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MEMBANGUN SISTEM INFORMASI GEOGRAFIS (SIG) BERPARADIGMA QUR’ANI Muhammad Faisal
El-QUDWAH El-Qudwah (04-2008)
Publisher : lp2m-uin malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1160.859 KB)

GIS ( Geographical Information System) be organization pool hardware, computer software and geographical data design efficiently in taking, saving,updating, manipulation, analyses and presents all part of geographical information. Geographical yields region map. At era now Peta region have been tidy and diolah into a computer peripheral, this komputrer peripheral which will analyse existing of a place and arranges it by corresponding to model representing the original. Increasinglyly berkembangya research about regional map especially in information technology area, finally is formed an geographical information system. This system of course can give certainty of the decision takers about settlement of region ( region mapping). If evaluated from a real far flung Indonesia state region, hence would hardly requires a GIS which is accurate and precise, why that way? because very very vital position of GIS a region / area having extraordinary effect to pursue existence of events of disaster which bia ought to be prevented early possibly. Why Indonesia region that way respects reputedly overseas, but simply happened a lot of disaster that is still not after, disaster coming silih to change, altogether having element at one particular nature phenomenon which we might not take care of reallyly and serious. Of course settlement of the region needs existence of involvement various party(sides to manage it, but in reality a lot of Iameness happened, why the Iameness Iameness happened, possibly this happened because basis nation believe we still weakening and hardly unable to esteem nature. Result of this research proves that we must be wise wise and in managing nature.Keyword : GIS, nature phenomenon

Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy Suhartono Suhartono; Fresy Nugroho; Muhammad Faisal; Muhammad Ainul Yaqin; Suyanta Suyanta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy Suhartono Suhartono; Fresy Nugroho; Muhammad Faisal; Muhammad Ainul Yaqin; Suyanta Suyanta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy Suhartono Suhartono; Fresy Nugroho; Muhammad Faisal; Muhammad Ainul Yaqin; Suyanta Suyanta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

STUDI KOMPARASI FUNGSI KEANGGOTAAN BERBEDA PADA FUZZY MAMDANI UNTUK KASUS PENYIRAMAN AIR OTOMATIS Fresy Nugroho; Muhammad Faisal
Conference on Innovation and Application of Science and Technology (CIASTECH) CIASTECH 2019 "Inovasi Cerdas dan Teknologi Hijau untuk Industri 4.0"
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.676 KB)

Penggunaan teknologi mikrokontroler dan logika fuzzy sebagai solusi penentuan jumlah dan waktu penyiraman yang tepat dapat menghindarkan kematian tanaman dan pembusukan akar. Artikel ini bertujuan meneliti dan menelaah karakter fungsi keanggotaan pada fuzzy Mamdani. Hasil dari penelitian ini menunjukkan bahwa fungsi keanggotaan Gaussian terbukti memiliki selisih rata-rata yang rendah yaitu 0,2 bila dibandingkan hasil percobaan nyata.

K-Means Binary Search Centroid With Dynamic Cluster for Java Island Health Clustering Muhammad Andryan; Muhammad Faisal; Ririen Kusumawati
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.511

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with deficient health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies

Uji Performa Prediksi Gempa Bumi di Jawa Timur dengan Artificial Neural Network Muhammad Aji Permana; M Faisal
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 11 Issue 1 June 2023
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/euler.v11i1.19291

East Java Province is an area directly adjacent to the Eurasian and Indo-Australian plate subduction zones, this has resulted in East Java province being an area prone to earthquakes. Predictions regarding the frequency of earthquake occurrences are very interesting to study. This needs to be done in order to increase our preparedness in an effort to reduce the risk of earthquakes. Research on earthquake prediction has been carried out, one of which is the artificial neural network method. The purpose of this study is to obtain the best network architecture that is applied to the data on the frequency of earthquake occurrences per month in East Java Province. Data on earthquake occurrences come from the BMKG Nganjuk Geophysics Station, which was recorded during the 2016-2021 period. The data was then grouped into the total frequency of events per month. The criteria for selecting the best network architecture are carried out by comparing each possible architecture's error values. The test method uses SSE (sum square error) criteria for each architectural model of the artificial neural network. The test results show that the input variation has a significant contribution and a greater correlation than the variation in the number of hidden neurons. The best prediction results are obtained in the model with 9-30-1 architecture with an error value of 0.1958.

