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All Journal Jurnal Ilmiah Informatika Komputer Infotech Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL MANAJEMEN INFORMATIKA (JUMIKA) Indonesian Journal of Applied Informatics Jurnal Teknologi Informasi dan Multimedia Jurnal Accounting Information System (AIMS) E-Link: Jurnal Teknik Elektro dan Informatika REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer INFORMATIKA Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Journal Innovations Computer Science Sintek Kuwera
Irfan Ali
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Other

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Analisa Penggunaan Metode Lexicon Based Dan Algoritma Naive Bayes Pada Sentimen Ulasan Aplikasi Duolingo Muhammad Abib Allesdio; Ade Irma Purnamasari; Irfan Ali; Nana Suarna; Agus Bahtiar
Jurnal Sistem Informasi dan Teknologi Vol 6 No 2 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i2.261

Abstract

Peningkatan jumlah ulasan pengguna pada aplikasi mobile membuka peluang untuk memahami persepsi dan pengalaman pengguna melalui analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Duolingo yang diambil dari Google Play Store menggunakan dua pendekatan, yaitu metode lexicon-based dan algoritma Naive Bayes berbasis Python. Metode lexicon-based digunakan untuk memberikan skor polaritas berdasarkan leksikon sentimen, sedangkan Naïve Bayes diterapkan sebagai model klasifikasi dengan dukungan fitur TF-IDF. Proses penelitian meliputi tahapan pengumpulan data, preprocessing teks (cleaning, case folding, tokenisasi, stopword removal, dan stemming), pembobotan sentimen, pelatihan model, serta evaluasi performa menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode lexicon-based mampu memberikan gambaran umum polaritas ulasan, namun performanya sangat dipengaruhi oleh kelengkapan leksikon dan variasi bahasa informal pengguna. Sementara itu, algoritma Naive Bayes menunjukkan performa yang lebih stabil dan akurasi lebih tinggi dalam mengklasifikasikan sentimen dibandingkan pendekatan leksikon. Perbandingan kedua metode memperlihatkan bahwa Naive Bayes lebih efektif dalam menangani data teks pendek, tidak terstruktur, serta mengakomodasi variasi kata dan ejaan. Temuan penelitian ini memberikan pemahaman yang lebih dalam mengenai persepsi pengguna terhadap Duolingo serta menjadi referensi metodologis bagi penelitian sentiment analysis selanjutnya, khususnya yang melibatkan kombinasi metode leksikon dan klasifikasi probabilistik.
Implementation of Deep Learning Based on Convolutional Neural Network for Detecting Images of Solar Panel Damage in Smart Grid Systems Camelia Putri Lestari; Nining Rahaningsih; Irfan Ali; Dodi Solihudin; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2225

Abstract

This study aims to implement Deep Learning based on Convolutional Neural Network (CNN) in detecting solar panel damage using thermal images as part of a Smart Grid system. The main problem addressed is the difficulty of early automatic identification of solar panel cell damage using conventional methods. Through the CNN approach, this study developed a classification model to distinguish between damaged (Defective) and undamaged (Non-Defective) solar panel conditions. The research stages included thermal image dataset collection, pre-processing, model training, and performance evaluation. The results showed that the CNN model was able to achieve an accuracy of over 87% with stable performance on the validation data. Visualization using the Grad-CAM method helps interpret the damaged areas that are the focus of the model's decision.
Pengembangan Pembelajaran Interaktif Sejarah Proklamasi Berbasis Game Mobile 2D Menggunakan Metode Game Development Life Cycle Habil Jabbal Firdausyi; Ade Irma Purnamasari; Irfan Ali; Indra Wiguna Marthanu; Fathurrohman Fathurrohman
Journal Innovations Computer Science Vol. 5 No. 1 (2026): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v5i1.413

Abstract

The History subject — particularly the Indonesian Proclamation of Independence — has long been delivered through conventional, one-directional methods that leave students disengaged and, in documented cases, visibly saturated. This study designed and developed an interactive digital learning medium as a direct response to that condition. The medium produced is a narrative-driven 2D Mobile Game, built using the Game Development Life Cycle (GDLC) across four phases: Concept, Pre-Production, Production, and Testing. The resulting prototype centers on a slide-controlling mechanism and a sequentially structured historical narrative, with an integrated quiz system embedded at the end of each story chapter. Validation by a Material Expert and a Media/Technology Expert placed the product in the "Highly Feasible" category, with an overall average score of 89.85% — a result that holds across both content accuracy and technical execution. Usability testing returned a System Usability Scale score of 81.0, rated "Excellent" and "Acceptable." These findings suggest the game is a credible alternative medium for reducing learning saturation and raising student engagement with History material.  
Analisis Dan Prediksi Risiko Kelahiran Bayi Menggunakan K-Means Dan Deep Neural Network (DNN) Mukhlisin Ilahudin; Nana Suarna; Agus Bahtiar; Mulyawan; Irfan Ali
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.206

