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All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Bianglala Informatika
Dede Rohman
STMIK IKMI Cirebon
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK PENGELOMPOKAN WILAYAH RAWAN BANJIR Jahrotun Khomsiyah; Alif Ramdhani; Ade Feby Damayanti; Dede Rohman
JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer Vol 12 No 3 (2021): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : LPPM Sekolah Tinggi Teknologi Pagar Alam

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

Abstract

The city of Cirebon is one of the cities that often experiences floods caused by very high rainfall and people often throw garbage in the sewers and into the river so that the river becomes clogged and overflows. To classify flood-prone areas in the city of Cirebon, an algorithm is needed. Therefore, the method used in this research is the K-Means Clustering Algorithm method. The data was processed using the RapidMiner application with this clustering stage producing 5 clusters consisting of Cluster 0 totaling 8 items, Cluster 1 totaling 15 items, Cluster 2 totaling 33 items, Cluster 3 totaling 10 items, and Cluster 4 having 32 items. The results of this study indicate that the K-Means Clustering Algorithm can group flood-prone areas in the city of Cirebon.
Pengembangan Model Pengelompokan Jenis Bencana Alam di Jawa Baratmenggunakan Algoritma K-Means Panji Adi Putra; Martanto; Arif Rinaldi Dikananda; Dede Rohman
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.v13i1.11998

Abstract

Salah satu masalah terbesar yang dihadapi masyarakat Jawa Barat adalah bencana alam.Analisis berbasis data diperlukan untuk memahami pola kejadian bencana dan mendukung kebijakanmitigasi yang efektif karena berbagai jenis bencana. Untuk menganalisis data kejadian bencana diJawa Barat selama periode 2020–2023, penelitian ini menggunakan pendekatan KnowledgeDiscovery in Databases (KDD).Tahapan KDD meliputi pembuatan dataset, preprocessing untuknormalisasi dan penanganan data hilang, serta transformasi guna menentukan atribut utama.Algoritma K-Means digunakan dalam proses data mining untuk mengelompokkan wilayahberdasarkan jenis bencana dan intensitasnya. Tahap terakhir adalah interpretasi hasil, yang bertujuanuntuk memahami pola distribusi bencana. Hasil klasterisasi menghasilkan lima kluster utama. Cluster0 menunjukkan dominasi kejadian banjir dan kebakaran lahan, sering ditemukan di dataran rendahdengan karakteristik lingkungan yang rawan pembakaran liar. Cluster 1 didominasi oleh kejadiantanah longsor di wilayah perbukitan yang curah hujannya tinggi. Cluster 2 mencerminkan kombinasikejadian hujan angin dan kekeringan di daerah pedesaan dengan sumber daya air terbatas. Cluster 3menunjukkan kejadian bencana dengan frekuensi rendah dan distribusi yang merata, seringkali terkaitdengan daerah urban. Sementara itu, Cluster 4 memiliki tingkat heterogenitas tertinggi, mencakupberbagai jenis bencana dengan intensitas bervariasi di wilayah pegunungan dan lembah. Kualitasklasterisasi diukur menggunakan Davies-Bouldin Index (DBI) sebesar 0.085, mengindikasikanpemisahan kluster yang baik. Selain itu, analisis Performance Vector menunjukkan jarak total antarkluster sebesar 2.311, dengan jarak terbesar pada Cluster 4 (4.672). penelitian ini diharapkan dapatmembantu dalam perencanaan dan alokasi sumber daya yang lebih tepat sasaran untuk mitigasibencana.
Naive Bayes Algorithm to Enhance Sentiment Analysis of Coursera Application Reviews on Google Play Store Masdarul Rizqi; Martanto; Arif Rinaldi Dikanda; Dede Rohman
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.v4i2.758

Abstract

Coursera is an online learning platform that provides various courses and certifications. This study aims to analyze user perceptions of the Coursera application after the reviews are translated into Indonesian, identify factors that influence positive and negative sentiment, and activate the effectiveness of the Naive Bayes algorithm in classifying review sentiment. The method used is Knowledge Discovery in Databases (KDD), with stages of data collection, preprocessing, and sentiment analysis using Naive Bayes. The results of the study show that the translation of reviews does not change the essence of user perception. Analysis of key words reveals positive experiences such as "kursus", "berguna", and "terima kasih", as well as criticism related to application performance. Factors such as price, content, and user experience play an important role in positive sentiment, while technical issues are the main cause of negative sentiment. The Naive Bayes model shows high accuracy with an accuracy value of 83.62%, precision of 83.34%, recall of 87%, and F1-score of 85.2%. These results indicate that the Naive Bayes algorithm is effective in analyzing sentiment of Coursera application user reviews. Further research is recommended to explore other algorithms or expand the analysis by considering additional factors that can influence user sentiment
Improvement of Students' Academic Achievement Classification Model Through the Analytical Hierarchy Process Algorithm in Elementary School Burujul Kulon III Candra Rahmawati Candra; Martanto; Arif Rinaldi Dikananda; Dede Rohman
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.v4i2.764

