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All Journal Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) MEANS (Media Informasi Analisa dan Sistem) Jurnal Dinamika Informatika (JDI) Journal of Artificial Intelligence and Engineering Applications (JAIEA) BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat
Tohidi, Edi
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing

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ANALISIS SENTIMEN TERHADAP BERHENTINYA TIKTOKSHOP PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES Sanjaya, Riki; Tohidi, Edi; Wahyudi, Edi; Kaslani, Kaslani
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 1 (2024): JATI Vol. 8 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i1.8443

Abstract

Perkembangan teknologi informasi telah membawa perubahan besar dalam dunia perdagangan, salah satunya adalah munculnya perdagangan dengan sistem elektronik (PSE) yang memungkinkan transaksi jual beli dilakukan secara online. Tiktokshop adalah salah satu platform PSE yang populer di Indonesia [1], yang merupakan bagian dari aplikasi Tiktok yang menyediakan fitur jual beli produk. Namun, pada tanggal 04 Oktober 2023, pemerintah Indonesia mengeluarkan kebijakan untuk menutup Tiktokshop berdasarkan surat Menteri perdagangan no 31 tahun 2023. Alasan pemerintah adalah untuk melindungi usaha mikro, kecil, dan menengah (UMKM) tradisional dari persaingan tidak sehat yang ditimbulkan oleh Tiktokshop. Kebijakan ini menuai banyak reaksi di media sosial, khususnya di Twitter, yang menjadi salah satu media untuk menyuarakan pendapat dan aspirasi masyarakat. Penelitian ini bertujuan untuk menganalisis sentimen terkait penutupan Tiktokshop di Twitter menggunakan algoritma Naive Bayes, yang merupakan salah satu metode klasifikasi teks berdasarkan kemunculan kata-kata tertentu. Hasil analisis menunjukkan bahwa sebagian besar netizen yang berkomentar terkait penutupan Tiktokshop menunjukkan bahwa respon positif mendominasi dengan nilai prediksi positif 965, sedangkan respon negatif hanya 535 nilai akurasi 85.00%, margin error +-2.92% Precision 99,33% recall 77,20 %.
ANALISA SENTIMEN KOMENTAR VIDEO YOUTUBE DI CHANNEL TVONENEWS TENTANG CALON PRESIDEN PRABOWO SUBIANTO MENGGUNAKAN SUPPORT VECTOR MACHINE Tohidi, Edi; Perdana Herdiansyah, Reza; Wahyudin, Edi; Kaslani, Kaslani
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 1 (2024): JATI Vol. 8 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i1.8560

Abstract

Indonesia merupakan negara demokrasi di mana rakyat memilih presiden melalui Pemilihan Umum Presiden (Pilpres) yang dilakukan 5 tahun sekali. Pada Pilpres 2024, ada 3 kandidat capres yaitu Anies Baswedan, Prabowo Subianto, dan Ganjar Pranowo. Youtube menjadi platform utama masyarakat menyampaikan opini politik. Penelitian ini menganalisis sentimen komentar video Youtube TVOneNews tentang calon presiden Prabowo sebagai capres 2024 dengan SVM. Tujuan penelitian ini adalah mengukur akurasi SVM dalam mengklasifikasi sentimen komentar video Youtube TVOneNews berjudul "Relawan dari Berbagai Daerah Deklarasikan Prabowo sebagai Capres 2024" serta melihat sentimen masyarakat terhadap calon presiden prabowo subianto. Metode penelitian ini menggunakan Knowledge Discovery in Database (KDD) yang terdiri dari lima tahapan yaitu Selection, Preprocessing, Transformation, Data Mining dan Interpretation. Datadalam penelitian ini berjumlah 927 komentar yang didapatkan melalui crawling setelah preprocessing tersisa 877 data dengan label positif 530 dan label negatif 346. Hasil penelitian menunjukkan terdapat sebuah perbedaan jumlah label sentimen awal dengan hasil SVM, dimana sentimen positif bertambah dari 530 menjadi 542 dan sentimen negatif berkurang dari 346 menjadi 334. Dan hasil klasifikasi SVM mendapatkan nilai akurasi 85%, presisi 87% dan recall 89%.
MARKET BASKET ANALYSIS PADA DATA PENJUALAN UMKM MENGGUNAKAN ALGORITMA FP-GROWTH Pratama, Denni; Kaslani, Kaslani; Tohidi, Edi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10939

