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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Dinamika Informatika (JDI) Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) Sintek Kuwera Miracle Get Journal Bianglala Informatika
Martanto
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Public Health

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The Improvement of Indonesian Film Genre Clustering Model Using the K-Means Algorithm in Film Production Decision-Making Wiratriyana; Martanto; Arif Rinaldi Dikananda; Mulyawan
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.v4i2.765

Abstract

The Indonesian film industry is expanding rapidly, but understanding audience preferences remains a significant challenge for producers. This study aims to cluster Indonesian films by genre and synopsis using the K-Means algorithm to aid in marketing strategies and content development. The dataset comprises 1,271 Indonesian film entries, including attributes like release year, genre, synopsis, and user ratings. The research follows the Knowledge Discovery in Databases (KDD) framework, which involves data selection, preprocessing, transformation, clustering with K-Means, and evaluation using the Elbow method to identify the optimal number of clusters. The results show that the K-Means algorithm successfully grouped the films into three clusters: drama, horror, and others. The analysis indicates that drama films dominate the high-rating cluster, while horror films are more commonly found in the low-rating category. The use of Principal Component Analysis (PCA) in the visualization aids in interpreting the clustering results, providing a clearer view of the data distribution. These findings highlight the potential for improving film production strategies by aligning content with audience preferences. By understanding genre patterns and ratings, producers can make more informed decisions in marketing and content development.
Use of K-Means Algorithm in Model Improvement Production Data Grouping for Determination Convection Production Strategy Ica, Ica Pandia; 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.
Development of Educational Game for Introduction Animal Types Using the ADDIE Method Smart Apps Creator In Improving Knowledge Students Artoti, Azzahra Rizky; Martanto; Dikananda, Arif Rinaldi; Mulyawan
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.v4i2.777

Abstract

The development of technology in education opens up opportunities for innovation to create interactive learning media, especially for early childhood. This research aims to develop educational games based on Smart Apps Creator using the ADDIE method to introduce animal species to Al-Washliyah kindergarten students. The method used is ADDIE, consisting of five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. in this study conducted validity, reliability, normality, homogeneity, and anova tests to measure the effectiveness of this learning media. The results showed that this animal species recognition educational game succeeded in improving student understanding with an average score before the use of learning media of 59.2% increasing to 87.73% after using learning media. Validity and reliability tests show that this learning media meets the criteria of effective, easy-to-use, and interesting learning media.
Application of the K-Means Algorithm in Enhancing the Clustering Model for Job Seekers in Cirebon City Laylatunna'imah; Martanto; Arif Rinaldi Dikananda; Ahmad Rifa’i
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.v4i2.785

Abstract

The development of information technology opens up opportunities to improve the efficiency of job search through the grouping of job seekers based on specific characteristics. This study uses the K-Means algorithm to analyze data on job seekers in Cirebon City for 2018–2022, focusing on education level and gender. The stages of the research include (1) data selection, (2) data preprocessing, (3) transformation of attributes into numerical format, (4) data grouping using RapidMiner, and (5) evaluation of clustering results using the Davies-Bouldin Index (DBI). The results showed that the optimal number of clusters was two (K=2), with a DBI value of 0.608 which indicates good cluster separation. The first cluster consists of job seekers with a higher level of education, while the second cluster has a lower level of education. Gender did not show a significant influence. These findings provide strategic insights for governments and companies in developing data-driven policies, such as more effective training or recruitment programs. The K-Means algorithm has proven its potential in supporting strategic decision-making in workforce management and being adaptable to other regions.
Simple Additive Weighting Method for Improving Decision Support Systems Laptop Selection Ika Riantika; Martanto; Arif Rifaldi Dikananda; 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.v4i2.790

