the lighting contrast. One method that is often used is histogram normalization, which can increase image contrast by balancing the distribution of pixels across a range of pixel values. The purpose of this research is to apply the histogram normalization method to images and compare the results before and after the normalization process. The images used in this study are self-made images and images from public databases. The results of the study show that normalized histograms can increase image contrast and improve low image quality due to inadequate lighting. Thus, histogram normalization can be used as a technique to improve image quality in various applications, including medical image processing, satellite image processing, and security surveillance.
A Review Learning Media Development Model Saluky; Marine, Yoni
International Journal of Technology and Modeling Vol. 1 No. 2 (2022)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v1i2.7

Abstract

This study aims to review various models of ICT-based learning media development. This study covers several development models such as ADDIE, SAM, RADD, Agile Development Model, Spiral Model, and DADD. The purpose of this research is to evaluate the advantages and disadvantages of each model and provide the best recommendations for the development of effective and efficient learning media. The results of this study are expected to contribute to the development of ICT-based learning media in the future.
Revolutionizing Natural Language Processing (NLP): Cutting-edge Deep Learning Models for Chatbots and Machine Translation Arif, Muhamad; Saefurohman, Asep; Saluky
International Journal of Technology and Modeling Vol. 3 No. 1 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i1.111

Abstract

Natural Language Processing (NLP) has undergone a transformative evolution with the advent of deep learning, enabling significant advancements in chatbots and machine translation. This article explores state-of-the-art deep learning models, including Transformer-based architectures such as GPT, BERT, and T5, which have revolutionized the way machines understand and generate human language. We analyze how these models enhance chatbot interactions by improving contextual understanding, coherence, and response generation. Additionally, we examine their impact on machine translation, where neural models have surpassed traditional statistical approaches in accuracy and fluency. Despite these advancements, challenges remain, including computational costs, bias mitigation, and real-world deployment constraints. This article provides a comprehensive overview of recent breakthroughs, discusses their implications, and highlights future research directions in NLP-driven AI applications.
Predicting Crop Water Requirements Using IoT Sensor Data for Deep Learning Saluky, Saluky; Fatimah, Aisya
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 7 No. 2 (2025)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v7i02.151

Abstract

The optimization of irrigation is a crucial factor in enhancing agricultural productivity and resource efficiency. This study proposes a deep learning-based approach to predict plant water requirements using data from IoT sensors. The system collects real-time environmental parameters such as soil moisture, temperature, humidity, and solar radiation, which are then processed using a deep learning model to generate accurate irrigation recommendations. The model is trained and evaluated on historical sensor data to ensure robustness and reliability in varying climatic conditions. The proposed method aims to minimize water wastage while maintaining optimal soil moisture levels, thereby improving crop health and yield. Experimental results demonstrate that the deep learning model outperforms conventional threshold-based irrigation systems in terms of prediction accuracy and water conservation. This research contributes to the advancement of smart farming by integrating IoT and artificial intelligence for precision agriculture.
Unmanned Aerial Vehicles (UAVs) for Pest and Disease Detection in Rice Cultivation: A Systematic Review Saluky, Saluky; Marine, Yoni; Fatimah, Aisya
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.158

Abstract

This paper presents a systematic review of the use of Unmanned Aerial Vehicles (UAVs) for pest and disease detection in rice cultivation, a critical challenge in maintaining yield stability and reducing chemical overuse in global food systems. The study aims to synthesize current approaches, technologies, and algorithms employed in UAV-based monitoring of rice pests and diseases, while identifying research gaps and future directions for precision rice farming. Following PRISMA-inspired guidelines, a Systematic Literature Review (SLR) was conducted across major scientific databases (Scopus, Web of Science, IEEE Xplore, and ScienceDirect) using predefined keyword combinations related to UAVs, rice, pest/disease detection, and remote sensing. Inclusion criteria focused on peer-reviewed studies that explicitly employed aerial platforms for detecting biotic stress in rice, while review papers, non-rice crops, and purely simulation-based works were excluded. The findings highlight three dominant technology dimensions: sensing modalities, with RGB and multispectral imagery being most prevalent, followed by hyperspectral and thermal sensors; analytical methods, ranging from traditional vegetation indices and thresholding to advanced machine learning and deep learning models; and operational considerations, including flight altitude, spatial resolution, and temporal frequency of data acquisition. The review contributes by proposing a conceptual framework linking sensor choice, image processing pipelines, and pest/disease symptom characteristics in rice, and by outlining open challenges regarding data standardization, smallholder adoption, and model transferability across regions.
Mengoptimalkan Kebutuhan Gizi Balita dengan Metode Simpleks Menggunakan POM-QM untuk Pencegahan Stunting Nurrochmah Sri Rahayu; Nurrochmah , Nurrochmah Sri Rahayu; Saluky
Perspectives in Mathematics and Applications Vol 1 No 1 (2025): Juni
Publisher : Kreasi Pustaka Mandiri (Krestama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66256/permata.v1i1.7

Abstract

This research aims to develop an optimization mathematical model to help meet toddlers' daily nutritional intake needs to prevent stunting. The Simplex method is applied in this model to determine food combinations that meet the standards of nutritional needs at minimal cost, considering budget constraints and the availability of foodstuffs so that they can be adapted to diverse socioeconomic conditions. The POM-QM application is used as a calculation tool to apply the Simplex method, making the optimization process more practical and accurate. Mathematical modeling is carried out by compiling a function of objectives by minimizing the cost of consuming foodstuffs that meet the daily nutritional needs of toddlers, including protein, fat, carbohydrates, iron, vitamin A, and vitamin C. The optimization results showed that nutritional needs could be optimally met with a combination of vegetable soup consumption of 22.5 grams, Tempe 8.476 g, and banana 4.5 g, with a total minimum cost of IDR 146,928. The nutrient content of the combination exceeds daily needs, such as protein (241.41 g), fat (274.06 g), Fe (82.93 mg), as well as vitamin A (450 RE) and vitamin C (45 mg), while carbohydrates are fulfilled exactly as much as 220 grams. These results show that the optimization approach can be used to design a balanced, nutritious diet that is cost-efficient as an effort to prevent stunting in toddlers.
Co-Authors Ade Saepudin, Ade Afiqoh Agustin Agus Pamuji Akbar, Reza Oktiana Alviyaturrohmah, Alviyaturrohmah Arif Muchyidin Asep Saefurohman Azzahra, Melia darwan darwan, darwan Fachry Abda El Rahman Farhatuaini, Lia Fathimah , Aisya Fatimah, Aisya HENDRI HANDOKO Heru Purnomo Hilmi Zaki Islahati Ilham Maulana Kurniawan, Heru Purnomo Kusmanto, Hadi Maimun, Muhammad Marine, Yoni Marinih Morin Prita Laura Muhammad Ali Misri, Muhammad Ali Muhyidin, Iid nurhana syamarro Nurrochmah , Nurrochmah Sri Rahayu Nurrochmah Sri Rahayu Nurul Bahiyah Onwardono Rit Riyanto Putri, Intan Nur Ramadhan S., M. Fathur Rezza Trie Kusdayati Santinah Santinah Santinah, Santinah Sirojudin Wahid Siti Rahmah Tuti Isnani Widodo Winarso Wulandari Wulandari Yoni Marine Yoni Marine