Article Per Year (5 Year)
p-Index From 2021 - 2026
1.066
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Rekayasa Elektronik Jurnal Dinamika Informatika (JDI) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Teknik Informatika dan Teknologi Informasi Scientica: Jurnal Ilmiah Sains dan Teknologi
Edi Tohidi
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Penerapan Algoritma K-Medoids Dalam Menentukan Cluster Kabupaten/Kota Berdasarkan Migrasi Penduduk Jawa Barat Bahrul Ulum; Edi Tohidi; Nisa Dienwati Nuris
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 4 No. 1 (2024): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v4i1.3581

Abstract

One of the three main factors influencing population dynamics is migration, along with births and deaths. Changes in population structure will definitely be influenced by migration. If in-migration is more than out-migration, the population will increase, but if out-migration is less than in-migration, the population will decrease. Therefore, it is necessary to know the grouping of regions based on population migration levels. To carry out this grouping, it is necessary to use Data mining methods. In this research, the Data mining used is Clustering using the K-Medoids algorithm. This method divides each district into predetermined groups. The K-Medoids method was chosen because it uses physical data that is not abstract and clear, which is suitable for the problem of grouping population migration data. By grouping migration levels based on districts/cities in West Java, it will be known which districts/cities in West Java have high levels of incoming migration, medium migration and high outmigration. Then recommendations can be given to the local government according to the migration level category.
PENERAPAN APLIKASI ABSENSI FACE RECOGNITION DENGAN OPENCV MENGGUNAKAN ALGORITMA HAARCASCADE CLASSIFIER DI SMK MUTHIA HARAPAN CICALENGKA Edi Tohidi; Rizki Fahrezi Maulana; Edi Wahyudin; Kaslani
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 4 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pencatatan kehadiran siswa merupakan aspek krusial dalam pengelolaan sekolah. Saat ini, di SMK Muthia Harapan Cicalengka, penanganan data absensi siswa masih dilakukan secara manual, menghabiskan waktu dan tenaga yang signifikan. Untuk menangani permasalahan ini, telah dikembangkan aplikasi bernama "SmartSchool Data Manager" yang memanfaatkan teknologi pengenalan wajah dengan menggunakan OpenCV dan algoritma HaarCascade Classifier. Aplikasi ini dirancang untuk secara otomatis melacak kehadiran siswa dengan mendeteksi wajah mereka menggunakan algoritma HaarCascade Classifier, yang menggunakan karakteristik wajah untuk identifikasi. Setelah diuji coba di SMK Muthia Harapan Cicalengka, aplikasi ini berhasil dengan akurasi mencapai 83.2%. Ini membuktikan kemampuan aplikasi dalam memantau kehadiran siswa secara efisien dan tepat. Selain meningkatkan akurasi pencatatan kehadiran siswa, aplikasi ini juga dapat membantu menghemat waktu dan usaha yang sebelumnya diperlukan oleh guru-guru dalam pencatatan manual kehadiran siswa. Dengan menggunakan aplikasi ini, prWoses pencatatan kehadiran siswa menjadi lebih mudah karena guru hanya perlu melakukan pemindaian wajah siswa untuk mencatat kehadiran mereka
Sentiment analysis to classify TikTok Shop Users on Twitter with Naïve Bayes Classifier Algorithm Lestari, Ayu; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
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.v4i2.748

Abstract

Advances in information technology have facilitated the use of social media as an e-commerce platform, with TikTok Shop enabling in-person transactions. This research addresses the gap in understanding user perceptions of TikTok Shop through sentiment analysis on Twitter. Sentiment classification is performed using the Naïve Bayes Classifier algorithm. The dataset consists of 1,907 Indonesian tweets, collected from January 2023 to July 2024, and processed using RapidMiner in the Knowledge Discovery in Database (KDD) framework. The preprocessing stages include data cleaning, normalization, tokenization, stopword removal, and stemming. To overcome data imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied. The model achieved 93.98% accuracy, with balanced precision and recall for positive, neutral, and negative sentiments. The sentiment distribution among TikTok Shop users on Twitter was 35.5% positive, 35.5% negative, and 29.0% neutral. This research provides insights into consumer behavior on social media and emphasizes the importance of sentiment analysis to increase user engagement and understand market perception. This research is expected to provide information to platform developers and businesses looking to improve TikTok
K-Means Algorithm for Grouping Models of Dengue Fever Prone Areas in Cirebon City Aida Safitri; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
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.v4i2.834

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease transmitted through the Aedes aegypti mosquito. DHF cases in Cirebon City show a significant increase every year. This study aims to classify dengue prone areas based on case data per health center in 2020-2024 obtained from the Cirebon City Health Office. The method used is the K-Means algorithm with the Knowledge Discovery in Database (KDD) approach, which includes data selection, preprocessing, data transformation, data mining, evaluation, and knowledge. Evaluation using Davies-Bouldin Index (DBI) showed optimal results at k = 6 with a DBI value of -0.445. The clustering results produced six clusters: cluster 5 (437 dengue cases in 34 health centers) showed high risk; cluster 0 (244 cases), cluster 2 (129 cases), and cluster 3 (279 cases) showed medium risk; while cluster 1 (69 cases) and cluster 4 (86 cases) showed low risk. This study shows that the K-Means algorithm is effective in identifying DHF risk distribution patterns and provides a strategic basis for the Cirebon City Health Office to prioritize interventions and develop more effective prevention strategies.
PENGELOMPOKAN PRESTASI AKADEMIK SISWA SD MENGGUNAKAN ALGORITMA K-MEANS Eneng Okta Srirahmawati; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
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.
Prediksi Stunting Menggunakan Algoritma Decision Tree Berbasis Synthetic Minority Over-sampling Technique (SMOTE) Asep Saepul Anwar; Ade Irma Purnama Sari; Agus Bahtiar; Edi Tohidi
Jurnal Dinamika Informatika Vol. 13 No. 2 (2024): Jurnal Dinamika Informatika Volume: 13 Nomer: 2
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v13i2.323

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.
Co-Authors Ade Irma Purnama Sari Ade Irma Purnamasari agus bahtiar Aida Safitri Asep Saepul Anwar AYU LESTARI BAHRUL ULUM Edi Wahyudin Eneng Okta Srirahmawati Kaslani Nisa Dienwati Nuris Rizki Fahrezi Maulana