Abstract

This study aims to improve the classification model of students' academic achievement at SD Negeri Burujul Kulon III using the Analytical Hierarchy Process (AHP) algorithm. This method is applied to analyze various student assessment criteria, including knowledge, skills, spiritual attitudes, and social attitudes, in order to create an objective and systematic evaluation system. The Knowledge Discovery in Databases (KDD) approach is used to ensure structured data management, including the stages of data collection, selection, transformation, analysis, and evaluation. The population of this study was all students of SD Negeri Burujul Kulon III with a sample size taken using the Stratified Random Sampling method, which ensures accurate representation of all grade levels. Data were collected through documentation studies, including exam result reports, skill assessments, and student behavior records observed by teachers. The analysis was carried out by applying the AHP algorithm to determine the priority weight of each assessment criterion through pairwise comparisons. The weights obtained are used to calculate the final grade which is the basis for classifying student achievement. The results of the study indicate that the AHP algorithm is able to produce a more accurate and relevant classification model to identify students with superior achievement, which is not only based on academic exam results but also includes student skills and attitudes. The resulting system provides significant benefits in academic decision-making, such as awarding outstanding students, identifying students who need special attention, and developing more effective learning strategies. This research also contributes to the development of data-based technology for educational evaluation, and can be an important reference for other educational institutions that want to improve the quality of evaluation, learning effectiveness, and student data management comprehensively, systematically, and sustainably in the future.
Use of K-Means Algorithm in Model Improvement Production Data Grouping for Determination Convection Production Strategy Ica Pandia Ica; Martanto; Arif Rinaldi Dikananda; Dede Rohman
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.v4i2.775

Abstract

This research was conducted to implement the K-Means Clustering algorithm in grouping convection production data to support the improvement of efficiency and effectiveness of production strategies. The data used is PT ABC's production data, which consists of important attributes, such as Production ID, Production Date, Product Name, Product Type, Color, Size, Raw Materials, and Order. The research method follows the stages of Knowledge Discovery in Database (KDD), which includes selection, preprocessing, transformation, data mining, and evaluation, so as to ensure that the data processed is relevant and ready to be analyzed. The grouping process is carried out using the K-Means algorithm, which groups data based on attribute similarity by determining the optimal number of clusters. The evaluation of the clustering results was carried out using the Silhouette Score and Davies-Bouldin Index metrics, where the results showed values that represented good cluster quality. A high Silhouette Score indicates that the data in the cluster has good uniformity, while a low Davies-Bouldin Index indicates a clear distance between clusters. The results of the grouping produce three main clusters that illustrate different production patterns, such as clusters with high, medium, and low order quantities. This analysis of the cluster provides important insights in supporting strategic decision-making, such as prioritizing resource management in high-order clusters and evaluating production efficiency in low-order clusters. This research is in line with previous literature that shows that the K-Means algorithm can be used effectively in big data grouping to support strategic planning. The practical contribution of this research is to help convection companies in understanding production patterns, so that production strategies can be designed more efficiently, responsively, and directionally. For further research, it is recommended to add new variables, such as production costs or work duration, as well as test other clustering algorithms to obtain more comprehensive results.
Sales Data Analysis using Linear Regression Algorithm on Raw Water Sales Eti Rohayati; Martanto; Arif Rinaldi Dikananda; Dede Rohman
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.v4i2.809

Abstract

This study aims to assess the effectiveness of linear regression algorithm in predicting raw water demand by considering customer transaction data, raw water volume, and seasonal variables. The method used is Knowledge Discovery in Databases (KDD), including data selection, preprocessing, transformation, data mining, and result evaluation. The dataset is divided 80% for training and 20% for testing. The analysis results show that the linear regression model has a coefficient of determination (R²) of 0.77, which means that the model can explain 77% of the data variability. The prediction error value is low, with Mean Absolute Error (MAE) 0.06, Mean Squared Error (MSE) 0.01, and Root Mean Squared Error (RMSE) 0.08, indicating good accuracy. In the comparison between actual and predicted values, for actual data of 7,000 liters, the model predicts 7,984.70 liters. The variable number of customer transactions has the greatest influence on raw water demand, with a coefficient of 16,940.46, while seasonal factors have less influence. Based on these findings, it can be concluded that the linear regression algorithm is effective in predicting raw water demand, however further development is required to improve accuracy at extreme values, by adding variables or using more complex algorithms.
Co-Authors Ade Feby Damayanti Alif Ramdhani Arif Rinaldi Dikananda Arif Rinaldi Dikanda Candra Rahmawati Candra Eti Rohayati Ica Pandia Ica Jahrotun Khomsiyah Martanto . Masdarul Rizqi Panji Adi Putra