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All Journal Information System for Educators and Professionals : Journal of Information System IndoMath: Indonesia Mathematics Education JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Digit : Digital of Information Technology Mosharafa: Jurnal Pendidikan Matematika Jurnal Informatika Terpadu Jurnal Dinamika Informatika (JDI) Jurnal Sistem Informasi dan Teknologi (SINTEK) Nuansa Informatika AMMA : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Bianglala Informatika Linier: Literatur Informatika dan Komputer
Ahmad Faqih
STMIK IKMI Cirebon
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Other

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SMOTE untuk Meningkatkan Performa Naïve Bayes dan Random Forest dalam Analis Sentimen aplikasi Digitalent Ahmad Faqih; Yusril Muhamad Izha Mahendra; Kaslani
Jurnal Dinamika Informatika Vol. 14 No. 2 (2025): Vol. 14 No. 2 (2025)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v14i2.347

Abstract

Sentiment analysis is critical to understanding how an app, such as a digital training app like Digitalent, is viewed by users. User reviews available on app distribution platforms provide ample data for this analysis. However, in sentiment analysis, data imbalance is a common problem; positive reviews tend to outnumber negative and neutral reviews. This imbalance can impact machine learning models, which can lead to inaccurate predictions of the majority class. The purpose of this research is to solve this problem by using SMOTE (Synthetic Minority Selection Technique) technique in sentiment analysis of Digitalent app reviews and comparing the performance of two machine learning algorithms, Naive Bayes and Random Forest. The research data was collected from Indonesian user reviews from the Digitalent platform. Before being processed for analysis, the data went through pre-processing processes such as cleaning, tokenization, and normalization. SMOTE technique was applied to balance the number of reviews for each sentiment class. Furthermore, Naive Bayes and Random Forest algorithms are used to categorize the sentiment. The results of the SMOTE application research successfully increased the proportion of negative and neutral classes, so that the distribution of the dataset became balanced. The test results show that the accuracy of Naïve Bayes increased from 68.25% to 92.16%, while Random Forest increased from 68.25% to 92.16%.Keywords: K-Means Clustering, education level, clustering, village education, RapidMiner
Algoritma K-Means Untuk Klasterisasi Kampung Di Desa Bojong BerdasarkanTingkat Pendidikan Yovi Yuliantin; Ahmad Faqih; Kaslani
Bianglala Informatika Vol. 13 No. 1 (2025): Maret 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v13i1.12008

Abstract

Desa Bojong menghadapi tantangan dalam memetakan tingkat pendidikan penduduknyasecara terstruktur. Meskipun data tersedia, kurangnya pengorganisasian menyebabkan kesenjanganakses pendidikan, terutama di wilayah-wilayah tertentu. Penelitian ini bertujuan untukmengelompokkan tingkat pendidikan warga Desa Bojong menggunakan algoritma K-Means, sehinggadapat memberikan wawasan yang lebih mendalam tentang kondisi pendidikan masyarakat. Data yangdigunakan mencakup 6.027 penduduk dari 10 kampung, dengan atribut seperti usia, pendidikanterakhir, pekerjaan, dan status pernikahan. Proses analisis mengikuti tahapan Knowledge Discovery inDatabase (KDD) dan dilakukan menggunakan perangkat lunak RapidMiner. Hasil penelitianmenunjukkan bahwa jumlah klaster yang ideal adalah 7, dengan nilai terbaik 0,467 untuk DaviesBouldin Index (DBI). Setiap klaster menunjukkan tingkat pendidikan tertentu. Cluster 6 memiliki tingkatpendidikan yang sangat tinggi, dengan banyak penduduk yang sampai perguruan tinggi. Sementaraitu, Cluster 0 terdiri dari orang-orang yang hanya tamat SD atau bahkan tidak sekolah. Studi inimenunjukkan distribusi pendidikan di Desa Bojong. Hasil ini dapat membantu pemerintah desamembuat program pendidikan yang lebih baik. Kampung dengan tingkat pendidikan rendah dapatberkonsentrasi pada program yang meningkatkan akses pendidikan dasar, seperti literasi dan subsidipendidikan, sedangkan kampung dengan tingkat pendidikan tinggi dapat berkonsentrasi padapengembangan program pendidikan lanjutan atau pelatihan vokasional. Metode ini diharapkan dapatmembantu Desa Bojong mengatasi kesenjangan pendidikan dan meningkatkan kualitas hidupwarganya dengan menyediakan program yang tepat sasaran. Selain itu, penelitian ini membantuimplementasi algoritma K-Means dalam pengelompokan data pendidikan di daerah pedesaan.
Optimisasi Model Backpropagation untuk Meningkatkan Deteksi Kejang Epilepsi pada Sinyal Electroencephalogram Odi Nurdiawan; Fathurrohman Fathurrohman; Ahmad Faqih
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 9 No 2 (2024): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Desember 2024)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v9i2.3187

