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Wahyudin, Edi
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Pengembangan Aplikasi Kasir Android Bagi Pelaku Usaha Mikro Wahyudin, Edi; Tohidi, Edi; Yoni Ardiansah, Muhamad; Agastya, Muhamad
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The use of Android-based cashier applications can improve the efficiency and accuracy of transactions for micro-entrepreneurs. This Community Partnership Program aims to provide training on creating simple Android-based cashier applications for micro-entrepreneurs. This training covers the introduction to the basics of Android application development using a specific platform, the design of an intuitive user interface (UI) for sales transactions, the implementation of key cashier application features such as transaction recording, calculation of total purchases, and simple report generation. It is expected that, through this training, micro-entrepreneurs can have the ability to create cashier applications that suit their business needs, thereby improving operational efficiency and financial management.
Pemanfaatan Teknologi IoT Untuk Monitoring Suhu Dan Kelembaban Gudang Pangan Fathurrohman; Wahyudin, Edi; Zamil Farhan, Muhammad; Putri Nindya, Nazwa
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The storage of food products in warehouses requires strict monitoring of environmental conditions, particularly temperature and humidity, to maintain the quality and shelf life of the products. Unstable temperature and humidity levels can trigger the growth of microorganisms, accelerate spoilage, and reduce the market value of the commodities. In this context, the Internet of Things (IoT) technology emerges as a strategic solution that enables real-time monitoring systems to operate automatically and efficiently. This Student Creativity Program (Program Kreativitas Mahasiswa/PKM) aims to design and implement an IoT-based monitoring system to control temperature and humidity in food storage warehouses. The system utilizes a DHT22 sensor to measure temperature and humidity, an ESP32 microcontroller as the control center, and a web-based or mobile application interface platform connected online to monitor the warehouse environment in real time. The collected data is periodically transmitted to a server and can be accessed by users through a dashboard equipped with notification features to alert when conditions exceed predefined thresholds. The methods employed in this project include system design, hardware and software integration, system testing in a food warehouse environment, and performance evaluation. The implementation results indicate that the IoT system can monitor temperature and humidity accurately and in real time. The system also successfully provides early warnings regarding unfavorable environmental conditions, allowing business operators to take immediate corrective actions. The application of this technology not only improves operational efficiency in food warehouse management but also serves as a form of technological education for local industry players, particularly MSMEs (Micro, Small, and Medium Enterprises) in the agribusiness sector. It is expected that this system model can be widely adapted across various food storage sectors to prevent losses due to improper storage conditions.
MODEL KLASIFIKASI SENTIMEN PADA ULASAN PENGGUNA APLIKASI GAME WEPLAY DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES Karunia Nurul Asry, Kintan; Irma Purnamasari, Ade; Bahtiar, Agus; Wahyudin, Edi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13909

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan sentimen ulasan pengguna pada aplikasi Game WePlay di Google Play Store menggunakan algoritma Naive Bayes. Dengan menganalisis sentimen, penelitian ini berupaya memahami persepsi dan pengalaman pengguna terhadap aplikasi game. Salah satu tantangan utama adalah keragaman data ulasan, seperti penggunaan bahasa informal dan distribusi data yang tidak seimbang. Data ulasan diambil dari Google Play Store melalui proses web scraping, kemudian diproses melalui tahapan praproses seperti normalisasi, tokenisasi, dan penghapusan kata-kata yang tidak relevan. Proses ekstraksi fitur dilakukan menggunakan pendekatan Term Frequency-Inverse Document Frequency (TF-IDF). Algoritma Naive Bayes digunakan untuk mengelompokkan sentimen menjadi kategori positif dan negatif. Hasil penelitian menunjukkan bahwa model memiliki tingkat akurasi sebesar 86%, dengan presisi rata-rata 84,9%, recall 82,7%, dan F1-score 83,6%. Dalam evaluasi lebih lanjut, sentimen positif tercatat memiliki F1-score sebesar 89,9%, sementara sentimen negatif mencapai F1-score sebesar 77,3%. Hasil ini mengindikasikan bahwa model lebih efektif dalam mengidentifikasi pola kata pada ulasan positif. Penelitian ini memberikan kontribusi penting untuk pengembangan aplikasi Game WePlay dengan menyediakan pemahaman yang lebih baik tentang ulasan pengguna.
PENINGKATAN MODEL KLASIFIKASI SENTIMEN PUBLIK DI YOUTUBE CNBC INDONESIA TERHADAP MOBIL LISTRIK MENGGUNAKAN ALGORITMA SUPPORT VEKTOR MACHINE Sehabudin, Sehabudin; Irma Purnamasari, Ade; Bahtiar, Agus; Wahyudin, Edi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14184

