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All Journal Jurnal technoscientia Infotech Journal Jurnal Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak INTERNAL (Information System Journal) JURNAL TEKNOLOGI TECHNOSCIENTIA Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Scientech Research and Development Jurnal Sistem Informasi dan Teknologi (SINTEK) Jurnal Teknik Mesin, Industri, Elektro dan Informatika Journal of Student Research AMMA : Jurnal Pengabdian Masyarakat INTERNAL (Information System Journal) Sintek Kuwera
Nana Suarna
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Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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K-Means Clustering Method to Make Credit Payment Groupinhg Efficient Siti Nur Illah; Nana Suarna; Irfan Ali; Dodi Solihudin
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.v4i2.815

Abstract

Credit payment management is one of the main challenges in the financial sector, especially in grouping customers based on risk and payment patterns. This study aims to evaluate the K-Means Clustering method in improving the efficiency of credit payment data clustering. The dataset used includes information on payment history, loan amount, tenor, and credit status from financial institutions. The research approach involves data processing stages, application of the K-Means algorithm, and evaluation of results using the Davies-Bouldin Index and Silhouette Score metrics. The results of the analysis show that the K-Means method is effective in identifying customer payment patterns and dividing them into three main clusters: high, medium, and low risk. In addition, this study found that determining the optimal number of clusters using the Elbow Method can improve the accuracy of the clustering results. The resulting model makes a significant contribution to credit risk management, helping financial institutions make strategic decisions related to credit policies and risk mitigation. This study offers practical implications, including increased operational efficiency and predictive ability against potential bad debts. Further studies are recommended to integrate this method with other algorithms to improve the performance of large-scale data analysis.
Pengembangan Aplikasi Informasi Posyandu dalam Meningkatkan Layanan Kesehatan Ibu dan Anak Nana Suarna; Nining Rahaningsih; Euis Fadilah; Farah Nur Farida
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

This Community Partnership Program aims to develop a Posyandu information system application to improve the efficiency and effectiveness of maternal and child health services. This application is designed to facilitate Posyandu officers in managing patient data, recording health histories, monitoring child development, and providing relevant health information. The application development includes needs analysis, user interface (UI) design, implementation of key features, and application usage training for Posyandu officers. It is expected that with this application, the quality of health services at Posyandu can be improved, and health information access for mothers and children can be facilitated.
Optimizing the Social Assistance Recipient Model in CangkringVillage Using the Naïve Bayes Algorithm Rotika; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
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.v4i2.849

Abstract

Social assistance is one of the methods used by the government to help the underprivileged. Cangkring Village is a village in Cirebon Regency that has inaccurate data on recipients of social assistance or underprivileged people. The Naive Bayes algorithm is one of the most effective techniques in machine learning for classifying data, in determining the eligibility of recipients of social assistance. The method works with a probabilistic approach to analyze data efficiently and accurately, can group data based on attributes and produce high accuracy. The problem in Cangkring Village, namely the accuracy of data on recipients of social assistance, is still a problem that requires special attention. This inaccuracy not only reduces the effectiveness of social assistance programs but also creates injustice for people in need. Invalid and inappropriate data causes the distribution of social assistance to be suboptimal. The purpose of this study is to optimize the accuracy model of social security recipients using the Naive Bayes algorithm, which can help improve the accuracy in determining eligible recipients.The method used in the study is secondary data processing taken from social assistance recipient data in Cangkring Village. This process includes data preprocessing stages, training and testing data distribution, and implementation or application of the Naive Bayes algorithm to perform classification. The results of the study show that the Naive Bayes algorithm is able to increase the accuracy of the classification of social assistance recipients with an accuracy rate of 90%, compared to the conventional method used previously. This study contributes to providing a more efficient and targeted method in selecting social assistance recipients, so that it can improve the social assistance distribution system in the future. Thus, the Naive Bayes algorithm can be an effective method for data-based decision making in the context of social policy.
Identify Rattan Sales Patterns Using the FP-Growth Algorithm on CV. Busaeri Rattan Robi; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
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.v4i2.877

Abstract

This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.
Sistem Informasi Pengelolaan Barang Inventaris Berbasis Web di Perumda BPR Bank Cirebon Dini Maryani; Nana Suarna
Journal of Student Research Vol. 1 No. 3 (2023): Mei: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i3.1159