Early Prediction System for Employee Attrition Company â€œXYZâ€ Using Support Vector Machine Algorithm Wikke Alvina Medyanti; Muhammad Faisal
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 2 (2023): July 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i2.46494

Pergantian karyawan merupakan masalah yang signifikan bagi organisasi karena dapat berdampak negatif pada produktivitas dan kinerja. Dalam penelitian ini, dikembangkan sebuah model Support Vector Machine (SVM) untuk memprediksi pergantian karyawan berdasarkan dataset yang berisi berbagai atribut karyawan. Dataset tersebut telah melalui tahap pra-pemrosesan dengan melakukan pemetaan nilai-nilai kategorikal dan pengkodean one-hot. Fitur-fitur kemudian dibagi menjadi data latih dan data uji, serta dilakukan penskalaan menggunakan StandardScaler. Hasil penelitian menunjukkan bahwa model mencapai akurasi sebesar 88,4%. Presisi untuk karyawan yang tidak mengalami pergantian (non-attrition) tinggi, yaitu sebesar 89,3%, menunjukkan kemampuan model dalam mengidentifikasi dengan benar karyawan yang kemungkinan akan bertahan. Namun, presisi untuk karyawan yang mengalami pergantian (attrition) lebih rendah, sebesar 69,2%, mengindikasikan adanya ruang untuk perbaikan dalam mengidentifikasi karyawan yang berisiko mengalami pergantian. Recall untuk karyawan non-attrition mencapai 98,4%, menunjukkan kemampuan yang tinggi dalam mengklasifikasikan dengan benar, sedangkan recall untuk karyawan attrition sebesar 23,1%. Nilai F1-score juga mencerminkan kinerja yang lebih baik untuk karyawan non-attrition dibandingkan karyawan attrition. Secara keseluruhan, model SVM menunjukkan potensi dalam memprediksi pergantian karyawan, namun perlu dilakukan pengembangan lebih lanjut untuk meningkatkan identifikasi karyawan yang berisiko, sehingga memberikan wawasan berharga dalam pengambilan keputusan SDM dan strategi retensi.Employee attrition is a significant concern for organizations as it can have a negative impact on productivity and performance. In this study, a Support Vector Machine (SVM) model was developed to predict employee attrition based on a dataset containing various employee attributes. The dataset was preprocessed by mapping categorical values and performing one-hot encoding. The features were then split into training and testing sets, and scaled using the StandardScaler.The results showed that the model achieved an accuracy of 88.4%. The precision for non-attrition employees was high at 89.3%, indicating the model's ability to correctly identify employees who are likely to stay. However, the precision for attrition employees was lower at 69.2%, suggesting room for improvement in identifying employees at risk of attrition. The recall for non-attrition employees was 98.4%, indicating a high ability to correctly classify them, while the recall for attrition employees was 23.1%. The F1-score also reflected a better performance for non-attrition employees compared to attrition employees. Overall, the SVM model showed promise in predicting employee attrition, but further enhancements are needed to improve the identification of employees at risk, thus providing valuable insights for HR decision-making and retention strategies.

Improving The Performance of the K-Nearest Neighbor Algorithm in the Selection of KIP Scholarship Recipients Manzilur Rahman Romadhon; M. Faisal; M. Imamudin
Jurnal Riset Informatika Vol 5 No 4 (2022): Periode September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.575

Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.

Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices Muhammad Sahi; Muhammad Faisal; Yunifa Miftachul Arif; Cahyo Crysdian
Applied Information System and Management (AISM) Vol 6, No 2 (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.v6i2.29648

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.

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