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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 Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik 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 Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Sistem Pengenalan Citra Dokumen Tanda Tangan Menggunakan Metode CNN (Convolutional Neural Network) Raudlatul Jannah; Miftahul Walid; Hoiriyah Hoiriyah
Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik Vol 12 No 2 (2022): Jurnal Energy Vol. 12 No. 2 November 2022
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v12i2.1116

Abstract

A signature is a marker or identity that is on a document. Signatures have an important role in verifying and legalizing documents. The signature is not just any sign but is a legal and original image of the owner. With the development of today's technology, identification of signature patterns can not only be done manually, but can also be done with the help of a computer. However, the computer does not automatically carry out the identification process, but requires a pattern recognition process first which can be done by extracting the signature feature. Identification of the signature pattern is needed to recognize and distinguish the signature of each individual based on the characteristics of the signature. Therefore, this study is expected to be an alternative to minimize signature recognition errors by using the CNN (convolutional neural network) method. The research stages include taking signatures from 10 respondents as many as 200 signature images for training data, and 50 signatures for testing data. then taken with a scanner. The results of this study are the Convolutional Neural Network method can recognize each signature image with an accuracy of 100% in the validation testing process and 85% in the testing process. Keywords: Signature, convolutional neural network, Digital Image Processing
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.
ANALISIS SENTIMEN PENGGUNA MEDIA SOSIAL TERHADAP APLIKASI M-HEALTH PEDULI LINDUNGI DENGAN METODE LEXICON BASED DAN NAÏVE BAYES Riky Iskandar Syah; Hoiriyah Hoiriyah; Miftahul Walid
Indonesian Journal of Business Intelligence (IJUBI) Vol 6, No 1 (2023): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v6i1.3275

Abstract

Pedulilindungi atau satusehat merupakan aplikasi yang dirilis secara resmi guna menangani penyebaran virus Covid-19 dan vaksinasi. Namun, dikarenakan suatu insiden besarnya kebocoran data pribadi, terutama identitas pribadi, kepercayaan masyarakat terhadap aplikasi tersebut sangat rendah. Untuk mengetahui pendapat masyarakat saat ini maka dilakukanlah penelitian dengan mengkombinasikan metode Lexicon Based dan Naïve Bayes. Hasil klasifikasi sentiment memperoleh nilai yaitu 62% negative, 32% netral, 6% positif pada Tiktok. 56% negative, 37% netral, 7% positif pada Youtube. 100% positif pada Twitter, dengan jumlah keseluruhan 118 skor negative, 69 skor netral, 113 skor positif, maka dapat disumpulkan masyarakat memiliki opini negative pada aplikasi peduli lindungi. Hasil evaluasi kinerja model memperoleh akurasi 91%, presisi 94%, recall 82%, f1_scores 86% pada Tiktok, pada Youtube akurasi sebesar 90%, presisi 93%, recall 81%, f1_scores 84%. Pada Twitter akurasi 70%, presisi 23%, recall 33%, f1-scores 28%. Pengkombinasian metode Lexicon Based dan Naïve Bayes ini memiliki akurasi yang sangat tinggi pada media sosial Tiktok dan Youtube, sehingga untuk penelitian selanjutnya pada media sosial Twitter perbanyak data yang diambil. Juga penelitian ini diharapkan dapat membantu membangun kembali aplikasi supaya lebih optimal.
Pengolahan Citra Digital Untuk Identifikasi Jenis Penyakit Kulit Menggunakan Metode Convolutional Neural Network (CNN) Sona Nova Ria; Miftahul Walid; Busro Akramul Umam
Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik Vol 12 No 2 (2022): Jurnal Energy Vol. 12 No. 2 November 2022
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v12i2.1118

Abstract

Skin disease is the most common disease and the fastest to infect the human body. This happens because the skin is the first organ to receive external stimuli in the form of touch, temperature and other stimuli. Skin diseases consist of several types that have almost the same color and texture with the naked eye. Thus, an approach is needed to identify the type of skin disease with the help of an image processing system, and an artificial neural network. The identification method used in this research is Convolutional Neural Network (CNN). The infected skin image is used as an input image for image processing. Before being identified, image preprocessing is done, namely resizing, grayscalling, using the Convolutional Neural Network method. The testing process in this study uses 70 types of skin disease images, validation data and 35 types of skin disease images for testing data. The results of this study are the Convolutional Neural Network method can recognize each type of skin disease image with an accuracy of 98% in the validation testing process and 85% in the testing process. Keywords : Skin disease, Convolutional Neural Network, Digital Image Processing
Pengembangangan Alat Penyiram Otomatis Dan Monitoring Budidaya Cabe Merah Berbasis Internet Of Things (Iot) Miftahul Walid; Sohibul Burhan
Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik Vol 13 No 1 (2023): Jurnal Energy Vol. 13 No. 1 Mei 2023
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v13i1.1047

