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All Journal Jurnal Buana Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Applied Information System and Management Jurnal ICT : Information Communication & Technology Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Indonesian Journal of Business Intelligence (IJUBI) Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Minfo Polgan (JMP) Jurnal Informatika Teknologi dan Sains (Jinteks) Nusantara Journal of Computers and its Applications Bajhrah : Jurnal Pengabdian Kepada Masyarakat
Walid, Miftahul
Universitas Islam Madura
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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SISTEM CERDAS PENDUGAAN SALINITAS AIR LAUT BERDASARKAN CITRA LANDSAT MENGGUNAKAN METODE Adaptive Neuro Fuzzy Inference System ( ANFIS ) Walid, Miftahul; Darmawan, Aang Kisnu
Jurnal Buana Informatika Vol 9, No 1 (2018): Jurnal Buana Informatika Volume 9 Nomor 1 April 2018
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v9i1.1283

Abstract

Abstract. The purpose of this research is to predict the sea surfce salinity, so that it can be used as refractory material for salt production. Salinity is the soluble salt content in water and the suitable the salinity standard in salt industry will give an impact on the quality of the salt produced. The method of this research is Adaptive Neuro Fuzzy Inference System (ANFIS). The system in this research works by extracting landsat 8 image to produce some value variable which is used as dataset in ANFIS system such as red , green, blue, Longitude and Latitude value. Its dataset will be divided to training and testing data. Training data is used to train the ANFIS system while testing data is used to measure the prediction accuracy resulted by ANFIS. in order to know the level of accuracy by using Root Means Square Error ( RMSE ) method is used to measure the accuracy level. The system has been able to make predictions with error rate of 2,0267 in average.Keywords: Salinity, Landsat Image, Smart System, ANFIS.Abstrak. Penelitian ini bertujuan untuk memprediksi salinitas air laut yang bisa dijadikan sebagai bahan refrensi untuk produksi garam. Salinitas adalah kadar garam terlarut dalam air, dengan salinitas yang sesuai standart dalam industri garam akan berdampak pada kualitas garam yang dihasilkan. Metode yang digunakan dalam penelitian ini adalah Adaptive Neuro Fuzzy Inference System ( ANFIS ). Sistem kerja dalam penelitian ini dengan mengekstraksi citra landsat 8 sehingga menghasilkan beberapa variabel yang dijadikan sebagai dataset dalam sistem ANFIS diantaranya adalah variabel red, green, blue, Longitude dan Latitude. Dataset tersebut akan dibagi menjadi data Training dan data Testing. Data Training digunakan untuk melatih sistem ANFIS sedangkan data Testing digunakan untuk mengukur akurasi prediksi yang dihasilkan oleh ANFIS. Pengukuran tingkat akurasi menggunakan metode Root Means Square Error ( RMSE ). Sistem yang dibuat telah mampu melakukan prediksi dengan tingkat error rata – rata 2,0267.Kata Kunci: Salinitas, Citra Landsat, Sistem Cerdas, ANFIS.
Seleksi Karyawan Baru Menggunakan Metode Composite Perfomence Index (CPI ) dan Rank Order Centroid (ROC) Miftahul Walid; Budi Satria; Masdukil Makruf
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 5, No 1 (2022): Januari
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v5i1.137

