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All Journal Information Management For Educators And Professionals (IMBI) Jurnal Informasi dan Komputer JURSIMA (Jurnal Sistem Informasi dan Manajemen) Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) JURSIMA
Arif Rinaldi Dikananda
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Library & Information Science Mathematics Social Sciences Other

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RANCANG BANGUN APLIKASI SISTEM INFORMASI PENDATAAN PELAUT BERBASIS WEB Arif Rinaldi Dikananda; Saefullah Fasa; Irfan Ali; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.473

Abstract

PT. Abdi Marine is one of the companies that has not used a web-based information system in the marine data collection section, where the data processing system is still manual. It often happens that seafarers' registration and flight date research takes up a lot of paper and seafarer data storage space, the calculation of the date is less accurate and making reports of incoming and outgoing seafarers' data takes a lot of time. To emphasize and learn in understanding the problems as described, the problem formulation that researchers can explain is to design a computerized marine crew data collection information system, create a database of data services for managers to carry out their work. The purpose of this research is to find out, develop and create an ongoing data collection application system into the PHP and HTML programming language using the MySQL database. So that researchers can draw conclusions in processing sailor crew data collection by implementing applications that have been designed and built in a systematic and structured manner, so that the level of damage in the process of implementing sailor crew data collection can be resolved.
RANCANG BANGUN SISTEM INFORMASI PERSEDIAAN BARANG BERBASIS WEB PADA PT PARAGON FURNITAMA INDUSTRY Arif Rinaldi Dikananda; Shofian Yunus; Saeful Anwar; Odi Nurdiawan
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.474

Abstract

PT. Paragon Furnitama Industry is one of the companies engaged in the production of fabric and leather sofas, back seats, and chair cushions, at this time the inventory process is still done manually because it still uses records in books and Microsoft Excel, the process sometimes finds several problems including data redundancy, discrepancies in stock of goods with records, and providing long reports because data validation is needed first. So that the information received by the parties concerned is very difficult to obtain quickly. To emphasize and study the problems as described, the problem formulation that researchers can explain is to design an inventory information system so that the company's performance is getting better. The Design and Build of this Goods Inventory Information System is built based on a website. The design of the information system uses the Software Development Life Cycle (SDLC) with the waterfall method so that this design system is expected to improve performance and performance, especially those related to processing inventory data to making inventory reports at PT. Paragon Furnitama Industry.
Bibliometrik Analysis: Konten Video Untuk Meningkatkan Daya Tarik Pariwisata Arif Rinaldi Dikananda; Dadang Sudrajat; Fatihanursari Dikananda; Rudi Kurniawan; Martanto
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The use of video content as a marketing tool in the tourism industry has seen a significant increase in recent years. This research aims to explore and develop effective video content strategies in increasing tourism appeal and influencing tourists' decisions to visit certain destinations. Research methods include bibliometric analysis of video content used in tourism marketing, as well as experiments to test the effectiveness of various video content strategies. The results of the study show that the characteristics of travel vlogs that include personal narratives, attractive visuals, and relevant information can increase user travel intentions. Additionally, audience engagement through short videos has proven to be a key factor in increasing travel interest. This research makes a new contribution in understanding the role of video content in tourism marketing and developing a video marketing strategy model that can be applied by the tourism industry to increase the attractiveness of tourist destinations. By utilizing the results of this study, the tourism industry can optimize the use of video content to reach a wider audience and increase positive perceptions of tourist destinations.
ANALISIS SEGMENTASI PELANGGAN VOUCHER WIFI DENGAN METODE K-MEANS Fahmi Naufal; Martanto; Arif Rinaldi Dikananda; Rohman, Dede
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5169

Abstract

AirNet Teknologi, yang bergerak di bidang konsultasi komputer dan penyedia layanan internet WiFi, melakukan penelitian untuk menerapkan teknik clustering menggunakan algoritma K-Means dalam menganalisis data penjualan voucher WiFi di cabang Talun dari 31 Oktober 2023 hingga 31 April 2024. Penelitian ini bertujuan untuk mengidentifikasi pola pembelian pelanggan berdasarkan jenis paket, durasi penggunaan, dan harga, guna meningkatkan strategi pemasaran perusahaan. Dataset yang dianalisis mencakup detail transaksi seperti tanggal, jenis produk, durasi penggunaan, dan total pembayaran. Proses analisis dilakukan menggunakan RapidMiner Studio dengan tahapan Knowledge Discovery in Database (KDD), termasuk seleksi data, praproses, dan evaluasi hasil clustering menggunakan Davies-Bouldin Index (DBI). Hasil menunjukkan jumlah klaster optimal adalah K = 6 dengan nilai DBI 0.182, menandakan kualitas clustering yang baik. Pelanggan dikelompokkan ke dalam enam klaster dengan karakteristik berbeda, yang dapat digunakan untuk menargetkan promosi dan program loyalitas. Penelitian ini menekankan pentingnya analisis data dalam pengambilan keputusan strategis, memungkinkan AirNet Teknologi untuk menyusun strategi pemasaran yang lebih efektif, meningkatkan kepuasan pelanggan, dan memperkuat posisinya di pasar layanan WiFi.
Improving the School Type Clustering Model on the Foundation Using the K-Means Algorithm (Case Study: Kebon Kelapa Al-Ma'rifah, Cirebon Regency) Hanifah Nur Aulia; 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.v4i2.739