Abstract

Technology of today has improved a number of spheres of life, including governance, agriculture, and education. Information technology use tries to provide efficiency in a number of ways. Given Indonesia's low level of technological development, particularly in the area of agriculture, innovation is unquestionably required if this country is to be competitive on the global stage. This is especially true in this era of globalization. Clearly, the agriculture sector has to develop technology that makes human labor more efficient and effective. Traditional farmers can benefit from technology in their operations to produce better yields and greater volumes. Red chili is one of the key agricultural products that needs to be developed because it has a high economic value, is a superior national and regional product, and occupies a significant place in the menu due to the fact that almost the entire population of Indonesia consumes it every day, albeit in small amounts. This plant can grow to a height of mdpl in both the lowlands and the highlands, but the lowlands are where it grows the best. The ideal air temperature range for growing chile plants is 18°C to 28°C. Fertilization might be hindered by temperatures between 16 and 32 degrees Celsius. The soil wetness of chili plants ranges between 60% and 80%. When limited space is available, growing red chilies either directly or in pots can be a solution. are hard to come by, especially for family needs. For farmers or plant owners, smart garden technology works and offers advantages, as well as a way to communicate with plants.
IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK AND RECURRENT NEURAL NETWORK METHODS TO PREDICT THE AMOUNT OF SALT PRODUCTION Miftahul Walid; Dini Fajariyah; Hozairi Hozairi; Budi Satria
NJCA (Nusantara Journal of Computers and Its Applications) Vol 8, No 1 (2023): June 2023
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v8i1.314

Abstract

Sumenep is one of the salt-producing regencies in Madura with 27 sub-districts where 11 sub-districts are salt producers which have a total area of 2,077.12 ha of ponds. Generally, people only cultivate salt in certain months because this salt production can only be done and depends on several factors, such as weather and land area. From the existing problems, this research was conducted using a Deep Learning approach, namely Artificial Neural Network (ANN) and Simple Recurrent Neural Network (SimpleRNN) to predict the amount of salt production. Weather data as input and salt production data as output taken from the last 6 years (2017-2022). The accuracy value in model training was used as a comparison to make predictions. the process of dividing training and testing data was also carried out with a ratio of 80%:20%. Furthermore, both methods was given 6 trainings each, so that the training of the two methods produces a different accuracy value. The ANN model produces an accuracy value of 53% and 71% for Simple RNN. Based on the resulting accuracy value, this base cased study is suitable for using the SimpleRNN algorithm model compared to ANN, provided that the amount of data used is large-scale
PENERAPAN WIRELESS SENSOR NETWORKS (WSN) UNTUK SISTEM PEMANTAUAN SAWAH TADAH HUJAN Miftahul Walid; Surifatul Hafiah; Bakir Bakir
NJCA (Nusantara Journal of Computers and Its Applications) Vol 4, No 2 (2019): Desember 2019
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v4i2.149

Abstract

Dalam penelitian ini teknologi Wireless Sensor Networks (WSN) digunakan untuk memantau kondisi sawah tadah hujan secara realtime, data yang dipantau antara lain kelembaban tanah, suhu dan kelembaban udara. Adapun proses pengambilan data di lapangan, sensor digunakan dan diintegrasikan dengan mikrokontroller, sensor berfungsi untuk mengambil data, kemudian dikirim dan disimpan di firebase’s cloud dengan mengunakan komunikasi wireless yang telah dikoneksikan ke jaringan internet, selanjutnya data yang disimpan tersebut ditampilkan ke aplikasi berbasis android.  Dari hasil  percobaan selama tiga hari, sistem mampu melakukan perekaman data secara realtime dengan pengaturan waktu 15 menit dalam satu proses perekaman, sedangkan rentang nilai  hasil perekaman sensor, untuk soil moisture  dihasilkan rentang nilai antara 31% - 76 % pada hari pertama, 45%-75% pada hari ke dua, 51%-56% pada hari ke tiga, untuk humadity antara 40%-84% pada hari pertama, 24%-95% pada hari ke dua, 52%-94% pada hari ke tiga, sedangkan untuk temperature 26 0C -370 0C pada hari pertama, 21 0C -43 0C pada hari kedua, 23 0C -29 0C pada hari ketiga.
K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups Miftahul Walid; Norfiah Lailatin Nispi Sahbaniya; Hozairi Hozairi; Fajar Baskoro; Arya Yudhi Wijaya
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p69-78

Abstract

The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited availability during the classification process, which involves dividing the transaction level attributes into three distinct groups: (0) low transactions, (1) medium transactions, and (2) high transactions. The clustering process encompasses three distinct features: (1) income, (2) spending, and (3) profit. These three traits will be utilized as input data throughout the categorization procedure. The classification procedure employing the Multilayer Perceptron technique involved processing a dataset including 1383 data points. The training data constituted 80% of the dataset, while the remaining 20% was allocated for testing. In order to evaluate the efficacy of the constructed model, the training error was assessed using K-Fold cross-validation, yielding an average accuracy score of 0.92. In the present study, the categorization technique yielded an accuracy of 0.96. This model aims to classify scenarios when the dataset lacks prior target data.
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