Abstract

UMKM di Indonesia menghadapi tingkat kegagalan yang tinggi, mencapai 78-80%, yang disebabkan oleh berbagai faktor termasuk pengelolaan inventori yang buruk, persaingan usaha, dan rendahnya penjualan. Pandemi COVID-19 juga memberikan dampak signifikan, terutama pada usaha sektor makanan dan minuman. Toko Rafa Cake, UMKM di Kota Cirebon yang memproduksi dan menjual makanan, mengalami dampak pandemi pada penjualannya. Pasca pandemi, Toko Rafa Cake, berusaha memperbaiki pengelolaan dan berinovasi dengan menghadirkan 155 varian produk, namun mengalami kesulitan dalam mengelola inventori dan menentukan strategi penjualan yang tepat. Penelitian ini menggunakan Market Basket Analysis dengan algoritma FP-Growth dan empat matriks evaluasi aturan asosiasi untuk menganalisis data transaksi penjualan. Kerangka penelitian menggunakan CRISP-DM. Dari 10.987 data transaksi, dihasilkan 6 aturan asosiasi umum dengan hasil yang bervariasi setiap bulannya. Produk Roti Regular All Varian memiliki support tertinggi sebesar 32,10%. Analisis menunjukkan bahwa pelanggan yang membeli Donat Ring Regular dan Glass Cake cenderung membeli Roti Regular All Varian dengan support 5,50%, confidence 98,80%, lift 3.075, dan conviction 55.436. Rekomendasi yang dihasilkan meliputi pengaturan tata letak produk yang sering dibeli bersamaan dan penerapan strategi bundling Roti Regular All Varian dengan produk lainnya. Hasil penelitian ini dapat membantu Toko Rafa Cake dalam menentukan strategi penjualan dan mengelola inventori dengan lebih efektif.
Prediksi Stunting Menggunakan Algoritma Decision Tree Berbasis Synthetic Minority Over-sampling Technique (SMOTE) Anwar, Asep Saepul; Sari, Ade Irma Purnama; Bahtiar, Agus; Tohidi, Edi
Jurnal Dinamika Informatika Vol 13 No 2 (2024): Jurnal Dinamika Informatika Volume: 13 Nomer: 2
Publisher : Universitas PGRI Yogyakarta

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Abstract

Stunting is a sign of a serious malnutrition problem and can cause toddlers to have a short height and affect the growth and development of toddlers. The data in the study used anthropometric data of toddlers from the Majasem Health Center in September 2024, a total of 1368 toddler data that had been recorded in that time span. The Decision Tree model used aims to predict the status of toddlers, based on the stunting category. However, the Decision Tree algorithm often faces the problem of non-optimal accuracy due to the imbalance of class data in the dataset. The SMOTE method is used as an effort to overcome the problem of class imbalance, so that the classes in the dataset will be balanced. This research successfully proves that the SMOTE method is able to improve the accuracy of the Decision Tree model, the accuracy of the model with the best SMOTE is 98.56% on training data and 96.91% on testing data with a data proportion of 80:20. This research is useful for helping health workers to provide better insight into the nutritional status of toddlers, besides that the developed model can increase the effectiveness of public health interventions.
K-Means Algorithm to Improve Leaf Image Clustering Model for Rice Disease Early Detection Gina Regiana; Irma Purnamasari, Ade; Bahtiar, Agus; Tohidi, Edi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.840

