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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Informatika dan Teknik Elektro Terapan Information Management For Educators And Professionals (IMBI) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer AL-TANZIM : JURNAL MANAJEMEN PENDIDIKAN ISLAM Indonesian Journal of Applied Informatics JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) JATI (Jurnal Mahasiswa Teknik Informatika) Media Informatika Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society MEANS (Media Informasi Analisa dan Sistem) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Informatika Terpadu International Journal of Social Science Instal : Jurnal Komputer Jurnal Janitra Informatika dan Sistem Informasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Software Engineering and Information System (SEIS) AMMA : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Kecerdasan Buatan dan Teknologi Informasi
Nining Rahaningsih
STMIK IKMI Cirebon
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PENINGKATAN KLASIFIKASI KEMISKINAN INDONESIA MENGGUNAKAN METODE DECISION TREE Danil, Supta; Rahaningsih, Nining; Dana, Raditya Danar; ., Mulyawan
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6336

Abstract

Kemiskinan masih menjadi permasalahan signifikan di Indonesia, terutama dalam hal ketidaktepatan sasaran dalam pemerataan ekonomi. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan model klasifikasi kemiskinan di tingkat kabupaten/kota di Indonesia menggunakan algoritma Decision Tree. Penelitian ini mengangkat beberapa rumusan masalah, antara lain pengembangan model klasifikasi, pengukuran performa model, dan analisis pengaruh pemilihan fitur terhadap akurasi model. Dataset yang digunakan bersumber dari Kaggle, terdiri dari 514 data dengan variabel seperti pengeluaran per kapita, Indeks Pembangunan Manusia (IPM), dan akses terhadap sanitasi layak.Proses penelitian mencakup tahapan preprocessing data, meliputi seleksi atribut, pembersihan data, dan transformasi atribut kategorikal menjadi numerik. Model klasifikasi yang dihasilkan menunjukkan akurasi hingga 87%, dengan analisis yang menyoroti pengeluaran per kapita dan akses terhadap sanitasi sebagai faktor utama yang memengaruhi tingkat kemiskinan. Evaluasi kinerja model dilakukan menggunakan matriks kebingungan, presisi, recall, dan F1-score, yang menunjukkan performa baik dalam membedakan kategori "miskin" dan "tidak miskin".
PEMANFAATAN ALGORITMA K-MEANS DALAM ANALISIS DATA PENJUALAN TOKO BUYUNG UPIK JS DI LAZADA Angraeni, Devita Fitri; Rahaningsih, Nining; Dana, Raditya Danar; Rohmat, Cep Lukman
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6438

Abstract

Banyaknya produk yang dijual oleh Toko Buyung Upik JS di Lazada menimbulkan kesulitan dalam menentukan produk yang laku dan kurang laku, sehingga terjadi ketidakseimbangan stok, seperti kelebihan pada produk yang kurang diminati dan kekurangan pada produk yang populer. Penelitian ini bertujuan mengelompokkan produk berdasarkan pola penjualan menggunakan teknik data mining untuk membantu strategi penjualan dan pengelolaan stok yang lebih efektif. Algoritma K-Means digunakan untuk clustering data penjualan, mencakup jumlah stok, transaksi, dan harga. Proses data mining meliputi tahapan Selection, Preprocessing, Transformation, Data Mining, dan Interpretation/Evaluation. Penentuan jumlah cluster optimal dilakukan dengan Elbow Method, sedangkan kualitas clustering dievaluasi menggunakan Davies Bouldin Index (DBI). Hasil penelitian menunjukkan jumlah cluster optimal adalah empat: Cluster 0 (83 produk, penjualan stabil), Cluster 1 (121 produk, penjualan tinggi), Cluster 2 (14 produk, kurang diminati), dan Cluster 3 (38 produk, penjualan moderat). Nilai rata-rata jarak dalam cluster adalah 54.941.560,812, dengan DBI sebesar 0,386 yang menunjukkan kualitas clustering cukup baik. Hasil ini memberikan wawasan bagi toko untuk memprioritaskan pengelolaan stok dan mengoptimalkan penjualan.
Creating Digital Literature through Transformational Leadership; Challenges and Solutions Sudrajat, Dadang; Dikananda, Arif Rinaldi; Rahaningsih, Nining; Cakranegara, Pandu Adi; Putra, Purniadi
JURNAL AL-TANZIM Vol 6, No 4 (2022)
Publisher : Nurul Jadid University, Probolinggo, East Java, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/al-tanzim.v6i4.3982

