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All Journal Jurnal Agroteknos SEIKO : Journal of Management & Business Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JISA (Jurnal Informatika dan Sains) MEANS (Media Informasi Analisa dan Sistem) Jurnal Dinamika Informatika (JDI) Journal of Artificial Intelligence and Engineering Applications (JAIEA) BULLET : Jurnal Multidisiplin Ilmu Jurnal Pengabdian Sosial Informasi interaktif : jurnal informatika dan teknologi informasi
Wahyudin, Edi
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences

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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.
PENINGKATAN KLASIFIKASI PENJUALAN PRODUK FASHION DI SABHIRA OFFICIAL DENGAN RANDOM FOREST Arraudhah, Nazwa; Irma Purnama Sari, Ade; Bahtiar, Agus; Wahyudin, Edi
Jurnal Dinamika Informatika Vol. 14 No. 1 (2025): Jurnal Dinamika Informatika Volume 14 Nomor 1
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v14i1.512

Abstract

Penelitian ini berfokus pada peningkatan akurasi model klasifikasi penjualan produk fashion di Toko Sabhira Official dengan menerapkan algoritma Random Forest. Pendekatan yang digunakan mengikuti tahapan Knowledge Discovery in Database (KDD), yang mencakup pemilihan data, prapemrosesan, transformasi, data mining, dan evaluasi. Data penelitian terdiri dari 1.559 transaksi dalam periode Agustus hingga Oktober 2023, dengan atribut seperti kategori produk, jumlah barang terjual, harga, serta kategori penjualan (rendah, sedang, tinggi). Model dikembangkan menggunakan perangkat lunak RapidMiner, dengan pembagian data sebesar 70% untuk pelatihan dan 30% untuk pengujian. Hasil analisis menunjukkan bahwa algoritma Random Forest mampu mencapai tingkat akurasi sebesar 99,81%, dengan precision untuk kategori “Tinggi” mencapai 100%, sementara kategori lainnya memiliki nilai di atas 99%. Evaluasi menggunakan confusion matrix menunjukkan tingkat kesalahan prediksi yang sangat rendah, sehingga model ini dapat mengklasifikasikan tingkat penjualan secara lebih akurat. Hasil penelitian ini memberikan wawasan yang berguna bagi Toko Sabhira Official dalam pengelolaan stok serta strategi promosi berbasis data.
Deep Learning-Based Consumer Preference Analysis for Batik Packaging Design Using Convolutional Neural Networks Wahyudin, Edi; Bahtiar, Agus; Ali, Irfan; Nurhidayat, Muhammad
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2494

Abstract

Packaging design plays an essential role in shaping consumers’ first impressions of a product, particularly in the batik industry, where cultural meaning and visual identity are deeply intertwined. This study aims to explore how a Convolutional Neural Network (CNN) can help identify consumer preferences toward various batik packaging designs. The dataset consists of real packaging from local SMEs as well as prototype designs created specifically for this research, incorporating variations in motifs, colors, and structural formats. All images were standardized and normalized to ensure consistency before being processed by the CNN model. The architecture consists of several convolutional layers, pooling layers, and fully connected layers, with dropout applied to reduce overfitting. Model training was conducted using the Adam optimizer and the sparse categorical cross-entropy loss function. The results demonstrate that the model achieved a testing accuracy of 92.51%. Stable performance across precision, recall, and F1-score indicates that the CNN effectively captures visual patterns associated with consumer appeal. These findings highlight the potential for batik SMEs to utilize deep learning as a decision-support tool, enabling them to design packaging that is more appealing, relevant, and aligned with contemporary consumer preferences.
Optimization of Convolutional Neural Networks Using Resizing Techniques for Banana Leaf Disease Classification Kurniawan, Aldiyansyah; Purnamasari, Ade Irma; Pratama, Denni; Tohidi, Edi; Wahyudin, Edi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1876