Abstract

The limitations of recording inventory of goods at Perumda BPR Bank Cirebon are still manual. This causes a relatively long inventory reporting time and the possibility of data errors. For this reason, an inventory web application can help manage inventory inventory at Perumda Bank Cirebon. Designing a web-based inventory information system is the purpose of this research at Perumda BPR Bank Cirebon. The method used includes going through the stages of analysis, design, execution, interviews, and observation. By using the PHP programming language and MySql database, this information system was created. The finding of this study is an inventory management information system that can facilitate agencies and companies in recording company inventories and minimizing the possibility of data errors
RANCANG BANGUN SISTEM PENCATATAN TRANSAKSI KEUANGAN BERBASIS WEBSITE PADA TOKO LATIFAH BUSANA PANGURAGAN KABUPATEN CIREBON Sumita Sumita; Nana Suarna
Journal of Student Research Vol. 1 No. 3 (2023): Mei: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i3.1186

Abstract

Toko Latifah Busana terletak di Desa Panguragan kulon, kecamatan panguragan, kabupaten Cirebon. Berdasarkan pada Observasi di Toko Latifah Busana masih menggunakan pencatatan dan transaksi secara manual yang menjadikan proses transaksi tidak efisien dalam pelayanan. rmasalahan pada sistem pencatatan transksi keuangan merupakan permasalahan yang ditemukan dan bisa terjadi pada suatu perusahaan dan usaha kecil menengah, Kesalahan pada saat menghitung maupun melakukan transaksi dapat memberikan kerugian bagi perusahaan. permasalahan yang muncul juga terjadi karena pembukuan yang tertumpuk seiring berjalannya waktu catatan pasti akan hilang dan mengakibatkan proses penyimpanan data tidak tertata dengan baik. Untuk membangun aplikasi pencatatan transaksi keuangan tersebut, Peneliti memperoleh data dengan menggunakan wawancara dan observasi. mekanisme yang digunakan adalah tahap perancangan sistem. Dengan bahan pemograman menggunakan software sublime text sebagai aplikasi editor. dengan adanya metode Waterfall membantu pembuatan aplikasi pencatatan transaksi keuangan menjadi lebih akurat dan sistemtis. Tujuan dari penelitian ini adalah untuk membangun sistem pencatatan transaksi keuangan berbasis website pada toko latifah busana Panguragan, dan dapat membantu proses pencatatan, stok barang, dan laporan keuangan pada Toko Latifah Busana lebih efisien dan terkomputerisasi untuk kedepannya.
Analisis Data Hasil Laporan Skripsi Berbasis Aspect Based Sentiment Analysis Menggunakan Algoritma K-Means Clustering Nana Suarna; Dadang Sudrajat; Umi Hayati; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study discusses the application of Aspect-Based Sentiment Analysis (ABSA) combined with the K-Means Clustering algorithm to analyze student thesis report data. The research scope includes text data processing from VAK (Visual, Auditory, Kinesthetic) learning style questionnaires to identify research aspects and automatically group thesis themes. The objective is to obtain a structured and representative mapping of students’ research themes based on their fields of study. The methodology involves several stages, including text preprocessing, TF-IDF weighting, aspect extraction using ABSA, and clustering with K-Means, validated through the Davies-Bouldin Index (DBI). The dataset consists of 976 textual entries derived from student questionnaire responses. The results indicate that the optimal cluster is achieved at k = 3 with a DBI value of 3.276, forming three main groups: (1) data mining, (2) statistical analysis, and (3) learning technology. The study concludes that the combination of ABSA and K-Means is effective in accurately classifying research themes and provides an analytical foundation for academic decision-making regarding student research trends.
Model Machine Learning Untuk Prediksi Risiko Penyakit Liver Dengan Random Forest Teroptimasi Rizky Andrea Arifa; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.204

Abstract

Penyakit liver merupakan salah satu kondisi kronis dengan tingkat mortalitas tinggi, sehingga diperlukan pendekatan prediksi yang akurat untuk mendukung deteksi dini. Penelitian ini bertujuan mengembangkan model machine learning untuk memprediksi risiko penyakit liver menggunakan algoritma Random Forest yang dioptimalkan dengan RandomizedSearchCV. Dataset yang digunakan terdiri dari 1.700 entri yang mencakup variabel klinis dan gaya hidup, termasuk usia, jenis kelamin, BMI, konsumsi alkohol, kebiasaan merokok, riwayat genetik, aktivitas fisik, diabetes, hipertensi, serta hasil Liver Function Test. Proses penelitian meliputi preprocessing, normalisasi skala, pembagian data menggunakan train-test split 80:20, pembangunan model baseline, dan optimasi hiperparameter. Hasil eksperimen menunjukkan bahwa optimasi menghasilkan peningkatan performa model, dengan akurasi 0.91, peningkatan recall sebesar 3.20%, dan AUC-ROC mencapai 0.96. Analisis feature importance menunjukkan bahwa LiverFunctionTest, BMI, dan AlcoholConsumption merupakan fitur paling berpengaruh terhadap prediksi risiko penyakit liver. Dengan demikian, Random Forest teroptimasi terbukti efektif dalam menghasilkan model prediksi yang akurat dan dapat digunakan sebagai alat pendukung keputusan dalam deteksi dini penyakit liver.
Analisis Dan Prediksi Risiko Kelahiran Bayi Menggunakan K-Means Dan Deep Neural Network (DNN) Mukhlisin Ilahudin; Nana Suarna; Agus Bahtiar; Mulyawan; Irfan Ali
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.206