Abstract

Abstrak: Dalam meminimalisir kesalahan serta subyektifitas keputusan untuk seleksi karyawan baru diperlukan sebuah system pendukung keputusan (Decision Support System / DSS ) yang dapat membantu bagian SDM untuk memutuskan karyawan yang akan diterima atau tidak, dalam penelitian ini digunakan kombinasi metode Composite Perfomence Index (CPI) dan Rank Order Centroid (ROC) pada system pembobotannya, luaran dari penelitian  ini adalah  berupa nilai perengkingan, dimana dari empat alternatif yang dihitung, alternatif pertama yaitu A1 memiliki rengking tertinggi dengan nilai 145,25 dikuti oleh A2 dengan nilai 140,25, selanjutnya A4 dengan nilai 128,3 dan rengking terahir adalah A3 dengan nilai 126. Dapat disimpulkan bahwa metode Composite Perfomence Index (CPI) yang dikombinasikan dengan metode Rank Order Centroid (ROC) dalam system pembobotan setiap kriteria dapat melakukan perengkingan yang baik dan mampu meminimalisir subjektifitas dari sistem pembobotan secara manual.Kata Kunci : DSS, CPI, ROC, Seleksi Karyawan baruAbstract: In minimizing errors and decision subjectivity for the selection of new employees, a decision support system (Decision Support System / DSS) is needed that can help the HR department to decide which employees will be accepted or not, in this study used a combination of Composite Performance Index (CPI) and Rank Order Centroid (ROC) in the weighting system, the output of this study is in the form of a ranking value, where of the four alternatives calculated, the first alternative, namely A1 has the highest ranking with a value of 145.25, followed by A2 with a value of 140.25, then A4 with a value of 128.3 and the last rank is A3 with a value of 126. It can be concluded that the Composite Performance Index (CPI) method combined with the Rank Order Centroid (ROC) method in the weighting system of each criterion can perform a good ranking and is able to  minimize the subjectivity of the weighting system as a whole. manually .Keywords: DSS, CPI, ROC, New Employee Selection
SISTEM CERDAS PENDUGAAN SALINITAS AIR LAUT BERDASARKAN CITRA LANDSAT MENGGUNAKAN METODE Adaptive Neuro Fuzzy Inference System ( ANFIS ) Miftahul Walid; Aang Kisnu Darmawan
Jurnal Buana Informatika Vol. 9 No. 1 (2018): Jurnal Buana Informatika Volume 9 Nomor 1 April 2018
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v9i1.1283

Abstract

Abstract. The purpose of this research is to predict the sea surfce salinity, so that it can be used as refractory material for salt production. Salinity is the soluble salt content in water and the suitable the salinity standard in salt industry will give an impact on the quality of the salt produced. The method of this research is Adaptive Neuro Fuzzy Inference System (ANFIS). The system in this research works by extracting landsat 8 image to produce some value variable which is used as dataset in ANFIS system such as red , green, blue, Longitude and Latitude value. Its dataset will be divided to training and testing data. Training data is used to train the ANFIS system while testing data is used to measure the prediction accuracy resulted by ANFIS. in order to know the level of accuracy by using Root Means Square Error ( RMSE ) method is used to measure the accuracy level. The system has been able to make predictions with error rate of 2,0267 in average.Keywords: Salinity, Landsat Image, Smart System, ANFIS.Abstrak. Penelitian ini bertujuan untuk memprediksi salinitas air laut yang bisa dijadikan sebagai bahan refrensi untuk produksi garam. Salinitas adalah kadar garam terlarut dalam air, dengan salinitas yang sesuai standart dalam industri garam akan berdampak pada kualitas garam yang dihasilkan. Metode yang digunakan dalam penelitian ini adalah Adaptive Neuro Fuzzy Inference System ( ANFIS ). Sistem kerja dalam penelitian ini dengan mengekstraksi citra landsat 8 sehingga menghasilkan beberapa variabel yang dijadikan sebagai dataset dalam sistem ANFIS diantaranya adalah variabel red, green, blue, Longitude dan Latitude. Dataset tersebut akan dibagi menjadi data Training dan data Testing. Data Training digunakan untuk melatih sistem ANFIS sedangkan data Testing digunakan untuk mengukur akurasi prediksi yang dihasilkan oleh ANFIS. Pengukuran tingkat akurasi menggunakan metode Root Means Square Error ( RMSE ). Sistem yang dibuat telah mampu melakukan prediksi dengan tingkat error rata – rata 2,0267.Kata Kunci: Salinitas, Citra Landsat, Sistem Cerdas, ANFIS.
Analysis and Development of Seawater Density Measurement Algorithm Using Arduino Uno and YL-69 Sensor Miftahul Walid; Hozairi; Madukil Makruf
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.239 KB) | DOI: 10.29207/resti.v4i5.2430

