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All Journal Jurnal technoscientia Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Informatics for Educators and Professional : Journal of Informatics KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL ILMIAH INFORMATIKA Conference SENATIK STT Adisutjipto Yogyakarta Jurnal ICT : Information Communication & Technology Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Perangkat Lunak JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom Budimas : Jurnal Pengabdian Masyarakat JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknologi Informasi dan Komunikasi Jurnal Manajemen Informatika Jayakarta Jurnal Informatika Terpadu Baselang: Jurnal Ilmu Pertanian, Peternakan, Perikanan dan Lingkungan Jurnal Janitra Informatika dan Sistem Informasi Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Dinamika Informatika (JDI) Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) Literasi: Jurnal Pengabdian Masyarakat dan Inovasi J-Icon : Jurnal Komputer dan Informatika Jurnal Teknologi Pangan dan Industri Perkebunan JURSIMA AMMA : Jurnal Pengabdian Masyarakat SmartComp Jurnal Informatika: Jurnal Pengembangan IT Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Branding: Jurnal Manajemen & Bisnis Jurnal Komtika (Komputasi dan Informatika) Smatika Jurnal : STIKI Informatika Jurnal Bianglala Informatika
Martanto .
STMIK IKMI Cirebon
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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.
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.
K-Means Algorithm for Clustering High-Achieving Student at Madrasah Tsanawiyah Yami Waled Muhammad Hilman; Martanto; Arif Rinaldi Dikananda; Ahmad Rifai
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.
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.
Optimization of Kebaya Product Grouping Using K-Means Algorithm for Marketing Strategy of Rental Services at Gifaattire Store Nuraeni; Martanto; Arif Rinaldi Dikananda; Ahmad Rifai
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.
Development of Educational Game for Introduction Animal Types Using the ADDIE Method Smart Apps Creator In Improving Knowledge Students Azzahra Rizky Artoti; 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.v4i2.777

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 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 A, Ronny Abdillah, Naufal Abdul Rosid, Rizal Adha Panca Wardanu, Adha Panca Ahmad Faqih Ahmad Rifa'i Ahmad Rifai Ahmad Rifai Ahmad Rifa’i Aji Dian Permana, Muhamad Aji Saputra, Mohammad AKBAR, MUHAMAD DENI Al-Giffary, Farhan Rizky Alfin Maulana Almadina, Muhammad Fitrian Shousyade Alpian Novansyah, Indi Andini, Eva APIPAH, KUSNATUL Ardhanur, Ichlas Arianti, Ira Arianti Arif Budi Setiawan Arif Rifaldi Dikananda Arif Rinaldi Dikananda Arif Rinaldi Dikanda Aryatama, Septian Asmana, Asmana Assrorudin, Assrorudin Augustian Pangestiazi, Irvanda Azahra, Amaliyah Putri Aziz Sahidin, Naufal Azzahra Moudy Fajria Azzahra Rizky Artoti Bagus Hermawan Barki, Khotimatul Betran Renaldi Bisma Mahendra Candra Rahmawati Candra Cep Lukman Rohmat Chrisna Basila Rahman, Muhammad Damar Widjaja Darmanto, Darmanto Dea Eryanti Putri Dede Rohman Denni Pratama Dewi Yuliyanti, Dewi Dian Ade Kurnia Dianawati Suryaningtyas Dias Bayu Saputra Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dilita Pramasmawari Lita Dita Rizki Amalia Diyanti yanti Djoko Untoro Suwarno Dwi Hastuti, Ningrum Eti Rohayati Fadhil Muhammad Bsysyar Faisal Adam, Faisal Faizal Rizqi, Muhammad Fathur Rezki Junaedi, Muhammad Fatihanursari Dikananda fatimah, lilis Fauzan Afrizal, Ricky Febriyani, Adinda Fihir, Muhammad Fithriyani, Nurul Muna Fitriarni, Dian Fuji Astri, Dewanti Gifthera Dwilestari Hamam, Moh Hanifah Nur Aulia Hardika Hardika, Hardika Harini, BW Hastuti, Ningrum Dwi Hayati , Umi Hayati, Umi Heliyanti Susana Herni, Indah Putri Hidayat, Fajar Ica Pandia Ica Ica Unari Ika Anikah Ika Riantika Iksan Maulana, Muhammad Indriawan, Rois Irfan Ali Irfan Ali, Irfan irfan cholid Iswanjono Iswanjono Jamaludin, Maulana Jamalul'ain, Abdul Kamil, Firmanilah Khaerul Anam Kharisma, Kharisma Khoirunisa, Pitria Kholilullah, Mohammad khusnul khotimah Laylatunna'imah Linggo Sumarno Lukmanul Hakim Lutfi Hakim Ma'arif Syaefullah, Muhammad Mahardika, Fathoni Masdarul Rizqi Maulana Jamaludin Maulana Yusuf, Muhammad Meida Nurus Melva Regina Arpratika Mirna Mirna Mochammad Fatha Mudzhaffar Moruk, Ewaldus Mu'min Azis, Muhammad Mubarok Mubarok Muhamad Djaelani Muhamad farhan Tholhah hidayat Muhamad Firly Ramadan Muhamad Jihad Andiana Muhamad Taufik Sugandi Muhammad Aditya Rabbani Adit Muhammad Fadhilah Muhammad Haikal Muhammad Hasan Fadlun Muhammad Hilman Muhammad Saifurridho Mujibulloh, Mujibulloh Mulyawan Mulyawan, Mulyawan Musyarofah Musyarofah, Musyarofah Muzani, Muhamad Muzilin, Elin Nailil Amani, Najiyah Nana Suarna Nanita, Nanita Narasati, Riri Narasati Nining Rahaningsih Nining Rahaninsih Nova Zulfahmi, A Nova Zulfahmi, A. Nur Aisyah, Devi Nur Asih, Nur Nur Hermawan, Ilham Nuraeni Nurdiawa, Odi Nurhanifah, Indah Nurus, Meida Odi Nurdiawa Odi Nurdiawan Panca Wardanu, Adha Panji Adi Putra Petrus Setyo Prabowo Prabowo, PS Prahara, Sukma Primawan, A.Bayu Puji Rahayu Putri, Niken Zeliana Raditya Danar Dana Ramdan Adi Surya, Muhamad Ridho Nugraha Rifa'i, Ahmad Rifa’I, Ahmad Rinaldi Dikananda, Arif Rinaldi, Arif Riskandi, Muhammad Rizal Rizal Rizka Amelia Rohman, Dede Roni Saputra, Roni Ronny Dwi Agusulistyo Rudi Kurniawan Ryan Hamongan Saeful Anwar Safrudin, Muhamad Saifurridho, Muhammad Salsabila Ainal Wasilah, Qonita Samsudin, Risma'ruf Setiyani, Th. Prima Ari Setiyani, TPA Siti Latifah Siti Paridah, Ninda Siti Romlah Sri Suwartini Subur, Muhamad Sulistiyana Sulistiyana Sumarno, L Suripno Suryaningsih Suryaningsih Suwarno, DU Syahri, Ibnu Nava Syam Al ghifari, Muhammad Syamsul Aripin Syaripah, Imas Syifa, Nurkhasanah Fadhila Tati Suprapti Thomas Agam Tjendro Tri Anelia Tri Gustiane, Indri Tuti Hartati Umi Hayati Ummiyati Ummiyati Vicky Pamungkas W Widyastuti, W Wibowo, Daniel Widjaja, D Wihadi, Dwiseno WIHADI, RB DWISENO Willy Prihartono Wiratriyana Wiwien Widyastuti Yudhistira Arie Wijaya Zulfahmi, A. Nova