Abstract

This study aims to improve the school type grouping model at the Kebon Kelapa Al-Ma'rifah Foundation, Cirebon Regency, using the K-Means algorithm. Data-based grouping is very important in supporting efficient education management, especially in environments that have various types of schools such as Madrasah Aliyah (MA), Vocational High School (SMK), Madrasah Tsanawiyah (MTs), and Madrasah Ibtidaiyah (MI). The data used comes from the New Student Registration (PPDB) dataset for the 2023–2024 school year, with demographic attributes such as name, place of birth, gender, and time of school entry. The evaluation of clustering quality was carried out using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results show that the optimal number of clusters is K=5 with the lowest DBI value of 0.201, which results in compact and well-separated clusters. The implementation of the K-Means algorithm helps the foundation understand the distribution pattern of students based on attributes such as gender, region, and entry time. This research provides practical benefits, including more targeted resource allocation, improved quality of education, and efficiency in school management. In addition, this research contributes to the development of data mining models in the education sector and opens up opportunities for the exploration of additional attributes such as academic achievement and socioeconomic conditions. Further research is suggested to use alternative algorithms such as K-Medoids or DBSCAN.
Improvement of Students' Academic Achievement Classification Model Through the Analytical Hierarchy Process Algorithm in Elementary School Burujul Kulon III Candra, Candra Rahmawati; 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.
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.
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.
Optimizing Naïve Bayes Algorithm Through Principal Component Analysis To Improve Dengue Fever Patient Classification Model Santi Nurjulaiha; Rudi Kurniawan; Arif Rinaldi Dikananda; Tati Suprapti
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.v4i2.798

Abstract

Dengue fever is an infectious disease that has a significant impact on public health in tropical regions, including Indonesia. Early detection and proper classification of DHF patients is essential to reduce severity and mortality. For this reason, a method that can improve the accuracy in diagnosing this disease is needed. Principal Component Analysis (PCA) and Naïve Bayes (NB) are two commonly used techniques in medical data analysis. PCA is used to reduce the dimensionality of data to reduce complexity, while Naïve Bayes is used for classification of data based on probability. This study aims to optimize the use of PCA and Naïve Bayes in improving the accuracy of the dengue patient classification model. The method used in this study involves processing a medical dataset of dengue patients containing various clinically relevant attributes. The dataset was then processed using PCA to reduce dimensionality and identify key features that affect classification. Next, Naïve Bayes was applied to classify the data based on the selected features. This study compares the performance of classification models that use a combination of PCA and Naïve Bayes with models that only use Naïve Bayes without dimensionality reduction. The results show that the use of PCA in data processing significantly improves the accuracy of the classification model compared to the model that only uses Naïve Bayes. The combination of PCA and Naïve Bayes produces a more efficient model and has a higher accuracy rate in identifying patients with DHF risk. Thus, the application of PCA and Naïve Bayes in the classification of DHF patients can be an effective tool in assisting the medical diagnosis process, which in turn can reduce misdiagnosis and improve patient recovery rates. This research contributes to the development of artificial intelligence technology in the medical field, especially to improve the accuracy of dengue disease diagnosis, and serves as a basis for further research in the use of machine learning techniques in healthcare. This study analyzes the performance of the Naïve Bayes algorithm in classifying dengue fever patient data, by comparing models that use Principal Component Analysis (PCA) as a dimension reduction method and models that do not use it. The results show that the Naïve Bayes model without PCA has an accuracy of 49.96%, which is close to the random guess rate. This finding indicates that the model is less effective in recognizing patterns in the data. In contrast, the application of PCA successfully increased the model's accuracy to 50.03%
Co-Authors Ade Irma Purnamasari Ahmad Faqih Ahmad Rifa’i Aria Pratama Candra, Candra Rahmawati Dadang Sudrajat Dede Rohman Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Fahmi Naufal Fatihanursari Dikananda Gifthera Dwilestari Hanifah Nur Aulia Ica, Ica Pandia Irfan Ali Laylatunna'imah Martanto Moch Feri Izulhaq Mulyawan Mulyawan Mulyawan Musofi Musofi Nana Suarna nur syarief abdullah Nurjana Adi Wijaya Odi Nurdiawan Rohayati, Eti Rohman, Dede Rudi Kurniawan Saeful Anwar Saefullah Fasa Santi Nurjulaiha Shofian Yunus Tati Suprapti Tio Prasetya Wiratriyana