Abstract

The development of information technology significantly benefits various activities, particularly for students, by facilitating access to information and supporting academic tasks. However, students majoring in Information Technology often face challenges in selecting a suitable laptop due to the wide range of options with varying specifications and prices. This study aims to develop a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to assist in choosing the best laptop. The SAW method was selected for its ability to evaluate multiple criteria through a weighting process. The study utilizes five main criteria: price, processor, RAM, storage type, and storage capacity. Data were collected through interviews and observations at the "IComp" laptop store. The analysis process involves matrix normalization and preference value calculation to determine recommendations. The DSS recommends the best laptop based on the highest preference score: Lenovo IP Flex 5 (0.78), followed by Lenovo IP3 (0.77) and HP Pav14 (0.76). The results indicate that these laptops offer an optimal balance between performance and price. The web-based sy stem designed accelerates the evaluation process, enhances objectivity, and improves user accessibility. The implementation of the SAW method proves effective and accurate in determining the best laptop, particularly in scenarios combining cost and benefit criteria. The system successfully meets the needs of Information Technology students by providing relevant and reliable results. This study successfully develops a DSS using the SAW method for selecting the best laptop. The system designed is effective and reliable for multi-criteria decision-making. Future research can integrate real-time data and broader user surveys to improve result generalization, making it applicable to other product selection contexts.
Sales Data Analysis using Linear Regression Algorithm on Raw Water Sales Rohayati, Eti; 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.
K-Means Algorithm for Clustering High-Achieving Student at Madrasah Tsanawiyah Yami Waled Muhammad Hilman; Martanto; Dikananda, Arif Rinaldi; Rifai, Ahmad
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 3 (2025): June 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i3.771

Abstract

This study aims to apply the K-Means algorithm to cluster students based on their mathematics grades at Madrasah Tsanawiyah Islamiyyah Yami Waled. By categorizing students into clusters of low, medium, and high academic achievement, the institution can develop more effective and targeted learning strategies. The data consisted of semester mathematics grades from 112 students, analyzed using the K-Means clustering algorithm. Clusters were evaluated using the Davies-Bouldin Index (DBI), with results showing three distinct clusters: Cluster 0 (low achievers, 54 students), Cluster 1 (medium achievers, 37 students), and Cluster 2 (high achievers, 21 students). The DBI score of 0.893 indicates good clustering quality, providing valuable insights for personalized learning approaches.
Optimization of Kebaya Product Grouping Using K-Means Algorithm for Marketing Strategy of Rental Services at Gifaattire Store Nuraeni; Martanto; Dikananda, Arif Rinaldi; Rifai, Ahmad
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 3 (2025): June 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i3.776

Abstract

This study aims to implement the K-Means algorithm to improve the kebaya clustering model to support the rental marketing strategy at Gifaattire Store. The K-Means algorithm was used to analyze eight months of historical kebaya rental data, focusing on the attributes of kebaya type and color. Using the Knowledge Discovery in Database (KDD) approach, the research conducted data selection, preprocessing, transformation, data mining, and evaluation of clustering results. Davies-Bouldin Index (DBI) was utilized to assess the quality of clustering, resulting in an optimal value of 6 clusters with a DBI of 0.580. The results showed that each cluster has unique characteristics that reflect customer demand patterns. Cluster 0, the largest cluster, indicates kebayas with high demand but limited color variations. In contrast, Cluster 1 indicates kebayas with a wide variety of colors but specific demand. This information enables Gifaattire Store to design more targeted data-driven marketing strategies and improve stock management efficiency. The research contributes to the development of literature on the application of K-Means in the fashion rental sector and offers practical insights into understanding customer preferences.
The Relationship Between Personal Protective Equipment Use and Reduction in Workplace Injuries Venny Yusiana; Muchlisinalahuddin; Martanto; Fandel Maluw; Daisy Debora Grace Pangemanan
Miracle Journal Get Press Vol 2 No 2 (2025): May, 2025
Publisher : CV. Get Press Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69855/mgj.v2i2.126