Abstract

Pertumbuhan teknologi kendaraan listrik di Indonesia memicu beragam opini publik, khususnya di platform media sosial seperti YouTube. CNBC Indonesia, sebagai salah satu sumber informasi terpercaya, sering menjadi pusat diskusi terkait isu ini. Penelitian ini bertujuan untuk meningkatkan model klasifikasi sentimen publik terhadap kendaraan listrik menggunakan algoritma Support Vector Machine (SVM), yang dikenal memiliki performa unggul dalam klasifikasi berbasis teks. Data penelitian berupa komentar publik dari kanal YouTube CNBC Indonesia yang dikumpulkan dari video-video bertema kendaraan listrik selama periode tertentu. Tahapan penelitian meliputi preprocessing data seperti tokenisasi, penghapusan stopwords, stemming, pembobotan dengan Term Frequency-Inverse Document Frequency (TF-IDF), dan implementasi algoritma SVM untuk klasifikasi sentimen menjadi positif dan negatif. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score, serta membandingkan hasilnya dengan algoritma lain seperti Naïve Bayes. Hasil penelitian menunjukkan bahwa algoritma SVM memiliki performa terbaik dengan akurasi mencapai 93% dan F1-score yang konsisten tinggi. Sebagian besar sentimen publik terhadap kendaraan listrik bersifat negatif, meskipun terdapat kritik terkait infrastruktur dan biaya yang masih menjadi tantangan. Keberhasilan algoritma SVM menunjukkan potensinya untuk analisis teks yang lebih kompleks di masa depan, meskipun penelitian ini menghadapi tantangan seperti bias data dan perlunya memperluas dataset.
Penerapan Algoritma Decision Tree Dalam Penentuan Karyawan Kontrak Alibasyah, Aziz; Ajiz, Abdul; Dwilestari, Gifthera; Kaslani; Wahyudin, Edi
MEANS (Media Informasi Analisa dan Sistem) Volume 7 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (736.756 KB) | DOI: 10.54367/means.v7i1.1844

Abstract

The problem that arises at this time is a complicated evaluation (assessment) process, meaning that what often happens now is that contract employees who get promoted to permanent employees are only seen on one criterion, but the employee is not necessarily superior on several other criteria. but still get promotions for permanent employees. And there are several problems that exist today, namely the process of evaluating contract employees which is still subjective. Data mining using the decision tree method is widely used to deal with problems with large amounts of data. This decision tree method is a classification method that is widely used because its construction is relatively fast, the results of the model built are easy to understand and the prediction results are very strong so that they can assist in decision making. This study uses 4 criteria, namely Achievement, Ability, Personality and Results. Prediction results accuracy obtained is 91.54% with the following details. Prediction results are accepted and it turns out to be true, 72 data are accepted. Prediction Result Accepted and it turns out True Not Accepted for 14 Data. Prediction Results Not Accepted and it turns out True Accepted 1 Data. Prediction Results Not Accepted and in fact True Not Accepted Amounting to 91 Data.
Pada minimarket, produk merupakan bahan pokok yang akan dijual belikan. Produk di minimarket ini akan menentukan pengelompokkan data stok barang di Toko Toba. Dengan adanya masalah ini, perlu untuk menciptakan sistem baru menggunakan Rapidminer yang dapat Fauziah, Irfa Mulhimah; Amalia, Dita Rizki; Wahyudin, Edi; Mulyawan, Mulyawan; Kaslani, Kaslani
MEANS (Media Informasi Analisa dan Sistem) Volume 7 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/means.v7i2.1852

