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All Journal JIKO (Jurnal Informatika dan Komputer) 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) Journal of Artificial Intelligence and Engineering Applications (JAIEA) 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 Automotive Engineering 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.
Optimalisasi Strategi Pemasaran melalui Segmentasi Pelanggan dengan Analisis RFM dan Algoritma K-Means untuk Bisnis Ritel Aliya Anisa Rahma; Ahmad Faqih; Ade Rizki Rinaldi
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 2 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i2.1737

Abstract

Industri ritel yang kompetitif memerlukan pemahaman mendalam tentang kebutuhan pelanggan untuk menyusun strategi pemasaran yang relevan dan efektif. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan di Toko Mitra 10 Cirebon menggunakan analisis Recency, Frequency, dan Monetary (RFM) yang dikombinasikan dengan algoritma K-Means. Segmentasi ini bertujuan mendukung strategi pemasaran yang lebih terarah dan meningkatkan loyalitas pelanggan. Data yang digunakan berasal dari catatan transaksi pelanggan dalam periode tertentu. Nilai RFM dihitung untuk setiap pelanggan berdasarkan Recency (waktu sejak transaksi terakhir), Frequency (jumlah transaksi), dan Monetary (total nilai transaksi). Metode K-Means digunakan untuk mengelompokkan pelanggan menjadi beberapa segmen, dengan jumlah kluster optimal ditentukan melalui metode elbow. Analisis menghasilkan tiga segmen utama: Lost Customers, dengan Recency tinggi, Frequency rendah, dan Monetary rendah; Potential Loyalists, dengan Frequency sedang dan Monetary bervariasi; serta Loyal Customers, dengan Frequency tinggi dan kontribusi Monetary signifikan. Hasil segmentasi ini mendukung penyusunan strategi pemasaran yang berbeda untuk setiap kluster: kampanye reaktivasi untuk Lost Customers, program loyalitas untuk Potential Loyalists, dan layanan eksklusif untuk Loyal Customers. Pendekatan berbasis data ini meningkatkan efektivitas pemasaran, loyalitas pelanggan, serta kontribusi pendapatan, sekaligus menegaskan pentingnya analisis data dalam pengambilan keputusan pemasaran yang relevan dan personal.
House Price Prediction Analysis Using a Comparison of Machine Learning Algorithms in the Jabodetabek Area Indah Ratna Ningsih; Ahmad Faqih; Ade Rizki Rinaldi
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.v4i2.733

Abstract

Jabodetabek, as the largest metropolitan area in Indonesia, has complex property price dynamics, making it difficult for developers and buyers to determine house prices. This study aims to analyze and compare the performance of the Multiple Linear Regression and Random Forest Regression algorithms in predicting house prices in the region. The data was obtained through scraping techniques from the rumah123.com website in October 2024, covering 999 data points with variables such as price, location, building area, land area, number of bedrooms, bathrooms, and garages. A comparative approach with cross-validation was applied to evaluate the performance of both algorithms using the metrics MAE, MSE, RMSE, MAPE, and R². The research results show that Random Forest Regression using GridsearchCV has better predictive performance, with an MAE value of Rp.645,764,815, MAPE of 28.12%, and R² of 0.864. The main factors influencing house prices in Jabodetabek include building size, land size, number of bedrooms, bathrooms, garages, and location. This finding emphasizes the superiority of Random Forest Regression in capturing complex data patterns and the significant role of these variables in determining house prices.
The Effect of SMOTE Application on Support Vector Machine Performance in Sentiment Classification on Imbalanced Datasets Dini Andriyani; Ahmad Faqih; Sandy Eka Permana
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.v4i2.742

Abstract

This research explores the effect of applying Synthetic Minority Oversampling Technique (SMOTE) on the performance of Support Vector Machine (SVM) algorithm in sentiment classification on imbalanced datasets. Public review data was collected from social media platform X (formerly Twitter) regarding the Free Lunch Program, with a total of 2,368 reviews automatically labeled using the BERT model into three categories: positive, negative, and neutral. Sentiment imbalance in the dataset was addressed by applying SMOTE to generate synthetic data on minority classes. The research method follows the stages of Knowledge Discovery in Databases (KDD), including data selection, preprocessing, labeling, transformation using TF-IDF, SVM model training, and performance evaluation. The experimental results show that the application of SMOTE successfully improves the accuracy of the SVM model by 12.48%, from 71.41% to 83.89%. Other evaluation metrics, such as precision, recall, and F1-score, also showed significant improvement from 0.69, 0.71, and 0.68 to 0.84, respectively. These findings confirm that SMOTE is effective in overcoming data imbalance, resulting in a more accurate and reliable sentiment classification model. This research contributes to the application of sentiment analysis in data-driven public policy evaluation.
Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm Anjar Ayuning Lestari; Ahmad Faqih; Gifthera Dwilestari
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.v4i2.743

