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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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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.
KLASIFIKASI DATA TWEET UJARAN KEBENCIAN DI MEDIA SOSIAL MENGGUNAKAN NAIVE BAYES CLASSIFIER Susanti, Noor Aliyah; Walid, Miftahul; Hoiriyah, Hoiriyah
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.v6i2.5174

Abstract

Ujaran kebencian banyak dilihat dan sering terjadi di dunia maya, terutama media sosial Twitter. Semenjak adanya pemilihan presiden di tahun 2014, masyarakat mulai mengenal bullying di dunia maya. Ujaran kebencian, berita-berita hoax, bahkan ancaman terhadap pemerintah dan tokoh publik kerap dilakukan. Untuk mengukur sentimen masyarakat terhadap suatu berita maka perlu dilakukan analisis sentimen, khususnya komentar pengguna Twitter. Pada penelitian ini, pengujian metode menggunakan Multinomial Naive Bayes (MNB) untuk mengukur akurasi klasifikasi ujaran kebencian dalam data tweet. Sebelum melakukan perhitungan nilai akurasi, data tweet harus diolah melalui teks preprocessing agar kata (term) dapat dikonversikan ke dalam bentuk matriks. Untuk kemudian diolah sebagai data numerik. Pengujian dilakukan pada dua kondisi pembobotan n-gram, yakni unigram dan bigram. Mulai menghitung nilai akurasi masing - masing pembobotan Unigram dan Bigram sehingga didapat hasilnya bahwa model perhitungan algoritma Naive Bayes Classifier memiliki nilai akurasi yang sama untuk masing - masing pembobotan n-gram, yakni 69,23076923076923.
KLASIFIKASI KASUS COVID-19 MENGGUNAKAN MODEL NAIVE BAYES CLASSIFIER Firdaus, Ahmad; Walid, Miftahul; Anwari, Anwari
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.v6i2.5333

Abstract

Covid-19 dengan berbagai macam penanganannya membuat banyak pertentangan menyertainya. Untuk mengetahui apakah benar kasus pasien terkonfirmasi positif covid-19 tinggi atau rendah. Dalam penelitian ini, model Naive Bayes Classifier memprediksi bahwa kasus covid-19 di Pamekasan tinggi (>= 30 orang) dan model Gaussian Naive Bayes menunjukkan kinerja yang sangat baik dengan nilai akurasi model sebesar 0,9688 serta nilai precision (0,97), recall (1,00) dan f-1 score (0,98). menunjukkan bahwa data yang menyatakan kasus covid-19 di Kabupaten Pamekasan termasuk tinggi dengan pengambilan data dalam rentang waktu Bulan Januari 2021 sampai dengan Desember 2021. Diharapkan untuk penelitian selanjutnya sistem dapat dikembangkan lagi karena data yang didapat saat ini masih terbatas
SISTEM CERDAS IRIGASI SPRINKLER PADA TANAMAN BAWANG BERBASIS IOT MENGGUNAKAN LOGIKA FUZZY: SISTEM CERDAS IRIGASI SPRINKLER PADA TANAMAN BAWANG BERBASIS IOT MENGGUNAKAN LOGIKA FUZZY Nuruddin, Nuruddin; Walid, Miftahul; Makruf, Masdukil
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 8 No. 2 (2025): Volume VIII - Nomor 2 - Februari 2025
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v8i1.671

Abstract

Abstract—Tanaman bawang merupakan salah satu komoditas pertanian yang memiliki peran penting dalam pemenuhan kebutuhan pangan dan nilai ekonomi. Produktivitas tanaman bawang sangat dipengaruhi oleh sejumlah faktor, termasuk ketersediaan air yang optimal. Oleh karena itu, penerapan sistem irigasi yang cerdas dan efisien menjadi kunci keberhasilan dalam meningkatkan hasil panen dan mengurangi penggunaan sumber daya air. sistem ini melibatkan peningkatan efisiensi penggunaan air, pengurangan biaya operasional, dan peningkatan hasil panen tanaman bawang. Selain itu, pendekatan berbasis menggunakan Logika Fuzzy memungkinkan sistem untuk beradaptasi dengan perubahan kondisi tanaman dan lingkungan, sehingga meningkatkan responsivitas dan ketahanan sistem terhadap fluktuasi cuaca dan kondisi tanah Dalam pengujian alat, akan memunculkan informasi tentang kelembaban tanah yang akan tampak di dashboard aplikasi Blynk, jika tanaman tersebut memiliki kelembaban tertentu pada tanah dan menghasilkan keputusan terhadap air yang akan diberikan, maka akan mendapatkan email dari codular. Hasil sensor kelembaban akan memengaruhi output, jika hasil sensor menunjukkan tanah kering maka luaran yang dihasilkan adalah “nyala” yang menandakan bahwa air akan mengalir untuk menyirami tanah. Sedangkan jika hasil sensor menunjukkan tanah lembab ataupun basah maka output yang dihasilkan adalah “mati” yang menandakan bahwa air akan berhenti menyirami tanah atau tidak mengalir. Kata kunci: Irigasi Sprinkler; Logika fuzzy; IoT; ESP32;Aplikasi Kodular.
Development of Drip Irrigation Monitoring and Control System Model Based on the Internet of Things Using Android Applications Walid, Miftahul; Horiyah, Horiyah; Rofiuddin, Rofiuddin; Susilo, Purnomo Hadi; Wahyudi, Muhammad Hasan
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23113

Abstract

Background: Efficient water management is crucial for sustainable agriculture, particularly in regions with limited water resources. Drip irrigation systems, when integrated with the Internet of Things (IoT), offer a promising solution to optimize water usage and enhance agricultural productivity. Objective: This study aims to develop an IoT-based drip irrigation system to improve water efficiency and support small-scale farmers by providing a cost-effective and adaptable solution. Methods: The system employs multiple sensors to monitor key environmental parameters, including soil moisture, air temperature, and water levels in the tank. The collected data is transmitted to the ThingSpeak cloud platform via an Android application for real-time monitoring and control. Performance metrics such as sensor reaction time, solenoid valve response time, and pump response time were analyzed to evaluate system effectiveness. Results: Experimental findings show that the system effectively monitors and regulates irrigation, with an average sensor reaction time of 2.95 seconds, a solenoid valve response time of 2.75 seconds, and a pump response time of 2.3 seconds. The system successfully automates irrigation, ensuring optimal water usage. Conclusion: The IoT-based drip irrigation system enhances water resource management, increases crop yield, and reduces operational costs. While the system demonstrates high efficiency, further research could focus on scalability, integration with predictive analytics, and adaptation to different crop types and environmental conditions.
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 Umam, Busro Akramul Wahyudi, Muhammad Hasan Yuli Sasmita Yuri Efenie