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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 Elkasista 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 Materials Science & Nanotechnology Mathematics Social Sciences Other

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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.
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.
PENENTUAN BIDANG MINAT TUGAS AKHIR MAHASISWA TEKNIK INFORMATIKA UNIVERSITAS ISLAM MADURA MENGGUNAKAN METODE K-MEANS Nurul Badriyah; Hozairi; Miftahul Walid
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 4 (2023): EDISI 18
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i4.2782

Abstract

Selection of areas of interest can be confusing for some students, so data grouping or clustering is needed for informatics engineering students as a determinant of the field of interest for the final project. The purpose of this study is to group data or clustering on informatics engineering students as a determinant of the field of interest in the final assignment based on course grades. The method to be used is k-means clustering, to determine the number of clusters in the final project interest grouping. After the trial process was carried out, the results obtained showed that the number of clusters was 3 clusters, where in this grouping process, it can be concluded that the majority of students (60%) have an interest in the SPK course. While the field of interest in IS is only owned by around 20% of students, and the field of interest in Data Mining is only owned by around 6% of students. Therefore, it can be said that students tend to have a stronger interest in the SPK field based on the calculation results of the grouping or clustering process.
Smart Drip Irrigation System Based on IoT Using Fuzzy Logic Walid, Miftahul; Ashar, Muhammad; Wahyudi, Muhammad Hasan
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 1 (2024): February 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i1.21351

Abstract

The absence of a water drip rate control system in drip irrigation systems has impacted water use efficiency and normalization of soil moisture. Therefore, this research aims to develop an intelligent system using the fuzzy logic method to control the rate of water droplets in a drip irrigation system and maintain soil moisture in normal conditions. The DHT22 sensor is used to obtain temperature and humidity values, which are then used as input data and processed by the ESP32 microcontroller, which includes a fuzzy system. The Internet of Things (IoT) is also used to send data from the microcontroller to the Thingspek web server. The Blynk application is used to make it easier to monitor temperature, humidity, and water droplet rate values. The results of this research show that the temperature accuracy values produced using the MSE evaluation were 6.66667 and RMSE were 2.58199, while for temperature, the values for MSE were 0.128333 and RMSE were 0.358236. The average value of soil moisture produced in the planting medium is 44.46%; this value is within normal conditions for chili plants, where normal soil moisture conditions range between 40% - 60%
PENGEMBANGAN SISTEM IRIGASI PERTANIAN BERBASIS INTERNET OF THINGS (IoT) Miftahul Walid; Hoiriyah, Hoiriyah; Fikri, Ali
Jurnal Mnemonic Vol 5 No 1 (2022): Mnemonic Vol. 5 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v5i1.4452

Abstract

Untuk mengikuti perkembangan industri yang telah masuk pada era revolusi industri 4.0, dimana era ini merupakan era pengembangan Internet of Things (IoT) dan big data, semua sektor secara tidak langsung dipengaruhi, tidak terkecuali sektor pertanian, perkembangan pertanian dengan memanfaatkan teknologi komunikasi dan informasi khususnya teknologi IoT dan big data di indonesia masih sangat minim sekali, bahkan bisa dikatakan masih belum berkembang, maka dalam penelitian ini peneliti melakukan peneltian di sektor pertanian, khususnya sektor irigasi pertanian dengan memanfaatkan teknologi IoT. Penelitian ini merupakan salah satu tahapan untuk memanfaatkan teknologi IoT yang diaplikasikan pada sistem irigasi pertanian, penelitian ini tidak hanya membangun sistem kontrol sensor yang diintegrasikan pada mikrokontroller namun juga membahas tentang arsitektur jaringan komunikasi, sehinga sistem irigasi ini bisa melakukan komunikasi dua arah dengan baik, cepat dan menjangkau area luas, sistem juga dibekali antarmuka yang mudah digunakan, antarmuka dibangun menggunakan aplikasi berbasis Mobile, antarmuka ini akan memudahkan user dalam mengakses informasi dan mengontrol sistem yang dibangun. penelitian ini mampu melakukan kontrol sistem irigasi jarak jauh dangan memanfaatkan teknologi IoT serta diharapkan berkontribusi dalam mewujudkan revolusi industri di indonesia yang dikenal dengan “ Making Indonesia 4.0” khususnya dalam bidang pertanian.
Lexicon-Based and Naive Bayes Sentiment Analysis for Recommending the Best Marketplace Selection as a Marketing Strategy for MSMEs Hoiriyah, Hoiriyah; Mardiana, Helva; Walid, Miftahul; Darmawan, Aang Kisnu
Jurnal Pilar Nusa Mandiri Vol 19 No 2 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i1.4176

