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All Journal Jurnal Informatika dan Teknik Elektro Terapan KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal ICT : Information Communication & Technology Infotekmesin Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer JCOSE Jurnal Bimbingan dan Konseling JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) MEANS (Media Informasi Analisa dan Sistem) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Dinamika Informatika (JDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) PENA ABDIMAS : Jurnal Pengabdian Masyarakat BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Sistem Informasi dan Manajemen INTERNAL (Information System Journal) Bianglala Informatika
Kaslani
STMIK IKMI Cirebon
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Journal : Journal of Artificial Intelligence and Engineering Applications (JAIEA)

 1 
Improving Regional Clustering Based on Tuberculosis Cases using the K-Means Algorithm of the Cirebon City Health Office Wilda Rusmiati Rahayu; Purnamasari, Ade Irma; Bahtiar, Agus; Kaslani
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.v4i2.727

Abstract

Tuberculosis (TB) is a highly infectious disease prevalent in Indonesia, including Cirebon City. This study utilizes the K-Means algorithm to optimize the clustering of areas based on TB case data from the Cirebon Health Office. By analyzing the number of cases, population density, and other factors, the study aims to identify regional clusters with similar TB case characteristics. The research employed Rapid Miner software and the Knowledge Discovery Database (KDD) methodology. The K-Means analysis categorized the study area into two clusters. Cluster_0, representing 20 areas, had lower TB risk, characterized by higher population density, smaller geographic size, and fewer TB cases. Cluster_1, representing two areas, exhibited higher TB risk, marked by lower population density, larger area, and more TB cases. The clustering quality was evaluated using the Davies-Bouldin Index (DBI), which yielded an optimal value of 0.189 at K=2K = 2. Additionally, the Avg within Centroid Performance Vector Analysis supported the clustering validity the clusters with value of 19851032.925.The results demonstrate that this clustering approach effectively identifies TB risk areas, aiding targeted interventions. The findings provide the Cirebon Health Office with a framework for better resource allocation, focusing intensive programs in high-risk regions and preventive measures in low-risk areas.
Optimization of the K-Nearest Neighbors (KNN) Algorithm in Imbalanced Dataset Classification Using the SMOTE Technique Abi Fajar Ahmad Fauzi; Ahmad Faqih; Kaslani
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.v4i2.756

Abstract

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.
Improving Student Achievement Clustering Model Using K-Means Algorithm in Pasundan Majalaya Vocational School Abdul mukhsyi, Sopian; Irma Purnamaari, Ade; Bahtiar, Agus; Kaslani
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.v4i2.793

Abstract

This study analyzes and enhances the student achievement clustering model at SMK Pasundan Majalaya using the K-Means algorithm. The Knowledge Discovery in Databases (KDD) method and RapidMiner AI Studio 2024.1.0 were used to process data from 125 students based on 15 metrics, including academic scores and attendance rates. For group evaluation, the Elbow method and Davies-Bouldin Index (DBI) were employed. The results showed optimal clustering with 2 groups and a DBI value of 0.893. Analysis results revealed significant differences in characteristics between the two groups. Cluster_1 consists of 38 students and has lower score patterns (60-80), with attendance rates of 94-100%, and a positive correlation between attendance and academic achievement. On the other hand, Cluster_0 consists of 86 students and shows higher score patterns (67.5-87.5), with attendance rates of 80-100%, and demonstrates a positive correlation between attendance and academic achievement. Schools can use this clustering model to create learning approaches that are better suited to each student group.
Clustering Analysis of Administrative Service Types Using K-Means (Study Case: Village bojongsalam) Wafiq Azizah; Ade Irma Purnamasari; Agus Bahtiar; Kaslani
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.v4i2.867

Abstract

Advances in information technology present significant opportunities for the improvement of public services, especially in relation to the administrative functions of Bojongsalam Village. Reliance on traditional methods often leads to inefficiencies and inaccuracies in administrative processes. This research uses the K-Means algorithm to categorize administrative service data based on service type, document number, printing date, and accompanying remarks. Utilizing the Knowledge Discovery in Databases (KDD) framework, the analysis includes data selection, pre-processing, transformation, and clustering analysis conducted through RapidMiner software. The dataset consisted of 718 administrative records that had undergone a rigorous cleaning process, including attribute normalization. The analysis resulted in an optimal Davies-Bouldin Index (DBI) value of -0.498 at K = 4, with each cluster representing a different service utilization pattern. The issuance of Family Cards (KK) and Birth Certificates showed higher demand compared to other available services. This classification promotes workload optimization, fair resource allocation, and formulation of effective operational strategies. The application of the K-Means algorithm demonstrated its effectiveness in data clustering and made a significant contribution to technology-based administrative management. The findings lay a basic framework for addressing the needs of the community in a timely manner.
FP-Growth for Data-Driven Purchase Pattern Analysis and Product Recommendations at Flanetqueen Store Marwah, Sopa; Rahaningsih, Nining; Ali, Irfan; Marthanu, Indra Wiguna; 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.
Mitigating Imbalanced Citrus Disease Image Datasets with Oversampling Gunawan, Arya; Suarna, Nana; Bahtiar, Agus; Marthanu, Indra Wiguna; 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.v5i2.1862