Abstract

This research aims to improve the accuracy of rice leaf image clustering in early disease detection using the K-Means algorithm. The approach used involves the Knowledge Discovery in Databases (KDD) method, which includes data selection, pre-processing, data transformation, data mining, evaluation, and presentation of results. The dataset used consists of images of healthy leaves and leaves infected with diseases such as Bacterial Leaf Blight, Brown Spot, and Leaf Smut. The images are processed through grayscale conversion, noise removal, size adjustment, and data augmentation. The K-Means algorithm is applied to cluster image features based on visual similarity. Evaluation results using Silhouette Score showed that the best clustering was obtained at K=2 with a score of 0.8340, resulting in two main clusters separating healthy and infected images. This study concludes that the K-Means algorithm is able to improve the efficiency and accuracy of rice disease detection, so that it can assist farmers in taking early preventive measures and increase agricultural productivity. This implementation shows significant potential in the development of smart agriculture technology.
PENGELOMPOKAN PRESTASI AKADEMIK SISWA SD MENGGUNAKAN ALGORITMA K-MEANS Srirahmawati, Eneng Okta; Purnamasari, Ade Irma; Bahtiar, Agus; Tohidi, Edi
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i1.1358

Abstract

Penelitian ini bertujuan untuk mengelompokkan prestasi akademik siswa di SDN Lebakwangi menggunakan algoritma K-Means. Algoritma K-Means adalah metode pengelompokan dalam data mining untuk mengelompokkan data berdasarkan kesamaan karakteristik antara data satu dengan data lainnya. Dalam penelitian ini, data yang digunakan berupa nilai rapor akademik siswa kelas 1, 2, 3, 4, dan 5 semester genap tahun 2024 sebanyak 171 data siswa, yang mencakup berbagai mata pelajaran yang digunakan sebagai atributnya, dengan menggunakan metode Knowledge Discovery in Database (KDD). Penelitian ini dapat mengidentifikasi pola pengelompokan siswa berdasarkan nilai akademik mereka yang lebih akurat dan efisien. Hasil evaluasi menggunakan algoritma k-means dan indeks evaluasi DBI menunjukkan bahwa nilai DBI terendah terdapat pada percobaan k=2, dengan nilai DBI 0.738. Pada percobaan tersebut, terbentuk 2 cluster, yaitu cluster_0  terdiri dari 141 siswa dengan kategori “baik” dan nilai rata rata 76.242, sedangkan pada cluster_1  terdiri dari 27 siswa dengan kategori  “sangat baik”  dan nilai rata-rata 85.181. Hasil penelitian ini dapat memberikan wawasan bagi guru untuk merancang cara pembelajaran yang lebih tepat sesuai dengan kebutuhan akademik masing-masing kelompok siswa.
Peningkatan Kompetensi Digital Lulusan SMK Kabupaten Cirebon Melalui Pelatihan Junior Network Administrator Arie Wijaya, Yudhistira; Tohidi, Edi; Azhar, Alwan; Ardiansyah, Andi
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 4 : Mei (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Junior Network Administrator training for SMK graduates in Cirebon Regency aims to improve skills and competencies in the field of computer networks. The program is designed as a solution to employment challenges by providing competency-based training that refers to industry standards. Training methods include theory, hands-on practice, and case studies relevant to the needs of the world of work. The program evaluation showed an increase in participants' understanding and skills in managing computer networks, configuring network devices, and basic troubleshooting. In addition, participants obtained certifications that can increase their competitiveness in the job market. The training results showed that most of the participants were successful in obtaining jobs or internships in technology companies and related institutions. The success of this program is supported by cooperation between educational institutions, local governments, and the industrial sector. Challenges faced include limited facilities and participants' readiness to deal with rapid technological developments. To improve the effectiveness of the program, it is recommended to improve training facilities, update the curriculum in line with industry developments, and collaborate more closely with the private sector.
Analisis Prediksi Tingkat Penjualan Brownies Tape Menggunakan Algoritma Naïve Bayes Tohidi, Edi; Danar Dana, Raditya; Mukhlashin, Khairul
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 1 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

