Article Per Year (5 Year)
p-Index From 2021 - 2026
2.737
P-Index
This Author published in this journals
All Journal JSiI (Jurnal Sistem Informasi) bit-Tech JUTECH : Journal Education and Technology KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Pengabdian Masyarakat Bhinneka Electronic Journal of Education, Social Economics and Technology AMMA : Jurnal Pengabdian Masyarakat
Amin Hidayat
Universitas Pamulang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Veterinary Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Transformasi Digital Pendidikan Generative AI untuk Guru SMA ALIA ISLAMIC SCHOOL Menggunakan Aplikasi Teachy Ade Putra Prima Suhendri; Meidy Fajar Wahyu; Amin Hidayat
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 7 : Agustus (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of Artificial Intelligence (AI) technology opens greatopportunities for educational transformation. However, AI utilization in schoolsstill faces challenges, particularly the low technology literacy among teachers. ThisCommunity Service Program (PKM) aims to enhance the competency of SMA AliaIslamic School teachers in utilizing Generative AI technology, specifically theTeachy platform, for creating learning materials. The implementation methodsinclude interactive lectures, simulations, hands-on practice, group discussions, andevaluation. The activity was conducted on April 24, 2025, at SMA Alia IslamicSchool, Tangerang, with 18 teacher participants from various subjects. The resultsshowed significant improvement in teachers' understanding of AI concepts and theirapplications in education. Teachers successfully practiced creating educationalmaterials using AI-based platforms independently. This activity also increasedteachers' motivation and confidence in integrating digital technology into thelearning process. Positive impacts were evident from the creation of moreinteractive and engaging learning media that can be used in both online and offlinelearning. The conclusion of this activity is that AI training proved effective insupporting digital transformation in educational environments. Recommendationsfor program sustainability include expanding training coverage, continuousmentoring, developing technology-based curricula, collaboration with universities,and strengthening school digital infrastructure. This PKM activity contributessignificantly to preparing teachers to face educational challenges in the digital erawhile maintaining Islamic values as the foundation of education at the school.
PENGEMBANGAN SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PADA KUCING MENGGUNAKAN METODE CERTAINTY FACTOR BERBASIS WEBSITE (STUDI KASUS: BALUNG PETS HOME) Aji Khoirunas; Amin Hidayat
JUTECH : Journal Education and Technology Vol 7, No 1 (2026): JUTECH JUNI (IN PRESS)
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v7i1.6258

Abstract

Pemelihara kucing seringkali mengalami kesulitan mendeteksi penyakit kucing berdasarkan gejala awal karena kurangnya pengetahuan tentang penyakit kucing. Ketidaktahuan ini sering kali menyebabkan penanganan medis tertunda, yang pada akhirnya dapat menyebabkan kondisi kesehatan kucing menjadi lebih buruk. Penelitian ini bertujuan untuk mengembangkan sistem pakar berbasis web menggunakan metode Certainty Factor (CF) untuk mendiagnosis awal suatu penyakit pada hewan kucing. Pengumpulan data diperoleh dari observasi dan wawancara yang dilakukan terhadap dokter di salah satu klinik hewan Balung Pets Home. Metode pengembangan sistem yang digunakan dalam penelitian ini adalah Rapid Application Development (RAD) dan untuk paradigma program menggunakan Procedural Programming. Pengujian dilakukan melalui metode BlackBox dan White Box. Hasil dari uji coba dengan 30 gejala dari 8 jenis penyakit menunjukkan bahwa sistem pakar yang dibangun dapat memberikan gambaran awal terhadap kemungkinan penyakit kucing dengan nilai keyakinan 0,724. Hasil dari penelitian ini adalah sistem dapat membantu para pemelihara kucing sejak dini untuk mengetahui apa saja gejala dari berbagai jenis penyakit kucing tersebut, serta solusi penanggulangannya dan dapat berkonsultasi ke dokter sebelum melanjutkan ke tahap yang lebih lanjut.
Adaptive Polynomial Regression Analysis For Predicting Indonesian Stock Exchange Movements With R-Square Optimization And Trading Channel Construction Ade Putra Prima Suhendri; Amin Hidayat; Yuda Samudra
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v%vi%i.1468