Abstract

Early and accurate identification of banana leaf diseases is essential for supporting digital agriculture, as visual symptoms often require rapid and reliable analysis. This study investigates the impact of three image resizing techniques squashing, letterboxing, and random resized crop on the performance of the MobileNetV2 architecture in classifying four categories of banana leaf images using the Banana Leaf Disease Dataset v4 consisting of 4,675 samples. The experiments were conducted using a transfer learning approach with an 80:10:10 data split, standardized normalization, and data augmentation. The results show that all resizing techniques achieved test accuracies above 92%. Squashing produced the highest accuracy and fastest training time, letterboxing demonstrated the most stable performance with the lowest validation loss, and random resized crop improved generalization to variations in object position. These findings confirm that resizing strategies significantly influence the stability and effectiveness of CNN models. Overall, MobileNetV2 proves capable of delivering accurate and efficient classification of banana leaf diseases when supported by an appropriate preprocessing pipeline. This study provides empirical evidence for developing image-based plant disease diagnosis systems within smart agriculture.
Pengaruh Promo Diskon Terhadap Loyalitas Pelanggan Mall Ramayana Kota Sorong Lumuhu, Jessica Adlin; Tanel, Ester Yulina; Jamin, Nur Hasan; Mukti, Novaningtyas Azhari; Kamsrar, Herlina; Kambado, Paniel; Yewen, Yosep; Kafiar, Joninho Albert; Wahyudin, Edi; Pahmi, Pahmi
SEIKO : Journal of Management & Business Vol 9, No 1
Publisher : Program Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/sejaman.v9i1.11394

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh promo diskon terhadap loyalitas pelanggan pada Mall Ramayana Kota Sorong. Dalam persaingan ritel modern yang semakin kompetitif, strategi promosi berupa diskon menjadi salah satu instrumen pemasaran yang efektif untuk menarik minat konsumen dan mempertahankan pelanggan. Penelitian ini menggunakan pendekatan kuantitatif dengan metode survei terhadap pelanggan yang pernah berbelanja dan memanfaatkan promo diskon di Mall Ramayana Kota Sorong. Teknik pengumpulan data dilakukan melalui penyebaran kuesioner, kemudian dianalisis menggunakan uji regresi linier sederhana untuk mengetahui besaran pengaruh variabel promo diskon terhadap loyalitas pelanggan. Hasil penelitian menunjukkan bahwa promo diskon berpengaruh positif dan signifikan terhadap loyalitas pelanggan. Semakin menarik dan konsisten program diskon yang ditawarkan, maka semakin tinggi tingkat kunjungan ulang, rekomendasi kepada orang lain, serta komitmen pelanggan untuk tetap berbelanja di Mall Ramayana Kota Sorong. Temuan ini mengindikasikan bahwa strategi diskon tidak hanya efektif dalam meningkatkan penjualan jangka pendek, tetapi juga berperan dalam membangun hubungan jangka panjang dengan pelanggan. Oleh karena itu, manajemen Mall Ramayana perlu merancang program diskon yang terstruktur, tepat sasaran, dan berkelanjutan untuk meningkatkan loyalitas pelanggan secara optimal.
Co-Authors Abdul Ajiz, Abdul Alibasyah, Aziz Amalia, Dita Rizki Amaliah, Rif'atul Amir Rudin, Rizki Aprilyani, Wiwin Arraudhah, Nazwa Budiarti, Reksa Damayanti, Selpiana Destriyanah, Riska Dikananda, Fatihanursari Eka Permana, Sandy Elsa Safutri Fadlan Abdul Rojak Fansuri, Rafly Fauziah, Irfa Mulhimah Firmansyah, Atep Gifthera Dwilestari GUSNAWATY, HS Hamonangan, Ryan Herdiana, Ruli Hery Widijanto Iin, Iin Irfan Ali, Irfan Irma Purnama sari, Ade Irma Purnamasari, Ade Jamin, Nur Hasan Kafiar, Joninho Albert Kambado, Paniel Kamsrar, Herlina Karunia Nurul Asry, Kintan Kaslani Khaer, Rijalul Kurniawan, Aldiyansyah Lumuhu, Jessica Adlin Lutfiah Lutfiah MUHAMMAD TAUFIK Mukti, Novaningtyas Azhari Mulyawan, Mulyawan Nurhidayat, Muhammad Pahmi Pahmi Perdana Herdiansyah, Reza Pratama, Denni Puri, Anita Purnamasari, Ade Irma Rizki Rinaldi, Ade Saeful Anwar, Saeful Salsabila, Putri Sazwati, Anggi Sehabudin Sehabudin, Sehabudin Solihudin, Dodi Subhiyanto, Fajar Sukma Maula, Intan Syahruf Efendi, Muhammad Tanel, Ester Yulina Tati Suprapti Tohidi, Edi Veithzal Rivai Zainal Vibrianti, Vera Yewen, Yosep