Abstract

Dataset imbalance is a critical challenge in plant disease image classification because it causes bias towards the majority class. This study evaluates the effectiveness of augmentation-based oversampling techniques on the classification performance of citrus leaf images using the MobileNetV2 architecture. The four leaf disease classes classified include Greening, Fresh, Canker, and Blackspot. The dataset was obtained from a public repository and processed through preprocessing (resize, normalization) and augmentation (rotation, flipping, zoom) stages. The model was trained and tested in two scenarios: baseline (unbalanced data) and mitigation (data balanced through augmentation). The experimental results show that the mitigation approach was able to increase accuracy from 91.92% to 93.94%. The F1-score, precision, and recall values also increased significantly, especially in the minority class. Evaluation using a confusion matrix reinforced the finding that augmentation-based oversampling is effective in reducing classification errors. This study shows that the integration of augmentation techniques and MobileNetV2-based transfer learning can significantly improve classification performance and contribute to the development of early detection systems for plant diseases in precision agriculture.
Co-Authors Abdul Ajiz Abdul Ajiz, Abdul Abdul Koda Abdul mukhsyi, Sopian Abi Fajar Ahmad Fauzi Ade Irma Purnamasari Ade Irma Purnamasari Adella, Luthfiyyah Iffah agus bahtiar Ahmad Faqih Alibasyah, Aziz Amalia, Dita Rizki Amir Rudin, Rizki Anana Rafly Andi Setiawan Andi Setiawan Andia, Rita Anwar Pauji Anwari, Saeful Aprilyani, Wiwin Aria Pratama Arya Gunawan Ayuningsih, Sri Bachtiar, Agus Bakri, Saeful Basysyar, Fadhil Muhammad Basysyar, Fadil M Burhanudin, Haris Cep Lukman Rohmat Dadang Sudrajat Deffan Febrian Dirmanthara Delisah Destriyanah, Riska Dian Ade Kurnia Dilla Eka Lusiana Dodi Solihin Edi Tohidi Edi Tohidi Edi Wahyudin Edi Wahyudin Ega Salsa Nugraha Eka Permana, Sandy Fansuri, Rafly Fathurrohman Fathurrohman Fathurrohman, Fathurrohman Fatihanursari, Fatihanursari Faturachman, Rifcki Aziz Faturrohman, Faturrohman Fauziah, Irfa Mulhimah Fitriyah, Anis Garsandi, Akmal Maulana Gifthera Dwilestari Haidar Fakhri Hamonangan, Ryan Handayani, Tineka Hartini, Tuti Hayati, Umi Herdiana, Ruli Hermawan, Eman Hery Widijanto Hilman Rifa'i Hira Wahyuni Azizah Iin, Iin Iqbal Agis Junizar Irfan Ali Irfan Ali, Irfan Irma Purnamaari, Ade Irma Purnamasari, Ade Jayawarsa, A.A. Ketut Kencana, Junaedi Surya Mahendra, Yusril Muhamad Izha Marthanu, Indra Wiguna Marwah, Sopa Muhalim, Alvy Muhammad Aji Pratama Mulyawan Mulyawan Mulyawan, Mulyawan Nining Rahaningsih Nur Atikah Odi Nurdiawan Oktaviani Putri , Farra Oktaviani Putri, Farra Pardiana, Firda Perdana Herdiansyah, Reza Pratama, Denni Puji Rahayu Purnama Sari, Ade Irma Purnamasari, Ade Irma Purnamasari, Ade Purnamasari Putri Siti Nur Hajijah, Regi Raditya Danar Dana Ramdhan, Dadan Rano Rayhan, Tubagus Muhammad Rifqi Aqila, Mochammad Rizki Fahrezi Maulana Rizki Lesmana, Ghali Rizky Wahyudi, Febri Rohmat, Cep Lukman Rudi Kurniawan Rudi Kurniawan Ryan Hamonangan Salsabila, Putri Sandy Eka Permana SANJAYA, RIKI siti azhar Sobari, Syahrul Suarna, Nana Subhiyanto, Fajar Sukma Maula, Intan Tati Suprapti Tengku Riza Zarzani N Tio Prasetya Tohidi, Edi Tohodi, Edi Tuti Hartati Umi Hayati Vibrianti, Vera Wafiq Azizah Wahyudi, Edi Wahyudin, Edi Wilda Rusmiati Rahayu Yuliantin, Yovi Zapar, Rizky