Abstract

Risiko kelahiran bayi merupakan indikator penting dalam evaluasi kesehatan ibu dan anak sehingga diperlukan pendekatan analitis yang mampu mengidentifikasi pola risiko secara akurat. Penelitian ini bertujuan menganalisis dan memprediksi risiko kelahiran bayi dengan mengintegrasikan metode K-Means dan Deep Neural Network (DNN). Dataset yang digunakan terdiri dari 983 data rekam medis ibu hamil yang telah melalui tahap pengumpulan data, pembersihan, dan preprocessing meliputi normalisasi, encoding variabel kategorikal, penanganan outlier, serta seleksi fitur. Metode K-Means digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik klinis guna membentuk representasi pola risiko awal, yang selanjutnya digunakan sebagai fitur tambahan pada model DNN. Model DNN dirancang menggunakan beberapa hidden layer dengan fungsi aktivasi ReLU dan regularisasi dropout. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 61,93% dan nilai ROC AUC sebesar 0,6402, yang mengindikasikan performa moderat dalam memprediksi risiko kelahiran bayi. Stabilitas kurva loss dan akurasi menunjukkan proses pelatihan yang berjalan dengan baik tanpa overfitting signifikan. Secara praktis, model ini berpotensi digunakan sebagai alat bantu awal bagi tenaga kesehatan dalam mengidentifikasi ibu hamil dengan risiko kelahiran lebih tinggi sehingga dapat dilakukan pemantauan dan intervensi lebih dini.
Penerapan Algoritma K-Means Clustering Untuk Mengelompokan Siswa SMK Al-Ma’rifah Berdasarkan Kehadiran Dila Nurhafidilah; Nana Suarna; Agus Bahtiar; Umi Hayati; Fatihanursari Dikananda
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.212

Abstract

Penelitian ini bertujuan untuk mengelompokkan siswa SMK Al-Ma’rifah berdasarkan pola kehadiran menggunakan algoritma K-Means Clustering. Data yang dianalisis merupakan catatan kehadiran siswa tahun ajaran 2023/2024 yang meliputi jumlah hadir, izin, sakit, alfa, dan persentase kehadiran. Tahapan pra-pemrosesan data dilakukan melalui pembersihan dan normalisasi sebelum proses clustering. Penentuan jumlah klaster optimal menggunakan Elbow Method dan Silhouette Coefficient menunjukkan bahwa tiga klaster merupakan struktur terbaik. Hasil pengelompokan menghasilkan tiga kategori siswa, yaitu sangat disiplin, cukup disiplin, dan kurang disiplin. Evaluasi kualitas klaster menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan pemisahan klaster yang baik. Penelitian ini membuktikan bahwa K-Means Clustering efektif dalam mengidentifikasi pola kehadiran siswa dan  dapat mendukung pengambilan keputusan sekolah berbasis data dalam meningkatkan kedisiplinan siswa.
Co-Authors Aby Febrian Ade Rizki Rinaldi agus bahtiar Ahmad Rifai Anita Anita Arif Rinaldi Dikananda Ayu Zulhijah Dadang Sudrajat Dendy Indriya Efendi Dila Nurhafidilah Dini Maryani Dodi Solihudin Euis Fadilah Fahmi Sulaiman Farah Nur Farida Fatihanursari Dikananda Gifthera Dwilestari Iin Irfan Ali Mukhlisin Ilahudin Mulyawan Musofi Musofi Nining Rahaningsih Puji Hartini Rizal Rayyan Firdaus Rizky Andrea Arifa Robi Rotika Siti Nur Illah Sumita Sumita Teguh Adrian Umi Hayati Willy Prihartono