Abstract

Tokopedia one of Indonesia's largest e-commerce platforms, offers a wide range of products with diverse customer reviews. These reviews reflect consumer opinions and provide valuable insights for service improvement and marketing strategies. Sentiment analysis is crucial for understanding customer perceptions, but processing large-scale, high-dimensional text data remains a challenge, impacting model efficiency and accuracy. This research uses Principal Component Analysis (PCA) to reduce data dimensionality without losing important information for sentiment classification. The study begins by collecting Tokopedia product reviews and preprocessing the text, including data cleaning, tokenization, stopword removal, and stemming. The reviews are then converted into numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. A Gaussian Naïve Bayes model is employed to classify sentiment into three categories: positive, neutral, and negative. The results demonstrate that PCA significantly improves model accuracy from 63.13% to 70.47%, with gains in precision (71.85%), recall (70.47%), and F1-score (71.06%). This research contributes to enhancing sentiment analysis techniques using PCA for Tokopedia reviews and offers a valuable approach that can be applied to other e-commerce platforms.
The Impact of Principal Component Analysis Dimensionality Reduction on Sentiment Classification Performance Using Support Vector Machine Azzahra Moudy Fajria; Ahmad Faqih; Gifthera Dwilestari
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.v4i2.744

Abstract

This study investigates the application of Principal Component Analysis (PCA) to enhance sentiment classification performance using the Support Vector Machine (SVM) algorithm. User reviews of the ChatGPT application from the Play Store were collected, preprocessed, and analyzed to identify the sentiment within the text (positive, negative, or neutral). The research follows the Knowledge Discovery in Databases (KDD) framework, starting with data selection, preprocessing, transformation, and applying PCA for dimensionality reduction. PCA was used to reduce the complexity of the high-dimensional text data, improving SVM's efficiency in sentiment classification. Evaluation results show that applying PCA led to an improvement in model performance, with accuracy increasing from 72.65% to 73.20%, precision from 71.58% to 72.24%, recall from 71.77% to 72.66%, and F1-score from 71.56% to 72.32%. Although the improvements were modest, the findings demonstrate that PCA effectively simplifies complex datasets and enhances SVM performance in sentiment classification, offering benefits in processing high-dimensional text data.
Naïve Bayes Optimization by Implementing Genetic Algorithm in Sentiment Analysis of BCA Mobile Reviews Muhammad Enricco Rizqy; Ahmad Faqih; Gifthera Dwilestari
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.v4i2.750

Abstract

The development of the digital era has encouraged the adoption of mobile banking applications that facilitate banking transactions, including the BCA Mobile application which is simple but still adheres to a slightly outdated, user-friendly appearance but to provide the best service, it is necessary to evaluate the various problems that arise through review analysis. This study aims to conduct sentiment analysis of BCA Mobile application reviews taken from the Google Play Store, with data totaling 1,200 reviews scraping results using Google Collaboratory python programming language, to categorize negative and positive reviews used manual labeling for more accurate results, the Naïve Bayes approach is used in classifying positive and negative category reviews due to the ability of this algorithm to handle text data. However, the weakness of Naïve Bayes which is sensitive to irrelevant features can cause a decrease in accuracy. This research implements Genetic algorithm to improve the performance of Naïve Bayes. The results showed that the application of Genetic algorithm successfully increased the accuracy, precision of Naïve Bayes classification 95%, precision 92% to accuracy 98%, precision 99%, which proved the effectiveness of Genetic algorithm in optimizing the model and improving the quality of sentiment analysis.
Co-Authors Abdul Mukhyidin Abi Fajar Ahmad Fauzi Ade Irma Purnamasari Ade Rizki Rinald Ade Rizki Rinaldi Ade Rizki Rinaldy Adisty Tri Putra Agung Triyono Ahmad Rifa'i Ahmad Rifai Ahmad Rifa’i Ainun Nisa Sari Aliya Anisa Rahma Andi Setiawan Anjar Ayuning Lestari Arga Esa Putra Arif Rinaldi Dikananda Ariska Sari Aulia Nur Rochmah Azis Alma’as Azzahra Moudy Fajria Bambang Irawan Bisma Mahendra Chulyatunni’mah Denni Pratama Dinar Faris Naufal Dini Andriyani Fadli Iqbal Syaidin Fahmi Rahman Fathurrohman Fathurrohman Fathurrohman Fatkhan Mubarok Gifthera Dwilestari Haris Abdul Hadi Heliyanti Susana Iin Indah Ratna Ningsih Kaslani Kevin Salsabil Arlandy Kurniawan Fajar Abdulloh Luthfiyyah Iffah Adelia Madyant Marfelio Muhammad Fajid Martanto . Mifta Almaripat Mohamad Riad Solihin Muhamad Dadan Rifai Muhammad Daffa Ayyasy Muhammad Enricco Rizqy Nanda Permatasari Nining Rahaningsih Nisa Dieanwati Nuris Odi Nurdiawan Rudi Kurniawan Salsa Billa Agistina Sandy Eka Permana Siti Sa'diah Sri Darmayanti Yovi Yuliantin Yubi Aqsho Ramadhan Yusril Muhamad Izha Mahendra