Abstract

Epilepsy is a chronic neurological disorder characterized by recurrent seizures caused by abnormal electrical activity in the brain. Fast and accurate seizure detection is crucial to support medical intervention and improve patients' quality of life. Currently, Electroencephalogram (EEG) signals are widely used to diagnose epilepsy as they record brain electrical activity in real-time. However, manual analysis of EEG signals requires time and precision, necessitating a more effective automated solution. This study aims to optimize the Backpropagation model for detecting epileptic seizures using EEG data. The research involved collaboration between Telkom University, Sumber Waras Hospital, and the University of Bonn. The EEG data collected was processed through Discrete Cosine Transform (DCT) to extract important features before being used to train the artificial neural network (ANN) model. The model was trained and tested using varying numbers of epochs to measure its accuracy. The results show that the Backpropagation model achieved optimal accuracy of 91.15% at 100 epochs and increased to 93.05% at 200 epochs. Although accuracy improved with more epochs, the longer computational time posed a risk of overfitting. This research demonstrates that the Backpropagation algorithm can be optimized to detect epileptic seizures accurately and efficiently. The implication for Sumber Waras Hospital is that this model can be implemented in EEG monitoring systems to detect seizures in real-time, supporting faster medical intervention and reducing reliance on manual analysis. Thus, this study contributes to providing a more efficient diagnostic solution and enhancing healthcare services for epilepsy patients.
IMPLEMENTASI INTEGRASI API DALAM WORKFLOW AUTOMATION MENGGUNAKAN N8N UNTUK MENDUKUNG TRANSFORMASI DIGITAL Ahmad Rifa’i; Ahmad Faqih; Muhamad Dadan Rifai; Fahmi Rahman
AMMA : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 : Juni (2026): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

This Community Service activity was carried out with the theme "Implementation of API Integration in Workflow Automation Using n8n to Support Digital Transformation." The target of this activity was vocational high school students throughout Cirebon City who needed to strengthen their digital competencies, especially in understanding system integration, workflow automation, and the use of technology-based applications. This activity was motivated by the needs of the modern workplace which increasingly demands vocational graduates to have the ability to use technology productively, understand data flows, and be able to develop simple digital solutions. The method of implementation of the activity was carried out through a hands-on workshop. Participants received material on digital transformation, the concept of APIs, webhooks, workflow automation, and the use of n8n. The activity continued with a demonstration of workflow creation, guided practice, development of school case studies, presentation of results, and evaluation. The case studies used included automation of activity registration, data storage to spreadsheets, participant recaps, and automatic notification sending. This approach made it easier for participants to understand technical concepts because the material was linked to real-life needs in the school environment. The results of the activity showed that participants were able to create simple workflows using n8n. Participants were able to understand the functions of triggers, nodes, data mapping, credentials, and outputs in the automation process. The satisfaction evaluation showed an average score of 4.50, categorized as very satisfied. The pre-test and post-test results also showed an increase in understanding from 58.25 to 83.40, representing a 25.15-point increase. These results indicate that the community service activity was effective in improving digital literacy and basic workflow automation skills. The activity outputs included training modules, practical worksheets, activity documentation, evaluation results, and simple workflow examples. This activity provided benefits to participants, accompanying teachers, and partner schools. Participants gained applied digital skills, and schools obtained technology-based learning resources. Overall, this activity supports the strengthening of the digital transformation of vocational education in Cirebon City.
Co-Authors Abdul Mukhyidin Ade Irma Purnamasari Ade Rizki Rinald Ade Rizki Rinaldi Adisty Tri Putra Ahmad Rifa'i Ahmad Rifai Ahmad Rifa’i Ainun Nisa Sari Andi Setiawan Arga Esa Putra Arif Rinaldi Dikananda Ariska Sari Aulia Nur Rochmah Azis Alma’as Bambang Irawan Bisma Mahendra Chulyatunni’mah Denni Pratama Dinar Faris Naufal Fadli Iqbal Syaidin Fahmi Rahman Fathurrohman Fathurrohman Fatkhan Mubarok Gifthera Dwilestari Haris Abdul Hadi Heliyanti Susana Iin Kaslani Kevin Salsabil Arlandy Kurniawan Fajar Abdulloh Luthfiyyah Iffah Adelia Madyant Marfelio Muhammad Fajid Martanto . Mohamad Riad Solihin Muhamad Dadan Rifai Muhammad Daffa Ayyasy Nanda Permatasari Nining Rahaningsih Nisa Dieanwati Nuris Odi Nurdiawan Rudi Kurniawan Salsa Billa Agistina Siti Sa'diah Sri Darmayanti Yovi Yuliantin Yubi Aqsho Ramadhan Yusril Muhamad Izha Mahendra