Abstract

This study aims to analyze and understand the principal's transformational leadership in realizing digital literacy. This study uses a qualitative approach to the type of phenomenology. Data collection techniques were carried out through interviews, observation, and documentation. Data analysis was carried out through several stages: data reduction, data presentation, and conclusion drawing or verification. The results showed that; transformational leadership strategy in increasing digital literacy by holding mini workshops and digital literacy examples. The challenges and solutions in transformational leadership are that teachers are still technology literate and not wholeheartedly in studying digitalization and inadequate internet access. This research has implications for the importance of designing educational institutions oriented toward developing digital literacy in responding to the challenges and demands of the times.
OPTIMASI MODEL XGBOOST UNTUK PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN OPTUNA Optarina, Yasni; Suarna, Nana; Bahtiar, Agus; Rahaningsih, Nining; Prihartono, Willy
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 1 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i1.10527

Abstract

Heart disease is one of the leading causes of mortality worldwide, emphasizing the need for accurate early detection systems. Machine learning models such as XGBoost have demonstrated strong performance in medical classification tasks; however, their effectiveness is highly dependent on optimal hyperparameter configurations. This study aims to improve the performance of XGBoost for heart disease classification by applying hyperparameter optimization using the Optuna framework with the Tree-structured Parzen Estimator (TPE) algorithm. The UCI Heart Disease dataset, consisting of 918 records, is used in this study. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to the training data. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The experimental results show that the optimized XGBoost model achieves an accuracy of 89.13%, outperforming the baseline model with 87.50%, and improves recall from 87.50% to 89.10%. In addition, the optimized model attains a higher ROC-AUC value of 0.9319, indicating improved classification stability. These findings demonstrate that Optuna-based hyperparameter optimization effectively enhances the performance and reliability of XGBoost, making it suitable for supporting early heart disease diagnosis in medical decision support systems.
Analysis and Visualization of Sales Transaction Patterns using Decision Tree and Tableau Public Akbar, Miftahul; Rahaningsih, Nining; Ali, Irfan; Dikananda, Fatihanursari; Hayati, Umi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1849

Abstract

This study aims to analyze sales transaction patterns of rubber waste at PT Mandiri Enviro Technosio by integrating the Decision Tree algorithm with interactive visualization using Tableau Public. The dataset consists of 405 sales transactions recorded during the 2024–2025 period, comprising attributes such as transaction date, product type, quantity, unit price, total value, delivery region, and buyer category. The research methodology includes data acquisition, preprocessing to ensure data quality and consistency, construction of a classification model using the CART algorithm, evaluation of model performance through a confusion matrix, and development of interactive dashboards for enhanced interpretability. The Decision Tree model achieved an accuracy of 88.24% in classifying transaction values into low, medium, and high categories. Unit price and transaction period were identified as the most influential attributes in determining transaction value. Visualization using Tableau Public effectively presented the distribution of transaction values, sales trends, and geographical patterns, thereby strengthening analytical insights and supporting data-driven decision making. The integration of classification techniques and interactive visualization contributes to improving business intelligence capabilities and enables the formulation of more adaptive, evidence-based sales strategies.
FP-Growth for Data-Driven Purchase Pattern Analysis and Product Recommendations at Flanetqueen Store Marwah, Sopa; Rahaningsih, Nining; Ali, Irfan; Marthanu, Indra Wiguna; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1850

Abstract

The advancement of information technology has encouraged the use of data analytics to support data-driven business decision-making. This study aims to analyze purchasing patterns of hoodie products and provide product recommendations for customers at Flanetqueen Store using the FP-Growth (Frequent Pattern Growth) algorithm. The research applies the Knowledge Discovery in Database (KDD) framework, consisting of five stages: data selection, preprocessing, transformation, data mining, and interpretation/evaluation. The dataset comprises hoodie sales transactions recorded from January to December 2024. Data analysis was conducted using RapidMiner Studio version 10.3 with a minimum support of 0.2 and minimum confidence of 0.4. The analysis produced 26 itemsets and 11 association rules indicating product correlations. The strongest rule, Bloods → Champion, achieved a confidence of 0.414, revealing that customers who purchased Bloods hoodies were also likely to buy Champion hoodies. These findings were used to design cross-selling strategies and generate relevant product recommendations. The study demonstrates that FP-Growth effectively extracts frequent purchase patterns and contributes to the development of data-driven recommendation systems in the local fashion retail industry.
Comparison of Balancing Strategies for Classifying Guava Fruit Diseases Putri Nabilla; Suarna, Nana; Bahtiar, Agus; Rahaningsih, Nining; Prihartono, Willy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1859

