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All Journal KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Angkasa: Jurnal Ilmiah Bidang Teknologi JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat Akuntansi: Jurnal Riset Ilmu Akuntansi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) Jurnal Ilmu Komputer Ruru Smatika Jurnal : STIKI Informatika Jurnal
Saeful Anwar
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Other

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Optimizing the Classification Model for Plant Medicine Supplies Using the Decision Tree Algorithm at the Anugrah Tani Shop, Brebes Regency: Inggris Saeful Amri; Rudi Kurniawan; Saeful Anwar
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.v4i2.825

Abstract

Retail businesses in the agricultural industry often face difficulties in estimating inventory needs, especially plant medicines which are important for protecting plants from pests and diseases. The lack of an accurate inventory prediction system can cause stock discrepancies, as happened at the Anugrah Tani Store, Brebes Regency, thereby disrupting operations and customer satisfaction. This research uses the Decision Tree classification technique to increase the accuracy of predicting the need for plant medicine supplies, with a clustering approach using the K-Means algorithm to determine the optimal K value through the Davies-Bouldin Index (DBI) calculation. A DBI value of -0.065 indicates good cluster quality with an optimal K of 2, where Cluster 0 has high inventory needs (1138 data) and Cluster 1 has low needs (4 data). The analysis results show that the accuracy level of the Decision Tree model is 98.25%, which is quite high. This model is not only able to predict inventory patterns accurately but also provides in-depth insights to support stock decision making. This research proves that the Decision Tree algorithm can help inventory management with a faster response to customer needs, while contributing to the development of machine learning-based classification models for the agricultural and retail sectors.
Optimizing Grocery Sales Data Grouping Using the Fuzzy C-Means Algorithm: Case Study of Nafhan Mart Store Nafhan Khairuddin Fathin; Rudi Kurniawan; Saeful Anwar
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.v4i2.842

Abstract

The sale of staple food products at Nafhanmart Store, Cirebon Regency, includes essential household items such as rice, cooking oil, sugar, and flour, which maintain stable demand as basic necessities. This study focuses on improving sales clustering models at Nafhanmart using the Fuzzy C-Means (FCM) algorithm, a prominent method in data mining. Key factors influencing sales include price, sales volume, demand, and remaining stock. Accurate clustering analysis is vital for strategic inventory management and profit maximization. The research applies the Knowledge Discovery in Database (KDD) methodology, encompassing data selection, preprocessing, transformation, FCM implementation, and evaluation using the Davies-Bouldin Index (DBI). Attributes analyzed include price, sales volume, demand, and remaining stock. The FCM algorithm clusters data based on patterns, with DBI evaluating clustering quality and determining optimal clusters. Data analysis and visualization were conducted using RapidMiner. Results show that the FCM algorithm achieves optimal clustering quality with a DBI score of 0.452 for two clusters, outperforming three clusters (DBI 0.474) and four clusters (DBI 0.536). Price and demand are identified as critical factors influencing clustering outcomes. These findings enhance the clustering model, offering actionable insights for inventory management and sales strategy, while showcasing the FCM algorithm's adaptability for other SMEs to support data-driven decision-making.
Penerapan Algoritma K-Means Clustering Pada Tingkat Inflasi Kota Di Indonesia Novia Wulandari; Nisa Dienwati Nuris; Saeful Anwar
Akuntansi Vol. 2 No. 2 (2023): Juni: Jurnal Riset Ilmu Akuntansi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/akuntansi.v2i2.235

Abstract

Inflation is a general tendency to increase the prices of goods and services, and it happens all the time. when the prices of domestic goods and services rise, inflation will rise. Depreciation causes the prices of goods and services to rise. Uncontrolled inflation can result in losses for society and the government. Therefore, an appropriate study is needed to map the dynamics of inflation in a region. One way to map the inflation rate is clustering. Clustering is dividing data into groups with the same characteristics. The author took the initiative to analyze the urban inflation rate in Indonesia from 2020 to 2022. The data is sourced from the Central Statistics Agency (BPS) website. This analysis uses the K-Means Clustering method with 5 clusters. the group with the highest inflation is in cluster 0, the high inflation group is in cluster 1, the moderate inflation group is in cluster 2, the low inflation group is in cluster 3, and the lowest inflation is in cluster 4. by categorizing the inflation rate of cities in Indonesia, it can be seen which cities in Indonesia have very high, high, medium, low and very low inflation rates.
Co-Authors Abdul Aziz Abdul Aziz Abdul Robi Padri Ade Irma Purnamasari Adila G Khiqmatiar Muchsin Afif Maulana Sidik Ahmad Faqih Ahmad Faqih Ali Ali Arif Rinaldi Dikananda Aulia Adisty Hervianne Basysyar, Fadhil Muhammad Cep Lukman Rohmat Chaidir Ali Dadang Sudrajat Dadang Sudrajat Denni Pratama Dian Ade Kurnia Dian Ade Kurnia Dias Bayu Saputra Dilita Pramasmawari Lita Dimas Aji Dita Rizki Amalia Edi Tohidi Fathurrohman Fatihanursari Dikananda Firman Yudatama Gifthera Dwilestari Gifthera Dwilestari Heliyanti Susana Ibnu Alfian Ibnu Hajar Iin Ike Suryani Dewi Indah Nur Hasanah Indah Ratna Ningsih Irfan Ali Kanda Muhamad Ishak Kevin Salsabil Arlandy Khamim Surya Hadi Kusuma Al Atros Kokom Komariyah Martanto Martanto . Muhamad Ilham Nur Faqih Nafhan Khairuddin Fathin Nana Suarna Nana Suarna Narasati, Riri Narasati Nining Rahaningsih Nisa Dieanwati Nuris Novia Wulandari Odi Nurdiawan Raditya Danar Dana Rahaditya Dasuki Rahmat Mauludin Revita Lestari Faujiah Risa Komala Sari Rudi Kurniawan Ruli Herdiana Ryan Hamonangan Saeful Amri Sandy Eka Permana Siti Rini Sari sri wahyuni nengsih Tati Supra Tati Suprapti Topan Hadi Tuti Hartati Windi Herlita Vidila Yoga Nugraha Yudhistira Arie Wijaya