Abstract

In minimarkets, the product is a staple that will be sold and bought. The products in this minimarket will determine the grouping of stock data at the Toba Store. Given this problem, it is necessary to create a new system using Rapidminer that can group stock data at Toko Toba, which was carried out at the Toko Toba Sedong minimarket and carried out in November 2021-January 2022. This k-means algorithm will not be affected by the order of objects which has been used.  In stock management that is carried out inaccurately and carelessly will cause very high and uneconomical storage costs, because there can be vacancies or excess goods and certain types of items. This study aims to group stock data using Rapidminer at Toba Stores into 2 clusters. The method that will be used in this research is using the K-Means Clustering method. This research is also strongly supported by 1 data mining tool, namely Rapidminer. Data mining on Rapidminer tools for cluster 0 there are 15 items and the data contained in it, for cluster 1 there are 9 data contained in it.
Pelatihan Kewirausahaan Bisnis Afiliasi Tiktok pada Siswa-Siswi SMA Muhammadiyah Tasikmalaya Khaer, Rijalul; Firmansyah, Atep; Wahyudin, Edi
Jurnal Pengabdian Sosial Vol. 1 No. 6 (2024): April
Publisher : PT. Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/wmtbsz42

Abstract

Aplikasi tiktok merupakan media sosial dengan konten utama video. Media sosial ini menyediakan fitur afiliasi untuk kebutuhan bisnis. Tujuan dari Pengabdian kepada Masyarakat ini untuk memeberikan pelatihan kepada siswa-siswi SMA Muhammadiyah agar menggunakan fitur afiliasi tiktok sebagai wadah mendapatkan penghasilan dari internet. Metode pengabdian yang dilakukan dengan cara memberikan pelatihan praktek secara langsung dengan mendaftar akun tiktok sebagai afiliasi. Hasil dari pengabdian masyarakat ini, siswa-siswi SMA Muhammadiyah mempunyai akun afiliasi tiktok serta meningkatkan pengetahuan terhadap peluang bisnis afiliasi sosial media tiktok. Dengan kegiatan pengabdian ini juga memberikan peluang penghasilan tambahan yang berasal dari bisnis afiliasi tiktok.
Co-Authors Abdul Ajiz, Abdul Agastya, Muhamad Alibasyah, Aziz Amalia, Dita Rizki Amaliah, Rif'atul Amir Rudin, Rizki Aprilyani, Wiwin Budiarti, Reksa Damayanti, Selpiana Destriyanah, Riska Dikananda, Fatihanursari Eka Permana, Sandy Elsa Safutri Fadlan Abdul Rojak Fansuri, Rafly Fathurrohman, Fathurrohman Fauziah, Irfa Mulhimah Firmansyah, Atep Gifthera Dwilestari GUSNAWATY, HS Hamonangan, Ryan Herdiana, Ruli Hery Widijanto Iin, Iin Irma Purnamasari, Ade Karunia Nurul Asry, Kintan Kaslani Khaer, Rijalul Lutfiah Lutfiah Martanto MUHAMMAD TAUFIK Mulyawan, Mulyawan Nasakh Perdana Herdiansyah, Reza Pratama, Denni Puri, Anita Purnamasari, Ade Irma Putri Nindya, Nazwa Rano Rizki Rinaldi, Ade Saeful Anwar, Saeful Salsabila, Putri Sazwati, Anggi Sehabudin Sehabudin, Sehabudin Solihudin, Dodi Subhiyanto, Fajar Sukma Maula, Intan Syahruf Efendi, Muhammad Tati Suprapti Tohidi, Edi Veithzal Rivai Zainal Vibrianti, Vera Yoni Ardiansah, Muhamad Zamil Farhan, Muhammad