Abstract

Stock price movements in financial markets often reflect rapid macroeconomic shifts and liquidity-driven volatility. This is particularly evident in large-cap, highly liquid stocks within the Indonesian LQ45 index. Traditional deep learning models often struggle with this market's unique volatility, suffering from overfitting, lack of transparency, and look-ahead bias. To address these issues, this study proposes an adaptive rolling polynomial regression (APR) framework for non-linear trend filtering and automated trading signal generation. The method uses a sliding window with endpoint locking to prevent look-ahead bias. At each step, it finds the best lookback window by optimizing a local OLS polynomial regression and filtering out noisy market regimes using a strict R-squared threshold. The resulting trends are converted into trading signals (BUY, SELL, HOLD, CLOSE) through dynamic Z-score volatility channels. When evaluated on daily historical data from 2015 to 2024 for five LQ45 stocks ANTM, ASII, BBCA, PTBA, and TLKM the framework shows an excellent fit, with R-squared values between 0.9881 and 0.9966. Backtesting indicates that the strategies yield positive returns and profit factors above one for all assets. ASII performed best, with a total return of 2680.16%. Despite low win rates (10%), the long-term profitability was maintained as large trend-following gains offset small losses. This study highlights that simple, interpretable mathematical models can provide effective, computationally efficient systematic trading options in emerging equity markets.
COMPARISON OF DECISION TREE AND NAIVE BAYES METHODS FOR RAINFALL CLASSIFICATION USING A WEATHER DATASET WITH A WEB-BASED APPLICATION Yuda Samudra; Amin Hidayat; Nanang
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/fxnw2631

Abstract

Rainfall prediction is an important component of weather analysis as it provides valuable information to support decision-making in sectors such as agriculture, transportation, and environmental management. Although various studies have compared machine learning algorithms for rainfall classification, many of them lack detailed discussion on dataset characteristics and practical system implementation. Therefore, this study aims to evaluate and compare the performance of Decision Tree and Naive Bayes algorithms for rainfall classification while considering dataset characteristics and implementing the model in a web-based application. The dataset used in this study consists of 2,500 records with meteorological parameters including temperature, humidity, wind speed, cloud cover, and atmospheric pressure. The data underwent preprocessing, including data cleaning and label encoding, where rainfall was represented as 1 and no rainfall as 0. The dataset was divided into training and testing sets, and both algorithms were applied to build classification models. Model performance was evaluated using confusion matrix, accuracy, and ROC curve analysis. The results show that the Decision Tree algorithm achieved an accuracy of 1.00 (100%), while the Naive Bayes algorithm achieved 0.972 (97.2%). Although Decision Tree shows superior performance, the perfect accuracy may indicate potential overfitting, and therefore the results should be interpreted carefully. Furthermore, the developed models were successfully implemented into a web-based application that enables users to perform rainfall prediction interactively. This study demonstrates that Decision Tree provides better performance for rainfall classification in the given dataset, while also highlighting the importance of considering dataset characteristics and evaluation methods. The integration of machine learning models into a web-based system provides a practical contribution for real-world weather prediction applications.   Keywords: Rainfall Classification, Decision Tree, Naive Bayes, Machine Learning, Weather Dataset, Web-Based Application
Spice Image Classification Based on Content-Based Image Retrieval Meidy Fajar Wahyu; Lely Panca Andriyanto; Amin Hidayat; Achmad Sehan; Eko Sutono
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.4149

Abstract

Indonesia possesses exceptional spice biodiversity, yet public familiarity with the original visual characteristics of many spices is declining because of packaged processing, reduced direct exposure, and changing food-consumption patterns. This study develops RempahID, a web-based spice identification system that integrates Content-Based Image Retrieval (CBIR) with machine-learning classification. The study addresses the limited availability of Indonesian spice recognition systems that simultaneously provide class predictions and visually similar reference images for user verification. The system uses a dataset comprising ten major spice categories, including ginger, turmeric, galangal, aromatic ginger, cinnamon, cloves, nutmeg, coriander, candlenut, and star anise. Each image is preprocessed through resizing, normalization, noise reduction, and Otsu-based segmentation. Visual representation combines 24 Hue-Saturation-Value color histogram features, four Gray-Level Co-occurrence Matrix texture descriptors, and seven Hu Moment shape features, producing a 35-dimensional feature vector. Euclidean Distance is employed to rank visually similar database images, while K-Nearest Neighbors, Support Vector Machine, and Random Forest are compared for classification. Performance is evaluated using accuracy, precision, recall, and F1-score. The Support Vector Machine with a radial basis function kernel achieved the best result, with 92.1% accuracy, 0.91 precision, 0.92 recall, and a 0.91 F1-score. Retrieved reference images also supported transparent visual comparison rather than presenting an isolated predicted label alone. These findings demonstrate that integrating complementary color, texture, and shape descriptors within a CBIR framework provides an effective and interpretable approach for Indonesian spice identification.
Co-Authors Achmad Sehan Ade Putra Prima Suhendri Ade Putra Prima Suhendri Ade Putra Prima Suhendri Aji Khoirunas Dea Khoirunnisa Dede Handayani Edi Mariyanto Eko Sutono Lely Andriyanto Lely Panca Andriyanto Lely Panca Andriyanto Lely Panca Andriyanto Andri Meidy Fajar Wahyu Meidy Fajar Wahyu Meidy Fajar Wahyu Meidy Nanang nanang nanang Nanang Nanang nanang nang Nurjanah, Elin Suraya, Gina Wahyu, Meidy Fajar Yuda Samudra