The problem of this covid pandemic has hit all parties, sales in this pandemic era must be observant of changes, entrepreneurs are obliged to manage finances properly so as not to experience colaps or bankruptcy. solutions made by entrepreneurs ranging from reducing the amount of production, reducing employees and or promoting massively. The criteria for this study were obtained from the journals used, namely the criteria, namely date, month, year, code, product name, price and quantity. Then this study uses primary data, which means that the data is used with brownie purchase data from tape products with sales records from 2021 in September. The method used is the naïve bayes algorithm with retrive operators, cross validation, naïve bayes, apply model and performance. The accuracy result in this study is 83.24% Prediction of Less Selling with true Less Selling as much as 2004 data. Prediction of Less Selling with true Selling as much as 350 data. In-demand predictions with less in-demand as much as 202 data. Laris prediction with true Laris as much as 737 data.
Pengembangan Model Prediksi Keberhasilan Mahasiswa Menggunakan Algoritma Machine Learning Dalam Learning Management System Tohidi, Edi; Ali, Irfan
BULLET : Jurnal Multidisiplin Ilmu Vol. 2 No. 1 (2023): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

The advancement of digital technology in education has driven the widespread adoption of Learning Management Systems (LMS) as effective platforms for online learning. This study aims to develop a predictive model for student success in LMS environments using machine learning algorithms. Student success is classified based on parameters such as participation levels, access frequency, assessment results, and punctuality in assignment submissions. Several machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors, are employed to build the prediction model. The performance of each model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that the Random Forest algorithm achieved the best performance with an accuracy of 89%, followed by Support Vector Machine and Decision Tree. The developed model is expected to assist educators and academic institutions in identifying students who may face learning difficulties at an early stage, allowing for timely and targeted interventions. This research contributes to the application of machine learning in supporting adaptive learning processes and enhancing data-driven educational quality.
Pelatihan Dasar Microsoft Office Bagi Remaja Putus Sekolah Sebagai Upaya Pemberdayaan Digital Tohidi, Edi; Solihudin, Dodi; Arief Saputra, Mochamad; Haris Maulana Ibrahim, Mochammad
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Basic skills in using Microsoft Office applications are essential competencies for enhancing job opportunities and participation in various activities in the digital era, especially for out-of-school youth who often have limited access to formal education. This Community Partnership Program aims to empower out-of-school youth through basic Microsoft Office training. This training is designed to provide understanding and practical skills in using Microsoft Word for document processing, Microsoft Excel for data processing and simple calculations, and Microsoft PowerPoint for creating presentations. It is expected that, through this training, out-of-school youth can improve their functional skills, open opportunities for jobs requiring basic administrative abilities, and increase their self-confidence in facing the challenges of the digital era.
Co-Authors Ade Rizki Rinaldi Agastya, Muhamad Amaliah, Rif'atul Andi Ardiansyah Anwar, Asep Saepul Arief Saputra, Mochamad Azhar, Alwan Basysyar, Fadil M Bayu Saputra Caswadi, Caswadi Danar Dana, Raditya Dienwati, Nisa Fansuri, Rafly Fathurrohman Fathurrohman Faturachman, Rifcki Aziz Fitriyah, Anis Garsandi, Akmal Maulana Gifthera Dwilestari Gina Regiana Hagi Badra, Muhammad Hamonangan, Ryan Hanani, Desri Haris Maulana Ibrahim, Mochammad Hayati, Umi Herdiana, Ruli Iin, Iin Irfan Ali, Irfan Irma Purnamasari, Ade Kaslani Mukhlashin, Khairul Mulyawan Mulyawan, Mulyawan Nining Rahaningsih NOVIYANTI Nugraha, Syahrul Nurrajtiari, Dira Perdana Herdiansyah, Reza Pratama, Denni Pratama, Hilda Fidyah Hadi Purnamasari, Ade Irma Putra, Arya Kamandanu Raditya Danar Dana Riptiyani, Anjela Rizki Rinaldi, Ade Saeful Anwar, Saeful SANJAYA, RIKI Sari, Ade Irma Purnama Solihudin, Dodi Srirahmawati, Eneng Okta Sukma Maula, Intan Susana, Heliyanti Syafiq, Mohammad Sayyid Tati Suprapti Tengku Riza Zarzani N Wahyudi, Edi Wahyudin, Edi Yoni Ardiansah, Muhamad Yudhistira Arie Wijaya Yulistiano, Irena