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All Journal International Journal of Electrical and Computer Engineering Sinergi Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat Publication Library and Information Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control LibTech : Library and Information Science Journal Jurnal Riset Mahasiswa Matematika Engagement: Jurnal Pengabdian Kepada Masyarakat Journal of Research on Community Engagement
Hisyam Fahmi
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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Penerapan Metode Segmentasi Gabor Filter Dan Algoritma Support Vector Machine Untuk Pendeteksian Penyakit Daun Tomat Habibullah, Muhamad; Fahmi, Hisyam; Herawati, erna
Jurnal Riset Mahasiswa Matematika Vol 2, No 6 (2023): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v2i6.22023

Abstract

This research discusses about processing a formulation that we can give to diseased tomato leaves. Gabor Filter is a method used to detect textures using frequency and orientation parameters. The Support Vector Machine (SVM) algorithm is an algorithm that can be used classifying tomato leaf diseases. The purpose of this research is to determine the accuracy of the Gabor Filter segmentation and the Support Vector Machine Algorithm for detecting tomato leaf disease to facilitate farmers in analyzing diseases on tomato leaves. The input will go through pre-processing of RGB pixels to Greyscale ones before being processed using Gabor Filter. This Gabor Filter process segments the image to produce a magnitude value. The results of the image magnitude values here will be seen and will enter the classification process using SVM. The SVM algorithm aims to find the best hyperlane on tomato leaves that have been segmented to separate classes in the input space. The application of the SVM method with class classification of tomato leaves by calculating the energy value and entropy of the extraction results, assisted by 12 features, namely: CiriR, Feature G, FeatureB, Standard DeviationR, Standard DeviationG, Standard DeviationB, SkewnessR, SkewnessG, SkewnessB, Mean, Energy, Entropy are used to the simplity classification process with a high degree of accuracy. The process of classification of tomato leaf disease with test data of 600 images managed to get an accuracy value of 74.1667%. In order to facilitate the performance of farmers in predicting tomato leaf disease.
Implementasi Metode ST-DBSCAN untuk Pengelompokan Pola Persebaran Titik Api pada Data Kebakaran Hutan di Indonesia Syurifah, Gita Ramadhani Wardatus; Fahmi, Hisyam
Jurnal Riset Mahasiswa Matematika Vol 3, No 5 (2024): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v3i5.27314

Abstract

Forest fires are a serious problem that almost always occur in Indonesia every dry season. In 2019 and 2023, many forest fires occurred, especially in Gambus land, due to the prolonged El-Nino phenomenon. This has a negative impact on the economy, social and environment. Therefore, clustering is an important effort to find out the location of areas where forest fires occur. Clustering is a technique used in data mining which works by searching and grouping data that has the same characteristics between one data and other data obtained. This research aims to determine the results of clustering of hotspot distribution patterns in forest fires in Indonesia using the ST-DBSCAN method. The ST-DBSCAN method is a clustering method used to group data using spatial and temporal parameters. The results of this research produced five clusters, noise of 31, and silhouette coefficient of 0.401 with optimal parameters, there are Eps1 = 0.3, Eps2 = 7, and MinPts = 14.
Implementasi Metode Jaringan Saraf Tiruan Backpropagation Pada Pengenalan Suara Manusia Prayugo, Mohammad Bagus Dimas; Fahmi, Hisyam
Jurnal Riset Mahasiswa Matematika Vol 3, No 3 (2024): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v3i3.22403

Abstract

Speech recognition is a process of voice identification using specific parameters taken by the sound catcher. The development of technology gave rise to an event that requires a calculation model on a computer system in speech recognition to be useful in science. One of the computer systems is the Backpropagation Artificial Neural Network (JST). This research uses the Backpropagation method in human speech recognition with the aim of knowing the architecture model and the level of accuracy obtained. Linear Predictive Coding (LPC) is used for voice feature extraction. Voice features in the time domain are converted into the frequency domain using Fast Fourier Transform (FFT). The voice data was divided into 80% training data and 20% testing data. A suitable JST architecture model is selected through training by calculating the optimal weights and biases to recognize the voice patterns well. The best architecture model found was 64-15-1-1. The model was tested using test data to test its ability to recognize voice patterns. Evaluation was done using K-Fold Cross Validation to measure the accuracy of the model. The accuracy value against the training data is 0.95, while against the testing data is 0.088886. The JST architecture model is very good at recognizing voices in training data, but less good in testing. Hopefully, this method can help in the research process related to recognition.
Implementasi Algoritma Floyd Warshall dalam Pencarian Rute Terpendek Lokasi Tower Base Transceiver Station (BTS) pada PT Citra Akses Indonusa Savitri, Bella Nafa; Jauhari, Mohammad Nafie; Alisah, Evawati; Fahmi, Hisyam
Jurnal Riset Mahasiswa Matematika Vol 2, No 4 (2023): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v2i4.16811