Abstract

The problem of class imbalance often poses an obstacle in deep learning-based image classification, especially in the domain of digital agriculture. The imbalance in data distribution makes it easier for models to recognize the majority class, while performance for the minority class declines. This study aims to analyze the effectiveness of three strategies for handling class imbalance: Weighted Loss Function, Oversampling, and a combination of Weighted Loss and Oversampling, in improving the performance of image classification of guava fruit diseases using a transfer learning-based MobileNetV2 architecture. The dataset consists of 3,784 images of three disease classes, namely Anthracnose, Fruit_Fly, and Healthy_guava, which show an imbalanced distribution. The research was conducted through the stages of Exploratory Data Analysis (EDA), pre-processing, augmentation, model training with four scenarios, and evaluation using Accuracy, Precision, Recall, F1-Score, and Macro Average F1-Score. The results showed that the Combination model (Oversampling and Weighted Loss) performed best on the minority class with an F1-score of 0.9630, the highest among all models. The Oversampling strategy produced the highest Macro F1-score of 0.9617, while Weighted Loss provided a significant improvement in classification sensitivity but was still below the combination model. Thus, it can be concluded that the combination strategy is the most effective approach in improving the sensitivity of the model to minority classes, while Oversampling excels in the overall performance stability of the model.
Segmentation of Coffee Purchasing Behavior Based on Transaction Time Using the K-Means Algorithm Yuslia Devitri; Rahaningsih, Nining; Ali, Irfan; Prihartono, Willy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1863