Abstract

This study investigates the relationship between the use of Personal Protective Equipment (PPE) and the reduction of workplace injuries among workers at PT X. Using a quantitative research design with a pre-test and post-test survey approach, data were collected from 110 respondents selected through purposive sampling. The results demonstrated a significant decrease in workplace injuries after the implementation of PPE, with the average injury score dropping from 2.27 before PPE use to 1.50 after PPE use (p-value = 0.000). These findings indicate that the use of PPE is highly effective in reducing both the frequency and severity of workplace injuries. Factors such as worker discipline, awareness, and knowledge significantly influence the effectiveness of PPE use. The study concludes that consistent and proper use of PPE, supported by worker education and the availability of quality equipment, can substantially enhance workplace safety and reduce occupational health risks
Analisis Kualitas Jaringan Hotspot Menggunakan Metode Quality of Service (QoS) dalam Mendukung Kegiatan Belajar Mengajar Di Sekolah Menengah Kejuruan Negeri 1 Gebang Fatha Mudzhaffar, Mochammad; Martanto; Rinaldi Dikananda, Arif; Rifai, Ahmad
Jurnal Dinamika Informatika Vol. 14 No. 1 (2025): Jurnal Dinamika Informatika Volume 14 Nomor 1
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v14i1.513

Abstract

Hotspot adalah jaringan nirkabel yang menyediakan akses internet kepada pengguna melalui perangkat Wi-Fi. Kualitas jaringan hotspot sangat penting dalam mendukung berbagai aktivitas, termasuk kegiatan belajar mengajar. Untuk menilai performa jaringan, metode Quality of Service (QoS) digunakan sebagai pendekatan standar dalam mengukur parameter-parameter utama jaringan, seperti throughput, packet loss, delay, dan jitter. Penelitian ini bertujuan untuk menganalisis kualitas jaringan hotspot di SMK Negeri 1 Gebang menggunakan metode QoS. Hasil penelitian menunjukkan bahwa nilai throughput berada dalam kategori "Buruk" hingga "Sangat Buruk" pada jam-jam trafik tinggi (12:00-15:00), dengan nilai berkisar antara 150-318 kbps, sehingga memerlukan optimasi jaringan. Di sisi lain, parameter packet loss tercatat 0%, yang menempatkannya dalam kategori "Sangat Baik." Nilai delay berkisar antara 10,12 ms hingga 30,01 ms, menunjukkan responsivitas jaringan yang baik dalam kategori "Sangat Baik." Sementara itu, nilai jitter berada dalam kategori "Baik" meskipun mengalami sedikit fluktuasi pada jam sibuk. Secara keseluruhan, meskipun performa jaringan dinilai baik dalam aspek packet loss, delay, dan jitter, peningkatan kualitas throughput sangat diperlukan untuk memastikan koneksi yang stabil dan berkualitas, khususnya pada jam trafik tinggi. Temuan ini memberikan dasar untuk pengembangan strategi optimasi jaringan guna mendukung kegiatan pendidikan secara lebih efektif.
Co-Authors Adi Putra, Panji Ahmad Faqih Ahmad Rifai Ahmad Rifai Akhmad Taukhid Arif Rifaldi Dikananda Arif Rinaldi Dikananda Arif Rinaldi Dikanda Artoti, Azzahra Rizky Bisma Mahendra Candra, Candra Rahmawati Dadang Sudrajat Daisy Debora Grace Pangemanan Dede Rohman Denni Pratama Dian Ade Kurnia Dikananda, Arif Rinaldi Dikananda, Fatihanursari Fachry Abda El Rahman Fahmi Naufal Fatha Mudzhaffar, Mochammad Fatihanursari Dikananda Hamongan, Ryan Hanifah Nur Aulia Hayati, Umi Heliyanti Susana Ica, Ica Pandia Ika Riantika Indra Wiguna Marthanu Irfan Ali Jannah, Eka Roehatul Jayawarsa, A.A. Ketut Khaerul Anam Laylatunna'imah Maluw, Fandel Masdarul Rizqi Muchlisinalahuddin Muhamad Abdul Fatah Muhammad Hilman Mulyawan Nana Suarna Nuraeni Puji Pramudya Marta Raditya Danar Dana Ramadan, Muhamad Firly Rifa'i, Ahmad Rinaldi Dikananda, Arif Rohayati, Eti Rohman, Dede Rudi Kurniawan Satria Turangga Siti Latifah Unari, Ica Venny Yusiana Wiratriyana Yudhistira Arie Wijaya