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All Journal Jurnal Simantec Applied Information System and Management Zeta - Math Journal Jurnal Mnemonic Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Buana Informatika Nusantara Journal of Computers and its Applications Intelmatics JIPI (Jurnal IPA dan Pembelajaran IPA) NERO (Networking Engineering Research Operation) Journal of Elektronik Sistem InformasI Bajhrah : Jurnal Pengabdian Kepada Masyarakat
Moh. Badri Tamam
Fakultas Teknik Uim Pamekasan
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Optimizing Agricultural Land Fertility through Nutrient Content and pH Analysis Tamam, Moh. Badri; Anwari; Rofiuddin; Supriatin
Intelmatics Vol. 5 No. 2 (2025): July-December
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i2.24051

Abstract

This research addresses the challenge of declining soil fertility in Pamekasan, East Java, by proposing a machine learning approach to improve the accuracy of soil fertility classification and provide data-driven recommendations. Conventional methods like linear regression and expert systems are limited in capturing the complexity of soil variables, leading to less accurate results. Therefore, this study compares the performance of two machine learning algorithms, Random Forest and XGBoost, in classifying soil fertility levels based on nutrient content (N, P, K, and micronutrients) and soil pH. The dataset, consisting of 880 soil samples from Pamekasan, revealed an imbalance, with the high-fertility class accounting for only 39 samples. After data preprocessing, both models were evaluated. The Random Forest model achieved an overall accuracy of 90.34%, slightly outperforming XGBoost, which reached 88.64%. Random Forest demonstrated superior performance in detecting low-fertility land (recall 0.97) and medium-fertility land (precision 0.93, recall 0.88). For the high-fertility minority class, Random Forest showed better recall (0.60) than XGBoost (0.40), while maintaining perfect precision (1.00). The study concludes that Random Forest is the optimal model for classifying soil fertility in Pamekasan. These findings provide a basis for more precise, efficient, and sustainable fertilization recommendations, which are expected to help farmers optimize productivity and support the sustainability of the local agricultural ecosystem by reducing excessive fertilizer use.
PENERAPAN AHP DALAM PENENTUAN TANAMAN ALTERNATIF PENGGANTI TEMBAKAU Tamam, Moh. Badri
Zeta - Math Journal Vol 5 No 1 (2020): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2020.5.1.21-25

Abstract

Tujuan dari penelitian ini adalah Menerapkan metode AHP untuk menentukan tanaman alternative pengganti dan Mendapatkan hasil validasi dan realibiliitas untuk tanaman alternative pengganti tembakau. Penelitian ini menggunakan metode AHP,1 Sistem pendukung keputusan banyak digunakan untuk kepentingan umum,contohnya pada bidang pertanian. Oleh karena itu penelitian ini akan membahas sistem pendukung keputusan yang diharapkan dapat membantu masyarakat dalam pemilihan dan mengetahui jenis tanaman pengganti tembakau. Data diolah dan diambil dari dinas pertanian Alternatif yang digunakan adalah semangka, jagung, pisang, cabe merah dan output adalah hasil peringkingan dari AHP. Berdasarkan hasil dan pembahasan, maka dapat ditarik kesimpulan yaitu dari hasil perhitungan manual dan matlab dengan menggunakan metode AHP, Dari hasil perhitungan peringkingan AHP dapat disimpulkan bahwa alternative tanaman pengganti tembakau dengan ranking pertama untuk petani 1 yaitu tanaman bawang merah.
Inquiry-Based Physics Learning Module with Physics Education Technology Assistance to Improve High School Students' Critical Thinking Skills and Scientific Literacy Kholida MS*, S. Ida; Tamam, Moh. Badri; Suprianto, Suprianto; Sumo, Maimon; Aprilita, Yeni Nur
Jurnal IPA & Pembelajaran IPA Vol 9, No 3 (2025): SEPTEMBER 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jipi.v9i3.48745