Abstract

PT Citra Akes Indonusa is a company that operates on the expertise of information technology services in Banten Province. One of the services provided by the company requires the construction of a Base Transceiver Station (BTS) tower. The tower requires maintenance if there is damage to the network signal. As a result, the shortest route is needed to make it easier for employees to reach the tower location to be more effective. There are eight BTS tower locations in Tangerang Regency and three BTS tower locations in Tangerang City. The process of finding the shortest route in this study uses the Floyd Warshall algorithm, which is unique in finding the shortest route by comparing each edge of all edges that are passed. The process of testing the shortest route is done by selecting the starting point, then selecting several BTS tower locations. Next, the shortest route will be searched using the Floyd Warshall algorithm from each point of destination for the BTS tower location, then the BTS tower location will be selected first and so on until the last destination. The effectiveness of this shortest route search involves a comparison of the routes presented by Google Maps. Based on the results of 30 randomized trials on BTS tower locations, the average shortest route effectiveness was 25.54% compared to the route generated by Google Maps. This is due to the selection at each BTS tower destination location so as to make the route more effective.
Implementasi Jaringan Syaraf Tiruan Backpropagation untuk Menentukan Prediksi Jumlah Permintaan Produksi Dodol Apel Sari, Farrah Nurmalia; Kusumastuti, Ari; Fahmi, hisyam
Jurnal Riset Mahasiswa Matematika Vol 2, No 2 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v2i2.14899

Abstract

Forecasting is importantly in accordance with the planning strategy; therefore it will affect the way of decision making. One of the forecasting methods is Artificial Neural Network with Backpropagation as the algorithm. This research aims to measure the accuracy of the network architecture which is being applied in order to calculate the prediction of the future’s apple paste product monthly demand which was obtained from CV. Bagus Agriseta Mandiri. The data which are being used are 36 monthly data from the year 2017, 2018 and 2019. Furthermore, the data obtained are normalized and divided into two, 66,66% as the data for training process and 33,33% as the data for testing process. Network architecture that is applied in this research is 12 : 10 :1, where 12 are neurons for input layer, 10 are neurons for one hidden layer and 1 is neuron for output layer. The Network with that framework obtained a result 20.161% for MAPE and 79.839% for the accuracy. That model is categorized as good enough for its forecasting ability. Moreover, the network was entirely validated using k-fold cross validation method with . The result obtained as follows: the average of MAPE is 47.079% and the average accuracy is 52.921%. According to it, the entire model can be categorized as good enough in order to run a forecast. As a comparison, another testing has been done with the same fold but different in the network architecture (model 6 – 8 – 1). The second model obtained results as follows: the average of MAPE is 26.74% and the average accuracy is 73.18%, so that the two prediction models’ ability are in the same category, it is good enough to run a forecast.
Penanaman Pohon pada Daerah Aliran Sungai di Desa Torongrejo Kota Batu dalam Mendukung Program Brantas Tuntas Fahmi, Hisyam; Abtokhi, Ahmad
JRCE (Journal of Research on Community Engagement) Vol 2, No 1 (2020): Journal of Research on Community Engagement
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrce.v2i1.9708

Abstract

Perubahan penggunaan lahan menjadi pemukiman dan lahan pertanian menyebabkan air hujan yang turun tidak dapat langsung meresap ke dalam tanah. Wilayah di Desa Torongrejo, Kecamatan Junrejo termasuk wilayah berlereng sehingga potensi banjir dan erosi di wilayah ini termasuk tinggi. Tindakan konservasi tanah dan air melalui gerakan menanam pohon dengan melibatkan masyarakat merupakan upaya strategis dalam menurunkan bahaya erosi dan mengalirkan air hujan ke dalam tanah melalui proses infiltrasi. Kegiatan ini dilaksanakan pada bulan Januari 2020 dipusatkan di Desa Torongrejo. Metode pengabdian yang digunakan dalam bentuk pendampingan masyarakat, analisis dan evaluasi kegiatan penanaman pohon dan bersih-bersih sungai. Kegiatan penaman pohon ini disambut antusias oleh komponen masyarakat. Ada sekitar 150 bibit pohon yang ditanam di sekitar aliran sungai Brantas.
Co-Authors Achmad Nizar Hidayanto Ahmad Abtokhi Angga Dwi Mulyanto Aniati Murni Arymurthy Ari Kusumastuti Devi Fitrianah Devi Fitrianah Devi Fitrianah Elnaz Putri, Hilda Zaqya Erna Herawati, Erna Evawati Alisah Habibullah, Muhamad Heni Widayani Mohammad Nafie Jauhari Mufidah, Hanifatul Muhammad Khudzaifah Pang Ning-Tan Prayugo, Mohammad Bagus Dimas Sari, Farrah Nurmalia Savitri, Bella Nafa Syihabuddin, Riyan Fahmi Syurifah, Gita Ramadhani Wardatus Wina Permana Sari Wina Permana Sari