Abstract

This studyaims to identify customer behavior patterns based on the time of purchaseof beverages at a coffee shop using the K-Means method.Transaction data includes purchase time, payment type, product name,time category, day, and month. The research stages include data cleaning, time attribute transformation, and numerical feature normalization. The optimal number of clustersis determined through testing k = 2–10 with four evaluation metrics,namely Inertia, Silhouette Score, Davies–Bouldin Index, and Calinski–HarabaszIndex. Based on the validation results, k = 3 was selected because it provided the best balancebetween compactness and cluster separation. The clustering results showedthree main customer groups based on transaction time trends:nighttime buyers with a peak around 10:27 p.m., afternoon to early evening buyerswith a centroid of 7:01 p.m., and morning to noon buyers with a centroid11:13. The frequency distribution indicates that the morning–afternoon buyer groupis the largest, while the early evening–night group is thesmallest. Visualization of scatter plots, boxplots, and time category graphsemphasizes the differences in characteristics between clusters. Overall,this study proves that K-Means is effective in mapping the temporal patternsof customer behavior. These findings can be used to develop time-based marketing strategies, operational arrangements, and product stock management,as well as form the basis for further analysis in the industry.
Strategi Otomatisasi Pemasaran Digital UMKM Melalui Pelatihan AI Dalam E-Commerce Fatihanursari Dikananda; Nining Rahaningsih; Ridho Nugroho; Vicky Pamungkas
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 : April (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The utilization of Artificial Intelligence (AI) offers significant potential for Micro, Small, and Medium Enterprises (MSMEs) to optimize their digital marketing strategies on e-commerce platforms. This Community Partnership Program is designed to provide training to MSMEs regarding the application of AI in various aspects of digital marketing. The training covers customer experience personalization, market data analysis, promotion content optimization, and improved online advertising efficiency. Through this activity, it is expected that MSMEs can enhance their understanding and skills in implementing AI-based solutions to expand market reach, improve customer interaction, and achieve more optimal digital marketing results in the e-commerce era.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Co-Authors ., Mulyawan Abdillah Fudholi, Luthfi Abdul Ajiz Abdul Rasyid Achmad Hidayat Ade Irma Purnamasari Ade Kurnia, Dian Ade Rizki Rinaldi Ahmad Faqih Akbar, Miftahul Al-Maulid, Hisyam Alvianatinova, Via Andini, Ayi Andriyanti, Rina Anggita Pratiwi, Eksadevi Angraeni, Devita Fitri Arif Rinaldi Dikananda Arif Sofyan, Mohamad Awaliyah, Lia Az Zahroh, Luthfia Fahmi Azarine, Divia Azhari, Shazifa Azizah, Maulidina Bakri, Saeful Basysyar, Fadhil Muhammad Basysyar, Fadil M Bustomi, Ziaudin Cakranegara, Pandu Adi Camelia Putri Lestari Cep Lukman Rohmat Dadang Sudrajat Danar Dana, Raditya Danar, Raditiya Danil, Supta Danya Rizki Chaerunisa Delisah Destiawati, Deby Dewanty Rafu, Maria Dienwati Nuris, Nisa Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dimin, Egi Susanto Dodi Solihudin Dwi Efranie, Priska Edi Wahyudin Elisa Sriyulia Euis Fadilah Fadhil M. Basysyar Fadhil Muhammad Basysyar Fadhil, Fadhil Yudistianto Fadilah, Mochammad Fauzan Fajar, Miftahul Farah Nur Farida Fatihanursari Dikananda Faturachman, Rifcki Aziz Faujatun Hasanah Fidya Arie Pratama Frihandiansah, Riyandi Gifthera Dwilestari Gita Budiarti, Mariani Gusmiarni, Mia Gusnanto, Ferdi Gustipartsani, Kamaludin Hadi, Melawati Haidar Fakhri Haryanto, Cep Hayati, Umi Herman Iin Ilham Kurniawan Ilham, Mokhamad Illahi, Asep Wahyu Imam Arifin imam maulana, imam Irfan Ali Irfan Ali, Irfan Jafar Jafar Kamelia Faridah Kaslani Khalda Rifdan, Ghina Kharomiyah, Kharomiyah Kholil, Kholil AldiYatna Kurmasih, Masih Laduni, Pasya Lili Purani Lisyana, Zita Lukman Rohmat, Cep M. Basysyar, Fadhil Mamluatul Hikmah, Lulu Martanto . Marthanu, Indra Wiguna Marwah, Sopa Medina Aprilia Putri Mira Miranda Moch Rifki Firdaus Muhamad Basysyar, Fadhil Muhammad Abdurohman Muhammad Basysyar, Fadhil Muhammad Taufik Hidayat, Muhammad Mulyana, Krisna Mulyawan Mulyawan, - Mulyawan, Mulyawan Nafilah, Mala Nana Mulyanasari Nana Suarna Narasati, Riri Narasati Nur Afrilia, Mela Nurhadiansyah Nurrochmah, Dina Siti Nursaniah, Rini Nurwijayanti Octavia Ningrum, Eka Puspita Odi Nurdiawan Optarina, Yasni Pii, Iwan Prasetia, Deni Pratama, Deni Pratama, Fidya Arie Pratama, Handreyan Rizki Prihartono, Willy Purnamasari, Ade Irma Purnamasari, Ade Purnamasari Putra, Purniadi Putri Nabilla Qodri M.A, M. Alifia Raditya Danar Dana Rahmasari, Fanny Rahmi Safitri, Rahmi Ranu Husna Ridho Nugroho Rifki Maulana, Muhamad Rini Astuti Riyandona, Siti Aiwastopa Rizki Ramadhan Rizky Wulandhari, Putri Roghib, Moh. Rohmat, Cep Lukman Rully Pramudita Ryanto, Bayu Saeful Anwar Saroji, Saroji Sekar Puspita Arum Siti Sa'diah Sofialaela, Annisa Sok Piseth Soni, Moh Sri Muflikah Kurniarti Sri Suwartini Suarna, Annisa Annastia Suarna, Nana Sutra Safira, Meita Syafi’i Bachtiar, Mochammad Syarif Maulana Yaasin Tati Suprapti Tengku Riza Zarzani N Tohidi, Edi Tohodi, Edi Tri Mukti, Aryanto Tuti Hartati Usup Supendi Vicky Pamungkas Vina, Vina Widiya, Putri Windy Mardiyyah, Nita Wulandari, Maryam Yahya, Jakaria Yayah Sarwiyah Yudhistira Arie Wijaya Yulia Mustafa, Iva Yuslia Devitri Zhahiran Herlambang, Prilanisa