Abstract

Risiko kelahiran bayi merupakan indikator penting dalam evaluasi kesehatan ibu dan anak sehingga diperlukan pendekatan analitis yang mampu mengidentifikasi pola risiko secara akurat. Penelitian ini bertujuan menganalisis dan memprediksi risiko kelahiran bayi dengan mengintegrasikan metode K-Means dan Deep Neural Network (DNN). Dataset yang digunakan terdiri dari 983 data rekam medis ibu hamil yang telah melalui tahap pengumpulan data, pembersihan, dan preprocessing meliputi normalisasi, encoding variabel kategorikal, penanganan outlier, serta seleksi fitur. Metode K-Means digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik klinis guna membentuk representasi pola risiko awal, yang selanjutnya digunakan sebagai fitur tambahan pada model DNN. Model DNN dirancang menggunakan beberapa hidden layer dengan fungsi aktivasi ReLU dan regularisasi dropout. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 61,93% dan nilai ROC AUC sebesar 0,6402, yang mengindikasikan performa moderat dalam memprediksi risiko kelahiran bayi. Stabilitas kurva loss dan akurasi menunjukkan proses pelatihan yang berjalan dengan baik tanpa overfitting signifikan. Secara praktis, model ini berpotensi digunakan sebagai alat bantu awal bagi tenaga kesehatan dalam mengidentifikasi ibu hamil dengan risiko kelahiran lebih tinggi sehingga dapat dilakukan pemantauan dan intervensi lebih dini.
Analysis and Visualization of Sales Transaction Patterns using Decision Tree and Tableau Public Miftahul Akbar; Nining Rahaningsih; Irfan Ali; Fatihanursari Dikananda; Umi Hayati
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.v5i2.1849

Abstract

This study aims to analyze sales transaction patterns of rubber waste at PT Mandiri Enviro Technosio by integrating the Decision Tree algorithm with interactive visualization using Tableau Public. The dataset consists of 405 sales transactions recorded during the 2024–2025 period, comprising attributes such as transaction date, product type, quantity, unit price, total value, delivery region, and buyer category. The research methodology includes data acquisition, preprocessing to ensure data quality and consistency, construction of a classification model using the CART algorithm, evaluation of model performance through a confusion matrix, and development of interactive dashboards for enhanced interpretability. The Decision Tree model achieved an accuracy of 88.24% in classifying transaction values into low, medium, and high categories. Unit price and transaction period were identified as the most influential attributes in determining transaction value. Visualization using Tableau Public effectively presented the distribution of transaction values, sales trends, and geographical patterns, thereby strengthening analytical insights and supporting data-driven decision making. The integration of classification techniques and interactive visualization contributes to improving business intelligence capabilities and enables the formulation of more adaptive, evidence-based sales strategies.
FP-Growth for Data-Driven Purchase Pattern Analysis and Product Recommendations at Flanetqueen Store Sopa Marwah; Nining Rahaningsih; Irfan Ali; Indra Wiguna Marthanu; Kaslani
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.v5i2.1850

Abstract

The advancement of information technology has encouraged the use of data analytics to support data-driven business decision-making. This study aims to analyze purchasing patterns of hoodie products and provide product recommendations for customers at Flanetqueen Store using the FP-Growth (Frequent Pattern Growth) algorithm. The research applies the Knowledge Discovery in Database (KDD) framework, consisting of five stages: data selection, preprocessing, transformation, data mining, and interpretation/evaluation. The dataset comprises hoodie sales transactions recorded from January to December 2024. Data analysis was conducted using RapidMiner Studio version 10.3 with a minimum support of 0.2 and minimum confidence of 0.4. The analysis produced 26 itemsets and 11 association rules indicating product correlations. The strongest rule, Bloods → Champion, achieved a confidence of 0.414, revealing that customers who purchased Bloods hoodies were also likely to buy Champion hoodies. These findings were used to design cross-selling strategies and generate relevant product recommendations. The study demonstrates that FP-Growth effectively extracts frequent purchase patterns and contributes to the development of data-driven recommendation systems in the local fashion retail industry.
Application of Decision Tree Algorithms to Classify the Sales Results of Kangen Kripik Sme Products Adila G Khiqmatiar Muchsin; Nining Rahaningsih; Irfan Ali; Dadang Sudrajat; Saeful Anwar
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.v5i2.1854