Abstract

Physics learning module is one of the teaching materials that can support the learning process so that one of the 21st century skills is achieved, critical thinking and scientific literacy. The current learning module has not fully trained these skills optimally. The purpose of this study is to develop a physics learning module based on integrated inquiry physics education technology (PhET) that is feasible (valid, practical, and effective) to improve critical thinking skills (CTS) and scientific literacy. The research method used is the development of Richey and Klein. The sample of this study was 70 students of class XI IPA of Public Senior High Schools in Pamekasan Regency. The research time was from July to August 2025 with a one group pretest posttest design research type. The research instruments were tests, implementation sheets, and expert validation sheets. The results showed that the developed learning module was very valid with an average value of 94%, Practical with a percentage of 94.8% implemented very practically. Effective with an N-gain score of 0.71 in the high category, the statistical test obtained sig (2-tiled) of 0.000, there is a significant difference in the pretest and posttest scores so that this module has a positive effect on improving CTS and scientific literacy. The conclusion based on the results of the study, the physics learning module developed is proven to be feasible to be applied in the learning process so that it has implications for improving CTS and scientific literacy
Classification of Sign Language in Real Time Using Convolutional Neural Network Tamam, Moh. Badri; Hozairi, Hozairi; Walid, Miftahul; Bernardo, Januario Freitas Araujo
Applied Information System and Management (AISM) Vol. 6 No. 1 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i1.29820

Abstract

Communication between people is essential for daily life activities. However, humans are created with their own strengths and weaknesses. One of them is the difficulty of communication and interaction for people with hearing and speech impairments. Sign language is a language for people who have difficulty hearing and speaking. However, sign language is not popular in society, and people who have it will have more difficulties. This research aims to classify hand gestures of sign language into letters using a convolutional neural network (CNN). The dataset is obtained from Kaggle, with a total of 34,627 data divided by the ratio of training and testing data of 80:20. From the test results, the letters of the alphabet that can be translated are: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, S, T, U, V, W, X, Y, and Z. Furthermore, validation accuracy is obtained. In this study, a very high validation accuracy was obtained. The easiest letters to guess are V and N, while the most difficult letters to guess are n, c, j, and z. With different preprocessing, the loss value can be reduced, giving a higher accuracy of 95.4%.
Implementation Of Convolutional Neural Network Algorithm For Tobacco Pest Detection Tamam, Moh Badri; Chafid, Nurul; Hozairi, Hozairi; Aini, Qurrotul; Santoso, Teguh Budi; Kurniawan, Wawan; Kuzairi, Kuzairi
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30044

Abstract

Agriculture plays a vital role in increasing Gross Domestic Product (GDP), providing employment, contributing to foreign exchange earnings, and supporting environmental conservation. Indonesia has great potential as an agricultural country where population majority relies on agricultural sector for their livelihood. Pamekasan Regency is center of tobacco production development in East Java, with a tobacco plantation area of over 30,000 hectares. However, pest attacks such as caterpillars often damage tobacco plants, reducing productivity and leaf quality. This study implemented AI technology, specifically Convolutional Neural Networks (CNN), to detect caterpillar pests in tobacco plants in Pamekasan. The main focus is on AI development in computer vision using deep learning techniques. The CNN training process involves several stages: convolution, ReLU layers, subsampling/pooling layers, and fully connected layers. The test scenario was conducted by dividing data by 85% training, 10% validation, and 5% testing, as well as tuning parameters for the learning rate and epochs. The model achieved a maximum accuracy of 85% without overfitting at a learning rate of 0.001 and epochs 15. This demonstrates that the CNN deep learning method can effectively identify disease features in tobacco plants. The application of this technology can increase productivity and efficiency in the agricultural sector, supporting a sustainable economy and ecology.Keywords: convolutional neural network, image detection, tobacco pest.
Co-Authors Adi Darma, Wawang Anwari Anwari . Anwari Anwari Aolia Ikhwanudin Aprilita, Yeni Nur Arisca Dyana, Motik Bernardo, Januario Freitas Araujo Chafid, Nurul Ernawati, Tia Fathorrozi Ariyanto Fitriyah, Eka Hozairi Hozairi Hozairi Januario Freitas Araujo Bernardo Kholida MS*, S. Ida Kusrini Kusrini Kusuma, Suteja Wira Dana Kuzairi Maimon Sumo Muhammad Agus Muljanto Muhsi, Muhsi Qurrotul Aini RAJAGUKGUK, SOFIA Rofiuddin Rozi, Khairul Sagito, Redy Wahyan Santoso, Teguh Budi Suprianto Suprianto SUPRIATIN Supriatin Supriatin Supriatin Supriatin, Supriatin Walid, Miftahul Wawan Kurniawan Zulkifli, Ahmad