Abstract

MSMEs (micro, small, and medium enterprises) play an essential role in the Indonesian economy, contributing to 60% of the country's GDP (gross domestic product), creating jobs, and increasing non-oil and gas exports. However, MSMEs in Indonesia face various challenges, including access to technology, digital marketing tools, financial resources, limited market distribution, and low technological literacy. Marketplaces provide an essential marketing channel for MSMEs to increase their competitiveness and sales. Sentiment analysis can assist businesses in making informed decisions about which marketplace to use to increase customer satisfaction. Apart from the importance of the marketplace for MSMEs in Indonesia, research on sentiment analysis for marketplace recommendations is still minimal. Therefore, this study aims to analyze six popular marketplaces in Indonesia using Lexicon-based and naïve Bayes research methods to provide the best marketplace recommendations for MSME marketing. The results showed that Blibli.com had the highest accuracy, followed by Tokopedia, Tiktokshop, Lazada, Shopee, and Bukalapak. Blibli.com received positive reviews with 96.33%, followed by Tokopedia with 95.25%, Tiktokshop with 94.61%, and Lazada with the highest accuracy. 94.22%, Shopee 92.18%, and Bukalapak 89.57%. This research has two significant contributions. First, making a scientific contribution by applying a combination model of lexicon-based and naïve Bayes to analyze market sentiment in Indonesia Second, offering a practical contribution by providing recommendations to MSME actors and policymakers in choosing the best marketplace for MSMEs marketing purposes in Indonesia. By utilizing the recommended marketplace, MSMEs can optimize their marketing strategy and increase their competitiveness in the digital marketplace.
SENTIMENT ANALYSIS ON LGBT ISSUES IN INDONESIA WITH LEXICON-BASED AND SUPPORT VECTOR MACHINE ALGORITHMS Hoiriyah, Hoiriyah; Qomariya, Nurul; Darmawan, Aang Kisnu; Walid, Miftahul; Efenie, Yuri
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i1.4183

Abstract

Non-heterosexual sexual orientation (LGBT) behavior today is one of the most pervasive issues in Indonesian culture. Because of its domino effect on social stability and physical and mental health, the phenomenon known as lesbian, gay, bisexual, and transgender (LGBT) has always been under scrutiny. The development of LGBT people in Indonesia reflects cultural changes that concern many people. Freedom of speech for LGBT people on social media has many public implications. Observation of this phenomenon gives rise to views of anomalies and discrepancies that have drawn criticism. Various attempts have been made to prevent the movement of LGBT people. However, until now, many still debate the pros and cons of this LGBT movement. The lexicon-based method uses a support vector machine to classify public opinion in TikTok video comments about LGBT issues. The lexicon-based method is used as a weighting method, and the support vector machine method is used as a classification method. The results show that the highest gain in sentiment is neutral, with percentage values of 61%, 56%, 68%, 69%, and 63%. The second is positive sentiment, with percentage values of 27%, 27%, 20%, 20%, and 29%. The rest have negative sentiments. With a relatively high accuracy of the five data sets sequentially at 93%, 89%, 95%, 97%, and 91%. This shows that the majority of Indonesians prefer to ignore the issue.
TWITTER TEXT MINING MENGENAI ISU VAKSINASI COVID-19 MENGGUNAKAN METODE TERM FREQUENCY, INVERSE DOCUMENT FREQUENCY (TF-IDF) Harieby, Edo; Hoiriyah, Hoiriyah; Walid, Miftahul
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 6 No. 2 (2022): JATI Vol. 6 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v6i2.5129

Abstract

Penyebaran informasi mengenai vaksin covid-19 menarik perhatian masyarakat. Berbagai macam isu bermunculan terkait halal dan tidaknya vaksinasi covid-19 dilakukan. Media sosial Twitter salah satunya yang memberikan ruang pada masyarakat untuk menanyakan dan berkomentar terkait vaksin covid-19 melalui cuitan (tweet) ataupun retweet. Dengan metode TF-IDF, penelitian ini dilakukan untuk menganalisis text (analisis sentimen) dari kumpulan tweet sehingga hasilnya diketahui banyaknya kata yang muncul dapat menjadi suatu kata kunci dalam perbincangan di Twitter, bahwa banyak masyarakat yang menyetujui adanya wajib vaksin covid-19. Hasil penelitian ini menampilkan 5 kata teratas yang paling banyak muncul, antara lain : vaksin (831.431911 kata), vaksinasi (748.304896 kata), covid (709.626652 kata), sehat (435.356173 kata), dukung (417.387094 kata) dan indonesia (404.432113 kata). Sedangkan hasil pembobotan TF-IDF adalah : mui (0.6436902527847653), vaksin (0.132185733888140), covid (0.1566272932497384), sinovac (0.4762729721904365), suci (0.8634345960912986), halal (0.5720637913580648), dan ncovid (0.543713657254659). Hasil penelitian ini masih memerlukan pembobotan n-gram dengan L1 atau L2 Normalization agar dapat digunakan sebagai data train dan data test pada proses analisa selanjutnya.
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 Eka Setiawan, Rian Putra 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 Wicaksono, Agung Tri Widiatmoko, Dekki Yuli Sasmita Yuri Efenie