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in strengthening the national economy; however, many still face challenges in managing and analyzing sales data effectively. This study aims to classify product sales results at UMKM Kangen Kripik Mang Acep by applying the Decision Tree algorithm as a data classification method based on machine learning. A quantitative experimental approach was employed to evaluate the model’s performance using one-year sales data, including attributes such as product variants, sales volume, sales channels, and marketing regions. Data processing was conducted using RapidMiner software following the Knowledge Discovery in Databases (KDD) framework, which includes data selection, preprocessing, transformation, data mining, and model evaluation. The results indicate that the Decision Tree algorithm successfully classified sales regions (Garut, Bandung, and Sumedang) with an accuracy rate of 96.48%, identifying “Units Sold (pcs)” as the most influential attribute for distinguishing marketing areas. These findings demonstrate that the Decision Tree method is not only effective in improving data analysis efficiency but also provides valuable strategic insights for data-driven business decision-making in MSMEs
Enhancing Election Staff Selection through Decision Tree-Based Classification Rizal Rayyan Firdaus; Nana Suarna; Irfan Ali; Ahmad Rifai
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.v4i2.768

Abstract

The selection of competent election committee members is a critical aspect in ensuring the success of a fair and transparent election process. However, the subjective nature of this selection process necessitates a data-driven approach to optimize the selection of officials who meet the required competency criteria. This research aims to classify the competencies of prospective election committee members using the Decision Tree algorithm based on demographic data and technological attributes of the population. The study employs the Knowledge Discovery in Databases (KDD) methodology, which includes the stages of data selection, preprocessing, transformation, data mining, and evaluation. In this process, data collected through various attributes are processed to build a classification model. The Decision Tree algorithm is applied to extract patterns from the data, resulting in a decision tree that can classify individuals into different competency classes based on existing features. The research findings indicate that the Decision Tree algorithm effectively classifies respondents into several competency classes that represent varying levels of skills and interest in the election process. The model shows that Class 4 is the dominant class, indicating that most respondents have moderate competency in technological skills and interest in elections. Class 3 represents individuals with higher technological skills but moderate interest, while Classes 2 and 1 represent individuals with varying combinations of interest and skills. This study demonstrates that using the Decision Tree algorithm in the KDD process is highly effective in objectively classifying the competencies of prospective election committee members. By analyzing the interactions among relevant attributes, the model provides insights that can improve the accuracy of election official selection. This data-driven approach can be adapted to other contexts requiring competency classification, offering broader benefits for various criteria-based selection systems.
Co-Authors Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Adila G Khiqmatiar Muchsin Agus Surip Agus Surip Ahmad Rifai Anwar Pauji Aria Pratama Arif Rinaldi Dikananda Arye Fandia Kusuma Camelia Putri Lestari Candi Prabowo Cep Lukman Rohmat Cep Lukman Rohmat Cep Lukman Rohmat Dadang Sudrajat Deffan Febrian Dirmanthara Dian Ade Kurnia Dita Rizki Amalia Dodi Solihudin Edi Tohidi Ega Salsa Nugraha Farid Ali Ma'ruf Farids Al Ma’ruf Fathurrohman Fathurrohman Fatihanursari Dikananda Faujatun Hasanah Firda Pardiana Gifthera Dwilestari Habil Jabbal Firdausyi Hegarmanah Muhabatin Heliyanti Susana Iin Indra Wiguna Marthanu Iqbal Agis Junizar Kaslani Kokom Komariah Lian Siliayani Lutfi Hakim Martanto . Miftahul Akbar Muhamad Aji Pratama Muhammad Abdurohman Muhammad Abib Allesdio Muhammad Aji Pratama Muhammad Fadhilah Mukhlisin Ilahudin Mulyawan Mulyawan . Nadia Putri Gantara Nana Suarna Nana Suarna Nining Rahaningsih Nining Rahaninsih Nizar Fazari Hidayat Odi Nurdiawan Raditya Danar Dana Ranu Husna Rheznandya Fahreza Ridho Nugraha Riko Saputra Rizal Rayyan Firdaus Saeful Anwar Siti Aisyah Siti Nur Illah Sopa Marwah Sri Ayuningsih Suripno Syaeful Annas Tati Suprapti Tio Prasetiya Tio Prasetya Umi Hayati Utami Aryanti Uyu Nuraeni Witriyani Witriyani Yudhistira Arie Wijaya Yuniar Niar