Abstract

In this research, an analysis was carried out to develop a measuring instrument for seawater density in salt production using a microcontroller (Arduino Uno) and YL-69 sensor, this sensor was commonly used to measure soil moisture. The experimental method was used in this research to produce initial data in the form of resistance and seawater density values, then calculations are carried out using statistical methods to find equations and produce a constant variable that connects the resistance and seawater density values. The equation was used to compile the algorithm into Arduino Uno. As for the results of this research, From six experiments conducted, two experiments produced the same sea water density value between the actual and the predicted, namely the 2nd and 5th experiments, while for other experiments there was a difference between the actual and predicted values, however, it was not too significant, the difference occurs between the value range 0 ~ 1, to determine the level of error, use the Mean Square Error (MSE) with an error level of = 0.5 and Mean Absolute Error (MAE) with an error level of = 0.6. The contribution of this research is an algorithm that can predict the density value (baume) based on the resistance value obtained from the YL 69 sensor.
IMPLEMENTASI METODE K-NEAREST NEIGHBOUR (K_NN) UNTUK MENDUGA SALINITAS AIR LAUT yuri efenie; Miftahul Walid
Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Vol 1 No 1 (2020): Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) April 2020
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/jatim.v1i1.755

Abstract

In this research, trying to predict the salinity of sea water using the K-Nearest Neighbor method, this method serves to clarify the input data using the distance measurement method with training data, the variable used in this study is the value of the location of coordinates (latitude and longitude) and the output is in the form of salinity, the case study in this study is the southern waters of Sumenep, the system has been able to make an estimate but with an error rate of 1.00 so that there is a need for re-analysis because the data used is only small, the need for additional data so that the results will be more optimal, it is also necessary to experiment with changing methods or simplifying rules or by adding input variables in the system that have been created so that it produces better accuracy values, because the existing system still requires a long time in estimating.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN POTENSI ANGIN UNTUK PEMBANGKIT LISTRIK TENAGA BAYU (PLTB) MENGGUNAKAN METODE FUZZY MAMDANI Busro Akramul Umam; Miftahul Walid
Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Vol 1 No 1 (2020): Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) April 2020
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/jatim.v1i1.759

Abstract

In order to assist the government in realizing the use of 23% of EBT in 2025, to support the direction of government policies and strategies to improve accessibility by providing electricity to remote islands and villages. so in this study, the researcher makes a decision support system for determining the potential of renewable energy, especially energy produced by wind for wind power plants, The method used in this research is the Mamdani Fuzzy Logic, a system consisting of 3 Input criteria, including wind speed, temperature and air pressure, and output is potential for wind energy, output is presented in the form of a percentage unit with a range of 0-100%. The research was conducted in Sumenep Regency, after processing with the Fuzzy Mamdani method, the value of wind potential was generated with the average of the total output data = 43.51%, the value of min = 31.27%, the max value = 49.87%.
Klasterisasi Perguruan Tinggi Swasta di Madura Berdasarkan Kinerja Sumber Daya Manusia dan Mahasiswa Menggunakan Metode K-Means Clustering Yuli Sasmita; Muhsi Muhsi; Miftahul Walid
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4431

Abstract

The number of private universities in Indonesia in 2020 is 3,044 private universities, in East Java 328 private universities and in Madura 30 private universities. The number of private universities in Indonesia causes intense competition. Colleges should strive to maintain and improve performance in order to ensure their activities. Therefore, it is necessary to do or group private universities based on the performance of human resources and students to encourage these universities to improve their performance. The grouping of private universities is carried out using the k-clustering method which groups data into several clusters based on data groups which are. The results of this study, the grouping of private universities in Madura into 3 clusters, namely: Cluster 1 there are 4 private universities, Cluster 2 there are 7 private universities, and Cluster 3 there are 19 private universities.
Klasifikasi Kemandirian Siswa SMA/MA Double Track Menggunakan Metode Naive Bayes Miftahul Walid; Finanatun Halimiyah; Hozairi .
Jurnal ICT: Information Communication & Technology Vol. 22 No. 2 (2022): JICT-IKMI, December 2022
Publisher : LPPM STMIK IKMI Cirebon

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

Abstract

Abstract- SMA /MA Double Track is a high school that carries out regular KBM (Teaching and Learning Activities) activities and organizes skills training activities side by side by utilizing local wisdom. The large number of participants in the SMA / MA Double Track program caused the East Java Provincial Education Office to have difficulty in determining the independence of participants. This is due to the absence of a method used to classify the independence of high school / MA Double Track students. Therefore, in this study tried to do a classification of student independence in SMA / MA Double Track. The method used is the Naive Bayes Classifier method because the Naive Bayes Classifier method is able to carry out the double track SMA / MA independence classification process with a good level of accuracy. The data set used was 40 data, with details of 30 training data and 10 test data, the input feature used consisted of six features, including (C1) making products, (C2) selling products, (C3) having a product catalog, (C4) having an online store, (C5) creating marketing media and (C6) sales transaction data, while for labels or outputs consisting of one feature, namely status. The results of the classification using the Naive Bayes Classifier method have an accuracy of 70%, from 10 test data there are 7 correct prediction data and 3 incorrect data. The research contribution is able to help the East Java Provincial Education Office map participants of the SMA / MA Double Track program who are independent (work or entrepreneurship) so that they are able to plan policies for next year.
Classification of Sign Language in Real Time Using Convolutional Neural Network Moh. Badri Tamam; Hozairi Hozairi; Miftahul Walid; Januario Freitas Araujo Bernardo
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%.
Inovasi Desain Pembelajaran Literasi Digital Untuk Anak Paud Berbasis Android Dengan Kodular Debi Nur Fadilah Ulfa; Bakir; Miftahul Walid
Jurnal Minfo Polgan Vol. 12 No. 1 (2023): Artikel Penelitian Juni 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v12i1.12452

Abstract

Penelitian ini bertujuan Untuk Mengembangkan desain pembelajaran dalam bentuk aplikasi berbasis android dengan menggunakan kodular.Untuk Menentukan keefektifan dan kelayakan media pembelajaran untuk guru dalam perantara proses belajar mengajar untuk meningkatkan pemahaman siswa. Metode yang digunakan pada penelitian dalam inovasi desain pembelajaran literasi digital adalah metode waterfall. Metode Waterfall memiliki model pengembangan yang berurutan dalam menyelesaikan suatu pengembangan perangkat lunak. Selain itu, model waterfall memiliki tahapan-tahapan yang jelas dan mudah dipahami. Sehingga metode waterfall dirasa cocok untuk digunakan pada penelitian ini. Model pengembangan perangkat lunak waterfall memiliki empat tahapan yaitu analisis kebutuhan, perancangan sistem, implementasi sistem, uji coba sistem dan penerapan sistem. Hasil penilaian kepuasan responden adalah 3,85 yang berarti bahwa aplikasi yang BAIK. Bagi siswa Dengan adanya aplikasi berbasis Android ini, diharapkan siswa lebih mudah menyerap ilmu yang ada di PAUD, serta menggunakan android secara lebih aktif. Dengan demikian meningkatkan kualitas pembelajaran dan efektifitas siswa dalam proses pembelajaran. Sedangkan Bagi guru Produk penelitian ini dapat digunakan sebagai media alternative belajar untuk mendukung kegiatan belajar profesional. Media Pembelajaran dalam bentuk aplikasi ini dapat memberikan banyak pembelajaran untuk menyampaikan informasi pembelajaran kepada siswa dan membantu guru meningkatkan kualitas mendidik dengan menyediakan fasilitas belajar dengan Gunakan teknologi untuk memenuhi kebutuhan siswa saat ini.
Co-Authors Aang Kisnu Darmawan Ahmad Firdaus, Ahmad Arya Yudhi Wijaya Ashar, Muhammad Bakir Bakir Bakir Bakir, Bakir Bernardo, Januario Freitas Araujo Budi Satria Budi Satria Busro Akramul Umam Debi Nur Fadilah Ulfa Dini Fajariyah Efenie, Yuri Fajar Baskoro Finanatun Halimiyah Fivien Muslihatinningsih Hafiah, Surifatul Harieby, Edo Hoiriyah Hoiriyah Hoiriyah, . Hoiriyah, Hoiriyah Horiyah, Horiyah Hozairi Hozairi Hozairi Hozairi Hozairi Hozairi Hozairi I Wayan Subagiarta Januario Freitas Araujo Bernardo Jauhari, Ach Kharisma, Putri Dewi M. Ali Fikri Madukil Makruf Mardiana, Helva Masdukil Makruf, Masdukil Moh. Badri Tamam Muhammad Agus Muljanto Muhsi Norfiah Lailatin Nispi Sahbaniya Nuruddin, Nuruddin Nurul Badriyah Purnomo Hadi Susilo, Purnomo Hadi Qomariya, Nurul Raudlatul Jannah Riky Iskandar Syah Rofiuddin Sohibul Burhan Sona Nova Ria Surifatul Hafiah Susanti, Noor Aliyah Syah, Riky Iskandar Umam, Busro Akramul Wahyudi, Muhammad Hasan Yuli Sasmita Yuri Efenie